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

Author

Listed:
  • Shaoliang Zhang

    (Division of Sport Science & Physical Education, Tsinghua University, Beijing 100084, China)

  • Miguel Ángel Gomez

    (Facultad de Ciencias de la Actividad Física y del Deporte (INEF), Universidad Politécnica de Madrid, 28040 Madrid, Spain)

  • Qing Yi

    (School of Physical Education and Sport Training, Shanghai University of Sport, Shanghai 200438, China
    Shanghai Key Lab of Human Performance, Shanghai University of Sport, Shanghai 200438, China)

  • Rui Dong

    (China Basketball College, Beijing Sport University, Beijing 100084, China)

  • Anthony Leicht

    (Sport and Exercise Science, James Cook University, Townsville, QLD 4810, Australia)

  • Alberto Lorenzo

    (Facultad de Ciencias de la Actividad Física y del Deporte (INEF), Universidad Politécnica de Madrid, 28040 Madrid, Spain)

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.

Suggested Citation

  • Shaoliang Zhang & Miguel Ángel Gomez & Qing Yi & Rui Dong & Anthony Leicht & Alberto Lorenzo, 2020. "Modelling the Relationship between Match Outcome and Match Performances during the 2019 FIBA Basketball World Cup: A Quantile Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-11, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:16:p:5722-:d:396082
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    References listed on IDEAS

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