1. Predicting NBA Player’s Points Scored
Doug Neu CS UW Madison

2. Executive Summary
Main Objective: Use a player’s previous seasons to predict how many points they will score in upcoming games. Useful for betting over/under on player points.
Tasks Completed:
Player stats scraped from NBA.com using their API
Model created with top 12 players from previous season.
Script written to iterate through all players and generate the projections
Changes from Proposal:
Decided k-means clustering wouldn’t be as useful as initially thought. Now a stretch goal.
Decided on basing model off of only top tier players with consistent statistical output.

3. Data Collection & Platform
Scraped player stats from NBA.com using their API.
Scraped more specialized stats (Defensive rating, pace) from ESPN using Selenium.
, seasons stored as training data
stored as testing data
Platform
Laptop

4. Program Created a new script in Python Used Tensorflow and Keras.
Linear Regression (still investigating Bayesian Ridge Regression)

5. Stats Used * Avoided using stats that are too result based
Last 10 Avg FGA
Last 10 Avg 3PA
Last 10 Avg FTA
Last 3 Avg Min
Last 10 FG%
Last 10 3P%
Last 10 FT%
Avg FGA for Season
Avg FTA for Season
Avg 3PA for season
Avg Min for season
Avg Pace of Game
Opponent’s defensive rating
* Avoided using stats that are too result based

6. Results MAE from Season AVG
MAE from Individual Player Model (10 run AVG)
MAE from All Player Model (10 run AVG)
Anthony Davis
7.50
7.86
7.72
Blake Griffin
5.19
5.56
5.27
Damian Lillard
6.33
6.47
6.43
DeMar DeRozan
6.85
7.01
6.82
Giannis Antetokounmpo
5.57
6.52
5.81
James Harden
6.90
6.62
6.74
Kawhi Leonard
6.31
6.14
6.07
Kemba Walker
6.48
6.37
Kevin Durant
5.85
6.04
5.79
LeBron James
6.15
6.22
6.29
Zach Lavine
6.28
6.09

7. Error Report

8. Future Improvements
Detect & eliminate extreme outliers as the result of Foul Trouble, Injuries, lack of minutes, coach’s decision.
Detect if minutes are well below average  outlier
Overtime can lead to a player playing 5 extra minutes  outlier
Increased usage due to teammate injuries  outlier
Add more players to model
Investigate and add more features to model