ArbitrageDetector

This project is a comprehensive analysis of arbitrage opportunities in sports betting, specifically focused on historical data from various seasons. Using datasets sourced from Kaggle, the project aims to identify and analyze potential arbitrage opportunities by examining betting odds for home wins, draws, and away wins.

Project Overview

Key features of this project include:

• Data Collection: Importing and processing datasets from multiple seasons to extract relevant betting odds.
• Data Cleaning: Subsetting and cleaning the data to focus on critical columns and handle missing values.
• Arbitrage Detection: Implementing functions to detect arbitrage opportunities by comparing probabilities derived from betting odds.
• Probability Normalization: Calculating normalized probabilities for different outcomes to better understand the betting landscape.
• Optimization: Using linear programming to maximize returns based on identified arbitrage opportunities.

This project serves as a practical application of statistical analysis and optimization techniques, providing insights into the potential profitability of arbitrage betting strategies.

Dataset

The datasets used in this project are historical sports betting data sourced from Kaggle. The primary dataset is final_dataset_with_odds.csv, along with individual season files from 2000-2001.csv to 2020-2021.csv.

Getting Started

Dependencies

To use this project, you need to have R and the following R packages installed:

install.packages(c("knitr", "kableExtra", "lpSolve"))

Installing

Clone the repository to your local machine and navigate to the project directory:

git clone https://github.com/yourusername/ArbitrageDetector.git
cd ArbitrageDetector

Executing program

To run the main analysis script, open R and execute the following command:

source("main_analysis.R")

This will import the necessary datasets, clean the data, and perform the arbitrage detection analysis.

Help

For common issues, ensure that all required packages are installed and that the dataset files are correctly placed in the project directory.

# Example command to check for missing packages
if (!require("lpSolve")) install.packages("lpSolve")

Authors

Contributors' names and contact info:

• Andrew Sun
• Jack Guan

Version History

• 0.1
  • Initial Release

License

This project is licensed under the MIT License - see the LICENSE.md file for details.

Acknowledgments

Inspiration, code snippets, etc.

• Kaggle