Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis

Abstract

The FIBA Basketball World Cup is one of the most prominent sporting competitions for men’s basketball, with coaches interested in key performance indicators (KPIs) that give a better understanding of basketball competitions. The aims of the study were to (1) examine the relationship between match KPIs and outcome in elite men’s basketball; and (2) identify the most suitable analysis (multiple linear regression (MLR) vs. quantile regression (QR)) to model this relationship during the men’s basketball tournament. A total of 184 performance records from 92 games were selected and analyzed via MLR and QR, using 10th, 25th, 50th, 75th and 90th quantiles. Several offensive (Paint Score, Mid-Range Score, Three-Point Score, Offensive Rebounds and Turnovers) and defensive (Defensive Rebounds, Steals and Personal Fouls) KPIs were associated with match outcome. The QR model identified additional KPIs that influenced match outcome than the MLR model, with these being Mid-Range Score at the 10th quantile and Offensive Rebounds at the 90th quantile. In terms of contextual variables, the quality of opponent had no impact on match outcome across the entire range of quantiles. Our results highlight QR modelling as a potentially superior tool for performance analysts and coaches to design and monitor technical–tactical plans during match-play. Our study has identified the KPIs contributing to match success at the 2019 FIBA Basketball World Cup with QR modelling assisting with a more detailed performance analysis, to support coaches with the optimization of training and match-play styles.

Modeling the Keys to Team’s Success in the Women’s Chinese Basketball Association

“…Therefore, more effective offensive actions that lead to two-point field goals, such as dribble penetration or post play, and greater physical ability would heighten the likelihood of team success. Unexpectedly, we found that three-point percentage had no significant relationship with the match outcome, which is inconsistent with a prior study from Zhang et al (2020) who reported that three-point score was a KPI that significantly associated with match outcome for men's basketball matches at the FIBA World Cup. This may be partly explained by the differences in the anthropometrical characteristics between men's and women's players (Garcia-Gil et al, 2018).…”

“…This may indicate that use of linear statistical approaches may underestimate the influence of free-throw percentage on the match outcome. However, differences between sexes may exist as a previous study for men's basketball reported that free-throw percentage was not the KPI that can significantly affect the match result based on both linear and non-linear approaches (Zhang et al, 2020), In this regard, differences in motor abilities between women and men basketball players may be a plausible reasons for the different trends regarding the relationship between free-throw percentage and match outcome.…”

“…The teams' technical matchrelated statistics of all 136 matches (n = 272 team observations) were selected as the sample. After disregarding the effect of multicollinearity among the explanatory variables, ten technical variables were analyzed and classified into two groups, offensive and defensive variables, according to previous studies (Gómez-Ruano et al, 2009;Sampaio et al, 2015;Zhang et al, 2020). The grouping information and operational definitions of these technical variables are presented in Table 1.…”

“…Therefore, the heterogeneities among the relationships could be revealed with QR modeling. Zhang et al (2020) compared the effectiveness between multiple linear regression (MLR) and quantile regression for identifying the KPIs of men's basketball matches at the FIBA Basketball World Cup, reporting that QR modeling explored additional KPIs (mid-range score at 10th quantile and offensive rebounds at 90th quantile) than MLR modeling. Therefore, QR modeling could be considered as a potentially superior tool for performance analysts to explain the match performance based on multivariate datasets.…”

Prediction of Offensive Possession Ends in Elite Basketball Teams

“…The variables examined included independent variables (Table 1). Definitions of these variables can be found in the previous studies [7,15,27,28]. Each variable has its values that allow it to accurately define the actions and situations analyzed (Table 1).…”