ianwhale / march_madness

CSE802 March Madness Project

CSE802 Project

This is a project focused on predicting the outcome of the NCAA Men's Basketball Tournament using past regular season data. See report.pdf for analysis of data and final prediction results.

Description of Experiments

Adaboost

See adaboost.py. The properties of the Adaboost algorithm allow us to easily determine the most important features to prediction with this simple ensemble method.

Correlation

See cov_plot.py. A simple exploration into the correlation of our feature set.

Ensemble Tournnament Scoring

See ensemble_serial.py. This script uses the ensemble methods trained in adaboost.py and random_forest.py to predict the 2017 NCAA Tournament in serial. Meaning, the games are predicted in their tournament format as if we were building a bracket.

Principal Component Analysis

See pca.py. We compute and plot the two largest principal components. Surprisingly, 99.8% of the variance in the data is account for by these two components.

Random Forest

See random_forest.py. With a bit more extra computation, the feature importances can be calculated using the average out of bag error of each feature. Luckily, Sklearn implements this functionality.

Glicko-2 Rating Deviation

See rd_experiment.py. The Glicko-2 rating metric assumes a team's true skill is normally distributed. In this experiment we explore how variable the average Glicko distribution becomes when considering data from further back in time. We conclude that it is actually more likely using as much data as possible---and therefore data from further in the past---is more beneficial.

Spending Experiment

See spending_experiment.py. A simple experiment to see how well correlated the all-time rating (calculated with Glicko-2 beginning from 1985) of the top 25 teams is with Men's Basketball spending. We remove a few outliers through Tukey fences and show, unsurprisingly, a correlation of r = 0.6058 between Glicko-2 and 2017 Men's Basketball spending.