The document discusses an attempt to predict NBA player injuries using statistical modeling. It collected player statistics over a three-week period to predict the likelihood of injury in the next three games. The model was able to predict injuries with an AUC score of 0.549. Important predictive features included a player's running speed, distance ran, and hustle statistics like charges drawn and balls recovered, which may indicate increased injury risk. The author proposes further improving the model by investigating additional hustle stats.

1. NBA Injury Prediction
Peter Wen
petwen1027@gmail.com
github.com/rayal34/Predicting_NBA_Injuries

2. MOTIVATION
● Warriors’ offensive rating with Steph Curry: 119.1
● Warriors’ offensive rating without Steph Curry: 105.3
105.3 = not good!

3. MOTIVATION
● Increased awareness of player health.

4. PROCESS
● Collected data by web scraping.
Data

5. PROCESS
● Used a three week aggregation moving window.
Predict if an injury
will occur in the next
3 games
Aggregate box score
statistics in the three
weeks leading up to the
prediction
Time

6. RESULTS
Winner!
AUC = 0.549

7. INSIGHTS
● Important features to the model.

8. INSIGHTS
● Important features to the model.

9. INSIGHTS
● Important features to the model.

10. INSIGHTS
● Speed: slower running speed might signal an injury.
● Distance: greater distance ran might also signal an injury.

11. FUTURE WORK
● Investigate hustle stats.
○ Ex: charges drawn, loose balls recovered.
○ Diving head-first into the stands = higher injury risk!

12. Thank you!
Any questions?
Peter Wen
petwen1027@gmail.com
github.com/rayal34/Predicting_NBA_Injuries