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Key game indicators in NBA players’ performance profiles
Kinesiology ( IF 0.9 ) Pub Date : 2019-01-01 , DOI: 10.26582/k.51.1.9
Rubén Dehesa 1 , Alejandro Vaquera 2 , Bruno Gonçalves 3 , Nuno Mateus 3 , Miguel Ángel Gomez-Ruano 4 , Jaime Sampaio 3
Affiliation  

The aim of the present study was to identify and describe different players’ performance profiles in National Basketball Association regular season and playoff games using new combined tracking and notational-based variables. The sample was composed by 535 balanced games (score differences below or equal to eight points) from the regular season (n=502) and the playoffs (n=33), for a total of 472 players were analysed. The variables included were: team points minus opponent points (on and off court), NET score (player's on values minus his off values), maximum negative and positive point difference, minutes on court, team’s winning percentage, game pace, defensive and offensive ratings, effective field-goal percentage, free-throws/ field-goals ratio, offensive rebound percentage, turnover percentage, game quarter and player position. The two step cluster analysis was performed to identify the players profiles during regular season and playoff games. The results identified five performance profiles during regular season games and four performance profiles during playoff games. The profiles identified were mainly characterized by the game quarter and the negative NET indicator (players’ performance on court minus their performance off court) in regular season games and the positive NET indicator during playoff games and second and third game-quarters. Coaching staffs can fine-tune these profiles to develop more team-specific models and, conversely, use the results to monitor and rebuild team constitution under the constrained dynamics of the game and competition stages.

中文翻译:

NBA球员表现档案中的关键比赛指标

本研究的目的是使用新的组合跟踪和基于记号的变量来识别和描述国家篮球协会常规赛和季后赛中不同球员的表现。该样本由常规赛(n = 502)和季后赛(n = 33)的535场平衡比赛(得分差异小于或等于8分)组成,总共分析了472名球员。变量包括:团队得分减去对手得分(场内外),净得分(球员的上场值减去他的场外值),最大负分和正分差,场上分钟,球队的获胜百分比,比赛节奏,防守和进攻等级,有效投篮命中率,罚球/投篮命中率,进攻篮板率,失误率,比赛季度和球员位置。进行了两步聚类分析,以识别常规赛和季后赛期间的球员资料。结果确定了常规赛期间的五个表现概况和季后赛期间的四个表现概况。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队构成。结果确定了常规赛期间的五个表现概况和季后赛期间的四个表现概况。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队构成。结果确定了常规赛期间的五个表现概况和季后赛期间的四个表现概况。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队构成。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队构成。所确定的配置文件的主要特征是常规赛中的比赛季度和净NET指标(球员在场上的表现减去场外表现),在季后赛以及第二和第三季度中表现为正NET指标。教练人员可以微调这些配置文件,以开发更多针对特定团队的模型,反之,在比赛和比赛阶段的动力有限的情况下,使用结果来监视和重建团队构成。
更新日期:2019-01-01
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