soccer research paper

150 Football Essay Topics & Soccer Research Topics

Are you a soccer player? If yes, then you will enjoy writing a soccer-themed essay! To make the writing process even easier, we present to you our list of football essay topics and samples. Check them out below!

• 🔝 Top 10 Football Research Topics for 2024

🏆 Best Football Topics to Write About

✍️ football essay topics for college, 👍 good football research topics & essay examples, 🎓 most interesting soccer research topics, 💡 simple football essay ideas, ❓ research questions about football, 🔝 top 7 football research topics for 2024.

• The 2022 FIFA World Cup
• Soccer and Basketball Differences
• Football Watching as Entertaining Action
• Liverpool Football Club’s Strategic (PESTLE) Analysis
• Environmental Impact of the Football Stadium Construction
• The Physics Behind Football
• Advertisement in Soccer Overview
• American Football League v. National Football League Case The American Football league, abbreviated as AFL, filed a lawsuit against the national football league (NFL) on the grounds of the Anti-Trust Act breach.
• Spanish Football League Spain is one of the countries that have dominated the game of football over the last couple of years. The country has achieved success with its senior and junior national teams.
• FIFA and Corruption In other words, FIFA is a kind of a football image, and it has to be deprived of various unethical and immoral practices.
• Should Football Be Banned for Being Too Violent and Dangerous? The essay ponders whether the game of football is dangerous and violent, and it should be banned, or are there other ways to reduce the possibility of players’ injuries.
• “Fordson: Faith, Fasting, Football” Documentary “Fordson: Faith, Fasting, Football” is a film about a community of Muslim Americans, who are focused on their passion and support for the Fordson High School football team.
• Organizational Theories in Australian Football League (AFL) This essay applies these two concepts to the operations of the Australian Football League. A brief background to the AFL will be presented before applying the individual theories.
• Football in Ghana and Its Relationship with the Rest of the World (Player Transfers) Football is one of the most popular sports not only in Ghana but in the region of Africa and the global society.
• Soccer in the US, Its Development and Popularity The main factor which impacted the development and popularity of soccer in the United States is the public’s area of interest.
• FIFA World Cup: History and Future The FIFA World cup is a soccer competition that is contested internationally by national soccer teams composed exclusively of male players.
• Event Management Analysis: Local Soccer Championship Even management requires careful and detailed analysis and planning in order to avoid a project failure and create an impressive and amazing setting for visitors.
• Impact of Qatar Hosting FIFA World Cup 2022 This paper discusses the impact of Qatar hosting the FIFA World Cup in 2022, specifically on its brand image and business operations.
• Soccer and Other Sports as a Communication Medium The paper discusses the ways sports communication potential is exploited by companies and organizations and how they use sports personalities charisma.
• Training Programs for a High School Quarterback Football Player The article addresses the specific exercises that the quarterback player should take and the duration, the number of sets, the reps, and the rest intervals.
• The National Football League Team Moving to the City of Omaha Moving the National Football League team to the city of Omaha, Nebraska, will have a positive financial influence on the citizens residing within its boundaries.
• The Football Impact on the European Region The European region has been considered to be the world’s most prominent fan organization, with around three million football fans.
• Reasons Why Kids Should Not Play Tackle Football The brain might repair itself, but the consequences of the injury usually last longer and include memory loss, headaches, and similar cognitive dysfunctions.
• American Sports: Football, Soccer, Basketball Some games have grown to be recognized as official sports with strict rules, governing institutions, and international events.
• Real Madrid and Barcelona Football Clubs History Real Madrid and Barcelona are the top European Football Clubs, which are usually opposed to each other. During the long time they applied different strategies to achievements in sport.
• The Qatar 2022 FIFA World Cup Bid The award of hosting the World Cup in 2022 for Qatar came as a surprise to Australia and the USA, which many people thought could win the rights.
• Goal Line Technology and Football Football matches are not only about the issue of teamwork but also about strict competition and the importance of the defining last-minute goal which can tip the scale between victory and defeat.
• Planning Franz Beckenbauer Charity Football Match Today I will be presenting my event management plan for the upcoming Franz Beckenbauer Charity Football Match.
• The Negotiation Process in Football The main issue being negotiated is the extension of a playing contract for A. J. Washington, a quarterback participating in the Los Angeles Spartans of National Football League.
• Qatar Hosting FIFA World Cup 2022 FIFA World Cup is one of the largest soccer tournaments in the world. It is scheduled to occur in 2022, and the host country will be Qatar.
• How to Play Defense in Football In fact, a strong defense and a well-developed strategy can cause turnovers from the rival, and turnovers can significantly influence football game results.
• Soccer League and Grassroots Strategy Soccer is one of the most popular sports in the world, with over 240 million registered players at all levels, and at least 3.5 billion fans.
• Is Watching Football Morally Acceptable The public opinion on the morality of watching football, or other competitive sports, is divided, as there are clear dangers associated with participating in football matches.
• FIFA, Zidane and Materazzi 2006 Debacle This paper examines FIFA, Zidane and Materazzi 2006 debacle. FIFA punishing both players was fair and helped them preserve their image.
• Opposing American Football Ban Due to Health Reasons One of the opposing views regarding American football from the perspective of players’ health is the dubious nature of the claim that the described risks are universal.
• Rhetorical Strategies of FIFA Franchise The website central to this review provides its viewers with reasons for either pre-ordering or waiting on the newest instalment in the FIFA franchise, FIFA 22.
• Negotiations Between National Football League and NFL Players Association Approving the proposed 2020 NFL CBA faced a significant amount of backlash from the players due to some of the issues that were not addressed in the agreement.
• Sport and Television: Football Support To retain its target audience and remain a popular activity, the sport needs the support of television as one of the main media tools.
• Racial Disparity in Professional Football: Rooney Rule An open conversation about equal rights and workplace diversity is reaching its peak in the form of viral social media campaigns and public demonstrations.
• Football Banned for Being Too Violent and Dangerous American football is a popular kind of sport in the United States, but scientific evidence demonstrates that this activity should be banned for being violent and dangerous.
• The National Football League Anti-trust Law The National Football League (NFL) during its long-lasting development is colored today, as the sphere where business interests seem to be more significant.
• American Football Is Too Dangerous and It Should Be Banned Regardless of American football being a major source of entertainment for many, it should be banned due to significant harm dealt with players’ brains, cognitive performance.
• The Review of Literature: American Football The articles included in the annotated bibliography research how violent and dangerous American football could be.
• American Football as a Popular Kind of Sport in the US American football is a popular kind of sport in the United States. A severe issue refers to the fact that professional players are often subject to health problems.
• Speed Drill: Agility Training in Young Elite Soccer Players The purpose of this paper is to describe and explain a speed drill for a specific athlete, using logical arguments and visual elements.
• Organizational Behavior Analysis: Japanese Soccer School Kurt Lewin’s theory of change is a framework most often used to describe and plan organizational change due to its relative simplicity, intuitive nature, and ease of use.
• Soccer: Effects of Sprint Training Training soccer players is an engaging and demanding activity, and it is crucial to make the most of this process to be a successful coach.
• Football and other Sports: Influence on Children’s Life Football is a very unique sport, as it helps a person establish a framework for life and attitude. It helps develop character and strengthen individuality.
• Training Football Athletes: Key Aspects Monotonous exercises should not bore them; trying activities appear to be more productive. Using many drills similar to deep ball drills in training practice is advantageous.
• 2010 FIFA Soccer World Cup Stadia Development in Cape Town: Resident Perceptions Bob & Swart’s Resident Perceptions of the 2010 FIFA Soccer World Cup Stadia Development in Cape Town assessed suggestions of the people on the venues of the FIFA.
• Nike’s Ad for Football Women’s World Cup 2019 Nike released its empowering advertisement ahead of the FIFA Women’s World Cup 2019 hosted in France. It claims that football is a game enjoyed by people of all races.
• Soccer and Sport as a New Medium of Communication Execution of physical tasks calls for smooth, self-controlled, and concerted effort. Athletes need emotional control if they are to successfully engage in sporting events.
• Soccer and Sport: New Medium of Communication The concentration of wealth in certain clubs and leagues makes them more lucrative and more entertaining. This influences and entices more fanatics to join the clubs and leagues.
• Ranking Systems: FIFA and US College Football The purpose of this paper is to compare the FIFA ranking system for international soccer and the Matrix-based Methods system used in US College football.
• FIFA, Corruption, and Its Effects on Business The paper studies how unethical behavior affects FIFA and how business relates to FIFA will be affected by news and how it can deal with such a situation.
• Football Tactics and How They Evolve over Time
• Long-Term Effects of Concussion on Football Players
• Commercialization’s Impact on Football Club Performance
• Football Hooliganism and Fan Violence
• The Role of Video Assistant Referee in Football
• American Football: Technology and Regulation of Helmet Safety
• Race and Quarterback Survival in the National Football League
• National Football League and Player Compensation Issues
• Competitive Balance and Consumer Demand in the English Football League
• Quarterback Mobility and Its Impact on College Football
• Action Plan For Fundraising for the Penn Hills Football and Cheer Association
• American Football and Coin Toss
• Fitness Requirements for Football
• Broadcaster and Audience Demand for Premier League Football
• Assessing Methods for College Football Rankings
• Football Scholarships and Football Recruiters
• Being Special: The Rise of SuperClubs in European Football
• Ajax Football Club: Strategic Alternatives
• College Football Players Should Get Paid
• Beer Availability and College Football Attendance
• Exercise Program for Football Team
• American Football and Positive Latitude
• Concussions and American Football
• Football Helmets Are Insufficient to Stop Concussions
• American Football and Ice Hockey
• Strategic Behaviour and Risk-Taking in Football
• Professional Asian Football Leagues and the Global Market
• Concussions Are the Most Common Football Injury
• Justice, Professional Football, and Minority Coaches
• Market Size and Attendance in English Premier League Football
• Spanish Football: Competitive Balance and the Impact of the Uefa Champions League
• Cheshire Football Club and Management of a Soccer Team
• College Football and Its Social and Cultural Importance in the USA
• Motor and Cognitive Growth Following a Football Training Program
• High School Football Women Play
• The Growth and Challenges of Women’s Soccer
• Football Talent Identification and Training Programs
• The Impact of Football Events on Local Economies
• Analysis of Mental Strategies in Football
• How Digital Platforms Influence Soccer Fan Engagement
• America’s Football and the World’s Soccer
• Football Concussions and Head Injuries
• Football: The United Kingdom and English Public
• Joe Robbie Professional Football Stadium History
• American Football and High School
• Football Has Impacted Our Society in Many Ways
• Greatest Football Players Throughout History
• Domestic Violence and the National Football League
• British Culture, Economy and Society and the Role of Football
• High School and School Football Team
• Baseball, Football, and Basketball: Models for Business
• Football and Its Effect on Society
• International Women’s Football and Gender Inequality
• College Football Conferences and Competitive Balance
• Globalization and the Future of Indigenous Football Codes
• Football Hooliganism, Society, and Culture
• Acquiring and Performing the Football Passing Skill
• Football: History, Rules, and Influential Individuals
• Floating European Football Clubs in the Stock Market
• Gender-Specific Relative Age Effects in Politics and Football
• Health Risks Involved With Playing Football
• College Football Rivalry Between Ohio and Michigan
• Economics, Uncertainty, and European Football
• Deviations From Equity and Parity in the National Football League
• Professional Sports and Its Impact on the National Football
• Fantasy Football Provides Fans With Interactivity
• Concussions and Head Injuries in the National Football League
• Migrating Football Players, Transfer Fees, and Migration Controls
• Fantasy Sports and Its Effect on the National Football League
• Eliminating College Football Team
• What Are the Risk Factors for Injuries in Football?
• How Does the Players Behavior Off Field Affect the Game of Football?
• What Is the Role of Football in Everyday Life?
• How Did the Financial Crisis Influence European Football?
• What Is the Americanization of European Football?
• What Is Italian Football’s Status in an Age of Globalization?
• What AI Can Do for Football, and What Football Can Do for AI?
• What Are the Dynamics of Group Sports With Special Reference to Football?
• What Is the Data Collection on the Incidence of Injuries in Football?
• What Is the Network Theory Analysis of Football Strategies?
• What Is the Role of Corporate Social Responsibility in the Football Business?
• What Is the Impact of College Football Telecasts on College Football Attendance?
• Is There a Relationship Between Climatic Conditions and Injuries in Football?
• What Are the Determinants of Football Match Attendance?
• What Are the Biomechanical Properties of Concussions in High School Football?
• What Are the Fitness Determinants of Success in Men’s and Women’s Football?
• What Is Bayesian Hierarchical Model for the Prediction of Football Results?
• What Is the Effect of Altitude on Football Performance?
• What Are the Psychological and Sport-specific Characteristics of Football Players?
• What Is the Relationship Between Football Playing Ability and Performance Measures?
• What Is the Predictive Power of Ranking Systems in Association Football?
• What Are the Peculiar International Economics of Professional Football in Europe?
• What Are the Medical, Morphological and Functional Aspects of Football Referees?
• What Is the Epidemiology of Injuries in First Division Spanish Football?
• What Are the Common and Unique Network Dynamics in Football Games?

Cite this post

• Chicago (N-B)
• Chicago (A-D)

StudyCorgi. (2022, March 1). 150 Football Essay Topics & Soccer Research Topics. https://studycorgi.com/ideas/football-essay-topics/

"150 Football Essay Topics & Soccer Research Topics." StudyCorgi , 1 Mar. 2022, studycorgi.com/ideas/football-essay-topics/.

StudyCorgi . (2022) '150 Football Essay Topics & Soccer Research Topics'. 1 March.

1. StudyCorgi . "150 Football Essay Topics & Soccer Research Topics." March 1, 2022. https://studycorgi.com/ideas/football-essay-topics/.

Bibliography

StudyCorgi . "150 Football Essay Topics & Soccer Research Topics." March 1, 2022. https://studycorgi.com/ideas/football-essay-topics/.

StudyCorgi . 2022. "150 Football Essay Topics & Soccer Research Topics." March 1, 2022. https://studycorgi.com/ideas/football-essay-topics/.

These essay examples and topics on Football were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

This essay topic collection was updated on December 31, 2023 .

Evolution of soccer as a research topic

Affiliation.

• 1 James R. Urbaniak, MD Sports Sciences Institute Duke Health, Durham, North Carolina. Electronic address: [email protected].
• PMID: 32599029
• DOI: 10.1016/j.pcad.2020.06.011

Soccer has not only the largest number of worldwide participants, it is also the most studied sport, with nearly 14,000 citations listed on Pubmed and nearly 60% more articles than the next most studied sport. Research about soccer was limited until the late 1970s when exponential growth began; approximately 98% of all soccer-related research publications have occurred since 1980. This vast repository of soccer research shows trends in various major (e.g., 'sex' or 'age group' or 'performance' or 'injury') and specialty (e.g., agility, deceleration, elbow-head impact injuries, behavior) topics. Examining trends of the various topics provides insights into which subjects have come in and out of favor as well as what topics or demographics have been neglected and worthy of inquiry. A further examination can be used by students to learn the most productive researchers, which programs have a strong history of inquiry, and what journals have demonstrated a commitment to publishing research on soccer.

Keywords: Association football; Pubmed; Research history.

Copyright © 2020 Elsevier Inc. All rights reserved.

Publication types

• Age Factors
• Biomedical Research / trends*
• Cardiorespiratory Fitness
• Health Status
• Middle Aged
• Periodicals as Topic / trends*
• Sex Factors
• Soccer / trends*
• Time Factors
• Young Adult

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Match-related physical performance in professional soccer: Position or player specific?

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations TSG ResearchLab gGmbH, Zuzenhausen, Germany, Department for Performance Analysis, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – review & editing

Affiliation Department for Performance Analysis, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Roles Formal analysis, Investigation, Software, Validation, Visualization, Writing – review & editing

Affiliations TSG ResearchLab gGmbH, Zuzenhausen, Germany, TSG 1899 Hoffenheim, Zuzenhausen, Germany

Roles Methodology, Validation, Writing – review & editing

Affiliation TSG ResearchLab gGmbH, Zuzenhausen, Germany

Roles Conceptualization, Methodology, Writing – review & editing

Affiliation TSG 1899 Hoffenheim, Zuzenhausen, Germany

Roles Funding acquisition, Methodology, Resources, Software, Supervision, Writing – review & editing

Affiliations Department for Performance Analysis, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany, Department for Social and Health Sciences in Sport, Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany

Roles Conceptualization, Funding acquisition, Methodology, Resources, Software, Supervision, Validation, Writing – review & editing

• Stefan Altmann, 
• Leon Forcher, 
• Ludwig Ruf, 
• Adam Beavan, 
• Timo Groß, 
• Philipp Lussi, 
• Alexander Woll, 
• Sascha Härtel

• Published: September 10, 2021
• https://doi.org/10.1371/journal.pone.0256695
• Reader Comments

The purpose of this study was to examine to what extent the physical match performance of professional soccer players is both position and player specific. First, official match data from the 2019/20 German Bundesliga season was used to search for players that met the inclusion criteria of playing a minimum of four entire matches in at least two different playing positions. Overall, 25 players met the criteria prior to the COVID-19 induced break, playing a minimum of eight matches. Second, the physical match performance of these players was analyzed separately for each position they played. The following four parameters were captured: total distance, high-intensity distance, sprinting distance, and accelerations. Third, the 25 players’ physical match performance data was then compared to normative data for each position they played to understand whether players adapted their physical performance (position dependent), or maintained their performance regardless of which position they were assigned to (position independent). When switching the position, the change in physical match performance of the respective players could be explained by 44–58% through the normative positional data. Moreover, there existed large individual differences in the way players adapted or maintained their performance when acting in different positions. Coaches and practitioners should be aware that some professional soccer players will likely incur differences in the composition of physical match performance when switching positions and therefore should pay special consideration for such differences in the training and recovery process of these players.

Citation: Altmann S, Forcher L, Ruf L, Beavan A, Groß T, Lussi P, et al. (2021) Match-related physical performance in professional soccer: Position or player specific? PLoS ONE 16(9): e0256695. https://doi.org/10.1371/journal.pone.0256695

Editor: Dragan Mirkov, University of Belgrade, SERBIA

Received: March 29, 2021; Accepted: August 12, 2021; Published: September 10, 2021

Copyright: © 2021 Altmann et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data that support the findings of this study are available from the Deutsche Fußball Liga (DFL). Restrictions apply to the availability of these data, which were used under license for this study. The data were provided by a commercial company (Deltatre) and are therefore not freely available. Requests to access the datasets should be directed to the DFL ( [email protected] ), asking for the official match data of the Bundesliga season 2019/2020. Interested researchers can replicate our study findings in their entirety by directly obtaining the data from the DFL and following the protocol in the Methods section. The authors did not have any special access privileges that others would not have.

Funding: The funder TSG 1899 Hoffenheim provided support in the form of salaries for authors [SH, TG, PL]. The funder TSG ResearchLab gGmbH provided support in the form of salaries for authors [SA, LR, AB]. Both funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Competing interests: The authors have read the journal’s policy and have the following competing interests to declare: The authors [SH, TG, PL] were employed by the commercial affiliation TSG 1899 Hoffenheim. The authors [SA, LR, AB] were employed by the non-profit limited liability company TSG ResearchLab gGmbH. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no patents, products in development or marketed products associated with this research to declare.

Introduction

Soccer is characterized as an intermittent team-sport requiring professional players to cover total distances between 10 and 13 km per match [ 1 , 2 ]. While the majority of the total distance occurs at lower intensities, 22–24% is spent at intensities above 15 km/h, 8–9% above 20 km/h, and 2–3% above 25 km/h. In addition, the players can perform between 600–650 accelerations during a match [ 3 ]. Hence, the typical match-related physical performance is reflected by a complex interaction of the aerobic and anaerobic energy systems [ 3 ].

Physical match performance has been shown to differ between playing positions [ 4 ]. The greatest total and high-intensity distance is commonly covered by central midfielders, wide defenders, and wide midfielders; while strikers and central defenders record lower distances [ 5 , 6 ]. Regarding sprinting behavior, wide defenders and wide midfielders have been consistently reported to demonstrate the greatest sprinting distance, with similar values being obtained for forwards. Central midfielders demonstrate shorter total distances while sprinting, followed by central defenders [ 1 , 5 , 7 – 9 ]. In line with this, Mohr et al. [ 10 ] and Ingebrigtsen et al. [ 11 ] found that wide players exhibit greater sprinting distances than central players. Finally, as with sprinting, wide players seem to perform more accelerations than central players [ 10 – 12 ]. In sum, physical performance during matches differs between playing positions, both in the total distance itself as well as in its composition (i.e., high-intensity runs, sprints, accelerations).

Several studies have confirmed that a relationship exists between the players’ physical capacities (e.g., derived from endurance-, sprint-, and repeated-sprint tests) and their physical match performance (e.g., total distance, high-intensity distance, maximal sprinting speed) [ 13 , 14 ]. That is, players with higher endurance or sprint capacities display higher total and high-intensity distances and reach higher maximal sprinting speeds during matches.

Therefore, it seems plausible that players in different positions also display distinct physical capacities. However, findings on position-specific single-sprint and repeated-sprint performance are inconclusive and no outfield position has been shown to constantly outperform other positions across several studies [ 15 – 17 ]. Moreover, recent research indicates that endurance capacities of professional outfield players are rather independent of their playing position [ 18 , 19 ]. Consequently, it can be concluded that the above-described position-specific performance during matches is not always reflected by the players’ physical capacities.

Combined with the finding that there exists a high variability in physical match performance between players of the same position [ 8 , 20 , 21 ], a possible explanation for this observation might be that the physical match performance is not only dependent on the playing position but also to some extent on the individual players themselves [ 19 ]. In other words, while taking contextual factors such as team tactics, game location, opponent strength, congested period or match status into account that have all been shown to influence physical performance [ 22 , 23 ], it might be further possible that some players always show a similar physical performance during matches, independent from the position they are instructed to play.

So far, only one study [ 24 ] has addressed this topic, showing a trend of players adapting their physical performance when switching playing positions. However, while reporting results on a group level, some limitations were not addressed in this study such as the inclusion of normative positional data from the same data set or the physical performance of individual players. Overcoming these limitations would allow for more meaningful conclusions to be drawn regarding whether the players’ physical match performance is position and player (in)dependent.

Therefore, the aim of this study was to examine to what extent the physical match performance of professional soccer players is attributed to being position and player specific by analyzing the individual data of players switching positions and normative positional data in relation to each other.

Materials and methods

Study design.

In the present study, official match data from the 2019/2020 season of the German Bundesliga were used. To investigate to what extent the physical match performance of players is not only position but also player specific, first, all players that played at least in two different positions during the season were identified. Second, the physical match performance of these players (total distance, high-intensity distance, sprinting distance, number of accelerations) was analyzed separately for each position they played. Third, the obtained data were examined in relation to normative data for each position, thereby allowing the interpretation of whether the players in question either maintained or adapted their performance according to the normative positional data. The study was approved by the institutional review board of the Institute of Sports and Sports Science, Karlsruhe, Germany. Data were collected as a condition of employment in which player performance is routinely measured during match play. Therefore, informed consent by the players was not required for this study [ 25 ]. Nevertheless, to ensure team and player confidentiality, all data were anonymized prior to analysis.

Data were collected from the first 25 matchdays (i.e., before the COVID-19 induced break) during the 2019/2020 season of the German Bundesliga. To be included in the study, players must have completed at least four entire matches (full 90 min) in at least two different positions (i.e., in sum, a minimum of eight matches per player). A minimum of four matches per position was chosen to minimize the effect of contextual factors and to account for variability in physical performance [ 20 – 22 , 26 ]. Moreover, only matches without a red card were included.

In total, 116 players were identified who completed at least one entire match in at least two different positions. However, only 25 players across 15 clubs met the inclusion criteria of at least four matches per position, thereby constituting the study sample. Collectively, from the 224 matches played in the study period, 163 matches were taken into account for the current study.

Normative data for each position were determined through all other players who were not included in the current study that also completed the full 90 min in one or more of the 163 matches in question, meaning that the 25 players included in the study sample did not also contribute to the normative data.

Each player of the study sample as well as those constituting the normative data were assigned to one of the following six outfield positions: central defender, wide defender, wing back, central midfielder, wide midfielder, forward. Regarding playing formation, in a system with four defenders (e.g., 4:4:2 or 4:2:3:1 system), the defensive players were coded as two central defenders and two wide defenders. Conversely, in a system with five defenders (e.g., 5:3:2 system), the defensive players were coded as three central defenders and two wing backs. For each player of the study sample, the main position, the secondary position, and, where applicable, the tertiary position was determined based on the number of matches played in the respective positions. Nevertheless, the order of position (main, secondary, tertiary) did not impact further analyses.

Furthermore, the physical match performance for each player and each position, respectively, was determined. The following four parameters were captured: total distance, high-intensity distance (17–23.99 km/h), sprinting distance (≥ 24 km/h), accelerations (positive acceleration values in each frame for ≥ 1.5 s). All definitions are based on the catalog of the German soccer league [ 27 ].

Both playing position and physical match performance data were derived from the official match data of the German Bundesliga. The latter was determined by means of a multiple-camera computerized tracking system (TRACAB, Chyron Hego, Melville, NY, USA) which has recently been validated [ 28 ].

Statistical analysis

The data were analyzed using SPSS statistical software version 26.0 (SPSS, Inc., Chicago, IL). Mean values and standard deviations (SD) for each physical performance parameter were calculated regarding both the positional normative data and each player of the study sample for each position he played.

Possible differences in the normative data between playing positions were analyzed using one-way repeated measures analysis of variance (ANOVA) and subsequent pairwise comparisons with Bonferroni corrected p values. Cohen’s d effect sizes (ES) were calculated to quantify the magnitude of differences between positions. The ES was considered as small (0.2 ≤ ES < 0.5), moderate (0.5 ≤ ES < 0.8), and large (ES ≥ 0.8) [ 29 ].

To determine whether the players of the study sample either maintained or adapted their performance according to the normative positional data when playing in different positions, the data of the study sample and the normative data were examined in relation to each other. Specifically, the difference between the physical performance in the main position, the secondary position, and where applicable, the tertiary position was computed for each player of the study sample and examined by means of independent t-tests and ES. Moreover, the difference between the physical performance in the normative data for the position combinations that were evident in the study sample, e.g., central defender and wide defender, was computed. Lastly, Pearson’s product-moment correlations (r) with 95% confidence intervals (95% CI) were run between the positional difference in physical performance of the players in the study sample and the associated positional difference in the normative data. The magnitude of the correlation coefficient was considered as small (0.1 ≤ r < 0.3), moderate (0.3 ≤ r < 0.5), large (0.5 ≤ r < 0.7), very large (0.7 ≤ r < 0.9), and nearly perfect (r ≥ 0.9) [ 30 ]. The significance level for all statistical tests was set to 0.05.

Descriptive statistics (mean ± SD) of the normative positional data are reported in Table 1 and S1 Fig . The ANOVA revealed significant differences between playing positions for all physical performance parameters. While central midfielders showed both the largest total (11.66 ± 0.92 km, ES = 0.68–1.86) and high-intensity distance (1.57 ± 0.83 km, ES = 0.08–0.84) compared to all other positions, wide midfielders demonstrated the largest sprinting distance (0.42 ± 0.14 km, ES = 0.34–2.39), and wing backs performed the highest number of accelerations (512 ± 37, ES = 0.05–0.90) (see S1 Table ).

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

Results are presented as mean values ± SD.

https://doi.org/10.1371/journal.pone.0256695.t001

Regarding the study sample, 23 players played in two different positions and two players in three different positions. In the latter case, all three positional comparisons were included for the two players in question (i.e., player 24/1, 24/2, 24/3, and player 25/1, 25/2, 25/3). The most common combinations of positions were wide defender and wing back (n = 9), central defender and wide defender (n = 6) as well as central midfielder and wide midfielder (n = 5).

Large to very large correlations (r = 0.66–0.76, r 2 = 44–58%) were found between the positional difference in physical performance of the players in the study sample and the associated positional difference in the normative data ( Table 2 ). Figs 1 – 4 illustrate the physical performance of each player of the study sample in relation to the positional normative data. Descriptive statistics (mean ± SD) and t-test results of each player of the study sample in relation to playing position are reported in S2 Table . Eight players clearly adapted their physical performance when changing the playing position supported by large observed ES differences between positions for at least three of the four performance parameters examined. Eleven players rather maintained their physical performance indicated from the large observed ES differences between positions for a maximum of one performance parameter. Nine players (representing 10 position combinations) displayed an inconsistent physical-performance pattern in relation to their playing positions demonstrated by large ES differences between positions for two performance parameters and trivial-to-moderate ES differences for two performance parameters). Moreover, large individual differences were observed in the way players behaved when acting in different positions.

Data are presented as mean values ± SD. Light grey diamonds and dashed lines indicate significant differences in performance between the two positions for the respective player.

https://doi.org/10.1371/journal.pone.0256695.g001

https://doi.org/10.1371/journal.pone.0256695.g002

https://doi.org/10.1371/journal.pone.0256695.g003

https://doi.org/10.1371/journal.pone.0256695.g004

https://doi.org/10.1371/journal.pone.0256695.t002

The purpose of this study was to examine to what extent professional soccer players competing in the German Bundesliga adapted (position dependent), or maintained their performance regardless of which position they were assigned to (position independent).

The analysis of the normative data revealed pronounced positional differences regarding physical match performance serving as a basis for further analysis. Our results further indicate that changes in physical match performance of players can be explained by 44–58% by their playing positions while the remaining variance can be attributed to other factors such as the individual players themselves. In a similar fashion, there were pronounced individual differences in the way the players adapted or maintained their performance in relation to their positions.

Our findings on normative positional data in physical match performance support previous literature, while also adding several new insights. Regarding total and high-intensity distance, the highest values were achieved by central midfielders and wide midfielders, which is in line with previous research [ 5 , 6 ]. Moreover, our results demonstrate that wide defenders (e.g., 4:4:2 or 4:2:3:1 system) displayed lower total and high-intensity distances compared to wing backs (e.g., 5:3:2 system), which is a new finding that highlights the necessity of distinguishing between these two positions. Wide midfielders, wing backs, and wide defenders followed by forwards demonstrated the greatest sprinting distance, while central midfielders and central defenders showed shorter distances while sprinting. These findings are generally supported by previous literature [ 1 , 5 , 7 – 9 ].

The last physical-performance parameter investigated in the present study is the number of accelerations. Here, wing backs, central midfielders, and wide defenders followed by wide midfielders accelerated most frequently. The high number of accelerations found in central midfielders contradicts recent studies [ 10 – 12 ] who reported wide players to perform more accelerations than central players. However, these studies included small sample sizes, used different definitions of accelerations, and were performed in different countries compared to our study, which could explain these discrepancies regarding central midfielders [ 31 ]. Besides, another interesting finding in relation to this parameter was that forwards accelerated least often of all positions, while central defenders demonstrated the lowest performance for the remaining parameters (i.e., total distance, high-intensity distance, sprinting distance). In summary, findings from our normative data reinforce that physical performance during matches differs between playing positions.

To investigate whether the players of the study sample either maintained or adapted their performance when playing in different positions, we analyzed the data of the study sample and the normative data in relation to each other. Correlation analyses revealed large to very large relationships between the positional difference in physical performance of the players in the study sample and the associated positional difference in the normative data. More specifically, changes in playing position explained 53–58% of the study sample’s variance in changes for total distance, high-intensity distance, and sprinting distance, and 44% for the number of accelerations. The remaining variance can be attributed to other factors such as the playing style of the individual players themselves.

Differences in the physical performance of each player of the study sample in relation to the normative data are clearly depicted within Figs 1 – 4 and S2 Table . From the study sample, eight players (players 3, 4, 11, 14, 17, 18, 19, and 24/1) clearly adjusted their physical performance according to the playing position. More specifically, one out of these eight players represented the position combination of wide defenders vs. wing backs, wide defenders vs. central midfielders, and wide midfielders vs. forwards, respectively. The remaining five players represented the combination of central defenders vs. wide defenders. Importantly, according to the normative data of the latter, wide defenders showed higher performance with large ES compared to central defenders for the three parameters total distance, high-intensity distance, and sprinting distance (see S1 Table ). Therefore, distinct differences in the normative data might explain why some players from the study sample adjusted their physical performance according to the position. Our finding relating to the position combination of central defenders and wide defenders is supported by previous research [ 24 ] that also indicated large increases in performance when players switched from central to wide defender.

Another 11 players (player 1, 5, 8, 9, 12, 15, 20, 22, 23, 24/3, and 25/1) from the study sample maintained their physical performance irrespective of playing position. These players mainly represent position combinations with less distinct and less consistent differences according to the normative data (e.g., forwards vs. wide midfielders, wide defenders vs. wing backs, wing backs vs. central midfielders; see S1 Table ). Therefore, it seems that the respective players from the study sample barely changed their performance as there was no need according to the positional normative data. Similarly, the behavior of players of the position combination of forwards and wide midfielders was comparable to that reported by Schuth et al. [ 24 ] who found only trivial to moderate ES differences within players interchanging between these two positions.

Lastly, nine players representing 10 position combinations (players 2, 6, 7, 10, 13, 16, 21, 24/2, 25/2, and 25/3) displayed a rather inconsistent physical-performance pattern in relation to their playing positions and, therefore, could not be attributed to one of the two aforementioned groups of players.

Besides this descriptive overview, large individual differences were observed in the way players behaved when acting in different positions. For instance, out of the three players representing the position combination wide defender and central midfielder, two players (players 2 and 24/1) decreased their sprinting distance by a large ES when playing as a central midfielder compared to playing as a wide defender. This change in sprinting performance is in accordance with the respective normative data. Conversely, the third player (player 10) representing this position combination increased his sprinting distance by a moderate ES, thereby contradicting the respective normative data. Another example with a similar pattern can be found in the position combination wide defender and wing back when looking at high-intensity distance. In agreement with the normative data (wing backs cover more high-intensity distance compared to wide defenders), out of nine players, four players (players 7, 14, 15, and 24/1) increased their performance by large ES and two players (players 22 and 23) by moderate ES when playing as a wing back, while 2 players (players 8 and 25/1) maintained their performance. By contrast, one player (player 6) of the same position combination decreased his high-intensity distance by a large ES in the wing-back position.

This is one of the first studies to investigate to what extent the physical performance during matches is not only position but also player specific. The importance of this topic is reflected by the fact that a total of 116 players completed at least one entire match in at least two different positions, leading to 178 single position combinations. Furthermore, considering the final study sample of 25 players, our results highlight that the playing position has a strong influence on the physical performance of players who act in two or more different positions, thereby supporting previous findings [ 24 ]. Albeit, there were pronounced individual differences in the way the players adapted or maintained their performance in relation to their positions.

While these individual differences can to some extent be explained by the individual playing style, another important factor that should be acknowledged in this regard is the variability of physical match performance [ 8 , 20 , 21 , 32 , 33 ]. In particular, it has been shown that variability differs between playing positions and the performance parameter in question [ 32 , 33 ]. Therefore, especially on the individual level, it is complex to determine whether a real change in performance has occurred [ 32 ].

To account for this variability, we chose a minimum of four entire matches for a player to be included in the study sample. A drawback of this approach using a relatively high number of matches required is that it led to a relatively small sample size in which the playing positions were not evenly distributed. For example, only three players of the study sample acted as forwards, while 16 players acted as wide defenders. A possible explanation for this might be that offensive players are more likely to be substituted during a match compared to defensive players, thereby not fulfilling the inclusion criteria of completing the full 90 min [ 34 ]. Nevertheless, future studies including larger sample sizes and a more even distribution of positions are warranted to investigate whether our findings are generalizable. Moreover, such large-scale studies could also take contextual factors (e.g., team tactics, opponent strength) into account which were not considered in the present study [ 4 ]. Lastly, based on the large individual differences in the way players behaved when acting in different positions, it would be interesting to know which type of players (e.g., strong or weak physical capacities) adapt or maintain their performance.

The findings of our study provide a number of potential practical applications, with the first relating to the connection between players adapting performance according to position and their physical capacities (e.g., sprinting and endurance performance). In particular, a change in playing position has a strong influence on the physical match performance of the players. Moreover, previous studies have shown that physical capacities are rather similar between players irrespective of their main playing position [ 15 – 19 ]. Therefore, players may experience different external and internal loads when changing between positions with commonly large performance differences, for example from central defender to wide defender. This change in load and the subsequent individual responses should be taken into consideration by coaches and practitioners in terms of the recovery process after matches. Second, the large individual differences observed highlight that physical match performance should not only be interpreted according to playing position but also to the individual players. Hence, coaches and practitioners should design training programs accounting for both the position(s) the players are supposed to act in and individuality.

Supporting information

S1 fig. normative data (mean values ± sd) for total distance, high-intensity distance, sprinting distance, and number of accelerations separated by playing position..

https://doi.org/10.1371/journal.pone.0256695.s001

S1 Table. Mean difference, ANOVA, post-hoc test, and ES for total distance, high-intensity distance, sprinting distance, and number of accelerations between playing positions.

https://doi.org/10.1371/journal.pone.0256695.s002

S2 Table. Mean values ± SD, t-test results, and ES of each player of the study sample in relation to playing position for total distance, high-intensity distance, sprinting distance, and number of accelerations.

https://doi.org/10.1371/journal.pone.0256695.s003

Acknowledgments

The authors thank the Deutsche Fußball Liga (DFL) for providing the match data used in this study.

• View Article
• PubMed/NCBI
• Google Scholar
• 27. Deutsche Fußball Liga (DFL). Definitionskatalog Offizielle Spieldaten. [Definition Catalogue Official Match Data]. Document Not Publicly Accessible.; 2019.
• 29. Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. New Jersey: Lawrence Erlbaum; 1988.
• 30. Hopkins WG. A scale of magnitudes for effect statistics. A new view of statistics. 2002: http://www.sportsci.org/resource/stats/effectmag.html .

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Data Descriptor
• Open access
• Published: 28 October 2019

A public data set of spatio-temporal match events in soccer competitions

• Luca Pappalardo   ORCID: orcid.org/0000-0002-1547-6007 1 ,
• Paolo Cintia 2 ,
• Alessio Rossi 2 ,
• Emanuele Massucco 3 ,
• Paolo Ferragina 2 ,
• Dino Pedreschi 2 &
• Fosca Giannotti 1  

Scientific Data volume  6 , Article number:  236 ( 2019 ) Cite this article

90k Accesses

80 Citations

393 Altmetric

Metrics details

• Information technology
• Interdisciplinary studies
• Medical research

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.

Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.9711164

Similar content being viewed by others

Predicting the in-game status in soccer with machine learning using spatiotemporal player tracking data

Steffen Lang, Raphael Wild, … Daniel Link

SC2EGSet: StarCraft II Esport Replay and Game-state Dataset

Andrzej Białecki, Natalia Jakubowska, … Jan Gajewski

Synchronization of passes in event and spatiotemporal soccer data

Henrik Biermann, Rumena Komitova, … Daniel Memmert

Background & Summary

Soccer analytics has attracted interest for a long time 1 , 2 . In the early 1950s Charles Reep collected statistics by hand to suggest that “the key to scoring goals is to transfer the ball as quickly as possible from back to front” 3 , thereby indirectly starting the long-ball movement in English football 4 .

Apart from a few sporadic attempts, it is only in recent years that soccer statistics have developed, thanks to sensing technologies that provide high-fidelity data streams extracted from every match. There are three main data sources available 5 : (i) soccer-logs describe the events that occur during a match and are collected through proprietary tagging software 6 , 7 , 8 , 9 ; (ii) video-tracking data describe the trajectories of players during a match and are collected through video recordings 10 , 11 ; (iii) GPS data describe the trajectories of players during training sessions and are collected through GPS devices worn by the players 12 . Despite this wealth of data, we cannot avoid noticing that soccer datasets are rarely available for scientific research. This limits the development of scientific methods for soccer analytics.

In this paper, we describe an open collection of soccer-logs that cover seven prominent male soccer competitions. The collection has been used recently during the Soccer Data Challenge initiative ( https://sobigdata-soccerchallenge.it/ ) and, to the best of our knowledge, it is the largest collection of soccer-logs ever released to the public. Soccer-logs describe match events , each containing information about its type (pass, shot, foul, tackle, etc.), a time-stamp, the player(s), the position on the field and additional information (e.g., pass accuracy). We believe that these data are greatly beneficial to the scientific community because they can contribute to research in several directions, such as the ones we outline below.

Performance analysis

Soccer-logs can be used to design algorithms for relevant problems such as the evaluation of performance and the discovery of tactics 1 , 5 . The problem of performance evaluation 9 , 13 , 14 is crucial for many actors in the sports industry: from broadcasters who want to solicit critical analysis among the fans, to managers who want to monitor the quality of their players and scouts who aim to improve the retrieval of talents. The automatic discovery of tactics 6 , 15 is also becoming a crucial task: while most tactical analyses are currently performed by reviewing video and matches in person, soccer-logs can be used to perform automatic discovery of tactics, simplifying the complex process of match analysis. While different approaches have been proposed in the literature using different datasets to attack these problems, our dataset is much larger and can serve as a common ground to compare and validate different solutions.

Complex systems analysis

Two soccer teams in a match represent a complex system whose global behavior depends in subtle ways on the dynamics of the interactions among the players. Soccer-logs enable the representation of a team as a network , in which nodes represent players and the edges interactions between nodes, usually passes 7 , 14 . While the structure of passing networks is proven to be linked to a team’s strength 7 , 14 , the potential of a multiplex and dynamic representation of networks in soccer has not been much investigated 16 . Soccer-logs allow the definition of different types of interactions between both teammates and opponents by relying on the several event types they encode. Such a richness of information, combined with the dichotomous nature of soccer matches (where collaboration and competition coexist), provides an unprecedented opportunity to investigate novel aspects about the dynamics of complex networks.

Science of success

The availability of a large dataset of sports performance also creates the opportunity to explore the relationship between performance and success, where a team’s success can be intended as its outcome in a competition and the player’s as their popularity or market value. While this relationship has been investigated for individual sports 17 , 18 , apart from a few attempts 19 , 20 there is not much work for soccer, partly due to the absence of publicly available datasets of performance. Our dataset gives the unprecedented opportunity to answer fascinating questions like: ‘What are the tactical patterns of successful teams?’, ‘What are the factors influencing a player’s popularity and market value?’ and ‘To what extent is success predictable from the observable performance?’

The data described in this paper have been collected and provided by Wyscout, a leading company in the soccer industry which connects soccer professionals worldwide, supports more than 50 soccer associations and more than 1,000 professional clubs around the world. The procedure of data collection is performed by expert video analysts (the operators), who are trained and focused on data collection for soccer, through a proprietary software (the tagger). The tagger has been developed and improved over several years and it is constantly updated to always guarantee better and better performance at the highest standards. Based on the tagger and the videos of soccer games, to guarantee the accuracy of data collection, the tagging of events in a match is performed by three operators, one operator per team and one operator acting as responsible supervisor of the output of the whole match. Optionally for near-live data delivery a team of four operators is used, one of them acting to speed up the collection of complex events which need additional and specific attributes or a quick review.

The tagging of a match consists of three main steps.

Step 1: setting formations

At the beginning of the match, an operator sets the teams’ starting formations, the positions of the players on the pitch and their jersey number. The formation of a team consists of the list of players in the starting lineup and the list of players on the bench.

Step 2: event tagging

For each ball touch in the match, the operator selects one player and creates a new event on the timeline. The operator then adds the type (e.g., pass, duel, shot, etc.) and subtype (e.g., a duel can be aerial or ground) of the event by using a special custom keyboard which gives operators the possibility to insert events and data in a streamlined way (Fig.  1a ). The operator finally adds the coordinates on the pitch and all the additional attributes for the event. These can be different depending on the event type: such as pass high/low, foot, dribbling side and so forth (Fig.  1b ). When a player shoots on goal, like in the example of Fig.  1b for player n.6 (Koke), the system asks the operator to fill a shot specific module that collects where the shot ends (on goal, out of goal, on post and exact position).

The process of tagging the soccer events from a match video. ( a ) Screenshot from the tagging software. An action is tagged by an operator via a special custom keyboard, thus creating a new event on the match timeline. ( b ) When the event position on the pitch is set, the shot specific input module appears (top). Event related input modules also appear for setting additional attributes of the occurring event (bottom).

Step 3: quality control

After the tagging, a procedure of quality control for each match is performed, mainly consisting of two different steps. The first step is automatic: an algorithm is used to avoid the majority of the errors made by operators, considerably reducing the margin of error. For example the algorithm matches the events tagged by both operators to crosscheck if they both collected events involving both teams, like duels, with the same positioning and interpretation. Similarly, the algorithm suggests events missed by the operators or searches for impossible combinations of event sequences. The second step of quality control is manual and supervised by quality controllers. It mainly consists of an in-depth check that is carried out once the match is completed. Going through each event of some sample matches, the controller can see and eventually correct any entered parameter. Sample matches for quality control are chosen by another algorithm in order to guarantee a well distributed and statistically meaningful coverage with respect to the kind and amount of analyzed matches.

Data Records

The data sets are released under the CC BY 4.0 License and are publicly available on figshare 21 .

The data refer to season 2017/2018 of five national soccer competitions in Europe: Spanish first division, Italian first division, English first division, German first division, French first division. These competitions are the most important in Europe according to the UEFA country coefficient, which is used to rank the football associations of Europe and thus determine the number of clubs from an association that will participate in the UEFA Champions League and the UEFA Europa League ( https://www.uefa.com/memberassociations/uefarankings/country/#/yr/2019 ). In addition, we provide the data of the World cup 2018 and the European cup 2016, which are competitions for national teams. In total, we provide seven data sets corresponding to information about all competitions, matches, teams, players, events, referees and coaches. Each data set is provided in JSON format (JavaScript Object Notation), an open-standard file format that uses human-readable and machine-processable text to transmit data objects consisting of attribute-value pairs and array data types (or any other serializable value). Table  1 shows the list of competitions we make available with their total number of matches, events and players. The data covers a total of around 1,941 matches, 3,251,294 events and 4,299 players.

Competitions

The competitions data set describes the seven competitions. Each competition is a document consisting of the following fields 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ):

area : denotes the geographic area associated with the league as a sub-document, using the ISO 3166-1 specification;

format : the format of the competition. All competitions for clubs (Spanish first division, Italian first division, English first division, German first division, French first division) have value “Domestic league”. The competitions for national teams (World cup 2018, European cup 2016) have value “International cup”;

name : the official name of the competition (e.g., Spanish first division, Italian first division, World cup 2018, etc.);

type : the typology of the competition. It is “club” for the competitions for clubs (Spanish first division, Italian first division, English first division, German first division, French first division) and “international” for the competitions for national teams (World cup 2018, European cup 2016);

wyId : the unique identifier of the competition, assigned by Wyscout.

Box  1 shows a document in the competitions data set referring to the Italian first division (“name”: “Italian first division”), a competition for clubs (“type”: “club” and “format”: “Domestic League”) held in Italy (see field “area”).

The matches data set describes all the matches we make available. Each match is a document consisting of the following fields 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ):

competitionId: the identifier of the competition to which the match belongs to. It is a integer and refers to the field “wyId” of the competition document;

date and dateutc : the former specifies date and time when the match starts in explicit format (e.g., May 20, 2018 at 8:45:00 PM GMT + 2), the latter contains the same information but in the compact format YYYY-MM-DD hh:mm:ss;

duration : the duration of the match. It can be “Regular” (matches of regular duration of 90 minutes + stoppage time), “ExtraTime” (matches with supplementary times, as it may happen for matches in continental or international competitions), or “Penalities” (matches which end at penalty kicks, as it may happen for continental or international competitions);

gameweek : the week of the league, starting from the beginning of the league;

label : contains the name of the two clubs and the result of the match (e.g., “Lazio - Internazionale, 2–3”);

roundID : indicates the match-day of the competition to which the match belongs to. During a competition for soccer clubs, each of the participating clubs plays against each of the other clubs twice, once at home and once away. The matches are organized in match-days: all the matches in match-day i are played before the matches in match-day i  + 1, even tough some matches may be postponed to facilitate players and clubs participating in Continental or Intercontinental competitions. During a competition for national teams, the “roundID” indicates the stage of the competition (eliminatory round, round of 16, quarter finals, semifinals, final);

seasonId : indicates the season of the match;

status : it can be “Played” (the match has officially started and finished), “Cancelled” (the match has been canceled before it started), “Postponed” (the match has been postponed and no new date and time is available yet) or “Suspended” (the match has been suspended by the referee because of conditions which make it impossible to continue play, such as inclement weather or power failure, and no new date and time is available yet);

venue : the stadium where the match was held (e.g., “Stadio Olimpico”);

winner : the identifier of the team that won the game, or 0 if the match ended with a draw;

wyId : the identifier of the match, assigned by Wyscout;

teamsData : it contains several subfields describing information about each team that is playing that match, such as lineup, bench composition, list of substitutions, coach and scores:

hasFormation : it has value 0 if no formation (lineups and benches) is present, and 1 otherwise;

score : the number of goals scored by the team during the match (not counting penalties);

scoreET : the number of goals scored by the team during the match, including the extra time (not counting penalties);

scoreHT : the number of goals scored by the team during the first half of the match;

scoreP : the total number of goals scored by the team after the penalties;

side : the team side in the match (it can be “home” or “away”);

teamId : the identifier of the team;

coachId : the identifier of the team’s coach;

bench : the list of the team’s players that started the match on the bench and some basic statistics about their performance during the match (goals, own goals, cards);

lineup : the list of the team’s players in the starting lineup and some basic statistics about their performance during the match (goals, own goals, cards);

substitutions : the list of team’s substitutions during the match, describing the players involved and the minute of the substitution.

Box  2 shows a document describing a match between Lazio and Internazionale (“label”: “Lazio - Internazionale, 2–3”) of the Italian first division (“competitionId”: 524), held on May 20th 2018 (see fields “date” and “dateutc”). Box  3 shows the structure of the formation subdocument for one of the teams, which includes the list of players on the bench, the list of players in the starting lineup and the list of substitutions made by the team.

The teams data set describes the clubs or national teams playing in the seven competitions. Each document in this data set consists of the following fields 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ):

city : the city where the team is located. For national teams it is the capital of the country;

name : the common name of the team;

area : information about the geographic area associated with the team;

wyId : the identifier of the team, assigned by Wyscout;

officialName : the official name of the team (e.g., Juventus FC);

type : the type of the team. It is “club” for teams in the competitions for clubs (Spanish first division, Italian first division, English first division, German first division, French first division.) and “national” for the teams in international competitions (World cup 2018, European cup 2016);

Box  4 shows a document describing team Juventus (“name”: “Juventus”) which is located in Turin (“city”: “Torino”) in Italy (see field “area”).

The players data set describes all players in the seven competitions 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ). Each document in this data set consists of the following fields:

birthArea : geographic information about the player’s birth area;

birthDate : the birth date of the player, in the format “YYYY-MM-DD”;

currentNationalTeamId : the identifier of the national team where the players currently plays;

currentTeamId : the identifier of the team the player plays for. The identifier refers to the field “wyId” in a team document;

firstName : the first name of the player;

lastName : the last name of the player;

foot : the preferred foot of the player;

height : the height of the player (in centimeters);

middleName : the middle name (if any) of the player;

passportArea : the geographic area associated with the player’s current passport;

role : the main role of the player. It is a subdocument containing the role’s name and two abbreviations of it;

shortName2 : the short name of the player;

weight : the weight of the player (in kilograms);

wyId : the identifier of the player, assigned by Wyscout.

Box  5 shows a document describing player Lionel Andres Messi Cuccittini (“shortName2”: “L. Messi”), who was born in Argentina (see field “birthArea”) and has the Spanish passport (see “passportArea”). From the document we observe that Messi’s preferred foot is the left foot (“foot”: “left”), his height and weight are 170 centimeters (“height”: 170) and 72 kilograms (“weight”: 72) respectively, he preferably plays as a forward (see field “role”) and he was born in 1987 (“birthDate”: “1987-06-24”).

The events data set describes all the events that occur during each match 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ). Each event document contains the following information:

eventId : the identifier of the event’s type. Each eventId is associated with an event name (see next point);

eventName : the name of the event’s type. There are seven types of events (see Table  2 ): pass, foul, shot, duel, free kick, offside and touch;

subEventId : the identifier of the subevent’s type. Each subEventId is associated with a subevent name (see next point);

subEventName : the name of the subevent’s type. Each event type is associated with a different set of subevent types (see Table  2 );

tags : a list of event tags, each describing additional information about the event (e.g., accurate). Each event type is associated with a different set of tags (see Table  2 ). The Wyscout documentation provides a mapping of the tag identifiers to the corresponding names and descriptions ( https://apidocs.wyscout.com/ );

eventSec : the time when the event occurs (in seconds since the beginning of the current half of the match);

id : a unique identifier of the event;

matchId : the identifier of the match the event refers to. The identifier refers to the field “wyId” in a match document;

matchPeriod : the period of the match. It can be “1H” (first half of the match), “2H” (second half of the match), “E1” (first extra time), “E2” (second extra time) or “P” (penalties time);

playerId : the identifier of the player who generated the event. The identifier refers to the field “wyId” in a player document;

positions : the origin and destination positions associated with the event. Each position is a pair of coordinates ( x , y ). The x and y coordinates are always in the range [0, 100] and indicate the percentage of the field from the perspective of the attacking team. In particular, the value of the x coordinate indicates the event’s nearness (in percentage) to the opponent’s goal, while the value of the y coordinates indicates the event’s nearness (in percentage) to the right side of the field;

teamId : the identifier of the player’s team. The identifier refers to the field “wyId” in a team document.

Box  6 shows an example of pass event (“eventId”: 8, “eventName”: “Pass”) generated by player 3344 (“playerId”: 3344) of team 3161 (“teamId”: 3161) in match 2576335 (“matchId”: 2576335) at second 2.41 of the first half of the match (“eventSec”: 2.4175, “matchPeriod”: “1H”). The pass started at position (49, 50) of the field and ended at position (38, 58) of the field (see field “positions”). Moreover, the pass was accurate as indicated by the presence of tag 1801 (field “tags”).

The coaches data set describes all coaches of the clubs and the national teams of the seven competitions we make available 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ). It consists of the following fields:

wyId : the identifier of the coach, assigned by Wyscout.

shortName : the short name of the coach;

firstName : the first name of the coach;

middleName : the middle name (if any) of the coach;

lastName : the last name of the coach;

birthDate : the birth date of the coach, in the format “YYYY-MM-DD”;

birthArea : geographic information about the coach’s birth area;

passportArea : the geographic area associated with the coach’s current passport;

currentTeamId : the identifier of the coach’s team. The identifier refers to the field “wyId” in a team document.

Box  7 shows a document describing coach Maurizio Sarri (“shortName”: “M. Sarri”), who was born in Italy (see field “birthArea”), has the Italian passport (see “passportArea”) and he was born in 1959 (“birthDate”: “1959-01-10”).

The referees data set describes all referees in the national and international competitions we make available 21 (see Wyscout documentation for further details at https://apidocs.wyscout.com/ ). It consists of the following fields:

wyId : the identifier of the referee, assigned by Wyscout.

shortName : the short name of the referee;

firstName : the first name of the referee;

middleName : the middle name (if any) of the referee;

lastName : the last name of the referee;

birthDate : the birth date of the referee, in the format “YYYY-MM-DD”;

birthArea : geographic information about the referee’s birth area;

passportArea : the geographic area associated with the referee’s current passport;

Box  8 shows a document describing referee William Collum (“shortName”: “W. Collum”), who was born in Scotland (see field “birthArea”), has a Scottish passport (see “passportArea”) and was born in 1979 (“birthDate”: “1979-01-18”).

Box 1 Example of document in the competitions data set describing the Italian first division.

Box 2 Example of a document in the matches data set describing a match between Lazio and Internazionale.

Box 3 Example of team document describing the club Juventus FC.

Box 4 Information about a team in the teams data set.

Box 5 Information about a player contained into the players data set.

Box 6 Information about a pass event contained into the events data set.

Box 7 Information about a coach in the coaches data set.

Box 8 Information about a referee contained into the referee data set.

Technical Validation

In general, based on the events data set, a soccer match consists of an average of 1,682 ± 101 events (Fig.  2b ), with an inter-time between two consecutive events of 3.59 ± 7.42 seconds. There are on average 59 ± 29 events observed for a player in a match, one every 78.78 ± 105.64 seconds, confirming that soccer players are typically in ball possession for less than two minutes 22 . Passes are the most frequent events, accounting for around 50% of the total events (Fig.  2a ). Duels (e.g., tackles and dribbles) are the second most frequent events (≈28%), while shots account for about 1.5% of the total events. The goals scored, the most important events in soccer since they determine a match outcome, are the rarest ones accounting for less than 1% of the total number of events. We provide an example of all the events (1,620) observed for the match “Lazio - Internazionale” of the Italian first division (May 20, 2018), plotted on the position of the field where they have occurred (Fig.  2c ).

Statistics of the events data set. ( a ) Frequency of events per type. ( b ) Distribution of the number of events in soccer matches. ( c ) Events produced by the two teams in the match Lazio (cyan points) vs. Internazionale (black squares). The events are plotted on the position of the field where they occurred.

Spatial dimension

By looking at the position of the field where the events occur, we can investigate interesting aspects of a soccer match, such as the spatial distribution of players and events. For example the kernel density plot in Fig.  3a shows that passes are distributed mostly in the center of the field, where actually most of the match takes place. As one could expect, we observe differences in the spatial distribution of events when we select the players by their role: while the events of forwards are observed mainly in the opponent’s half of the field (Fig.  3h ), the events of defenders are observed mostly in the own half and on the sides of the field (Fig.  3g ). Similarly, as expected the spatial distribution of events change with their type: attacking events (e.g., shots) are mostly observed close to the opponent’s goal (Fig.  3b ), while defensive events (e.g., clearances) are mostly observed close to the team’s own goal (Fig.  3f ). The spatial dimension of match events can provide us with information about a player’s behavior during a match, giving for example the possibility to determine a player’s profile from his average position during a match 13 .

Distribution of positions per event type. ( a–f ) Kernel density plots showing the distribution of the events’ positions during match. The darker is the green, the higher is the number of events in a specific field zone. ( g–i ) Distribution of the passes’ position during a match for each player’s role. The darker is the color, the higher is the number of passes in a specific field zone.

Temporal dimension

By looking at when the events occur during a game, we can investigate interesting dynamics of teams and players. For example, Fig.  4 shows that goals are scored more frequently in the second half of the match 23 , 24 , mirroring several of the possible factors that could affect scoring, such as a decrease of attention by the defenders towards the end of the match due to a loss of stamina, or a more offensive attitude of the opponents who try to win or equalize the match. Similarly, we observe that the frequency of other rare events like yellow and red cards is the highest in the recovery time. This aspect could highlight the presence of a bias by the referees who are less prone to award a card in the beginning of a match (as suggested in 25 ), a reduction of stamina or an increment of aggression of players at the end of the match.

In-match evolution of the number of events. Number of events (i.e., goals on the top plot, yellow cards in the middle plot and the red cards in the bottom plot) that occur in all the matches in the data set, with time windows of 5 minutes.

Another aspect that can be investigated by combining the spatial and the temporal dimensions of soccer-logs are the so-called invasion index , a measure of how close to the opponent’s goal a team plays during a match (i.e., its dangerousness), and acceleration index , a measure of how fast a team reaches the closest position to the opponent’s goal 26 . By exploiting the spatial and temporal dimension of soccer-logs, the invasion index can be computed on each possession phase, which is defined as a sequence of events on the ball made by a team before the opponents gain the possession. To compute the invasion index of a possession phase we compute: (i) for each event in the possession phase, the probability of scoring from the position where the event occurs (defined as the fraction of goals that have been scored from that position); (ii) we take the highest of these probabilities. A team’s overall invasion index during a match is simply the average invasion index across its possession phases. Figure  5 shows the invasion and acceleration index of the teams throughout the match Roma - Fiorentina (0–2), played on April 7, 2018. We observe that Fiorentina has on average a higher invasion index than Roma (0.27 ± 0.33 and 0.23 ± 0.31, respectively).

Invasion index and acceleration index for a game in the match data set. Bold lines represent the rolling mean of, respectively, invasion index ( a ) and acceleration index ( b ), while thin lines represent the individual values computed for each possession phase of each team. Purple vertical lines refer to the two goal scored by Fiorentina during the match, while the red vertical line indicates the half time of the match.

A team’s average acceleration index is another measure of its playing efficacy during a match. The acceleration index of a team’s possession phase is computed as the ratio between its invasion index and the square of the time between the first event and most dangerous event of the possession phase. A team’s average acceleration index during a match is the average acceleration index across its possession phases. Similarly to the invasion index, Fiorentina has a higher average acceleration than Roma (Roma: 0.06 ± 0.16, Fiorentina: 0.07 ± 0.15).

Both the invasion and the acceleration indices show that Fiorentina (the winner of the match) was more dangerous during the match, staying closer to the opponent’s goal and reaching dangerous zones faster than Roma.

Team analysis

Soccer-logs enable the analysis of the interactions between players through the reconstruction of a team’s passing network 7 , 14 , a representation of the movements of the ball between teammates during a match. A passing network allows identifying the key players in the team, i.e., the ones having more connections to the teammates or a high passing activity 27 , 28 . Figure  6 shows two examples of a team passing network for the match Napoli - Juventus (Italian first division). Although Napoli engaged in more passes than Juventus (666 vs. 332), the two passing networks show similar average weighted out-degrees (1.01 ± 0.93% and 1.10 ± 0.84%, respectively). However, Juventus’ playing style resulted in a higher connectivity 29 , defined as the network’s second smallest eigenvalue (i.e., a root of the characteristic equation of a matrix). This value indicates the robustness of a team, i.e., the strength of the links between its players. As a matter of fact, large values of connectivity between teammates are associated with a better overall team performance.

Representation of the player passing networks of the match Napoli-Juventus. Nodes represent players, edges represent passes between players. The size of the nodes reflects the number of ingoing and outgoing passes (i.e. node’s degree), while the size of the edges is proportional to the number of passes between the players.

The reconstruction of passing networks from soccer-logs enables several performance analyses 7 . For example, by using the passing network and the players’ position during a pass it is possible to identify the most efficient tactical patterns across teams 30 , 31 .

Player analysis

Soccer-logs can be used to compare the performance of players and track their evolution in time. As an example, we compare three forwards with different characteristics – L. Messi (FC Barcelona), C. Ronaldo (Juventus FC) and M. Salah (Liverpool). We observe that L. Messi has the highest passing activity: while he produces 49 ± 19 passes per match on average, C. Ronaldo and M. Salah produce 26 ± 6 and 25 ± 9 passes per matches, respectively. Additionally, we observe that L. Messi engages in more duels per match (25 ± 8) than C. Ronaldo and M. Salah (15 ± 5 and 21 ± 7 duels per match). The data we release to the public also enable the computation of several performance metrics, such as Flow Centrality 14 and PlayeRank 13 . A player’s flow centrality in a match is defined as his betweenness centrality in the passing network 14 . Figure  7a shows the distribution of flow centrality of L. Messi, C. Ronaldo and M. Salah for the matches in season 2017/2018. L. Messi results in a higher flow centrality (0.10 ± 0.01) than C. Ronaldo and M. Salah (0.09 ± 0.01 and 0.09 ± 0.01, respectively).

Distribution of flow centrality and PlayeRank score for three top players. ( a ) Distribution of the flow centrality of L. Messi (red line), C. Ronaldo (blue line) e M. Salah (black line) during the soccer season 2017/2018. ( b ) Performance quality calculated as the PlayeRank score of L. Messi (red line), C. Ronaldo (blue line), and M. Salah (black line).

The performance quality of the players during the season can be assessed using PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the soccer players’ performance quality in a match or in a series of matches 13 . Figure  7b shows that the three aforementioned players have different performance trends during the season. M. Salah obtained his best performance in the first part of the season, then decreasing during the course of the season. In contrast, L. Messi significantly increases his performance quality throughout the season while C. Ronaldo, who was not playing the first part of the season due to an injury, has on average a performance quality slightly higher than Salah but lower than Messi. We can conclude that, according to two measures computed on soccer-logs, Messi performs the best both in terms of passing centrality and performance quality.

Code availability

The code to reproduce the plots in the paper is available upon request by writing at [email protected] or [email protected].

Bornn, L., Cervone, L. D. & Fernández, J. Soccer analytics: Unravelling the complexity of “the beautiful game”. Significance 15 , 26–29 (2018).

Article   Google Scholar  

Anderson, C. & Sally, D. The Numbers Game: Why Everything You Know About Football is Wrong. Penguin Books (2013)

Reep, C. & Benjamin, B. Skill and Chance in Association Football. Journal of the Royal Statistical Society 131 , 581–585 (1968).

Sykes J. & Paine N. How One Man’s Bad Math Helped Ruin Decades Of English Soccer. Five Thirty Eight (2016)

Gudmundsson, J., Butte, A. J. & Horton, M. Spatio-Temporal Analysis of Team Sports. ACM Computing Surveys 50 (2), 22:1-22:34 (2017).

Decroos, T., Van Haaren, J. & Davis, J. Automatic Discovery of Tactics in Spatio-Temporal Soccer Match Data. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD ’18) , 223–232 (2018).

Cintia, P., Pappalardo, L., Pedreschi, D., Giannotti, F. & Malvaldi, M., The harsh rule of the goals: Data-driven performance indicators for football teams. Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA) , 1–10 (2015).

Cintia, P., Rinzivillo, S. & Pappalardo, L. Network-based Measures for Predicting the Outcomes of Football Games. Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (MLSA) , 46–54 (2015).

Brooks, J., Kerr, M. & Guttag, J. Developing a Data-Driven Player Ranking in Soccer Using Predictive Model Weights. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16) , 49–55 (2016).

Bornn, L. & Fernendez, J. Wide Open Spaces: A statistical technique for measuring space creation in professional soccer. MIT Sloan Sports Analytics Conference 2018 (2018).

Wei, X., Sha, L., Lucey, P., Morgan, S. & Sridharan, S. Large-Scale Analysis of Formations in Soccer. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) , 1–8 (2013).

Rossi, A. et al . Effective injury forecasting in soccer with GPS training data and machine learning. PloS One 13 (7), 1–15 (2018).

Pappalardo, L. et al . PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach. ACM Transactions on Intelligent Systems and Technology (TIST), 10 (5), 59:1-59:27 (2018).

Duch, J., Waitzman, J. S. & Amaral, L. A. N. Quantifying the Performance of Individual Players in a Team Activity. PlosOne 5 (6), 1–7 (2010)

Bialkowski, A. et al . Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM) , 725–730 (2014).

Buldú, J. M. et al . Using Network Science to Analyse Football Passing Networks: Dynamics, Space, Time, and the Multilayer Nature of the Game. Frontiers in Psychology 9 , 1664–1078 (2018).

Yucesoy, B. & Barabasi, A.-L. Untangling performance from success. EPJ Data Science 5 (1), 1–17 (2016).

Cintia, P., Pappalardo, L. & Pedreschi, D. “Engine Matters”: A First Large Scale Data Driven Study on Cyclists’ Performance. Proceedings of the 13th IEEE International Conference on Data Mining Workshops , 147–153 (2013).

Pappalardo, L. & Cintia, P. Quantifying the relation between performance and success in soccer. Advances in Complex Systems 21 (3) (2017).

Pappalardo, L., Cintia, P., Pedreschi, D., Giannotti, F. & Barabasi, A.-L. Human Perception of Performance. Preprint at, http://arxiv.org/abs/1712.02224 (2017).

Pappalardo, L. & Massucco, E. Soccer match event dataset. figshare , https://doi.org/10.6084/m9.figshare.c.4415000.v2 (2019).

Link, D. & Hoerning, M. Individual ball possession in soccer. PLoS One 12 (7), 1–15 (2017).

Armatas, V., Yiannakos, A., Papadopoulou, S. & Skoufas, D. Evaluation of goals scored in top ranking soccer matches: Greek “Superleague” 2006-07. Serbian Journal of Sports Sciences 3 (1), 39–43 (2009).

Alberti, G., Iaia, F. M., Arcelli, E., Cavaggioni, L. & Rampinini, E. Goal scoring patterns in major European soccer leagues. Sport Sciences for Health 9 (3), 151–153 (2013).

Unkelbach, C. & Memmert, D. Game Management, Context Effects, and Calibration: The Case of Yellow Cards in Soccer Journal of Sport and Exercise Psychology 1 , 95–109 (2008).

Link, D. & Weber, H. Using individual ball possession as a performance indicator in soccer. Workshop on Large-Scale Sports Analytics (2015)

Gama, J. et al . Network analysis and intra-team activity in attacking phases of professional football. International Journal of Performance Analysis in Sport  14 (3), 692–708 (2014).

Passos, P. et al . Networks as a novel tool for studying team ball sports as complex social systems.  Journal of Science and Medicine in Sport 14 (2), 170–176 (2011).

Article   CAS   Google Scholar  

Clemente, F. M., Martins, F. M. L., Kalamares, D., Wong, D. P. & Mendes, R. S. General network analysis of national soccer teams in FIFA World Cup 2014. International Journal of Performance Analysis in Sport 15 , 80–96 (2015).

Wang, Q., Zhu, H., Hu, W., Shen, Z. & Yao, Y. Discerning tactical patterns for professional soccer teams: an enhanced topic model with applications. 21 th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015).

Rein, R. & Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5 (1) (2016).

Download references

Acknowledgements

This work has been partially funded by EU project SoBigData RI, grant #654024.

Author information

Authors and affiliations.

ISTI-CNR, Pisa, Italy

Luca Pappalardo & Fosca Giannotti

Department of Computer Science, University of Pisa, Pisa, Italy

Paolo Cintia, Alessio Rossi, Paolo Ferragina & Dino Pedreschi

Wyscout, Chiavari, Italy

Emanuele Massucco

You can also search for this author in PubMed   Google Scholar

Contributions

L.P. designed data sets, processed data, made plots and wrote the paper. P.C. designed data sets and processed data. A.R. processed data, made plots and wrote the paper. E.M. designed data sets and wrote the paper. P.F. wrote the paper. D.P. and F.G. provided funding.

Corresponding author

Correspondence to Luca Pappalardo .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ .

The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article.

Reprints and permissions

About this article

Cite this article.

Pappalardo, L., Cintia, P., Rossi, A. et al. A public data set of spatio-temporal match events in soccer competitions. Sci Data 6 , 236 (2019). https://doi.org/10.1038/s41597-019-0247-7

Download citation

Received : 28 February 2019

Accepted : 08 August 2019

Published : 28 October 2019

DOI : https://doi.org/10.1038/s41597-019-0247-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Validation of a motion model for soccer players’ sprint by means of tracking data.

• Takuma Narizuka
• Kenta Takizawa
• Yoshihiro Yamazaki

Scientific Reports (2023)

• Henrik Biermann
• Rumena Komitova
• Daniel Memmert

Influence of Red and Yellow cards on team performance in elite soccer

• Llorenç Badiella
• Martí Casals

Annals of Operations Research (2023)

Analyzing the feature extraction of football player’s offense action using machine vision, big data, and internet of things

• Baiqing Liu

Soft Computing (2023)

A simultaneous electroencephalography and eye-tracking dataset in elite athletes during alertness and concentration tasks

• Xinzhen Pei

Scientific Data (2022)

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• Review Article
• Open access
• Published: 21 September 2020

Reducing Injuries in Soccer (Football): an Umbrella Review of Best Evidence Across the Epidemiological Framework for Prevention

• Oluwatoyosi B. A. Owoeye   ORCID: orcid.org/0000-0002-5984-9821 1 , 2 ,
• Mitchell J. VanderWey 1 &
• Ian Pike 3 , 4  

Sports Medicine - Open volume  6 , Article number:  46 ( 2020 ) Cite this article

39k Accesses

41 Citations

44 Altmetric

Metrics details

Soccer is the most popular sport in the world. Expectedly, the incidence of soccer-related injuries is high and these injuries exert a significant burden on individuals and families, including health and financial burdens, and on the socioeconomic and healthcare systems. Using established injury prevention frameworks, we present a concise synthesis of the most recent scientific evidence regarding injury rates, characteristics, mechanisms, risk and protective factors, interventions for prevention, and implementation of interventions in soccer. In this umbrella review, we elucidate the most recent available evidence gleaned primarily from systematic reviews and meta-analyses. Further, we express the exigent need to move current soccer injury prevention research evidence into action for improved player outcomes and widespread impact through increased attention to dissemination and implementation research. Additionally, we highlight the importance of an enabling context and effective implementation strategies for the successful integration of evidence-based injury prevention programs into real-world soccer settings. This narrative umbrella review provides guidance to inform future research, practice, and policy towards reducing injuries among soccer players.

This review provides a one-stop evidence reference regarding the prevention of soccer injuries, including evidence and perspectives on the implementation of proven interventions.

Overall evidence supports the use of the 11+ neuromuscular training warm-up and focused strength training, and there is emerging evidence for load management programs to mitigate injury risk among soccer players.

Theory-driven dissemination and implementation studies are needed to improve the adoption, adherence, appropriate adaptation, scale-up, and sustainment of evidence-based injury prevention interventions in soccer.

The findings from this review provide guidance to inform future research, practice, and policy towards reducing injuries among soccer players.

Soccer (football) is the most popular sport in the world [ 1 ], with some 270 million involved in the sport worldwide in 2006 [ 2 ]. For approximately 110,000, it is a profession and thus a source of income; for some 38 million registered players, it is a team game organized within leagues and competitions; and for about 226 million others, it is an enjoyable exercise surrogate for fitness and health [ 2 ]. The health benefits of soccer as “medicinal exercise” are well documented, for example, improved cardiovascular health, mental health, and bone health [ 3 ]. However, there is a paradoxical negative effect of soccer on health when players get injured (e.g., obesity or post-traumatic osteoarthritis after an anterior cruciate ligament injury) [ 4 , 5 ]. Furthermore, soccer injuries exert a significant burden on socioeconomic and healthcare systems [ 6 ]. Founded on established epidemiological frameworks describing the sequence of research steps to effective injury prevention practice [ 7 , 8 ]—from identifying injury rates to the implementation of effective interventions—we present a narrative umbrella review that articulates best available evidence to inform guidelines, practice, and policy towards mitigating the risk of injuries in soccer, and in turn maximizing the benefits of participation among individuals.

To achieve the above-mentioned purpose, we conducted methodical searches across five databases (MEDLINE, SPORTDiscus, PsycINFO, CINAHL, and Cochrane Database of Systematic Reviews) from January 2010 to January 2020 to identify all systematic reviews, meta-analyses, reviews, and original research (where limited or no reviews were available) across soccer injury studies that investigated injury incidence, characteristics, mechanisms, risk and protective factors, interventions for prevention, and implementation and evaluation of interventions. A summary of the search records for our primary source of data (systematic and narrative reviews) is presented in Table 1 , and details of the search terms used—key concepts and search words—are presented in an additional file ( Supplementary File ). Our search strategy involved the use of relevant search descriptors of “OR” and “AND” to combine search/key words and key concepts, respectively, after each search word was exploded (exp) to capture all literature possible. Search records were limited to articles with full text, written in the English language, and relating to humans. The same methodology was used to obtain primary research articles where no reviews were available.

Injury Rates

Injury incidence among soccer players differs across levels of participation, age, type of exposure, and sex. The incidence of injuries in soccer is mostly significant during games/matches, ranging from 9.5 to 48.7 injuries/1000 h among competitive male youth players, 2.5 to 8.7 injuries/1000 h among male professional players, and 12.5 to 30.3 injuries/1000 h among female players [ 9 , 10 , 11 , 12 ] (Table 2 ). The incidence of injuries appears higher among males vs. females, and injury incidence is higher during games/matches vs. practice/training for all participation categories, among both male and female players [ 10 , 11 , 12 ]. Soccer players younger than 12 years of age have a lower injury rate (1.0–1.6 injuries per 1000 h) compared to older players [ 9 ].

Injury Location and Type

Most soccer injuries occur in the lower limbs (60–90%), especially the ankle, knee, and thigh [ 10 , 11 , 12 , 13 , 14 ]. Among male players, the most common injuries affect the hamstring muscles followed by the ankle, knee, and groin [ 11 , 13 ]. Comparably, among female players, knee and ankle injuries are the most common, followed by thigh/hamstring injuries [ 10 , 13 ].

Thigh, Knee, and Ankle Injuries

Most thigh injuries result from strains with a high proportion of hamstring injuries, despite quadriceps injuries leading to longer absence from play [ 15 ]. The prevalence and history of hamstring injury is greater among adult professional players (40%) compared to under-20 players (18%) [ 16 ]. Up to 18% of severe soccer injuries presenting at hospital emergency departments involve the knee [ 17 ]. One such injury involves the anterior cruciate ligament (ACL). The ACL injury rate among females (2.0/10,000 athlete exposures) is 2.2 times higher than that of males (0.9/10,000 athlete exposures), independent of participation level [ 18 ]. Ankle injuries account for up to 20% of all soccer injuries with ankle sprains constituting 77% of all ankle injuries [ 14 , 19 ].

The prevalence of concussion in youth soccer appears to be relatively low with an incidence of 0.19 (95% CI 0.16–0.21) concussions per 1000 athletic exposures and 0.27 (95% CI 0.24–0.30) concussions per 1000 athletic exposures among male and female players, respectively [ 20 ]. A higher concussion incidence has been consistently reported among females [ 10 , 20 ].

Injury Mechanisms

Overall, about two-thirds of soccer injuries are traumatic and the other one-third (27–33%) are caused by overuse [ 11 , 12 , 21 ]. These findings are based on a medical attention/time-loss injury definition, and emerging evidence from studies using an all-complaint injury definition suggests that overuse onset injuries may be as prevalent as acute onset injuries [ 22 ]. About two-thirds of traumatic injuries are contact injuries, of which 12–28% are caused by foul play. Notably, non-contact injuries account for 26–58% of all injuries [ 13 , 21 ]. Injuries occur primarily during the initial or final 15 min of the match, indicating the significance of an appropriate warm-up and the effects of fatigue on players [ 23 ].

Risk and Protective Factors

Non-modifiable risk factors, player position.

Goalkeepers are at a lower overall risk of injury compared to outfield players in the male game [ 24 ]. Independent of goalkeepers, current evidence is inconsistent regarding the association between player position and injury risk; however, it appears that strikers may be at a greater risk as compared with other outfield players during matches [ 24 ].

Previous Injury

A history of previous injury continues to be the most consistent and strongest risk factor for future injury, and this also holds true for specific injuries [ 9 , 25 , 26 , 27 , 28 , 29 ]. For example, a history of previous hamstring injury is associated with future hamstring injury among male players [ 25 , 28 ], previous ACL injury is associated with risk of future ACL injury [ 29 ], and previous ankle sprain injury is related to the emergence of new ankle sprain injuries [ 27 ].

Current evidence regarding age as a risk factor for soccer injury is limited. One systematic review suggested that increasing age was a risk factor for future hamstring injury among male players [ 25 ]. Another systematic review concluded that existing literature was insufficient to infer any relationship between age and the risk of ACL injury among soccer players [ 29 ]. In a single prospective study, age > 14 years was a significant risk factor for future acute knee injury among female players [ 30 ].

Familial predisposition for ACL injury is associated with increased risk of ACL injury and acute knee injury [ 29 , 30 ].

Overall, the incidence of injuries is higher among males vs. females [ 10 , 11 ]; however, female sex is associated with increased ACL injury risk [ 29 ].

Competitive Setting

Game exposure demonstrated increased injury risk compared to practice for both male and female soccer players [ 29 , 31 ]. Furthermore, within the practice setting, the risk of injury is higher for scrimmage compared to normal practice and walk-through [ 29 ].

Shoe-Surface Interaction

Current research suggests there is an association between higher shoe-surface interaction and increased ACL injury risk [ 29 ].

Pre-season Knee Complaints

Females presenting with pre-season knee complaints appear to be at increased risk for acute knee injury during the season [ 30 ].

Early Sport Specialization

Though there is a lack of substantive evidence for soccer specifically, early sport specialization has been found to be associated with a greater risk for overuse injuries across multiple youth sports [ 9 ]. One study showed that female soccer players 12–15 years of age playing on more than one team had increased risk for lower extremity overuse injuries [ 32 ].

Growth and Leg Length

Elite male youth soccer players are at greater risk for traumatic injury in the year of peak height velocity [ 33 ]. A recent prospective study of male soccer players aged 10–12 years shows an association between an increase in leg length throughout the season and risk for overuse injury [ 34 ]. The same study suggests an association between longer leg length and risk of overuse injury among male soccer players aged 13–15 years. Additionally, they found a higher weight and a decreased growth rate to be associated with an increased risk of acute injury.

Modifiable Risk Factors

Evidence regarding load-injury relationships among soccer players is still emerging as reviews remain sparse in this area of inquiry. Current evidence across team sports indicates that load, in terms of player exposure and/or exertion, could either be an independent protective or risk factor for injury, depending on whether load administration is optimal and progressive or suboptimal (e.g., load spike), respectively, and that this relationship is likely moderated by other risk factors for injury [ 35 , 36 , 37 , 38 , 39 , 40 ]. Prospective studies showed that a high amount of absolute (accumulated or cumulative) load, based on different calculations of load measures (e.g., 1-weekly, 2-weekly), was associated with greater risk of injury among elite youth and professional soccer players [ 39 , 40 , 41 ]. These findings suggest that it may be expedient to have an absolute load threshold, for example, weekly load threshold, to further mitigate injury risk in soccer, especially youth soccer [ 39 , 40 ]. Altogether, available evidence suggests that avoiding a spike in load (e.g., the acute to chronic workload ratio) is associated with less soccer injuries [ 39 , 40 , 41 ].

Neuromuscular Factors

Hamstring/quadriceps strength ratio imbalance is a key risk factor for hamstring muscle injury; specifically, decreased hamstring strength relative to quadriceps strength is a risk factor for knee ligamentous injuries in both male and female youth soccer players [ 29 , 42 ]. Decreased single leg hop distance is also associated with increased hamstring injury risk [ 43 ]. While current evidence is inconclusive for muscle strength asymmetry (i.e., right vs. left) as a risk factor, eccentric hamstring strength asymmetry is specifically indicated as a key predictor of injury among male youth soccer players [ 26 ]. Furthermore, eccentric hamstring strength (< 256 N) and single leg hamstring bridge scores of less than 20 reps on the right leg are associated with increased risk of hamstring strain [ 43 ]. Poor landing mechanics, specifically, increased dynamic knee valgus, is associated with increased risk for lower limb injury, including ACL injury [ 9 , 42 , 43 ]. Leg dominance and leg asymmetry also relates to increased risk of injury; a difference of 15% or greater, between an individual’s dominant and non-dominant limb, has been shown to predict future injury [ 42 ]. An asymmetry of greater than 4 cm on the anterior reach portion of the Y-balance test places athletes at 2.5 times greater risk for injury among male youth soccer players [ 42 , 43 ]. Hip external rotation strength scores using handheld dynamometry of less than 18% of the individual’s body weight is associated with lower extremity and back injuries [ 43 ]. Additionally, the literature suggests that the risk of injury may increase with altered neuromuscular firing during dynamic movements like cutting or landing, and dynamic stability deficits may increase lower extremity injury risk for male youth soccer players [ 42 ].

Protective Factors

Although mention of protective factors in review level evidence did not exist at the time of this evidence review, findings from original research previously described (under modifiable risk factors) signify load management as a viable target for mitigating injury risk in soccer. For example, an in-season relative load measure of acute to chronic workload ratio of 1 to 1.25 significantly reduced injuries among youth players [ 40 ], and a reduced absolute load significantly reduced injuries among youth and adult professional players [ 39 , 40 ]. Additionally, current evidence suggests that improved neuromuscular capacity and control, including increased quadriceps, hamstring, hip flexor strength, and movement control are protective against injuries among soccer players [ 9 , 26 , 29 , 42 , 43 ].

Opportunities for Prevention

Effective interventions.

Drawing from available evidence regarding modifiable risk factors and protective factors for soccer injuries, injury prevention experts have developed and tested interventions for reducing musculoskeletal injuries in soccer. There is extensive high-quality evidence (including two reviews of systematic reviews) showing the clinical effectiveness of exercise-based interventions in the form of neuromuscular training (NMT) warm-up programs in reducing all soccer-related injuries across sex, ages, and skill levels. Specifically, the 11+ (formerly called the FIFA 11+) warm-up program reduces overall injury rate (i.e., all injuries) by 30 to 47% [ 23 , 44 , 45 , 46 ], lower limb injury rate by 39 to 44% [ 44 , 45 ], overuse injury rate by 55%, and knee injury rate by 52% [ 47 ]. Emerging evidence also suggests that the 11+ Kids (a version for children under 12 years old) is efficacious (48% reduction for all injuries) for reducing injuries in younger players [ 48 ]. Additionally, the “Knee Injury Prevention Program” (KIPP) has the potential to significantly reduce non-contact lower limb injury and overuse injury among young female soccer players by 50% and 56%, respectively [ 47 ].

In a recent systematic review, the application of a variety of exercise-based injury prevention programs for youth players was found to reduce injury rates by up to 46% [ 49 ]. Furthermore, the risk of hamstring injuries can be reduced by up to 51% when the Nordic Hamstring exercise is implemented in isolation [ 50 ]. A recent meta-analysis showed that ankle injuries can be reduced by as much as 40% [ 51 ] and a meta-analysis of meta-analyses [ 52 ] demonstrated that a 50% reduction can be achieved for all ACL injuries in a heterogeneous sample of athletes, including soccer players, when NMT warm-up is implemented.

Specific instructions on how to perform aforementioned NMT warm-up programs can be found in the International Olympic Committee’s “Get Set” app, an innovative and accessible mobile app that provides continued access to illustrative and video information regarding effective sport- and body-specific NMT warm-up programs, including the 11+ program. The 11+ program can also be accessed from the following website: https://www.youtube.com/watch?v=RSJIp7e7fyY

Although concussions are not frequent in soccer, sustaining a concussion may present severe and lasting negative health consequences [ 53 ]. It is important for coaches, parents, and administrators to be aware of concussion signs and symptoms and know what to do if concussion is suspected. For concussion prevention, there is evidence that education about concussion among key stakeholders, e.g., coaches, referees, and parents, can reduce the incidence of concussion and facilitate improved outcomes [ 54 ]. Interventions for primary (e.g., rule change and avoiding a slippery playing surface) and secondary (e.g., concussion recognition and decision on return to playtime) prevention are mainly informational for coaches and parents/guardians. A popular evidence-based educational tool is the Concussion Awareness Training Tool, available at https://cattonline.com .

Cost-Effectiveness of Interventions

Literature regarding the cost-effectiveness of injury prevention interventions in soccer is limited. A reduction of 43% was reported in healthcare costs in the training group that underwent an NMT warm-up similar to the 11+ program with additional use of a wobble-board, when compared to a standard practice control group [ 55 ]. Similarly, the “11+ Kids” program showed a 51% reduction in healthcare costs when compared with a regular warm-up [ 56 ].

Implementation and Evaluation

Literature regarding the evaluation of the implementation of efficacious/effective interventions such as the 11+ and other NMT warm-up programs is advancing despite the lack of reviews [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 ]. However, of all the studies currently available, only two reported using an implementation framework to evaluate a preventative program. The Reach Effectiveness Adoption Implementation Maintenance Framework was used in both studies: one to evaluate an NMT warm-up program for knee/ACL prevention, and the other to evaluate the Adductor Strengthening Program for groin injury prevention [ 57 , 59 ]. Overall, the execution of NMT warm-up programs when implemented ranged between low and moderate [ 60 , 68 ].

To improve the spread and implementation of evidence-based injury prevention intervention in soccer, an understanding of implementation contexts is imperative. Although more rigorous theory-driven studies are needed to further understand potential contextual moderators of successful/unsuccessful implementation, a small number of studies have investigated perceived facilitators and barriers to NMT programs across levels of soccer participation (Table 3 ).

Current Best Practices for Implementation

Literature regarding best practices for onward translation of evidence-based injury prevention programs into routine practice in community and professional soccer remains sparse, and the urgent need for research in this field of inquiry has been identified [ 70 ]. The following conclusions have been reached in existing literature:

Preseason structured coaching workshops have the potential to effectively increase coach attitudes, perceived behavioral control, self-efficacy, and intention and subsequent implementation of NMT programs [ 64 , 71 , 72 ]. However, it remains unclear whether high levels of behavioral determinants, i.e., cognitive and psychosocial factors, would ultimately result in high levels of program adherence and maintenance over time [ 57 ].

Coach-led delivery of the 11+ appears to be relatively sufficient in implementing the program; evidence on the advantage of having additional support or supervision from research or team staff, e.g., strength and conditioning coach, an athletic trainer, or physiotherapist, is mixed [ 57 , 60 , 71 ].

For maximum effectiveness, coaches need to ensure quality delivery to their teams by performing NMT warm-up exercises with proper technique and adhering to the program guidelines, while adapting it to fit their local setting. A minimum of 2× weekly appears to be optimal and thereby recommended [ 58 , 61 ].

Quality implementation requires soccer associations and organizations at the federal, provincial, and community levels to enact policies that enforce injury prevention programs and education and policies that require coaches to use proven NMT warm-up programs such as the 11+ [ 60 , 73 , 74 ].

Conclusions and Call to Action

This review provides guidance to inform future research, policy, and practice towards reducing injuries among soccer players. It presents a one-stop evidence reference regarding the burden, etiology, and prevention of soccer injuries, including current opportunities for evidence-based interventions and their implementation. To achieve desired outcomes and population-level impact from injury prevention research evidence, evidence-based interventions need enabling contexts and effective implementation strategies for a successful integration into real-world settings. Consequently, innovators (e.g., researchers) and implementation actors at the organizational (e.g., football associations, government/public health agencies, non-profit organizations, football clubs) and individual (e.g., coaches, strength and conditioning personnel, medical staff) levels have critical roles to play and are urged to rise to the occasion.

Researchers need to acquire an appreciable level of proficiency in dissemination and implementation research designs to build upon current literature to advance dissemination and implementation science in soccer injury prevention. Specifically, theory-driven dissemination and implementation studies are needed to improve the adoption, adherence, appropriate adaptation, delivery, scale-up, and sustainment of evidence-based injury prevention interventions such as the 11+ in soccer. Researchers should move beyond randomized controlled trials evaluating efficacy in NMT programs (considering that there is extensive evidence supporting NMT efficacy ) to evaluating strategies for implementation in randomized controlled and pragmatic (e.g., quasi-experimental) trials. Further, researchers should use current information on implementation barriers to and facilitators of evidence-based interventions and knowledge from implementation science to conceptualize and test potential implementation strategies. In addition, soccer organizations and their staff, especially coaches, have the obligation of ensuring safety among their players. Collectively, researchers, knowledge brokers, policymakers, leaders, and administrators in soccer and other related organizations need to work collaboratively to move current injury prevention evidence into action in order to protect players’ current and future health.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated during the current study.

Abbreviations

Anterior cruciate ligament

Neuromuscular training

Top-10 trending most popular sports in America 2019 | SportyTell [Internet].

FIFA Big Count 2006: 270 million people active in football [Internet]. 2007 [accessed 2020 Apr 1]. https://www.fifa.com/mm/document/fifafacts/bcoffsurv/bigcount.statspackage_7024.pdf .

Krustrup P, Dvorak J, Junge A, Bangsbo J. Executive summary: the health and fitness benefits of regular participation in small-sided football games. Scand J Med Sci Sports. 2010.

Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: osteoarthritis. Am J Sports Med. 2007.

Toomey CM, Whittaker JL, Nettel-Aguirre A, Reimer RA, Woodhouse LJ, Ghali B, et al. Higher fat mass is associated with a history of knee injury in youth sport. J Orthop Sports Phys Ther. 2017;.

Fuller CW. Assessing the return on investment of injury prevention procedures in professional football. Sports Med. 2019.

van Mechelen W, Hlobil H, Kemper HCG. Incidence, severity, aetiology and prevention of sports injuries: a review of concepts. Vol. 14, Sports Medicine: An International Journal of Applied Medicine and Science in Sport and Exercise. 1992. p. 82–99.

Finch C. A new framework for research leading to sports injury prevention. J Sci Med Sport. 2006;9(1–2):3–9.

Article   Google Scholar  

Watson A, Mjaanes JM. Soccer injuries in children and adolescents. Pediatrics. 2019.

Junge A. Epidemiology in female football players. In: Football traumatology: new trends: Second Edition. 2015.

Pfirrmann D, Herbst M, Ingelfinger P, Simon P, Tug S. Analysis of injury incidences in male professional adult and elite youth soccer players: a systematic review. J Athl Train (Allen Press. 2016 May;51(5):410–424.

López-Valenciano A, Ruiz-Pérez I, Garcia-Gómez A, Vera-Garcia FJ, De Ste CM, Myer GD, et al. Epidemiology of injuries in professional football: a systematic review and meta-analysis. Br J Sports Med. 2020.

Junge A, Dvorak J. Soccer injuries: a review on incidence and prevention. Sports Med. 2004;34(13):929–38.

Feria-Arias E, Boukhemis K, Kreulen C, Giza E. Foot and ankle injuries in soccer. Am J Orthop (Belle Mead NJ). 2018;.

Pfirrmann D, Herbst M, Ingelfinger P, Simon P, Tug S. Analysis of injury incidences in male professional adult and elite youth soccer players: a systematic review. J Athl Train. 2016.

Ribeiro-Alvares JB, Dornelles MP, Fritsch CG, de Lima-e-Silva FX, Medeiros TM, Severo-Silveira L, et al. Prevalence of hamstring strain injury risk factors in professional and under-20 male football (soccer) players. J Sport Rehabil. 2019.

Roth TS, Osbahr DC. Knee injuries in elite level soccer players. American journal of orthopedics (Belle Mead, N.J.). 2018.

Montalvo AM, Schneider DK, Silva PL, Yut L, Webster KE, Riley MA, et al. “What’s my risk of sustaining an ACL injury while playing football (soccer)?” A systematic review with meta-analysis. British Journal of Sports Medicine. 2019.

Fong DT-P, Hong Y, Chan L-K, Yung PS-H, Chan K-M. A systematic review on ankle injury and ankle sprain in sports. Sports Med. 2007;37(1):73–94.

Pfister T, Pfister K, Hagel B, Ghali WA, Ronksley PE. The incidence of concussion in youth sports: a systematic review and meta-analysis. Br J Sports Med. 2016 Mar;50(5):292–7.

Bizzini M, Dvorak J. FIFA 11+: an effective programme to prevent football injuries in various player groups worldwide-a narrative review. Br J Sports Med. 2015;49(9).

Harøy J, Clarsen B, Thorborg K, Hölmich P, Bahr R, Andersen TE. Groin problems in male soccer players are more common than previously reported. Am J Sports Med. 2017;45(6):1304–8.

Sadigursky D, Braid JA, De Lira DNL, Machado BAB, Carneiro RJF, Colavolpe PO. The FIFA 11+ injury prevention program for soccer players: a systematic review. BMC Sports Sci Med Rehabil. 2017.

Della Villa F, Mandelbaum BR, Lemak LJ. The effect of playing position on injury risk in male soccer players: systematic review of the literature and risk considerations for each playing position. American journal of orthopedics (Belle Mead, N.J.). 2018.

Hughes T, Sergeant JC, Parkes MJ, Callaghan MJ. Prognostic factors for specific lower extremity and spinal musculoskeletal injuries identified through medical screening and training load monitoring in professional football (soccer): a systematic review. BMJ Open Sport Exerc Med. 2017.

McCall A, Carling C, Davison M, Nedelec M, Le Gall F, Berthoin S, et al. Injury risk factors, screening tests and preventative strategies: a systematic review of the evidence that underpins the perceptions and practices of 44 football (soccer) teams from various premier leagues. Br J Sports Med. 2015 May;49(9):583–9.

Owoeye OBA, Palacios-Derflingher LM, Emery CA. Prevention of ankle sprain injuries in youth soccer and basketball: effectiveness of a neuromuscular training program and examining risk factors. Clin J Sport Med. 2018;28(4):325–31.

van Beijsterveldt AMC, van de Port IGL, Vereijken AJ, Backx FJG. Risk factors for hamstring injuries in male soccer players: a systematic review of prospective studies. Scand J Med Sci Sports. 2013;23(3):253–62.

Volpi P, Bisciotti GN, Chamari K, Cena E, Carimati G, Bragazzi NL. Risk factors of anterior cruciate ligament injury in football players: a systematic review of the literature. Muscles, Ligaments and Tendons Journal. 2016.

Hägglund M, Waldén M. Risk factors for acute knee injury in female youth football. Knee Surgery, Sport Traumatol Arthrosc. 2016;24(3):737–46.

Emery CA, Meeuwisse WH, Hartmann SE. Evaluation of risk factors for injury in adolescent soccer: implementation and validation of an injury surveillance system. Am J Sports Med. 2005;33(12):1882–91.

O’Kane JW, Neradilek M, Polissar N, Sabado L, Tencer A, Schiff MA. Risk factors for lower extremity overuse injuries in female youth soccer players. Orthop J Sport Med. 2017.

Read P, Oliver JL, De Ste Croix MBA, Myer GD, Lloyd RS. Injury risk factors in male youth soccer players. Strength Cond J (Lippincott Williams Wilkins). 2015 Oct;37(5):1–7.

Google Scholar  

Rommers N, Rössler R, Goossens L, Vaeyens R, Lenoir M, Witvrouw E, et al. Risk of acute and overuse injuries in youth elite soccer players: body size and growth matter. J Sci Med Sport. 2020.

Gabbett TJ. The training-injury prevention paradox: should athletes be training smarter and harder? Br J Sports Med. 2016;.

Malone S, Hughes B, Doran DA, Collins K, Gabbett TJ. Can the workload–injury relationship be moderated by improved strength, speed and repeated-sprint qualities? J Sci Med Sport. 2019;22(1).

Gabbett TJ, Hulin BT, Blanch P, Whiteley R. High training workloads alone do not cause sports injuries: how you get there is the real issue. Br J Sports Med. 2016;50(8):444–5.

Owoeye OBA. Digging deep into the etiology of basketball injuries: a complex systems approach for risk mitigation. In: Lavar L, Kocaoglu B, Bytomski J, Cole B, Arundale A AN, editor. The basketball sports medicine and science book. Springer; 2020.

Watson A, Brickson S, Brooks A, Dunn W. Subjective well-being and training load predict in-season injury and illness risk in female youth soccer players. Br J Sports Med. 2017.

Bowen L, Gross AS, Gimpel M, Li F-X. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. Br J Sports Med. 2016;bjsports-2015-095820.

Malone S, Owen A, Newton M, Mendes B, Collins KD, Gabbett TJ, et al. The acute:chonic workload ratio in relation to injury risk in professional soccer. J Sci Med Sport. 2016;0(0):646–8.

Read PJ, Oliver JL, De Ste Croix MBA, Myer GD, Lloyd RS. Neuromuscular risk factors for knee and ankle ligament injuries in male youth soccer players. Sports Medicine. 2016.

Read PJ, Oliver JL, De Ste Croix MBA, Myer GD, Lloyd RS. A review of field-based assessments of neuromuscular control and their utility in male youth soccer players. Journal of Strength and Conditioning Research. 2019.

Al Attar WSA, Alshehri MA. A meta-analysis of meta-analyses of the effectiveness of FIFA injury prevention programs in soccer. Scand J Med Sci Sports. 2019.

Al Attar WSA, Soomro N, Pappas E, Sinclair PJ, Sanders RH. How effective are F-MARC injury prevention programs for soccer players? A systematic review and meta-analysis. Sports Med. 2016;46(2):205–17.

Thorborg K, Krommes KK, Esteve E, Clausen MB, Bartels EM, Rathleff MS. Effect of specific exercise-based football injury prevention programmes on the overall injury rate in football: a systematic review and meta-analysis of the FIFA 11 and 11+ programmes. Br J Sports Med. 2017;bjsports-2016-097066.

Herman K, Barton C, Malliaras P, Morrissey D. The effectiveness of neuromuscular warm-up strategies, that require no additional equipment, for preventing lower limb injuries during sports participation: a systematic review. BMC Med. 2012.

Rössler R, Junge A, Bizzini M, Verhagen E, Chomiak J, aus der Fünten K, et al. A multinational cluster randomised controlled trial to assess the efficacy of ‘11+ Kids’: a warm-up programme to prevent injuries in children’s football. Sport Med. 2018;.

Hanlon C, Krzak JJ, Prodoehl J, Hall KD. Effect of injury prevention programs on lower extremity performance in youth athletes: a systematic review. Sport Heal A Multidiscip Approach. 2020;12(1):12–22.

Al Attar WSA, Soomro N, Sinclair PJ, Pappas E, Sanders RH. Effect of injury prevention programs that include the Nordic hamstring exercise on hamstring injury rates in soccer players: a systematic review and meta-analysis. Sports Med. 2017;47(5):907–16.

Grimm NL, Jacobs JC, Kim J, Amendola A, Shea KG. Ankle injury prevention programs for soccer athletes are protective: a level-I meta-analysis. J Bone Joint Surg (Am Vol). 2016.

Webster KE, Hewett TE. Meta-analysis of meta-analyses of anterior cruciate ligament injury reduction training programs. J Orthop Res. 2018.

Gardner RC, Yaffe K. Epidemiology of mild traumatic brain injury and neurodegenerative disease. Molecular and Cellular Neuroscience. 2015.

Tator CH. Sport concussion education and prevention. J Clin Sport Psychol. 2012.

Marshall DA, Lopatina E, Lacny S, Emery CA. Economic impact study: neuromuscular training reduces the burden of injuries and costs compared to standard warm-up in youth soccer. Br J Sports Med. 2016;bjsports-2015-095666-.

Rossler R, Verhagen E, Rommers N, Dvorak J, Junge A, Lichtenstein E, et al. Comparison of the “11+ Kids” injury prevention programme and a regular warmup in children’s football (soccer): a cost effectiveness analysis. Br J Sports Med. 2019.

Frank BS, Register-Mihalik J, Padua DA. High levels of coach intent to integrate a ACL injury prevention program into training does not translate to effective implementation. J Sci Med Sport. 2015;18(4):400–6.

Hägglund M, Atroshi I, Wagner P, Waldén M, Hagglund M, Atroshi I, et al. Superior compliance with a neuromuscular training programme is associated with fewer ACL injuries and fewer acute knee injuries in female adolescent football players: secondary analysis of an RCT. Br J Sports Med. 2013;47(15):974–9.

Harøy J, Wiger EG, Bahr R, Andersen TE. Implementation of the adductor strengthening programme: players primed for adoption but reluctant to maintain — a cross-sectional study. Scand J Med Sci Sports. 2019.

Joy EA, Taylor JR, Novak MA, Chen M, Fink BP, Porucznik CA. Factors influencing the implementation of anterior cruciate ligament injury prevention strategies by girls soccer coaches. J Strength Cond Res. 2013;27(8):2263–9.

Soligard T, Nilstad A, Steffen K, Myklebust G, Holme I, Dvorak J, et al. Compliance with a comprehensive warm-up programme to prevent injuries in youth football. Br J Sports Med. 2010;44(11):787–93.

Steffen K, Emery CA, Romiti M, Kang J, Bizzini M, Dvorak J, et al. High adherence to a neuromuscular injury prevention programme (FIFA 11+) improves functional balance and reduces injury risk in Canadian youth female football players: a cluster randomised trial. Br J Sports Med. 2013;47(12):794–802.

Lindblom H, Carlfjord S, Hägglund M. Adoption and use of an injury prevention exercise program in female football: a qualitative study among coaches. Scand J Med Sci Sports. 2018;28(3):1295–303.

Article   CAS   Google Scholar  

McKay CD, Merrett CK, Emery CA. Predictors of FIFA 11+ implementation intention in female adolescent soccer: an application of the health action process approach (HAPA) model. Int J Environ Res Public Health. 2016;13(7).

O’Brien J, Finch CF. Injury prevention exercise programs for professional soccer: understanding the perceptions of the end-users. Clin J Sport Med. 2017.

Norcross MF, Johnson ST, Bovbjerg VE, Koester MC, Hoffman Marc F.; ORCID: http://orcid.org/0000-0001-5329-3925 MAAI-O http://orcid. org/Norcros. Factors influencing high school coaches’ adoption of injury prevention programs. [Internet]. Ardern Comstock, Donaldson, Finch, Finch, Finch, Finch, Frank, Gilchrist, Glasgow, Glasgow, Hagglund, Joy, Kamath, LaBella, Lindblom, McKay, Morrissey, Orr, Pate, Rogers, Rogers, Sadoghi, Sawyer, Soligard, Twomey, Van Tiggelen, White B, editor. Vol. 19, Journal of Science and Medicine in Sport. Norcross, Marc F., [email protected] : Elsevier Science; 2016. p. 299–304.

Donaldson A, Callaghan A, Bizzini M, Jowett A, Keyzer P, Nicholson M. Awareness and use of the 11+ injury prevention program among coaches of adolescent female football teams. Int J Sports Sci Coach. 2018;13(6):929–38.

Junge A, Lamprecht M, Stamm H, Hasler H, Bizzini M, Tschopp M, et al. Countrywide campaign to prevent soccer injuries in Swiss amateur players. Am J Sports Med. 2011.

O’Brien J, Young W, Finch CF. The use and modification of injury prevention exercises by professional youth soccer teams. Scand J Med Sci Sports. 2017.

Owoeye OBA, McKay CD, Verhagen EALM, Emery CA. Advancing adherence research in sport injury prevention [Internet]. Vol. 52, British Journal of Sports Medicine. 2018. p. 1078–9.

Steffen K, Meeuwisse WH, Romiti M, Kang J, McKay C, Bizzini M, et al. Evaluation of how different implementation strategies of an injury prevention programme (FIFA 11+) impact team adherence and injury risk in Canadian female youth football players: a cluster-randomised trial. Br J Sports Med. 2013;47(8):480–7.

Pryor JL, Root HJ, Vandermark LW, Pryor RR, Martinez JC, Trojian TH, et al. Coach-led preventive training program in youth soccer players improves movement technique. J Sci Med Sport. 2017;.

Bizzini M, Dvorak J. FIFA 11+: an effective programme to prevent football injuries in various player groups worldwide-a narrative review. Br J Sports Med. 2015;49(9):577–9.

Bizzini M, Junge A, Dvorak J. Implementation of the FIFA 11+ football warm up program: How to approach and convince the Football associations to invest in prevention. Br J Sport Med. 2013;47(12).

Download references

Acknowledgements

The funding for this review was managed by Pike, I. and Babul, S. of the British Columbia (BC) Injury Research and Prevention Unit and BC Children’s Hospital Research Institute and coordinated by Richmond, S. of the Canadian Injury Prevention Trainee Network.

Provided by the British Columbia Alliance for Healthy Living Society, Canada, and supported by the Saint Louis University, MO, USA.

Author information

Authors and affiliations.

Department of Physical Therapy and Athletic Training, Doisy College of Health Sciences, Saint Louis University, Allied Health Professions Building, 3437 Caroline Street, St. Louis, MO, 63104, USA

Oluwatoyosi B. A. Owoeye & Mitchell J. VanderWey

Sport Injury Prevention Research Centre, Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada

Oluwatoyosi B. A. Owoeye

Department of Paediatrics, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada

BC Injury Research and Prevention Unit, BC Children’s Hospital Research Institute, Vancouver, BC, Canada

You can also search for this author in PubMed   Google Scholar

Contributions

OO conceived the study and did the initial systematic literature search. MV did the updated literature search. OO and MV drafted the manuscript, and OO and IP substantially revised it. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Oluwatoyosi B. A. Owoeye .

Ethics declarations

Ethics approval and consent to participate.

Not applicable

Consent for publication

Competing interests.

The authors, Oluwatoyosi Owoeye, Mitchell VanderWey, and Ian Pike, declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Additional file 1:..

Search terms

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Owoeye, O.B.A., VanderWey, M.J. & Pike, I. Reducing Injuries in Soccer (Football): an Umbrella Review of Best Evidence Across the Epidemiological Framework for Prevention. Sports Med - Open 6 , 46 (2020). https://doi.org/10.1186/s40798-020-00274-7

Download citation

Received : 24 April 2020

Accepted : 19 August 2020

Published : 21 September 2020

DOI : https://doi.org/10.1186/s40798-020-00274-7

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Home — Essay Samples — Life — Sports — Soccer

Essays on Soccer

Soccer persuasive speech, the beauty and benefits of soccer: a comprehensive analysis, made-to-order essay as fast as you need it.

Each essay is customized to cater to your unique preferences

+ experts online

The Importance of Soccer and Soccer

Two football stars: messi vs ronaldo, different positions in a soccer team, the globalization of soccer, let us write you an essay from scratch.

• 450+ experts on 30 subjects ready to help
• Custom essay delivered in as few as 3 hours

The Role of Finance in The Football World

Soccer – the game where i can be me, the effect of cristiano ronaldo as a football model, football: history, rules and influential individuals, get a personalized essay in under 3 hours.

Expert-written essays crafted with your exact needs in mind

Most Popular in The World: Soccer

Gender pay discrimination in the us soccer, soccer is a team game, a specific sport like soccer, types of training to increase the efficiency of the soccer player, help to permanently injured soccer players, the inequality in men and women's soccer in spain and the usa, review of the history and benefits of soccer, factors that lead to the success of a football career, personal writing: improving my talent as a football player, evaluation of whether footballers are overpaid, a look at leadership by a football player, the first world cup, race towards the goal, personal experience of connecting with my football team, developing the spirit of football in africa, fifa 19: top 5 overrated players, the risk of concussions in professional football, review of our last game against watford, the premier league on american television.

Soccer is a team sport played with a spherical ball between two teams of 11 players.

The two teams compete to get the ball into the other team's goal (between the posts and under the bar), thereby scoring a goal. Players are not allowed to touch the ball with hands or arms while it is in play, except for the goalkeepers within the penalty area. Players may use any other part of their body to strike or pass the ball and mainly use their feet. The team that has scored more goals at the end of the game is the winner; if both teams have scored an equal number of goals, either a draw is declared or the game goes into extra time or a penalty shootout, depending on the format of the competition.

Hook Examples for Soccer Essays

World cup fever hook.

""The world unites every four years in a frenzy of excitement and passion known as the FIFA World Cup. Join me as we kick off our exploration of soccer, a sport that transcends borders and brings people together.""

The Beautiful Game's Global Impact Hook

""Soccer isn't just a sport; it's a cultural phenomenon that influences nations and inspires generations. Explore the profound impact of soccer on societies, politics, and identity.""

From the Streets to the Stadium Hook

""For many, soccer starts as a simple game on the streets or in dusty fields, but it can lead to dreams fulfilled on grand stadiums. Delve into the journeys of players who rose from humble beginnings to international stardom.""

Passion and Loyalty in Soccer Hook

""Soccer fans are known for their unwavering passion and loyalty to their teams. Analyze the unique bond between supporters and their clubs, exploring the chants, rituals, and the electric atmosphere of soccer matches.""

The Artistry of Soccer Hook

""Soccer is not just about scoring goals; it's about the poetry of movement, the artistry of passing, and the precision of tactics. Join me in exploring the elegance and creativity that define the game.""

Soccer's Role in Social Change Hook

""Soccer has the power to drive social change and break down barriers. Investigate how the sport has played a role in addressing social issues, promoting inclusivity, and advocating for equality.""

The Thrill of the Penalty Kick Hook

""In the high-stakes world of soccer, the penalty kick is a moment of intense pressure and drama. Join me as we dissect the psychology and strategies behind this exhilarating aspect of the game.""

Diego Maradona, Michel Platini, Zinedine Zidane, David Beckham, Cristiano Ronaldo, Gareth Bale, Lionel Messi, etc.

No one knows exactly when soccer was created, but the earliest versions of the game can be traced back 3,000 years. Soccer is the most popular game in the world. In many countries it is known as “football”. In England, soccer was formed when several clubs formed the Football Association about 150 years ago.

Relevant topics

• Muhammad Ali
• Paying College Athletes
• Jackie Robinson
• Jesse Owens
• Lebron James
• College Football

By clicking “Check Writers’ Offers”, you agree to our terms of service and privacy policy . We’ll occasionally send you promo and account related email

No need to pay just yet!

We use cookies to personalyze your web-site experience. By continuing we’ll assume you board with our cookie policy .

• Instructions Followed To The Letter
• Deadlines Met At Every Stage
• Unique And Plagiarism Free

Pools RSK Papers – Soccer, Capital, Bob Morton

Pools rsk papers, rsk pool papers ,rsk pool papers this week, pool rsk paper for this week, soccer research pool paper, Bob Morton, rsk pool papers, rsk papers this week.

Week 41 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 41 RSK Pool Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 40 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 40 RSK Pool Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 39 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 39 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 38 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 38 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 37 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 37 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 36 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

  Week 36 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, ...

Week 35 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 35 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 34 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 34 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 33 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 33 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 32 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 32 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 31 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 31 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 30 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 30 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 29 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 29 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 28 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 28 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 27 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 27 Pools RSK Papers 2024: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 26 Pool RSK Papers 2023: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 26 Pools RSK Papers 2023: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 25 Pool RSK Papers 2023: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 25 Pools RSK Papers 2023: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 24 Pool RSK Papers 2023: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 24 Pools RSK Papers 2023: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Week 23 Pool RSK Papers 2023: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 23 Pools RSK Papers 2023: Soccer X Research, Bob Morton, Capital Intl, Winstar, BigWin ...

Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans Research Paper

Introduction, origin and history, the first fifa world cup, the modern version of soccer, soccer’s popularity.

This paper entails issues as it appertains to soccer. The coverage of the history of this game which is also popularly known as soccer is quite diverse and different ideas have been put across as to where the game originated and its evolution to the current state.

Soccer in America is a relatively new game when compared to other sports. Its popularity is not as in other parts of the world due to many historical factors. Other games dominate the sporting scene and only recently have there been efforts to popularize this sport among American citizens.

Different countries in the world treat soccer differently and as it turned out, it’s difficult to get one standard view on soccer or its future. Everyone who has heard about the game or who knows much about it has a personal opinion on soccer.

History holds it that many cultures played games that resembled soccer but the first format of what evolved to the modern soccer game goes way back to 3000 years ago in Japan. In Japan, a game in which a ball was kicked around a pitch, though a very small one, can be followed back to 1004 B.C. This is got from texts from way back in 50B.C that describe games played between teams from China and those from Japan. The Chinese used a leather ball that was puffed with hair and the same text explains that a soccer game between China and Japan was played in the capital of Japan in 611A.D.

The Romans also played a game that resembled soccer, though a very rough version, in the early Olympics in Rome. In this game, more than half of the players were hospitalized after the matches. It is not known as to when the sport spread from Asia to Europe since historians focused on other issues like war and tended to ignore soccer and other sports.

Later during King Edward of England’s reign of 1307-1327, soccer was banned in England due to the amount of noise the crowds were making as they cheered their teams during the matches that were played in the streets. Laws were put in place for the imprisonment of anyone who would be found playing soccer. However, these laws could not stop the fan’s love for soccer and as a result, they were sanctioned in 1681.

The modern soccer version was created from rugby and rules that governed it also followed those that governed rugby.

Soccer in America dates back to the year 1620 where native Indians used to play a game similar to soccer which they called “Pasuckquakkohwog”. This is according to Pilgrim Fathers who first settled in Portsmouth.

By the year 1820 many colleges in America were playing soccer but no intercollegiate games went on as the rules changed often.

In 1880 British immigrants brought along their soccer teams to America and other parts of the world. They played their games with a lot of enthusiasm thus making the game even more popular among Native Americans.

In 1884, in Newark, the American Football Association was established. And in 1886 it organized the first international game outside British rule, between America and Canada.

In 1904 Federation Internationale de Football (FIFA) was formed by charter members to oversee the running of soccer in the world and on August 15 1913 the United States Football Association joined FIFA as an associate member.

The first FIFA world cup was played in 1930 and it continued to be held every five years up to date. This first world cup had only thirteen teams participating and ninety thousand football fans watching. The American team which was the highest-ranked to win the cup came third overall as Argentina clinched the cup.

The British are the makers of modern soccer by creating the rules and commercializing the sport in the world. Their league turned out to be the most well organized and popular in the world due to the participation of foreign players in the teams.

The modern version of soccer was introduced to America from Britain by the early from Britain immigrants. Britain being the country that colonized America was not very popular among the Americans at the time and thus anything that was thought to have originated in Britain was also despised. To show that they were free of British rule, Americans, therefore put soccer off. Other games may be quite popular in America yet had been imported from Britain, for example, baseball which was created to be played by children and not adults in Britain, but popularized by the Americans at the time as a way of expressing their uniqueness from the British.

Traditional American “jocks” use soccer hating as their favorite pastime activity. Soccer has been unpopularised in America by anti soccer websites which are financed by other game coaches thus discouraging the youth in America from playing football. They use defaming terms “like soccer is communist”.

Again, America is not known for producing world renowned football players and thus its popularity fails. In continents like Europe and Africa very good players are produced who make a living from playing soccer especially in European clubs. The lack of professional players from America lies in the fact that Americans prefer other sports in which they only need to learn a single skill and become good over soccer where they have to be athletic and still master the skills of the game. Another factor is that Americans prefer high-scoring sports over soccer which is low scoring. The early soccer organizations in America were corrupt and the many scandals that befell soccer then made people view it as a sport that had no future in America thus focusing on other more organized sports.

The media in America gives soccer very little coverage to a level of 2-1 to other sports like baseball and basketball. Also, America is made up of immigrants with different origins who don’t have a sport that unifies them. Soccer seasons take the whole year and thus people figure it as being boring to participate in and opt for other short-season games.

In other countries like Italy and Brazil soccer is very popular and a way of life to them. Soccer in these nations is treated as a matter of life and death. People who take part in playing or in the running of soccer in such nations make good business from their activities. Popular players are usually treated as the countries heroes and run down the countries history books.

The future of soccer looks very promising the world over. Even in countries where soccer is not very popular as in America, efforts are being put in place to popularize the sport. Currently, soccer is ranked as the second most popular sport in America, which is a very good picture. Africa is also catching up with the rest of the world by producing quality players and organizing continental and subcontinental soccer competitions. The evolution of soccer is continuing with the current inclusion of modern technology in refereeing and the creation of new soccer rules by FIFA.

Soccer becomes more and more popular nowadays all over the world. First of all, it is closely connected with the process of commercialization of sports, and huge amounts are invested into the teams, stadiums, players, etc.

Soccer (or football as it is called in the continent of its origin) is considered to be the most popular sport all over the world; in South America, Brasilia, France, U.K people play football, attend football matches, watch matches on TV and discuss them with friends and read updated football news. The matches between elite football groups magnetize millions of people. For example, the capacity of Salt Lake Stadium in India is 120,000 people, and of Beaver Stadium in U.S.A – 107,282 people. In World Championship the stadiums are often filled up to the throat and lots of people can not cope to get tickets for the matches.

Football attractiveness often directs to rivalry, which occasionally enhances into hooliganism. Battles between fans of different teams generally occur after football games. Sometimes football rivalry outlines in tragedy in arenas. At the mass brawl in May 2007, hundred Liverpool followers fought each other in Athens, expecting to get a ticket for the match this way. Another disastrous occasion took place at the Hillsborough stadium on April 15, 1989, and resulted in the death of 96 people. The exceptional incursion of fans through a narrow tunnel leading to the stadium has originated a major crush.

Football has always supported a foothold in the fans’ eagerness. Most of the biggest stadiums in the world are built especially for football, as it is enormously popular. One of the key notices in this sport is the football move gossips part. Raising consciousness of the aggression among football fans might help to decrease the risk of tragedies and save the lawfully owned reputation of the game.

Though soccer’s popularity is unquestionably due to the exploits of the national team, the J. League is also displaying signs of recuperation. This year’s watcher numerals are previously significantly above those evidenced. With the aperture of a chain of 40,000-capacity stadiums for use in the World Cup and the endorsement of accepted regional clubs like Urawa Reds and Consadole Sapporo to J. League Division One, even places that were once abandoned are now crowded with fans.

It is not soccer itself that is popular. The J. League is not droning with enthusiasm. Watchers just feel understanding for the national team battling with other states. Yet the J. League has constantly aimed to care for clubs with profound roots in their local societies rather than concentrating on teams’ national reputation.

Benson, M. English Loan Words in Russian Sports Terminology American Speech > Vol. 33, No. 4 (1958), pp. 252-259.

Carroll S.. The Disempowerment of the Gender Gap: Soccer Moms and the 1996 Elections PS: Political Science and Politics > Vol. 32, No. 1 (1999), pp. 7-11.

Dyte. D.; Clarke R. A Ratings Based Poisson Model for World Cup Soccer Simulation The Journal of the Operational Research Society > Vol. 51, No. 8 (2000), pp. 993-998.

Edelman, R. A Small Way of Saying “No”: Moscow Working Men, Spartak Soccer, and the Communist Party, 1900-1945. The American Historical Review > Vol. 107, No. 5 (2002), pp. 1441-1474.

Giulianotti, R. Soccer Goes Glocal Foreign Policy > No. 131 (2002), pp. 82-83.

Kaulard L. The Transatlantic Soccer Bridge: How to Get a Kick out of Soccer and… German! Die Unterrichtspraxis / Teaching German > Vol. 37, No. 1 (2004), pp. 56-57.

Kinloch, G. Changing Racial Attitudes in Zimbabwe: Colonial/Post-Colonial Dynamics Journal of Black Studies > Vol. 34, No. 2 (2003), pp. 250-271.

Koning, R. Balance in Competition in Dutch Soccer The Statistician > Vol. 49, No. 3 (2000), pp. 419-431.

Roadburg, A. Factors Precipitating Fan Violence: A Comparison of Professional Soccer in Britain and North America The British Journal of Sociology > Vol. 31, No. 2 (1980), pp. 265-276.

Stevenson, T. Football in Newly United Yemen: Rituals of Equity, Identity, and State Formation Journal of Anthropological Research > Vol. 56, No. 4 (2000), pp. 453-475.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2021, September 1). Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans. https://ivypanda.com/essays/soccer-its-history-origin-and-evolution/

"Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans." IvyPanda , 1 Sept. 2021, ivypanda.com/essays/soccer-its-history-origin-and-evolution/.

IvyPanda . (2021) 'Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans'. 1 September.

IvyPanda . 2021. "Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans." September 1, 2021. https://ivypanda.com/essays/soccer-its-history-origin-and-evolution/.

1. IvyPanda . "Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans." September 1, 2021. https://ivypanda.com/essays/soccer-its-history-origin-and-evolution/.

Bibliography

IvyPanda . "Soccer in America: Its History, Origin, Evolution, and Popularize This Sport Among Americans." September 1, 2021. https://ivypanda.com/essays/soccer-its-history-origin-and-evolution/.

• Sports Stadium for Team Organization and Community
• The Development of Soccer Analysis
• The Lack of Soccer Popularity in the U.S.
• FIFA 10 Football Simulation Video Game
• A Summary of "What The Dog Saw"
• FIFA’s Public Image: Issues and Mitigate the Damage
• FIFA World Cup
• Cultural Values Embeded in Soccer
• Football Industry in Africa: Socially Responsible Business?
• Soccer and National Identity
• Soccer: Transmitting Real Information on the Game
• High Jump Game and Its Health Effects
• Soccer in the US and American Exceptionalism
• Lacrosse Equipment and Associations in Canada
• South Carolina Gamecocks - Michigan Wolverines Match

Academia.edu no longer supports Internet Explorer.

To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to  upgrade your browser .

•  We're Hiring!
•  Help Center

Football (soccer)

• Most Cited Papers
• Most Downloaded Papers
• Newest Papers
• Save to Library
• Last »
• Sociology of Sport Follow Following
• Soccer Follow Following
• Sport Follow Following
• Sports History Follow Following
• Football Follow Following
• Football Culture Follow Following
• Economics of Football (soccer) Follow Following
• Football Fan Behaviour Follow Following
• Sports Management Follow Following
• History of Football Follow Following

Enter the email address you signed up with and we'll email you a reset link.

• Academia.edu Publishing
•   We're Hiring!
•   Help Center
• Find new research papers in:
• Health Sciences
• Earth Sciences
• Cognitive Science
• Mathematics
• Computer Science
• Academia ©2024

• Advertise Here
• Bet9ja & Betking Codes
• Football Pools Draws
• Half-Time Results
• Bigwin, Telegraph Papers
• Calculate Pools Winning
• Cookies Policy
• Terms of Services

Week 41 Pool RSK Papers 2024: Bob Morton, Capital Intl, Soccer X Research, BigWin

Week 41 RSK Pool Papers 2024: Soccer, Bob Morton, Capital, Winstar, BigWin

Week 41 rsk papers 2024 : Welcome to Fortune Soccer here we provide you with Pools RSK Papers: Soccer X Research, Bob Morton, Capital International , WinStar, Bigwin Soccer and pool papers from other publishers such as Dream International Research, Fortune ‘X’ Matrix, Special Advance Fixtures, Right On Fixtures, Weekly Pools Telegraph, Pools Telegraph, Temple of Draws, Soccer Standard, and Dream International Fixtures.

CBK PRESS POOLS PAPERS © (POOLS JOURNALS)

Ω  DREAM INTERNATIONAL FIXTURES

Ω   DREAM INTERNATIONAL RESEARCH

Ω  FORTUNE ‘X’ MATRIX

SPECIAL ADVANCE FIXTURES

CAPITAL INTERNATIONAL

SOCCER ‘X’ RESEARCH

SOCCER PERCENTAGE AND LEAGUE TABLE

BIGWIN SOCCER

POOLS TELEGRAPH

CBK Pools Papers (with Pool Late News)

[PUBLISHED EVERY WEDNESDAY, THURSDAY AND SUNDAY]

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Recommended Websites

Football pools results.

• Week 41 Pool Result for Sat 13 Apr 2024 – UK 2023/2024
• Week 40 Pool Result for Sat 6 Apr 2024 – UK 2023/2024
• Week 39 Pool Result for Sat 30 Mar 2024 – UK 2023/2024
• Week 38 Pool Result for Sat 23 Mar 2024 – UK 2023/2024
• Week 37 Pool Result for Sat 16 Mar 2024 – UK 2023/2024
• Week 42 Pool Result for Sat 20 Apr 2024 – UK 2023/2024
• Week 36 Pool Result for Sat 9 Mar 2024 – UK 2023/2024
• Week 35 Pool Result for Sat 2 Mar 2024 – UK 2023/2024
• Week 34 Pool Result for Sat 24 Feb 2024 – UK 2023/2024
• Week 33 Pool Result for Sat 17 Feb 2024 – UK 2023/2024
• Week 32 Pool Result for Sat 10 Feb 2024 – UK 2023/2024
• Week 31 Pool Result for Sat 3 Feb 2024 – UK 2023/2024
• Week 30 Pool Result for Sat 27 Jan 2024 – UK 2023/2024
• Week 29 Pool Result for Sat 20 Jan 2024 – UK 2023/2024
• Week 28 Pool Result for Sat 13 Jan 2024 – UK 2023/2024
• Week 27 Pool Result for Sat 6 Jan 2024 – UK 2023/2024
• Week 26 Pool Result for Sat 30 Dec 2023 – UK 2023/2024
• Week 25 Pool Result for Sat 23 Dec 2023 – UK 2023/2024
• Week 24 Pool Result for Sat 16 Dec 2023 – UK 2023/2024
• Week 23 Pool Result for Sat 9 Dec 2023 – UK 2023/2024
• Week 22 Pool Result for Sat 2 Dec 2023 – UK 2023/2024
• Week 21 Pool Result for Sat 25 Nov 2023 – UK 2023/2024
• Week 20 Pool Result for Sat 18 Nov 2023 – UK 2023/2024
• Week 19 Pool Result for Sat 11 Nov 2023 – UK 2023/2024
• Week 18 Pool Result for Sat 4 Nov 2023 – UK 2023/2024

• International edition
• Australia edition
• Europe edition

Great Barrier Reef discovery overturns belief Aboriginal Australians did not make pottery, archaeologists say

Paper dates 82 pottery pieces found in single dig site at between 3,000 and 2,000 years old

• Follow our Australia news live blog for latest updates
• Get our morning and afternoon news emails , free app or daily news podcast

Groundbreaking archaeological research may have upended the longstanding belief that Aboriginal Australians did not make pottery.

A paper published in the Quaternary Science Reviews on Wednesday details the finding of 82 pottery pieces from a single dig site on a Great Barrier Reef island, dates them at between 3,000 and 2,000 years old and determines that the pots were most likely made by Aboriginal people using locally sourced clay and temper.

The pieces are the oldest securely dated pottery discovered in Australia and weave Indigenous Australians into an ocean-going network of people in Papua New Guinea, the Torres Strait and Pacific Islands who formed a “community of cultures across the Coral Sea”, the paper finds. Fragments of pottery have also been found on the Torres Strait.

The archaeologists say the finds open “a new chapter in Australian, Melanesian and Pacific archaeology”.

The Walmbaar Aboriginal Corporation chair, Kenneth McLean, is a Dingaal clan member and traditional owner of the group of islands on which the pottery was unearthed.

“For our elders Jiigurru was always a sacred place,” McLean said. “It was always a place of trading and ceremony.”

The James Cook University distinguished professor Sean Ulm, who co-led the dig alongside Monash University’s Prof Ian McNiven and with the Dingaal and Ngurrumungu communities, says the finds not only overturn notions about Aboriginal people and pottery but a number of “very common tropes” about Indigenous Australians.

Sign up for Guardian Australia’s free morning and afternoon email newsletters for your daily news roundup

One is that they were all isolated from the rest of the world. Another related to the simplicity of Aboriginal watercraft.

The chain of islands of Jiigurru – of which the 10 sq km Lizard Island is the largest – surround a lagoon about 33km off Cape Flattery.

The 2.4-metre deep dig unearthed evidence of continuous occupation going back more than 6,000 years on the islands, cut off from the mainland by sea level rise at least 10,000 years ago.

McLean believes that his ancestors would have used the clay pots to carry resources such as water and shellfish on the long canoe voyages to the islands.

“Holding the pieces of pottery that were locally made, on country, that was feeling my ancestors’ presence,” he said. “It was an emotional moment, holding something that was ancient.”

Much remains to be learned about how the pots were made and appeared. The average size of the sherds is less than 2cm – too small and fragmented to reveal much of their original form and function.

So momentous a story told by such tiny pieces of worked earth explains why the research took years to reach publication, Ulm says.

The dig began in mid 2017 and ended 14 months later. But its findings would challenge a view widely held among academics since fragments of pottery were first spotted on Jiigurru by a holidaying archeologist from New Zealand while he snorkelled the shallow lagoon in 2006.

Attempts to date those pottery pieces were inconclusive, though they were interpreted by many as direct evidence of the presence of Lapita people in Australia.

From the islands of eastern Papua New Guinea and over the span of a few centuries, the Lapita and their descendants settled vast swathes of the remote Oceania, taking pigs, dogs and chickens, taro and breadfruit, and their distinctive pottery to the Solomon Islands and eastwards across the Pacific into Vanuatu, New Caledonia, Fiji, Tonga and Samoa.

after newsletter promotion

Ulm’s paper describes it as “one of humanity’s great maritime settlement accomplishments”.

But when Ulm and his colleagues went looking for more pottery pieces using the kind of methodical dig that could shed light on who made the Jiigurru pottery and when, they found none of the telltale signs – chicken bones or traces of banana – that would suggest Lapita occupation. Instead the shellfish and fish bones of the midden site spoke of continuous Indigenous occupation. And none of the ceramic sherds bore the hallmark designs of Lapita potters.

The find begs the question of why pottery pieces weren’t found on the site after about 2,000 years ago, though seasonal occupation continued. That, Ulm says, is a question which cannot be answered from one dig site and will require more research to unravel.

McLean, too, hopes the research will encourage further collaboration between Indigenous communities and archeologists that will “find more of the ancient artefacts that could rewrite Australia’s ancient history”.

University of Southern Queensland professor, Bryce Barker, who was not involved in the study, says it “certainly is very significant” and an “exemplary piece of research”.

“The science in that paper is exemplary – you can’t fault the science,” he said. “I don’t think there is any question that there is pottery there at 3,000.”

But the claim that Aboriginal people made the pottery, he said, was “a little bit contentious”.

“Perhaps the more parsimonious explanation for why that pottery is on Lizard Island is that it is part of that trade and interaction with those people from the north, rather than it being something that Aboriginal people manufactured,” he said.

The researchers involved argue that the ancient pottery of the Great Barrier Reef “points to the likelihood” of more remains, perhaps including Lapita, scattered about “the vast and archaeologically unknown north-east Queensland coastline”.

“To me, that’s what’s exciting about the find, that it’s a little glimpse into the extraordinary knowledge that we are yet to unfold about the deep history of this country,” Ulm said.

“If one excavation of one metre by one metre can tell us all these ‘new things’, what does the rest of the coastline have to teach us?”

• Indigenous Australians
• Archaeology
• Great Barrier Reef

Most viewed

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Video Analytics in Elite Soccer: A Distributed Computing Perspective

* Northwestern University, USA,

Ashish Rauniyar

† SINTEF Digital, Norway,

Håvard D. Johansen

§ UiT The Arctic University of Norway, Norway,

Dag Johansen

Michael a. riegler.

‡ SimulaMet, Norway,

Pål Halvorsen

¶ Oslo Metropolitan University, Norway

Ubiquitous sensors and Internet of Things (IoT) technologies have revolutionized the sports industry, providing new methodologies for planning, effective coordination of training, and match analysis post game. New methods, including machine learning, image and video processing, have been developed for performance evaluation, allowing the analyst to track the performance of a player in real-time. Following FIFA’s 2015 approval of electronics performance and tracking system during games, performance data of a single player or the entire team is allowed to be collected using GPS-based wearables. Data from practice sessions outside the sporting arena is being collected in greater numbers than ever before. Realizing the significance of data in professional soccer, this paper presents video analytics, examines recent state-of-the-art literature in elite soccer, and summarizes existing real-time video analytics algorithms. We also discuss real-time crowdsourcing of the obtained data, tactical and technical performance, distributed computing and its importance in video analytics and propose a future research perspective.

I. Introduction

Technology has a vast impact on sport industry, in particular soccer [ 1 ]. Advancements in online connectivity and the rapid proliferation of smartphones and social media websites has brought fans closer to the action than ever before. Spectators now have more alternatives of equipment and experience than ever before [ 2 ]. Elite sports clubs are continually looking for new methods and strategies that can provide them with a competitive edge over their competitors. Thus, the rise of real-time sports data analytics in favourite sports such as basketball, soccer, field hockey, and baseball has changed the nature of sports science. This science is driven by the advanced technique to gather a massive amount of data while the match is in progress. Metrics related to game events like passes, shots, and tackles are gathered carefully. Using acquired data to gain a competitive advantage, such as supporting players in real-time or during training, recruitment, and preparation, is a major challenge these days.

Today, most professional sports teams have either an analytics department or data scientists [ 3 ]. Teams frequently scan scout notes from clipboards, gather video-based or sensor in formation and hand over those files to the elite data scientists. This information is sent to the mathematicians who analyzes the results produced by the programmer. The results even help to decide which player fits the best in their club. This is why analytics is now touted as the “present and future of sports” [ 3 ]. Any team that does not fully utilize these will be at a competitive disadvantage. The rise in popularity of data-driven prediction in sports has energized fans, who are consuming more analytical content than ever before. For example, various websites are devoted to the analysis and investigation of sports statistics and how they relate simply to make a prediction. As a result, developing data culture within a sports club’s organization is critical for sustaining a competitive edge in today’s extremely demanding digital world.

On the other hand, crowdsourcing technology has scaled up the fans’ engagement beyond 90 minutes of the match. Crowdsourcing technology is now used by the clubs by broadcasting certain tasks and asking followers to provide input. This feedback is utilized to help solve specific problems. They use the method to reaffirm fans’ experiences, which aids in the development of trust among audiences and clubs. Despite the fact that there are few works and case studies on sports analytics, they are either only focused on real-time analytical systems or on predictive analytics [ 4 ], [ 5 ] or position of the specific player [ 6 ] or post-match monitoring fatigue in professional soccer [ 7 ], [ 8 ]. The current study intends to highlight real-time analysis of the integrated system, motion analysis, crowdsourcing, tactical and technical performance, individual subjective report, and the role of distributed computing in sport analytic.

The main contributions of this paper are: (i) we provide examples of some of the state of the art video analytics, (ii) we describe the connection between elite soccer analysis and distributed computing; and finally, (iii) we provide future perspectives and directions in the field.

II. Literature review

The sport industry spends an enormous amount of resources for performance analysis of their games. They use both manual and analytical tools, enabling the team managers and trainers to analyze the game to boost performance. For example, in Interplay-sports [ 9 ], video streams are analyzed and annotated manually using soccer ontology classification strategy. Prozone [ 10 ] uses video-analysis software that automates some of the manual annotation processes. Specifically, it quantifies players’ gestures such as speed, velocity, and position during the game. The measurement of such characteristics has already proven successful in Old Trafford in Manchester and Reebok Stadium in Bolton (now called as University of Bolton Stadium) [ 11 ]. Likewise, STATS SportVU Tracking Technology [ 12 ] utilizes video cameras to accumulate the positional data of those players over the playing field in real-time. These data are used to improve the players’ performance. Although Camargus [ 13 ] produces a superior video technology infrastructure, it lacks other analytical applications. The TRACK [ 14 ] and ZXY Sport Tracking [ 5 ] systems use the global positioning system and radio-based systems for capturing performance measurements in athletes. The player’s statistics can be presented along with the fitness graph, speed profiles and accumulated distances in many ways like charts and animations. Osgnach et al. [ 15 ] proposed a match analysis strategy by performing a comprehensive evaluation of soccer players’ metabolic requirements by video match analysis to consider accelerations.

The Muithu system [ 16 ] merges coach annotations with associated video sequences. However, the video has to be manually transferred and mapped to the game’s timeline. Halvorsen et al. [ 17 ] and Saegrov et al. [ 18 ] demonstrated a system named Bagadus. This system combines a camera array video capture platform with the ZXY Sport Tracking system for participants’ statistics along with a method for human expert annotations. Mortensen et al. [ 19 ] introduced the automatic video extraction capabilities of Bagadus. Barros et al. [ 20 ] showed a system based on the use of distributed mobile devices that allows the annotation of soccer matches in real-time or after the game is finished (by the observation from another media). In this paper, we cover the basics of sports analytics system to the application of distributed fog computing and IoT to sports analytics.

III. Methods

Match analysis can be used to assess the physical abilities of skilled soccer players, particularly in identifying high intensities, which are also considered as fast running speeds. It is divided into three categories: technical (skill performance), tactical or strategic, and physical. The systems that provide a solution to sport analytics are described below.

A. Motion Analysis

Motion analysis is the most extensively utilized technique in sports bio-mechanics and rehabilitation for individual player analysis, emphasizing movement patterns and ground activity. The essential information such as total distance travelled, the time taken to complete a specific activity, and efforts applied during varying movement categories, for instance, walking, jogging, standing, sprinting are collected [ 21 ], [ 22 ]. This information is utilized to create an extensive players activity profile [ 23 ], defining the average physical requirements of each participant along with their playing position on the ground. These obtained data points assist data scientists, trainers, and other professionals in monitoring fluctuations in physical performance over time, allowing them to quantify players’ training burden or compare players with similar features or attributes. Furthermore, this information allows the player’s activity profile to be correlated to a similar demographic (e.g., teammate or opponent).

B. Tracking using LPM (Radio Signals) and GPS in a Professional Football Club

The Local Position Measurement (LPM) system is one of the most accurate sports tracking systems. This system uses high-tech RFID technology. LPM system consists of both base stations (antenna) and transponders. Base stations are strategically placed around the ground to collect data. The players wear transponders. The position of the transponders is quantified by the base station, which means that the positions of the players are likewise quantified. The positions are calculated in real time. As a result, the measured data could even be examined while the measurement was taking place. These positioned data are processed to separate standard values like as distance covered, speed and acceleration, and so on. In GPS, the devices are passive receivers of signals from aerial satellites, but LPM systems use wearable emit signals that are relayed to local receivers, which perform the specific triangulation.

C. ZXY Sports Tracking

The ZXY Tracking system is a commercial product by ChyronHego [ 24 ]. ZXY Arena wearable tracking employs RF-based technology and is designed for permanent installations such as specialized training facilities or contest arenas. This entire system is currently positioned at Alfheim arena. The system contains eleven stationary radio receivers mounted around the field. Each of the receivers has a nearly 90-degree field of view. This forms an overlapping zone in the soccer field, which provides high immunity to indicating signal blocking and occlusion. For signal transmission and radio communication, the installed system is dependent on the 2.45 GHz ISM band. Each radio receiver calculates the positional data depending upon the radio signals obtained. Wearable belts with sensors that the players wear during the game collect the radio signal. This belt has a compass, a heart-rate monitoring system, a gyroscope, and accelerometers. All of these instruments work together to produce positioning data for the players on the field as well as performance measures. Using a sensor, the ZXY system determines the direction of the players on the field, heart rate frequency, location, and step frequency with a sample rate of around 20 times per second.

Bagadus [ 4 ], [ 8 ], [ 17 ], [ 18 ] is an integrated sport analysis system for real-time panorama video demonstration of athletics events. Figure 1 shows the overall block diagram of the Bagadus system. This system is presently used at Alfheim stadium (Tromsø IL) and Tromsø, and Ulleval stadium (Norwegian National soccer team, Oslo). A Bagadus system integrates the three main sub-systems: a sensor system (ZXY), a coach annotation system, and a video system. By utilizing these sub-systems, Bagdus installs and combines several components to stimulate the desired sports application-specific scenario. The system’s modernity stems from the integration and combining of many components that allow for the autonomous demonstration of video events that are primarily dependent on data analytics and positioning sensors and are coordinated with the video system. Bagdus, for example, can extract videos of a player who is racing faster than 10 miles per hour, as well as when all of the defenders are in the 18-yard penalty box [ 19 ]. Furthermore, we may follow and pick one or more participants in the video, as well as repeat and retrieve the specific occurrences that have been marked by specialists. It used to take a long time to evaluate sports summaries and write reports. Bagdus functions as an integrated platform, handling basic processes including video synchronization automatically.

Overall Bagdus architecture [ 4 ].

E. Player Tracking System

There are different player tracking systems. Example includes optical-based technology, GPS and radio etc. Bagdus possibly can use any tracking. For example, at Alfheim stadium, the player tracking system imports position of the players using the ZXY Sport Tracking system [ 5 ].

F. Coach Annotation System

The coach annotation system [ 16 ] replaces the conventional pen-and-paper annotations, which were used previously by the coaches for soccer game annotations. Currently, with a single press of a button on Bagadus mobile app, the coaches can annotate the video very quickly. The system is based on hindsight recording, in which the end point of the event is identified and the system then takes the video prior to this mark. Furthermore, the documented occurrences are preserved in an analytics database, which can subsequently be displayed along with the related video of that specific incident. The timing of the tagged event and video must be synced here. As a result, the mobile device synchronizes its regional time with NTP machines [ 25 ]. Nonetheless, it is simply a second-granularity time-sync requirement.

IV. Individual Subjective Reports (PMSYS)

The Player Monitoring SYStem (PMSys) [ 26 ] is a smart-phone self-reporting tool that allows for the tracking of several phenotypic parameters via recurring questionnaires that players answer to via their mobile devices. PMSys supports both the IOS and Android platforms since it provides dependable and systematic reports from all team members at regular intervals. It was created as a hybrid-mobile application based on the Ionic 2+ Framework to lower the expense of multi-platform support [ 27 ]. The current versions of the framework build apps that feel and look like native ones, and previous aspects and performances have been greatly reduced [ 28 ]. PMSys is now available in both Google Play and the iTunes store for Android and iOS devices. A mobile application with graphical visualization feedback provides the players with a timeline overview. The data collected via the PMSys system is also used in several follow up research that is focusing on players performance [ 8 ], [ 29 ]–[ 31 ].

V. Distributed Fog Computing for the Big Data Generated from the Sport Analytics

Fog computing is a potential technology that tries to address current IoT paradigm challenges. Figure 2 shows the different interaction model present in the fog computing. It is appropriate in conjunction with wide-area distributed systems, with several clients at the edge of the network [ 32 ]. The clients may be the consumer devices (for example, smartphones, smartwatches) that are interactively used by individuals or accessories which are part of IoT (for example, cameras installed at the stadium). The client can serve both a user and an actuator that accepts control signals (for example, a fitness notification on a smartwatch), as well as a data producer (for example, heart-rate monitor from the smartwatch, video streams from cameras installed at the stadium).

Interaction models present in the fog computing.

Fog computing assists as a computing layer within the edge devices and the cloud in the network topology [ 33 ]. When opposed to fog computing, cloud computing has a higher uptime and requires a constant internet connection. Applications that use fog computing can circumvent network performance limitations in cloud computing by processing data close to edge devices. As a result, they provide a beneficial counter-balance to the prevailing paradigms. It is usually assumed that defining feature of fog computing indicates a lower network distance from the edge, however there are other network topology models [ 33 ]. One of the most important reasons for employing fog computing is the large amount of data created at the network’s edge. IoT deployments encourage fog computing. Common web clients who solely use WWW services and content noted the expansion of Content Distribution Networks (CDN) to help those with low latency. The data from the IoT sensors is used by the player to measure and analyze several metrics in real-time [ 34 ]. In this situation, fog computing has been operating like an inverse CDN [ 35 ].

VI. Discussions

Athlete performance analysis is a growing subject of interest in the sports industry. Wearables, electronic performance, and tracking devices are already having an impact on training and matches. The data can also be used to determine which player would be best suited to their club. As a result, analytics is referred to as the “present and future” of sports. Any team that does not fully utilize these will be at a competitive disadvantage. The emergence of wearables and electronic devices has expedited athlete quantification technology research and development. The German national soccer team, for example, used wearable devices to profile their players. Based on the data, coach Joachim Low made the critical substitute of Mario Gtze, who scored the game-winning goal in the 2014 World Cup final in Brazil [ 36 ]. It is crucial to do research on mental fatigue and post-match retrieval using mechanical workload metrics that have a logical link with neuro-muscular demands. Regardless of technical difficulties in the implemented setting, additional research on the effect of cognitive and central nervous system function, travel, sleeping behavior, feedback from the trainer, and nutritional status on Post Match Fatigue (PMF) responses would be necessary to expand the literature base. The development of mentally challenging activities with high ecological validity for soccer is critical for determining the extent to which emotional fatigue occurs in players and then tracking its time-course for recovery post-match.

Adapting crowdsourcing and human computation methodologies aids in the collection of real-time and interactive crowdsourced data [ 37 ]. During user study, spectators are required to execute monitoring tasks such as finding the players on the field, identifying ball passes etc., of a real-life soccer game. With crowdsourcing, players can acquire extensive and very complicated information in real-time. However, there are also challenges in maintaining the quality of the data source, managing the crowd and sourcing the right crowd. One of the potential solutions for addressing challenges would be transparency in data, method and research process [ 38 ].

An enormous amount of IoT data originates as observational streams in the context of distributed computing for sport analytics, or as time-series data gathered from widely distributed sensors [ 39 ], [ 40 ]. These data are high-velocity data streams that are latency-sensitive that require online analytics and decision-making to give, say, health alerts of the player on/off the ground. A substantial amount of data is also generated through the video streams supplied by the stadium’s cameras. As high-definition cameras become more affordable, the bandwidth necessary to drive data from the edge to the cloud can expand in volume, but network capacity remains constant [ 35 ]. The applications that must be approved can include real-time video analytics to appropriately record footage for future implementation.

Fog computing is prone to security attacks such as forgery [ 41 ], tampering [ 42 ], and Jamming [ 43 ]. Additionally, there is also concern related to privacy issue such as ‘user privacy’, ‘data privacy’, ‘identity privacy’, ‘usage privacy’, ‘location privacy’, and ‘network privacy’ [ 44 ]. Most of these attacks can be mitigated by applying countermeasures. For example, “Efficient encryption techniques” can be developed by designing complex encryption algorithms. Similarly, “decoy technique” can be used for data authentication, “authentication scheme” can be used for user credentials, and “blockchain security” can be used for network transactions [ 44 ]. In this context, we argue that fog computing can be used to move decision-making closer to the edge in order to reduce latency and analyze data in real-time. It can minimize the amount of bandwidth utilized in the core internet and limit data movement to the local network [ 33 ]. Furthermore, fog nodes placed near IoT devices, including end-users, can reduce both propagation delay and bandwidth utilization.

VII. Conclusion and future perspective

Sports analysis based on video analytics is a demanding area of research. The live data from broadcasting has been shown to be useful for match analysis and media coverage. These statistics are used by coaches, sports scientists, and the media to classify matches based on specific patterns or visual qualities that allow the categorization of match style. The motion analysis has supplied crucial information about the physical state of the players. PMSYS has been found to be advantageous to the team.

According to our study, we found that fog computing can be an excellent solution for real-time distributed processing of video streams generated by cameras and other IoT sports-related applications. As a result of the benefits of distributed fog computing and IoT, we believe it can be a possible performance evaluation solution for sports analytics. Many aspects will be improved in the future, such as the development of transparent data and advanced machine learning-based algorithms that may be used to examine performance analysis and pattern recognition in depth. Multiple sources of data can be collected in order to analyze various elements of the player and the team. More research on positioning data tracking methods, as well as fatigue and injury monitoring warrants, is necessary to offer alternative solutions.