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

TL;DR

This study introduces a new method to evaluate soccer team defense by predicting more frequent events like ball recovery and attacks using player and ball positional data, offering a more reliable performance indicator.

Contribution

The paper presents a novel predictive approach for assessing team defense based on common game events, improving reliability over traditional score-based evaluations.

Findings

Proposed classifiers achieved mean F1 scores > 0.483, outperforming existing classifiers.

The new index showed a moderate correlation (r=0.397) with long-term season outcomes.

The method provides a more consistent evaluation of team defense than traditional score predictions.

Abstract

With the development of measurement technology, data on the movements of actual games in various sports can be obtained and used for planning and evaluating the tactics and strategy. 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 because they predict rare events throughout the game. Besides, it is difficult to evaluate various plays leading up to a score. In this study, we propose a method to evaluate team defense from a comprehensive perspective related to team performance by predicting 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 predicted the true events (mean F1 score $>$ 0.483) better than the existing classifiers which were based on rare events or goals (mean F1 score $<$ 0.201). Also, the proposed index had a moderate correlation with the long-term outcomes of the season ($r =$ 0.397). These results suggest that the proposed index might be a more reliable indicator rather than winning or losing with the inclusion of accidental factors.