Modelling the relationship between match outcome and match performances during the 2019 FIBA basketball world cup: A quantile regression analysis
Zhang, Shaoliang, Gomez, Miguel Ángel, Yi, Qing, Dong, Rui, Leicht, Anthony, and Lorenzo, Alberto (2020) Modelling the relationship between match outcome and match performances during the 2019 FIBA basketball world cup: A quantile regression analysis. International Journal of Environmental Research and Public Health, 17 (16). 5722.
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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.
| Item ID: | 66751 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1660-4601 |
| Keywords: | Basketball performance analysis, Elite athletes, Quantile regression, Team sport |
| Copyright Information: | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/) |
| Funders: | Shanghai University of Sport (SUS) |
| Projects and Grants: | SUS Grant No. 11DZ2261100 |
| Date Deposited: | 11 Mar 2021 01:08 |
| FoR Codes: | 42 HEALTH SCIENCES > 4207 Sports science and exercise > 420799 Sports science and exercise not elsewhere classified @ 100% |
| SEO Codes: | 13 CULTURE AND SOCIETY > 1306 Sport, exercise and recreation > 130602 Organised sports @ 100% |
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