A datadriven method for understanding and increasing 3-point shooting percentage
(Eine datengetriebene Methode zum Verständnis und Steigerung des Prozentsatzes von 3-Punkte-Würfen)
Although 3-point shooting is an essential aspect of winning games, shooting percentages have remained stagnant for decades. Here, we analyze 6 shooter factors from over 1.1 million 3-point shots captured by the Noah shooting system to quantitatively define high percentage shooting and shooter improvement. We find significant associations between all of these 6 shooter factors and shooting percentage.
Furthermore, we use the interaction of these factors to define the region in the hoop where shots are guaranteed to score. Of the 6 factors, 4 are directly actionable using new technologies for instant feedback. We use machine learning to predict shooting percentage within 1.5% using only these 4 factors as input. Finally, we grouped players by their proficiency at these 4 factors and show case studies about the dissimilar training approaches that will lead to optimal improvement for two of these groupsDeskriptoren
© Copyright 2017 MIT Sloan Sports Analytics Conference 2017. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Spielsportarten Naturwissenschaften und Technik |
| Veröffentlicht in: | MIT Sloan Sports Analytics Conference 2017 |
| Sprache: | Englisch |
| Veröffentlicht: |
2017
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| Online-Zugang: | http://www.sloansportsconference.com/wp-content/uploads/2017/02/1505.pdf |
| Seiten: | 1-14 |
| Dokumentenarten: | Kongressband, Tagungsbericht |
| Level: | hoch |