Capstone - Predicting the Philadelphia 76ers' Success

By David Cortes

Problem Statement

This capstone aims to build a regression model with Houston Rockets data in order to predict how many three-point field goals the Philadelphia 76ers make in the upcoming NBA season.

Data

The Houston Rockets and Philadelphia 76ers data used in this project was scraped from basketball-reference.com. Once the data was craped, they were transferred to CSV files.

• Training data: game logs for each player on the 2017-2018 Rockets roster
• Testing data: game logs for each player on the 2020-2021 Sixers roster

Modeling

Linear, Ridge, and Lasso regression models were each tested.

• PolynomialFeatures was used (degree=2)
• Mean cross-val score:
• Lasso: 0.79696671
• Ridge: 0.798621953
• Linear: 0.8

The Lasso Regression model returned the best accuracy score.

• Training Score: 0.999241585099331
• Testing Score: 0.997952083858883

Findings and Conclusion

• The Lasso model turned out very accurate, with a score of over 99%
• Using data from the Sixers’ first 18 games, the model was able to predict about 161 three-pointers
• I'm hopeful that this data and model can be useful for purposes like training, fantasy basketball, etc.

Next Steps

• Find a way to gather data that shows the number of field goals made from right under the basket.
• Regularly update the testing data as the 2020-2021 NBA season continues.
• Feature engineer more advanced stats(3-point attempt rate, turnover percentage, etc.)
• Increase the size of the training data (combine two or three years of Houston Rockets game logs)
• Explore more models like ElasticNet

Works Cited

• https://www.basketball-reference.com/
• https://github.com/vishaalagartha/basketball_reference_scraper
• https://pypi.org/project/Unidecode/