Prediction Models with Sports Data

This module introduces the regression models in dealing with the categorical outcome variables in sport contest (i.e., Win, Draw, Lose). It explains the Linear Probability Model (LPM) and Logistic Regression for categorical dependent variables.

This module explores the relationship between probability and betting markets. It explains the concept of odds, and the relationship between betting odds and probabilities. It develops a measure of the accuracy of betting odds using sports examples.

This module shows how to forecast the outcome of EPL soccer games using an ordered logit model and publicly available information. It assesses the accuracy of these forecasts against the betting odds.

This module assesses the efficacy of the EPL forecasting model by replicating the model in the context of three North American team sports leagues (NHL, NBA, MLB). It shows how to forecast game outcomes using an ordered logit model.

In this module we examine the historical and social consequences of gambling, and the relationship between gambling and statistics. Gambling is explored from the perspective of different ethical and religious systems.