Maanav-G/NBA-rookie-statistics-analysis

A machine learning model that predicts the statistics of NBA's 2019-2020 season rookies
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2019DraftClass.csv
College and Rookie Trends.ipynb
College&Rookie Data Scrapper.ipynb
CollegeRookieStatLog.csv
CollegeRookieStatLog2.csv
CollegeRookieStatLog2_temp.csv
CollegeStatLog2.csv
Fixed CollegeRookieStatLog2.ipynb
Full College Clustering.ipynb
Prospect Scraper (2019).ipynb
Rookie Predictions .ipynb
RookiePredictions.csv

NBA Rookie Statistics

Summary

A mathematical machine learning model designed to predict the regular season averaging statistics for incoming rookies, by applying the trend of all the NBA player’s (1980 - 2018) college careers compared to their first season in the league, to the 2019 rookies college career statistics. I built this model using Python, and I’m currently working on a UI to display the calculated data.

Data Preparation

Before actually playing with and analyzing the data, I used BeautifulSoup to scrape all the required data, which included the following:

• Year-by-year in-game NBA statistics of every player from 1980 to 2019
• Year-by-year in-game College statistics of the every player accounted for above
• In-game College statistic of every in-coming NBA rookie for 2019

Analysis

Following are the concepts that were applied to predict the 2019 in-game statistics of incoming rookies:

• Correlation Matrix
• Linear Regression
• Extra Trees
• Random Forest
• XGBoost
• Feed Forward Neural Network
• Differences

Examples

The following are a few examples of the predicted statistics for the 2019-2020 Rookies:

Players Points Assists Rebounds Steals Blocks Draft Pick
Zion Williamson 13.0 2.4 6.2 1.1 0.8 #1
Darius Garland 8.1 1.9 2.9 0.6 0.2 #5
Coby White 7.8 2.3 2.2 0.6 0.2 #7
Cam Reddish 6.1 1.1 1.6 0.6 0.2 #10
Bol Bol 9.4 1.1 4.9 0.5 0.8 #44

Tools

Data: BeautifulSoup, Pandas

Visualization: SeaBorn, Matplotlib

Analysis: TPOT, XGBoost, SkLearn

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