Sport Betting

Sport betting, a multi-billion dollar industry, is increasingly studied using data-driven approaches to predict outcomes and optimize betting strategies. Current research focuses on applying machine learning algorithms, such as XGBoost and recurrent neural networks (RNNs including LSTM and GRU), to analyze diverse data sources (e.g., historical match results, player statistics, betting market dynamics) and improve prediction accuracy and calibration. These advancements have implications for both the profitability of betting and the understanding of market efficiency, with studies demonstrating the importance of model calibration over raw accuracy in achieving positive returns. Furthermore, the rise of decentralized betting platforms using blockchain technology and automated market makers is also a significant area of investigation.