Predicting Soccer Game Outcomes

Context:

Predicting the outcome of soccer games is a complex task influenced by various factors such as team performance, player statistics, injuries, weather conditions, and more. Accurate predictions can benefit fans, bettors, and sports analysts by providing insights into potential game results.

My Contribution:

I developed a machine learning model to predict the outcomes of soccer games based on historical match data and various influencing factors. The project involved several critical steps:

Data Collection and Preprocessing:

Gathered extensive historical match data from various leagues, including team statistics, player performance metrics, weather conditions, and injury reports.

Cleaned and preprocessed the data, handling missing values and encoding categorical variables.

Feature Engineering:

Created new features such as recent form, head-to-head records, home/away advantage, and player availability.

Applied feature selection techniques to identify the most relevant features for prediction.

Model Training and Evaluation:

Trained multiple classification models including logistic regression, random forests, gradient boosting, and neural networks.

Evaluated model performance using metrics such as accuracy, precision, recall, and F1-score.

Fine-tuned the models through hyperparameter optimization to improve prediction accuracy.

This project provided valuable insights into the factors influencing soccer game outcomes and demonstrated the potential of machine learning in sports analytics. The predictions resulted in positive expected value when placing bets on the website bet365.com.