Blunder prediction in chess

Abstract The ability to predict blunders in chess plays a crucial role in improving players’ performance and enabling strategic decision-making. We introduce a novel, scalable, and personalized blunder prediction model for chess. Unlike prior work requiring a separate model per player, our unified architecture learns a collaborative user embedding space, allowing it to generalize weaknesses across players and new users. Our hybrid model, inspired by Deep Factorization Machines (DeepFM), fuses a frozen pre-trained CNN (for board embeddings) with dynamically learned user embeddings to model player-board interactions while still utilizing metadata about the state of the board and the user. We demonstrate that this latent ’blunder profile’ is a significantly more powerful predictor of error than a player’s explicit Elo rating. The system achieves state-of-the-art performance (0.801 AUC) on both immediate and non-immediate blunders, offering an efficient and data-sparse-friendly solution for personalized chess analysis. Ultimately, this approach demonstrates the practical viability of deep personalization in complex strategy games, facilitating highly efficient, user-centric learning environments.