Football Match Prediction Using Machine Learning and Betting Strategy Analysis

This article explores the application of machine learning techniques to predict football match outcomes and develop profitable betting strategies. The research utilizes two primary categories of data: team fundamental information (including team strength, pre-match form, historical head-to-head records, venue effects, and offensive/defensive capabilities quantified into 17-dimensional features) and betting odds from 17 major bookmakers (51-dimensional features).

The study compares multiple prediction models, starting with linear models like Logistic Regression (LR) achieving 38.18% accuracy on the English Premier League, then progressing to non-linear models. Support Vector Machine (SVM) improved accuracy to 51.23%, while Random Forest achieved over 53% accuracy across most European leagues. The research identifies that French Ligue 1 has the highest "chaos score" due to greater competitive imbalance, making prediction more challenging.

Deep Neural Network (DNN) approaches were also explored, utilizing unsupervised feature learning to automatically extract meaningful representations from the raw data. The ensemble method combining DNN with SVM achieved 54.55% prediction accuracy on the Premier League.

For score prediction, two approaches are presented: the Poisson distribution method and multi-class classification treating score prediction as a 25-class problem (5x5 matrix for goals up to 4 each).

The betting strategy analysis derives a profit condition formula: 1/accuracy < average_odds. Analysis shows that betting only when prediction probability falls below 0.4 or above 0.9 satisfies this condition, yielding a 55% profit rate in backtesting with 20 out of 100 matches selected.

The article concludes by extending the methodology to stock prediction, discussing signal mining, feature correlation analysis, and the application of LSTM and transformer models for time-series financial data.