School of Sport and Exercise Science, University of Worcester, Worcester, United Kingdom.
Faculty of Engineering, Environment and Computing, Coventry University, Coventry, United Kingdom.
PLoS One. 2021 Jan 7;16(1):e0244257. doi: 10.1371/journal.pone.0244257. eCollection 2021.
The purposes of this study were to (i) develop a field-goal shooting performance analysis template and (ii) explore the impact of each identified variable upon the likely outcome of a field-goal attempt using binary logistic regression modelling in elite men's wheelchair basketball. First, a field-goal shooting performance analysis template was developed that included 71 Action Variables (AV) grouped within 22 Categorical Predictor Variables (CPV) representing offensive, defensive and game context variables. Second, footage of all 5,105 field-goal attempts from 12 teams during the men's 2016 Rio De Janeiro Paralympic Games wheelchair basketball competition were analysed using the template. Pearson's chi-square analyses found that 18 of the CPV were significantly associated with field-goal attempt outcome (p < 0.05), with seven of them reaching moderate association (Cramer's V: 0.1-0.3). Third, using 70% of the dataset (3,574 field-goal attempts), binary logistic regression analyses identified that five offensive variables (classification category of the player, the action leading up to the field-goal attempt, the time left on the clock, the location of the shot, and the movement of the player), two defensive variables (the pressure being exerted by the defence, and the number of defenders within a 1-meter radius) and 1 context variable (the finishing position of the team in the competition) affected the probability of a successful field-goal attempt. The quality of the developed model was determined acceptable (greater than 65%), producing an area under the curve value of 68.5% when the model was run against the remaining 30% of the dataset (1,531 field-goal attempts). The development of the model from such a large sample of objective data is unique. As such it offers robust empirical evidence to enable coaches, performance analysts and players to move beyond anecdote, in order to appreciate the potential effect of various and varying offensive, defensive and contextual variables on field-goal success.
（i）开发一种实地投篮表现分析模板；（ii）利用二项逻辑回归模型，探索在精英男子轮椅篮球比赛中，每个已识别变量对实地投篮结果的影响。首先，开发了一种实地投篮表现分析模板，其中包含 71 个动作变量（AV），分为 22 个分类预测变量（CPV），代表进攻、防守和比赛情境变量。其次，利用模板分析了 2016 年里约热内卢残奥会男子轮椅篮球比赛中 12 支球队的所有 5105 次投篮尝试的录像。皮尔逊卡方分析发现，18 个 CPV 与投篮尝试结果显著相关（p<0.05），其中 7 个 CPV 达到中度相关（Cramer's V：0.1-0.3）。第三，使用数据集的 70%（3574 次投篮尝试），二项逻辑回归分析确定了 5 个进攻变量（球员的分类类别、投篮前的动作、时钟上剩余的时间、投篮位置和球员的移动）、2 个防守变量（防守施加的压力和 1 米半径内的防守人数）和 1 个情境变量（球队在比赛中的结束位置）影响成功投篮的概率。所开发模型的质量被认为是可接受的（大于 65%），当该模型应用于数据集的剩余 30%（1531 次投篮尝试）时，产生了 68.5%的曲线下面积值。从如此大量的客观数据中开发模型是独特的。因此，它提供了可靠的经验证据，使教练、绩效分析师和球员能够超越轶事，以了解各种不同的进攻、防守和情境变量对投篮成功的潜在影响。
