Evaluation of soccer team defense based on prediction models of ball
  recovery and being attacked
  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.
