Machine Learning in Sport Science

Articles, technical reports and other scientific publications on theory, fundamentals and applications of Machine Learning in Sports Science.

References will be organized into categories

References:

1. Chmait Nader, Westerbeek Hans; Artificial Intelligence and Machine Learning in Sport Research: An Introduction for Non-data Scientists; Frontiers in Sports and Active Living, Volume 3, 2021, DOI=10.3389/fspor.2021.682287, ISSN=2624-9367, URL=https://www.frontiersin.org/articles/10.3389/fspor.2021.682287.

Abstract = "In the last two decades, artificial intelligence (AI) has transformed the way in which we consume and analyse sports. The role of AI in improving decision-making and forecasting in sports, amongst many other advantages, is rapidly expanding and gaining more attention in both the academic sector and the industry. Nonetheless, for many sports audiences, professionals and policy makers, who are not particularly au courant or experts in AI, the connexion between artificial intelligence and sports remains fuzzy. Likewise, for many, the motivations for adopting a machine learning (ML) paradigm in sports analytics are still either faint or unclear. In this perspective paper, we present a high-level, non-technical, overview of the machine learning paradigm that motivates its potential for enhancing sports (performance and business) analytics. We provide a summary of some relevant research literature on the areas in which artificial intelligence and machine learning have been applied to the sports industry and in sport research. Finally, we present some hypothetical scenarios of how AI and ML could shape the future of sports."

2. Beal, R., Norman, T., & Ramchurn, S. (2019). Artificial intelligence for team sports: A survey. The Knowledge Engineering Review, 34, E28., URL=https://eprints.soton.ac.uk/436900/1/Artificial_Intelligence_for_Team_Sports_A_Survey.pdf

Abstract = "The sports domain presents a number of significant computational challenges for artificial intelligence (AI) and machine learning (ML). In this paper, we explore the techniques that have been applied to the challenges within team sports thus far. We focus on a number of different areas, namely match outcome prediction, tactical decision making, player investments, fantasy sports, and injury prediction. By assessing the work in these areas, we explore how AI is used to predict match outcomes and to help sports teams improve their strategic and tactical decision making. In particular, we describe the main directions in which research efforts have been focused to date. This highlights not only a number of strengths but also weaknesses of the models and techniques that have been employed. Finally, we discuss the research questions that exist in order to further the use of AI and ML in team sports."

3. Rein, R., Memmert, D. Big data and tactical analysis in elite soccer: future challenges and opportunities for sports science. SpringerPlus 5, 1410 (2016). , URL=https://springerplus.springeropen.com/articles/10.1186/s40064-016-3108-2

Abstract = "Until recently tactical analysis in elite soccer were based on observational data using variables which discard most contextual information. Analyses of team tactics require however detailed data from various sources including technical skill, individual physiological performance, and team formations among others to represent the complex processes underlying team tactical behavior. Accordingly, little is known about how these different factors influence team tactical behavior in elite soccer. In parts, this has also been due to the lack of available data. Increasingly however, detailed game logs obtained through next-generation tracking technologies in addition to physiological training data collected through novel miniature sensor technologies have become available for research. This leads however to the opposite problem where the shear amount of data becomes an obstacle in itself as methodological guidelines as well as theoretical modelling of tactical decision making in team sports is lacking. The present paper discusses how big data and modern machine learning technologies may help to address these issues and aid in developing a theoretical model for tactical decision making in team sports. As experience from medical applications show, significant organizational obstacles regarding data governance and access to technologies must be overcome first. The present work discusses these issues with respect to tactical analyses in elite soccer and propose a technological stack which aims to introduce big data technologies into elite soccer research. The proposed approach could also serve as a guideline for other sports science domains as increasing data size is becoming a wide-spread phenomenon."

4. Li, B., & Xu, X. (2021). Application of Artificial Intelligence in Basketball Sport. Journal of Education, Health and Sport, 11(7), 54–67., URL=https://apcz.umk.pl/JEHS/article/view/JEHS.2021.11.07.005

Abstract = "Basketball is among the most popular sports in the world, and its related industries have also produced huge economic benefits. In recent years, the application of artificial intelligence (AI) technology in basketball has attracted a large amount of attention. We conducted a comprehensive review of the application research of AI in basketball through literature retrieval. Current research focuses on the AI analysis of basketball team and player performance, prediction of competition results, analysis and prediction of shooting, AI coaching system, intelligent training machine and arena, and sports injury prevention. Most studies have shown that AI technology can improve the training level of basketball players, help coaches formulate suitable game strategies, prevent sports injuries, and improve the enjoyment of games. At the same time, it is also found that the number and level of published papers are relatively limited. We believe that the application of AI in basketball is still in its infancy. We call on relevant industries to increase their research investment in this area, and promote the improvement of the level of basketball, making the game increasingly exciting as its worldwide popularity continues to increase. "

5. Rodrigues, A. C. N., Pereira, A. S., Mendes, R. M. S., Araújo, A. G., Couceiro, M. S., & Figueiredo, A. J. (2020). Using Artificial Intelligence for Pattern Recognition in a Sports Context. Sensors, 20(11), 3040. MDPI AG., URL=https://www.mdpi.com/1424-8220/20/11/3040

Abstract = "Optimizing athlete’s performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN."

6. Chidambaram, S., Maheswaran, Y., Patel, K., Sounderajah, V., Hashimoto, D. A., Seastedt, K. P., McGregor, A. H., et al. (2022). Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation. Sensors, 22(18), 6920. MDPI AG., URL=https://www.mdpi.com/1424-8220/22/18/6920

Abstract = "Wearable technologies are small electronic and mobile devices with wireless communication capabilities that can be worn on the body as a part of devices, accessories or clothes. Sensors incorporated within wearable devices enable the collection of a broad spectrum of data that can be processed and analysed by artificial intelligence (AI) systems. In this narrative review, we performed a literature search of the MEDLINE, Embase and Scopus databases. We included any original studies that used sensors to collect data for a sporting event and subsequently used an AI-based system to process the data with diagnostic, treatment or monitoring intents. The included studies show the use of AI in various sports including basketball, baseball and motor racing to improve athletic performance. We classified the studies according to the stage of an event, including pre-event training to guide performance and predict the possibility of injuries; during events to optimise performance and inform strategies; and in diagnosing injuries after an event. Based on the included studies, AI techniques to process data from sensors can detect patterns in physiological variables as well as positional and kinematic data to inform how athletes can improve their performance. Although AI has promising applications in sports medicine, there are several challenges that can hinder their adoption. We have also identified avenues for future work that can provide solutions to overcome these challenges."

7. Van Eetvelde, H., Mendonça, L.D., Ley, C. et al. Machine learning methods in sport injury prediction and prevention: a systematic review. J EXP ORTOP 8, 27 (2021). , URL=https://jeo-esska.springeropen.com/articles/10.1186/s40634-021-00346-x

Abstract = _" Purpose: Injuries are common in sports and can have significant physical, psychological and financial consequences. Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention. The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention.

Methods: A search of the PubMed database was performed on March 24th 2020. Eligible articles included original studies investigating the role of ML for sport injury prediction and prevention. Two independent reviewers screened articles, assessed eligibility, risk of bias and extracted data. Methodological quality and risk of bias were determined by the Newcastle–Ottawa Scale. Study quality was evaluated using the GRADE working group methodology.

Results: Eleven out of 249 studies met inclusion/exclusion criteria. Different ML methods were used (tree-based ensemble methods (n = 9), Support Vector Machines (n = 4), Artificial Neural Networks (n = 2)). The classification methods were facilitated by preprocessing steps (n = 5) and optimized using over- and undersampling methods (n = 6), hyperparameter tuning (n = 4), feature selection (n = 3) and dimensionality reduction (n = 1). Injury predictive performance ranged from poor (Accuracy = 52%, AUC = 0.52) to strong (AUC = 0.87, f1-score = 85%).

Conclusions: Current ML methods can be used to identify athletes at high injury risk and be helpful to detect the most important injury risk factors. Methodological quality of the analyses was sufficient in general, but could be further improved. More effort should be put in the interpretation of the ML models."_

8. Huang, L., Liu, G. Functional motion detection based on artificial intelligence. J Supercomput 78, 4290–4329 (2022)., URL=https://link.springer.com/article/10.1007/s11227-021-04037-3#citeas

Abstract = "Sports injuries can be a major problem for athletes. Therefore, sports injury protection has become a key focus of attention in sports and medical circles. With widening participation in sport, related injuries can have an impact at national level. However, many areas around the world lack adequate medical resources. It takes more time and money for local people to get to the nearest rehabilitation department or physical therapy studio. Artificial intelligence (AI) has undergone vigorous development, leading to increased computing speed and accuracy. Nowadays, two-dimensional image signals can be used for body-posture recognition. This research is based on the Openpose limb-detection AI model, which has corrective exercise training elements and uses functional motion-detection technology as the diagnostic basis (combined with physical therapists’ clinical knowledge of rehabilitation interventions). We propose a 2-D imaging physical health detection system. The system is divided into four main parts: a mobile app user interface, the computing server, professional interface and database. A user video is recorded by the app. The computing server then calculates keypoints of the human body through Openpose and converts these data into clinical test indicators. Finally, health indicators are displayed, and the app registers action scores. The professional interface presents users with health feedback and recommends suitable rehabilitation videos. The computing server is divided into two parts: limb detection and the detection system. The limb-detection aspect is divided into four parts: sample collection, video processing, keypoint processing and results comparison. The detection system is subdivided into system architecture and a health evaluation (forming the basis for video recommendations). The key contributions of this paper are that the proposed system can calculate body posture and automatically detect the physical condition and health of the body. In addition to reducing dependence on professional human resources, the system can also save the trouble of manual angle measurement in traditional physical therapy."

9. Zexiu Ai, Quantitative CT study of martial arts sports injuries based on image quality, Journal of Visual Communication and Image Representation, Volume 60, 2019, Pages 417-425, ISSN 1047-3203, URL=https://www.sciencedirect.com/science/article/pii/S1047320319301075

Abstract = "Wushu is an outstanding cultural heritage of the Chinese nation and one of the most extensive mass sports in China. As a traditional sports event in China, martial arts are undergoing rapid changes. However, martial arts are a systemic sport with high requirements for speed, explosiveness and coordination. In recent years, with the rapid development of martial arts, competitive competitions have become increasingly fierce. It is easy for athletes to suffer physical damage during the practice of difficult movements. This not only affects the normal exercise and physical health of martial arts enthusiasts, but also affects the improvement of sports level and teaching quality. Therefore, it studies the common parts of martial arts sports injuries. Distribution, looking for its causes, proposing preventive measures, rapid development of image processing technology, digital image has become an indispensable part of multimedia information technology. Digital image is an important carrier for people to obtain information and communicate. Under this background, the research of image quality evaluation has become a hot spot in the field of image processing. The purpose of this paper is to analyze the anatomical characteristics of knee joints of martial arts athletes, the mechanics of injury, the pathophysiological changes after injury, and establish a mathematical model by computer algorithm to accurately perceive the image quality of martial arts sports damage, and finally achieve the use of computer instead of human vision. The system goes to view and recognize images. In this paper, the application value of quantitative CT parameters of martial arts exercise in the evaluation of martial arts injury joints is based on image quality, in order to provide valuable reference for the treatment of martial arts injury selection and prognosis evaluation."

10. Chen Huang, Lei Jiang, Data monitoring and sports injury prediction model based on embedded system and machine learning algorithm, Microprocessors and Microsystems, Volume 81, 2021, 103654, ISSN 0141-9331, URL=https://www.sciencedirect.com/science/article/abs/pii/S0141933120308000

Abstract = "Managing sports performance is very important in the sports industry. Performance, the executives, centers on boosting competitor execution and decreasing the danger of injury. Several factors contribute to these goals, including player health, emotional status, exercise load and physical intensity requirements. Generally speaking, injury prediction is an essential component of injury prevention, and successful identification of injury prediction is a primary indicator for effective prevention. The proposed Artificial Neural Network (ANN) objective is to develop and use early-doing ability and exercise load data to validate a hierarchical machine learning prediction system with accurate detection of player injuries. The physical and workload that requires detection of this early personalized damage can be avoided with specific help. The framework is used to test 21 soccer players’ sports information from various sources, including gathered and inside burden information, outside burden information, and review information. The entirety of this information is fused into the proposed framework to improve the exactness of harm expectation. This calculation distinguishes competitors in danger of injury, with their early intervention available."

11. Moustakidis S, Plakias S, Kokkotis C, Tsatalas T, Tsaopoulos D. Predicting Football Team Performance with Explainable AI: Leveraging SHAP to Identify Key Team-Level Performance Metrics. Future Internet. 2023; 15(5):174., URL=https://www.mdpi.com/1999-5903/15/5/174

Abstract = "Understanding the performance indicators that contribute to the final score of a football match is crucial for directing the training process towards specific goals. This paper presents a pipeline for identifying key team-level performance variables in football using explainable ML techniques. The input data includes various team-specific features such as ball possession and pass behaviors, with the target output being the average scoring performance of each team over a season. The pipeline includes data preprocessing, sequential forward feature selection, model training, prediction, and explainability using SHapley Additive exPlanations (SHAP). Results show that 14 variables have the greatest contribution to the outcome of a match, with 12 having a positive effect and 2 having a negative effect. The study also identified the importance of certain performance indicators, such as shots, chances, passing, and ball possession, to the final score. This pipeline provides valuable insights for coaches and sports analysts to understand which aspects of a team’s performance need improvement and enable targeted interventions to improve performance. The use of explainable ML techniques allows for a deeper understanding of the factors contributing to the predicted average team score performance."

12. Rory P. Bunker, Fadi Thabtah, A machine learning framework for sport result prediction, Applied Computing and Informatics, Volume 15, Issue 1, 2019, Pages 27-33, ISSN 2210-8327, URL="https://www.sciencedirect.com/science/article/pii/S2210832717301485"

Abstract = "Machine learning (ML) is one of the intelligent methodologies that have shown promising results in the domains of classification and prediction. One of the expanding areas necessitating good predictive accuracy is sport prediction, due to the large monetary amounts involved in betting. In addition, club managers and owners are striving for classification models so that they can understand and formulate strategies needed to win matches. These models are based on numerous factors involved in the games, such as the results of historical matches, player performance indicators, and opposition information. This paper provides a critical analysis of the literature in ML, focusing on the application of Artificial Neural Network (ANN) to sport results prediction. In doing so, we identify the learning methodologies utilised, data sources, appropriate means of model evaluation, and specific challenges of predicting sport results. This then leads us to propose a novel sport prediction framework through which ML can be used asa learning strategy. Our research will hopefully be informative and of use to those performing future research in this application area."

13. K. Apostolou and C. Tjortjis, "Sports Analytics algorithms for performance prediction," 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), Patras, Greece, 2019, pp. 1-4, URL=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8900754&isnumber=8900660

Abstract = "Sports Analytics is an emerging research area with several applications in a variety of fields. These could be, for example, the prediction of an athlete's or a team's performance, the estimation of an athlete's talent and market value, as well as the prediction of a possible injury. Teams and coaches are increasingly willing to embed such “tools” in their training, in order to improve their tactics. This paper reviews the literature on Sports Analytics and proposes a new approach for prediction. We conducted experiments using suitable algorithms mainly on football related data, in order to predict a player's position in the field. We also accumulated data from past years, to estimate a player's goal scoring performance in the next season, as well as the number of a player's shots during each match, known to be correlated with goal scoring probability. Results are very promising, showcasing high accuracy, particularly as the predicted number of goals was very close to the actual one."

14. Zhu Haiyun, Xu Yizhe, Sports performance prediction model based on integrated learning algorithm and cloud computing Hadoop platform, Microprocessors and Microsystems, Volume 79, 2020, 103322, ISSN 0141-9331, URL=https://www.sciencedirect.com/science/article/pii/S0141933120304816

Abstract = "This article discusses the classification and research performance information properties. It also discusses construction and application of the Hadoop cloud computing platform. The model presented in this article is a one piece learning algorithm which is a predictive model and a model of cloud based data collection. This model is supported by Hadoop which is suitable for computing with different data sizes. A large number of simulations are performed on the Hadoop platform, under different working conditions, to verify the accuracy and characteristics of the training skill. Spark framework of this research is to develop computational engine efficiency and improve rain prediction models successfully and effectively using big data and Hadoop learning. Therefore, the planned high timeliness and accuracy of real-time hurricane forecast with rain, can solve the problem."

15. Ruud J. R. Den Hartigh, A. Susan M. Niessen, Wouter G. P. Frencken & Rob R. Meijer (2018) Selection procedures in sports: Improving predictions of athletes’ future performance, European Journal of Sport Science, 18:9, 1191-1198, URL=https://www.tandfonline.com/doi/full/10.1080/17461391.2018.1480662

Abstract = "The selection of athletes has been a central topic in sports sciences for decades. Yet, little consideration has been given to the theoretical underpinnings and predictive validity of the procedures. In this paper, we evaluate current selection procedures in sports given what we know from the selection psychology literature. We contrast the popular clinical method (predictions based on overall impressions of experts) with the actuarial approach (predictions based on pre-defined decision rules), and we discuss why the latter approach often leads to superior performance predictions. Furthermore, we discuss the “signs” and the “samples” approaches. Taking the prevailing signs approach, athletes’ technical-, tactical-, physical-, and psychological skills are often assessed separately in controlled settings. However, for predicting later sport performance, taking samples of athletes’ behaviours in their sports environment may result in more valid assessments. We discuss the possible advantages and implications of making selection procedures in sports more actuarial and sample-based."

16. V. C. Pantzalis and C. Tjortjis, "Sports Analytics for Football League Table and Player Performance Prediction," 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA), Piraeus, Greece, 2020, pp. 1-8, URL=https://ieeexplore.ieee.org/abstract/document/9284352

Abstract = "Common Machine Learning applications in sports analytics relate to player injury prediction and prevention, potential skill or market value evaluation, as well as team or player performance prediction. This paper focuses on football. Its scope is long-term team and player performance prediction. A reliable prediction of the final league table for certain leagues is presented, using past data and advanced statistics. Other predictions for team performance included refer to whether a team is going to have a better season than the last one. Furthermore, we approach detection and recording of personal skills and statistical categories that separate an excellent from an average central defender. Experimental results range between encouraging to remarkable, especially given that predictions were based on data available at the beginning of the season."

17. Yongjun Li, Lizheng Wang, Feng Li, A data-driven prediction approach for sports team performance and its application to National Basketball Association, Omega, Volume 98, 2021, 102123, ISSN 0305-0483, URL=https://www.sciencedirect.com/science/article/pii/S0305048319302002

Abstract = "Performance prediction is an issue of vital importance in many real managerial applications. This paper will propose a prediction approach for sports team performance based on data envelopment analysis (DEA) methodology and data-driven technique. The proposed approach includes two steps: The first one conducts a multivariate logistic regression analysis to examine the relationship between the winning probability and game outcomes at the team-level. The other one addresses a DEA-based player portfolio efficiency analysis to optimally choose players and plan the playing time among players in the court. The second step aims to use players’ and team's historical data to train the future and obtain the most promising outcomes in terms of their average inefficiency status. Finally, we apply the proposed performance prediction approach to National Basketball Association and take Golden State Warriors as an example to illustrate its usefulness and efficacy. We obtain the prediction results for the 2015–16 regular season based on a four-season dataset from the 2011–12 season to the 2014–15 season. Further, we carry out multiple experiments to provide deeper discussion and analysis on according prediction results. It shows that the DEA-based data-driven approach can predict the sports team performance very well and can also provide interesting insights into the performance prediction problem."

18. S. Lotfi and M. rebbouj, “Machine Learning for sport results prediction using algorithms”, IJITAS, vol. 3, no. 3, pp. 148–155, Aug. 2021., URL= https://www.woasjournals.com/index.php/ijitas/article/view/114

Abstract = "This paper describes the use of machine learning in sports. Given the recent trend in Data science and sport analytics, the use of Machine Learning and Data Mining as techniques in sport reveals the essential contribution of technology in results and performance prediction. The purpose of this paper is to benchmark existing analysis methods used in literature, to understand the prediction processes used to model Data collection and its analysis; and determine the characteristics of the variables controlling the performance. Finally, this paper will suggest the reliable tool for Data mining analysis technique using Machine Learning."

19. Keisuke Fujii, Data-Driven Analysis for Understanding Team Sports Behaviors, Journal of Robotics and Mechatronics, 2021, p. 505-514, vol.33, no.3, 公開日 2021/06/20, Online ISSN 1883-8049, Print ISSN 0915-3942, URL=https://www.jstage.jst.go.jp/article/jrobomech/33/3/33_505/_article/-char/ja,

Abstract = "Understanding the principles of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. The rules regarding the real-world biological multi-agent behaviors such as those in team sports are often largely unknown due to their inherently higher-order interactions, cognition, and body dynamics. Estimation of the rules from data, i.e., via data-driven approaches such as machine learning, provides an effective way to analyze such behaviors. Although most data-driven models have non-linear structures and high predictive performances, it is sometimes hard to interpret them. This survey focuses on data-driven analysis for quantitative understanding of behaviors in invasion team sports such as basketball and football, and introduces two main approaches for understanding such multi-agent behaviors: (1) extracting easily interpretable features or rules from data and (2) generating and controlling behaviors in visually-understandable ways. The first approach involves the visualization of learned representations and the extraction of mathematical structures behind the behaviors. The second approach can be used to test hypotheses by simulating and controlling future and counterfactual behaviors. Lastly, the potential practical applications of extracted rules, features, and generated behaviors are discussed. These approaches can contribute to a better understanding of multi-agent behaviors in the real world."

20. Lam, Max W. Y.. "One-Match-Ahead Forecasting in Two-Team Sports with Stacked Bayesian Regressions" Journal of Artificial Intelligence and Soft Computing Research, vol.8, no.3, 2018, pp.159-171., URL=https://sciendo.com/article/10.1515/jaiscr-2018-0011

Abstract = "Thereisagrowinginterestinapplyingmachinelearningalgorithmstoreal-worldex-amplesbyexplicitlyderivingmodelsbasedonprobabilisticreasoning.Sportsanalytics,beingfavouredmostlybythestatisticscommunityandlessdiscussedinthemachinelearningcommunity,becomesourfocusinthispaper.Specifically,wemodeltwo-teamsportsforthesakeofone-match-aheadforecasting.WepresentapioneeringmodelingapproachbasedonstackedBayesianregressions,inawaythatwinningprobabilitycanbecalculatedanalytically.Benefitingfromregressionflexibilityandhighstandardofperformance,SparseSpectrumGaussianProcessRegression(SSGPR)–animprovedal-gorithmforthestandardGaussianProcessRegression(GPR),wasusedtosolveBayesianregressiontasks,resultinginanovelpredictivemodelcalledTLGProb.Forevaluation,TLGProbwasappliedtoapopularsportsevent–NationalBasketballAssociation(NBA).Finally,85.28%ofthematchesinNBA2014/2015regularseasonwerecorrectlypredictedbyTLGProb,surpassingtheexistingpredictivemodelsforNBA."

DATASETS:

1. Injury Prediction for Competitive Runners, URL=https://www.kaggle.com/datasets/shashwatwork/injury-prediction-for-competitive-runners
2. Performance Data on Football teams 09 to 22, URL=https://www.kaggle.com/datasets/gurpreetsdeol/performance-data-on-football-teams-09-to-22
3. FIFA World Cup 1930-2022 All Match Dataset, URL=https://www.kaggle.com/datasets/jahaidulislam/fifa-world-cup-1930-2022-all-match-dataset
4. 120 years of Olympic history: athletes and results, URL=https://www.kaggle.com/datasets/heesoo37/120-years-of-olympic-history-athletes-and-results
5. Crowdsourced Football Strategy Decisions, URL=https://www.kaggle.com/datasets/thedevastator/crowdsourced-football-strategy-decisions
6. Football Strategy - Scenarios & Coaching Decisions, URL=https://www.kaggle.com/datasets/thedevastator/how-to-win-football-games-a-data-driven-approach
7. Australian athletes data set, URL=https://www.kaggle.com/datasets/vikashrajluhaniwal/australian-athletes-data-set