Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study

Abstract

With the development of measurement technology, data on the movements of actual games in various sports are available and are expected to be used for planning and evaluating the tactics and strategy. In particular, defense in team sports is generally difficult to be evaluated because of the lack of statistical data. Conventional evaluation methods based on predictions of scores are considered unreliable and predict rare events throughout the entire game, and it is difficult to evaluate various plays leading up to a score. On the other hand, evaluation methods based on certain plays that lead to scoring and dominant regions are sometimes unsuitable to evaluate the performance (e.g., goals scored) of players and teams. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance based on the prediction of ball recovery and being attacked, which occur more frequently than goals, using player actions and positional data of all players and the ball. Using data from 45 soccer matches, we examined the relationship between the proposed index and team performance in actual matches and throughout a season. Results show that the proposed classifiers more accurately predicted the true events than the existing classifiers which were based on rare events (i.e., goals). Also, the proposed index had a moderate correlation with the long-term outcomes of the season. These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors.

Pitching strategy evaluation via stratified analysis using propensity score

“…The contribution of the input variables to the propensity score model prediction was computed by SHAP (SHapley Additive exPlanations) (Lundberg and Lee, 2017), which utilizes an interpretable approximate model of the original nonlinear prediction model and also used for sports analysis (Toda et al, 2021). Figure 5 shows the contribution of the input variables (SHAP).…”

Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts

“…Liu et al introduced Game Impact Metric (GIM) to aggregate the values of the actions of players in ice hockey [10]. In soccer, a method of Valuing Actions by Estimating Probabilities (VAEP) [14] and its variants [15] has been proposed. A survey on team sports was recently conducted in [16].…”