运用分类树在压力情境下测量运动表现并应用于篮球投篮
Measuring sport performances under pressure by classification trees with application to basketball shooting.
作者信息
Metulini Rodolfo, Le Carre Mael
机构信息
Department of Economics and Statistics, University of Salerno, Fisciano, Italy.
Big & Open Data Innovation Laboratory (BODaI-Lab) - Department of Economics and Management, University of Brescia, Brescia, Italy.
出版信息
J Appl Stat. 2019 Dec 19;47(12):2120-2135. doi: 10.1080/02664763.2019.1704702. eCollection 2020.
Measuring players' performance in team sports is fundamental since managers need to evaluate players with respect to the ability to score during crucial moments of the game. Using Classification and Regression Trees (CART) and play-by-play basketball data, we estimate the probabilities to score the shot with respect to a selection of game covariates related to game pressure. We use scoring probabilities to develop a player-specific shooting performance index that takes into account for the difficulty associated to score different types of shots. By applying this procedure to a large sample of 2016-2017 Basketball Champions League (BCL) and 2017-2018 National Basketball Association (NBA) games, we compare the factors affecting shooting performance in Europe and in the United States and we evaluate a selection of players in terms of the proposed shooting performance index with the final aim of providing useful guidelines for the team strategy.
衡量团队运动中球员的表现至关重要，因为教练需要根据球员在比赛关键时刻的得分能力来评估他们。利用分类回归树（CART）和逐场篮球数据，我们针对与比赛压力相关的一系列比赛协变量，估计投篮得分的概率。我们使用得分概率来制定一个针对球员的投篮表现指数，该指数考虑了不同类型投篮得分的难度。通过将此程序应用于2016 - 2017年篮球欧冠联赛（BCL）和2017 - 2018年美国职业篮球联赛（NBA）的大量比赛样本，我们比较了影响欧美投篮表现的因素，并根据提议的投篮表现指数对一些球员进行评估，最终目的是为球队战略提供有用的指导方针。
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