+

GameChooser

Matthew FolzInsight Data Science

I want to watch a NBA game tonight ... but

there’s too many choices!

GameChooser intelligently picks the best games for

you to watch.

The idea is as follows:• People prefer competitive games

• People prefer high-scoring games

• People prefer to watch good teams play

• People prefer to watch their favorite teams

GameChooser incorporates the first two factors by constructing a machine learning model that predicts the outcome of every NBA game

on a given night.

GameChooser uses random forest classifiers to predict winners, and support vector

regression to estimate point differentials and points scored by each team.

It’s trained on six seasons’ worth of NBA data -- over 5000 games in total.

It considers win-loss records, point differentials, scheduling, home/away differences, ‘momentum’ --

20 features in all.

But wait, there’s more!

Performance

• GameChooser picks winners correctly with 70% accuracy.

• GameChooser beats the spread with 54% accuracy.

• GameChooser beats the over/under with 54% accuracy.

Over a sample of 700 games in 2013:

GameChooser can simulate what would happen if every team played every other team at some instant in time.

Each week, it automatically generates a power ranking of every NBA team.

game-chooser.com

Tools

Matthew Folz

Ph.D in Mathematics (UBC, 2013)

Research: Probability Theory and Stochastic Processes