Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

Dijkhuis, Talko; Kempe, Matthias; Lemmink, Koen

doi:10.3390/e23080952

Cited by 16 publications

(10 citation statements)

References 29 publications

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“…Fundamental theories on learning in sports are important there. Our work should be consistent with instruction theory, pedagogy and motor learning theory 25 and general exercise and training principles (Brink et al, 2010;Dijkhuis et al, 2017;Borghuis et al, 2008), including various approaches to motor learning discussed below. 25 Jacobs and Michaels (2007), Jacobs et al (2012), Vilar et al (2012), Stolz and Pill (2014), Travassos et al (2012, Davids et al (2012bDavids et al ( , 2012aDavids et al ( , 2013Davids et al ( , 2015, Headrick et al (2015), Serra-Olivares et al (2015), Brymer and Renshaw (2010), Renshaw et al (2009Renshaw et al ( , 2019Renshaw et al ( , 2010Renshaw et al ( , 2016, Correia et al (2019), Chow et al (2007Chow et al ( , 2011, Carvalho et al (2013), Araújo et al (2006Araújo et al ( , 2016.…”

Section: Philosophical Accounts Of Skill Acquisition and Motor Learningmentioning

confidence: 58%

A Design Space of Sports Interaction Technology

Postma¹,

Delden²,

Koekoek³

et al. 2022

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With this monograph we introduce a new, systematic taxonomy of Sports Interaction Technology (Sports ITech) that defines a design space of existing and future work in this domain. We set the taxonomy in a context of our view on sport science and sports practice, target outcomes of sports and the underlying factors influencing them, and the role that sports technology plays to support sports science and practice. In that setting we systematically build and illustrate a taxonomy for the design space for Sports ITech as a sub-area of sports technologies, with specific attention for the adequate inclusion of knowledge from the sports

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Section: Philosophical Accounts Of Skill Acquisition and Motor Learningmentioning

confidence: 58%

A Design Space of Sports Interaction Technology

Postma¹,

Delden²,

Koekoek³

et al. 2022

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“…To this end, some authors applied machine learning models such as Random Forest and Decision Trees to in-match position tracking data from 302 competitive professional soccer matches to forecast player performance levels. The research, which utilised data acquired retrospectively, illustrated the capability of early match forecasts to assist coaches in making well-informed substitute judgments ( 53 ).…”

Section: Discussionmentioning

confidence: 99%

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Munoz-Macho,

Domínguez-Morales,

Sevillano-Ramos

2024

Front. Sports Act. Living

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IntroductionIn competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.MethodsSystematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.ResultsThe analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).ConclusionsThis study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

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“…An ML approach to support substitutions is presented in [17], the real-time physical performance of football players during the match is analysed and this is suggested as an additional parameter, to decide on the substitutions. The authors used data from 302 football matches to develop an ML model.…”

Section: Background Literaturementioning

confidence: 99%

Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning

Mohandas,

Ahsan,

Haider

2023

BDCC

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Football (also known as Soccer), boasts a staggering fan base of 3.5 billion individuals spread across 200 countries, making it the world’s most beloved sport. The widespread adoption of advanced technology in sports has become increasingly prominent, empowering players, coaches, and team management to enhance their performance and refine team strategies. Among these advancements, player substitution plays a crucial role in altering the dynamics of a match. However, due to the absence of proven methods or software capable of accurately predicting substitutions, these decisions are often based on instinct rather than concrete data. The purpose of this research is to explore the potential of employing machine learning algorithms to predict substitutions in Football, and how it could influence the outcome of a match. This study investigates the effect of timely and tactical substitutions in football matches and their influence on the match results. Machine learning techniques such as Logistic Regression (LR), Decision tree (DT), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Random Forest (RF) classifiers were implemented and tested to develop models and to predict player substitutions. Relevant data was collected from the Kaggle dataset, which contains data of 51,738 substitutions from 9074 European league football matches in 5 leagues spanning 6 seasons. Machine learning models were trained and tested using an 80-20 data split and it was observed that RF model provided the best accuracy of over 70% and the best F1-score of 0.65 on the test set across all football leagues. SVM model achieved the best Precision of almost 0.8. However, the worst computation time of up to 2 min was consumed. LR showed some overfitting issues with 100% accuracy in the training set, but only 60% accuracy was obtained for the test set. To conclude, based on the time of substitution and match score-line, it was possible to predict the players who can be substituted, which can provide a match advantage. The achieved results provided an effective way to decide on player substitutions for both the team manager and coaches.

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Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

Cited by 16 publications

References 29 publications

A Design Space of Sports Interaction Technology

A Design Space of Sports Interaction Technology

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Tactically Maximize Game Advantage by Predicting Football Substitutions Using Machine Learning

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