Identifying Key Factors for Securing a Champions League Position in French Ligue 1 Using Explainable Machine Learning Techniques.

Plakias, Spyridon; Kokkotis, Christos; Mitrotasios, Michalis; Armatas, Vasileios; Tsatalas, Themistoklis; Giakas, Giannis

Featured Application: The information obtained from this study can be useful for coaches and performance analysts of teams. By taking into account the factors that contribute to securing one of the top positions in the league standings, coaches are encouraged to adopt ball possession strategies with patience and short passes to create gaps in the opponent's defense and, at the right moment, execute through balls aiming to enter the attacking third and create goal opportunities. On the other hand, teams are advised to avoid long passes, as these may lead to a loss of team cohesion and create large distances between the lines. Finally, this information is also useful for team scouts in identifying players who are suitable for implementing the aforementioned strategies. Introduction: Performance analysis is essential for coaches and a topic of extensive research. The advancement of technology and Artificial Intelligence (AI) techniques has revolutionized sports analytics. Aim: The primary aim of this article is to present a robust, explainable machine learning (ML) model that identifies the key factors that contribute to securing one of the top three positions in the standings of the French Ligue 1, ensuring participation in the UEFA Champions League for the following season. Materials and Methods: This retrospective observational study analyzed data from all 380 matches of the 2022–23 French Ligue 1 season. The data were obtained from the publicly-accessed website "whoscored" and included 34 performance indicators. This study employed Sequential Forward Feature Selection (SFFS) and various ML algorithms, including XGBoost, Support Vector Machine (SVM), and Logistic Regression (LR), to create a robust, explainable model. The SHAP (SHapley Additive Explanations) model was used to enhance model interpretability. Results: The K-means Cluster Analysis categorized teams into groups (TOP TEAMS, 3 teams/REST TEAMS, 17 teams), and the ML models provided significant insights into the factors influencing league standings. The LR classifier was the best-performing classifier, achieving an accuracy of 75.13%, a recall of 76.32%, an F1-score of 48.03%, and a precision of 35.17%. "SHORT PASSES" and "THROUGH BALLS" were features found to positively influence the model's predictions, while "TACKLES ATTEMPTED" and "LONG BALLS" had a negative impact. Conclusions: Our model provided satisfactory predictive accuracy and clear interpretability of results, which gave useful information to stakeholders. Specifically, our model suggests adopting a strategy during the ball possession phase that relies on short passes (avoiding long ones) and aiming to enter the attacking third and the opponent's penalty area with through balls.

FEATURE selection; SUPPORT vector machines; ARTIFICIAL intelligence; STANDING position; K-means clustering

Applied Sciences (2076-3417), 2024, Vol 14, Issue 18, p8375

2076-3417

Academic Journal

10.3390/app14188375