A datadriven method for understanding and increasing 3-point shooting percentage
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. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | sport games technical and natural sciences |
| Published in: | MIT Sloan Sports Analytics Conference 2017 |
| Language: | English |
| Published: |
2017
|
| Online Access: | http://www.sloansportsconference.com/wp-content/uploads/2017/02/1505.pdf |
| Pages: | 1-14 |
| Document types: | congress proceedings |
| Level: | advanced |