Predicting NBA Player’s Points Scored Doug Neu CS 539 UW Madison

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.

Data Collection & Platform Scraped player stats from NBA.com using their API. Scraped more specialized stats (Defensive rating, pace) from ESPN using Selenium. 2015-16, 2016-17 seasons stored as training data 2017-2018 stored as testing data Platform Laptop

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

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

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

Error Report

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