The best performing model was a time-varying Bayesian Dynamic Linear Regression model that used ARIMA (Autoregressive Integrated Moving Average) methods to forecast the time-varying parameters that are used to forecast the point spread in the Dynamic Linear Regression Model.

You will learn how to combine your regression models from this chapter with principled uncertainty quantification through the Bayesian lens---a combination that many professional sports bettors consider indispensable.

Build predictive sports models using linear, logistic, Poisson, and regularized regression. Full derivations, NFL worked examples, and production-ready Python code for autonomous betting agents.

Module 1: Introduction to Linear Regression Module 2: Gathering Our Data Module 3: Creating a Linear Regression Model Module 4: More Advanced Linear Regression Models Module 5: Transformations and Model Interpretations Module 6: Betting Your Model

Learn how to use regression analysis in sports betting models to improve predictions, analyze data trends, and gain an edge in your wagering strategies.

They utilized Multiple Linear Regression to predict the first innings score by considering variables like the current run rate, the number of wickets fallen, and the venue of the match.

Our proposed FL-GLM converts betting odds to probabilities more accurately than other existing counterparts, which are multinomial logistic regression Baxter (1990) and ordered logistic regression McCullagh (1980).

To address this gap, the present study examined the extent to which demographic, psychological, and gambling-related constructs (e.g., harms) are endorsed by in-play sports bettors relative to...

ML for sports outcome prediction has been widely studied, however very little of this research extends to sports betting. In the bulk of this work, ML models are evaluated on accuracy achieved.

Abstract The global sports betting market is substantial and growing rapidly. Artificial intelligence and analytics o↵er investors a new, data-driven perspective. This study aims to build a betting framework by analyzing machine learning methods. It integrates outcome-prediction models with profitable betting strategies. The dataset contains over 5,000 real-world English Premier League ...