BASKETBALL SPATIAL PERFORMANCE INDICATORS

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This document discusses spatial performance indicators for basketball. It presents a method to analyze shooting performance by partitioning the court into rectangles using a classification tree to maximize scoring percentage differences between areas. A case study analyzes the shooting chart of a well-known NBA player from the 2017-2018 season, showing their scoring percentages and average time shooting in different court locations. A second case study looks at the same indicators for a team's shooting performance.

Sports

BASKETBALL SPATIAL
PERFORMANCE INDICATORS
Marica Manisera Rodolfo Metulini
Marco Sandri Paola Zuccolotto
University of Brescia, Italy

Manisera Metulini Sandri Zuccolotto - University of Brescia
paola.zuccolotto@unibs.it marica.manisera@unibs.it
SCIENTIFIC COORDINATORS OF BDsports

Manisera Metulini Sandri Zuccolotto - University of Brescia
Agenda:
• Performance Analysis
• Spatial Performance indicators
• Case studies:
o players’ performance
o team’s performance
• Concluding remarks
• Performance Analysis
• Spatial Performance indicators
• Case Study: players’ performance
• Case Study: team’s performance

Manisera Metulini Sandri Zuccolotto - University of Brescia
• Performance Analysis
• Spatial Performance indicators
• Case Study: players’ performance
• Case Study: team’s performance
In time
• with a time series approach
• with a multivariate analysis approach
(with respect to specific game situations)
(shooting)
Performance
Analysis

Thank You
References
Download a (regularly updated) list of references at
http://bodai.unibs.it/bdsports/basketball.htm

A new approach in team sports analysis consists in studying positioning and movements of players during the game in relation to team performance. State of the art tracking systems produce spatio-temporal traces of players that have facilitated a variety of research aimed to extract insights from trajectories. Several methods borrowed from machine learning, network and complex systems, geographic information system, computer vision and statistics have been proposed. After having reviewed the state of the art in those niches of literature aiming to extract useful information to analysts and experts in terms of relation between players' trajectories and team performance, this paper presents preliminary results from analysing trajectories data and sheds light on potential future research in this eld of study. In particular, using convex hulls, we find interesting regularities in players' movement patterns.

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BASKETBALL SPATIAL PERFORMANCE INDICATORS

1. BASKETBALL SPATIAL PERFORMANCE INDICATORS Marica Manisera Rodolfo Metulini Marco Sandri Paola Zuccolotto University of Brescia, Italy

2. Manisera Metulini Sandri Zuccolotto - University of Brescia BDsports, a network of people interested in Sports Analytics http://bodai.unibs.it/bdsports/

3. Manisera Metulini Sandri Zuccolotto - University of Brescia paola.zuccolotto@unibs.it marica.manisera@unibs.it SCIENTIFIC COORDINATORS OF BDsports

4. Manisera Metulini Sandri Zuccolotto - University of Brescia Agenda: • Performance Analysis • Spatial Performance indicators • Case studies: o players’ performance o team’s performance • Concluding remarks • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance

5. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance In time • with a time series approach • with a multivariate analysis approach (with respect to specific game situations) (shooting) Performance Analysis

6. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance In space • with respect to the teammates’ placement • with respect to the spots on the court today’s talk (shooting) Performance Analysis

7. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Spatial Shooting Performance Indicators Split the court into a number of rectangles that are then colored according to A. scoring percentages B. difference in scoring percentages with respect to other players, teams, etc. C. some aggregate statistic (e.g. average play length when the shot is attempted, average time in the quarter, etc.) Shot chart of James Harden, Source: NBA.COM

8. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Our Proposal Define the rectangles by means of a CLASSIFICATION TREE i.e.: find the best partition of the court according the criterion of maximizing the difference in the scoring percentages between rectangles

9. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Case Study: Players’ Performance Let’s consider a well-known NBA player (data from the regular season 2017-2018)

10. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance 0.4 0.5 0.6 0.7 result Classification tree • outcome: shot made/missed • predictors: shot coordinates Partition of the court induced by the classification tree (color: scoring percentages)

11. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance -0.10 -0.05 0.00 0.05 0.10 result 400 420 440 460 periodTime Difference in scoring percentages with respect to all the other teammates Average time in the quarter when the shot is attempted

12. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Case Study: Teams’ Performance We now focus on the team

13. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Classification tree • outcome: shot made/missed • predictors: shot coordinates Partition of the court induced by the classification tree (color: scoring percentages) 0.4 0.5 0.6 0.7 result

14. Manisera Metulini Sandri Zuccolotto - University of Brescia • Performance Analysis • Spatial Performance indicators • Case Study: players’ performance • Case Study: team’s performance Difference in scoring percentages with respect to all the opponents Average time in the quarter when the shot is attempted -0.10 -0.05 0.00 0.05 0.10 result 330 340 350 360 370 380 periodTime

15. Thank You References Download a (regularly updated) list of references at http://bodai.unibs.it/bdsports/basketball.htm

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