1. Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study
- Author
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Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro, and Keisuke Fujii
- Subjects
FOS: Computer and information sciences ,Computer and Information Sciences ,Decision Analysis ,Computer Science - Artificial Intelligence ,Physiology ,Science ,Social Sciences ,Pilot Projects ,Research and Analysis Methods ,Running ,Machine Learning ,Machine Learning Algorithms ,Mathematical and Statistical Techniques ,Artificial Intelligence ,Soccer ,Medicine and Health Sciences ,Psychology ,Humans ,Statistical Methods ,Statistical Data ,Behavior ,Multidisciplinary ,Biological Locomotion ,Applied Mathematics ,Simulation and Modeling ,Statistics ,Decision Trees ,Biology and Life Sciences ,Boosting Algorithms ,Models, Theoretical ,Sports Science ,Decision Tree Learning ,Artificial Intelligence (cs.AI) ,Collective Human Behavior ,Physical Sciences ,Medicine ,Recreation ,Engineering and Technology ,Team Behavior ,Games ,Management Engineering ,Mathematics ,Algorithms ,Research Article ,Sports ,Forecasting - 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 $, Comment: 15 pages, 5 figures
- Published
- 2022