Chess Match Outcome Prediction via Sequential Data Analysis with Deep Learning

Chess, a timeless game of strategy, has captivated minds for centuries and continues to challenge enthusiasts and grandmasters alike. “Chess Match Outcome Prediction via Sequential Data Analysis with Deep Learning” presents an innovative approach to forecasting the results of chess games by harnessing the sequential data inherent in each move. This research capitalizes on extensive datasets of chess games, encompassing diverse player ratings, time controls, and openings. The pre-process this data into a structured format, allowing for the encoding of chess positions and move sequences. Through the application of cutting-edge deep learning techniques such as Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, proposed model the intricate temporal relationships within the games. Suggested study addresses multiple prediction tasks, from binary classification (win/loss) to multi-class classification (win/draw/loss), enabling a comprehensive evaluation of the model's predictive capabilities. The research also delves into hyperparameter tuning and model interpretability, shedding light on the crucial factors influencing prediction accuracy. The results of proposed experiments unveil the potential of deep learning in chess outcome prediction, offering promising accuracy and performance. As chess remains an evolving game, proposed model serves as a dynamic tool adaptable to the ever-changing landscape of strategies and player behaviors. In an age where chess is both a game of tradition and innovation, this research bridges the two worlds, enriching the analytical toolkit of chess enthusiasts and offering practical applications in chess analysis tools and services.