Abstract

Several studies have attempted to predict soccer games using various machine learning algorithms. Few of them have succeeded in predicting soccer games with predictive accuracy (PA) as high as 54.6%. This paper aims to predict English Premier League (EPL) soccer games with PA higher than 54.6%. To reach this goal, we build several ordered probit models, and Artificial Neural Network (ANN) using EPL data from 8 seasons (2008-09 season to 2016-17 seasons). The results show that a simple statistical model comes closest to reaching the target set by a complex ANN model: an ordered probit model with 4 predictors has an average PA of 53.5%. However, the model is heavily reliant on betting odds data. Likewise, an ANN model with 4 predictors has an average PA of 50.8%. These results suggest that if we want to build a model with higher PA, which does not rely on betting odds data, then building more complex ANN model may be the key: specifically, building ANN models with more hidden layers and nodes.

Advisor

Long, Colby

Department

Mathematics; Computer Science

Disciplines

Applied Statistics | Artificial Intelligence and Robotics | Categorical Data Analysis | Data Science | Multivariate Analysis | Probability | Statistical Models

Keywords

soccer prediction, Artificial Neural Networks, sports prediction, ordered probit, machine learning

Publication Date

2021

Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis

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