Analyzing factors that lead to NBA regular season success
(Analyse der Faktoren, die zum Erfolg in der regulären NBA-Saison führen)
The National Basketball Association (NBA) values regular-season success and acknowledges the crucial role of a team`s roster composition in determining overall performance. This study uses machine learning techniques, specifically unsupervised learning clustering and decision tree models, to predict the composition of a winning roster. Our research identified three distinct clusters based on win percentage and the distribution of players across different skill levels. Successful teams typically have more top-tier players and a significant representation of players in the lowest skill level. In contrast, teams that spread their talent across the entire roster are less successful. We have noticed that players with average to above-average skills are notably affected by excessive playing time in the previous game, which leads to decreased performance and potential losses for the team in the next game. Considering the time of year and the gap between games, we recommend prioritizing the rest and recovery of top players, especially in the latter half of the season. It`s crucial to ensure that players who are not as skilled as the top players but still make significant contributions to the team maintain consistent performance, especially during the first half of the season. Analyzing height`s impact on basketball player performance has revealed practical insights that can empower coaches and management. We found that the shortest and tallest players often perform less than those of average height. Most top performers in the NBA tend to have heights closer to the average. However, for players who frequently operate near the net and encounter numerous rebound opportunities, it is generally preferable to have an average or taller player for slightly enhanced overall performance compared to below-average height players. Teams can use these insights to improve their roster construction and maximize player utilization by coaches from one game to the next. This research provides practical strategies that can be immediately implemented to enhance team performance.
© Copyright 2024 Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support icSPORTS - Volume 1. Veröffentlicht von Science and Technology Publications. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Spielsportarten Naturwissenschaften und Technik |
| Tagging: | maschinelles Lernen |
| Veröffentlicht in: | Proceedings of the 12th International Conference on Sport Sciences Research and Technology Support icSPORTS - Volume 1 |
| Sprache: | Englisch |
| Veröffentlicht: |
Porto
Science and Technology Publications
2024
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| Online-Zugang: | https://doi.org/10.5220/0013041500003828 |
| Seiten: | 83-94 |
| Dokumentenarten: | Artikel |
| Level: | hoch |