Machine learning is revolutionizing the world of sports betting, particularly in the context of the NHL. This innovative project delved into the realm of data science to develop cutting-edge machine learning models aimed at identifying lucrative betting opportunities in the NHL anytime goalscorer markets. The primary objective was to leverage player statistics and game-related variables to pinpoint mispriced bets in sportsbook odds, ultimately enhancing profitability for bettors.
Sportsbooks are known for skewing odds in their favor, posing a significant challenge for individuals looking to make consistent gains through betting. To combat these biases, this project meticulously collected data from various platforms such as DraftKings, Hockey Reference, and Rotowire through extensive web scraping techniques. A custom MySQL database was engineered to efficiently store and process this data, ensuring accuracy, integrity, and scalability.
One of the key components of this project was feature engineering, where over 250 unique features were created to quantify player performance. These features ranged from rolling window statistics like shots per 60 minutes to game-related variables such as rest days and home/away status. Additionally, advanced metrics like point streaks were incorporated to provide a comprehensive view of player capabilities.
Several machine learning algorithms were put to the test, including random forests, XGBoost, and neural networks. The performance of these models was evaluated based on metrics such as binary cross-entropy loss, expected value thresholds, and average profit per bet. While none of the models proved profitable against DraftKings, XGBoost emerged as the top-performing algorithm, offering the highest average profit per bet.
Insights gleaned from the project shed light on the complexities of overcoming biases inherent in sportsbook odds. By clustering players using techniques like k-means, distinct playing styles such as skill, defensive, and grinder were identified, revealing patterns that influenced model predictions. The project also highlighted the potential for improvement by incorporating advanced hockey statistics, injury data, and graph neural networks to capture team dynamics and player chemistry.
Moreover, the significance of “odds shopping” across multiple sportsbooks was underscored as a vital strategy to boost profitability and exploit variations in odds. This project not only demonstrated the promise of machine learning in analyzing sports betting markets but also underscored the challenges of competing with sophisticated sportsbook models. Through continued exploration of new features and modeling strategies, there remains a promising path towards developing a profitable solution in the future.
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