Can We Use Machine Learning To Predict Win/Loss Rates in Chess Using Meta-Game Data?
DOI:
https://doi.org/10.58445/rars.3133Keywords:
ChessAbstract
Artificial intelligence research in chess has traditionally centered on analyzing board positions and moves. While effective for engines, this perspective overlooks the wider context that shapes human play. Players face time pressure, emotional, and psychological influences beyond the board. This paper examines whether Machine Learning can predict win, loss, or draw outcomes using only meta-game data such as ELO differences, time management, and activity statistics. A Random Forest classifier trained on a large dataset achieved competitive accuracy, revealing correlations between contextual signals and results.
References
C. F. Chabris and E. Hearst, “Visualization, pattern recognition, and forward search: Effects of playing speed and sight of the position on grandmaster chess errors,” Cogn. Sci., vol. 27, no. 4, pp. 637–648, 2003. Link: Visualization, pattern recognition, and forward search: Effects of playing speed and sight of the position on grandmaster chess errors.
A. E. Elo, The Rating of Chessplayers, Past and Present. New York, NY, USA: Arco Publishing, 1978. Link: The Rating of Chess Players, Past and Present by Sam Sloan | Goodreads
S. Guntz, J. L. Crowley, D. Vaufreydaz, R. Balzarini, and P. Dessus, “The role of emotion in problem solving: First results from observing chess,” arXiv preprint arXiv:1810.11094, 2018. Link: The Role of Emotion in Problem Solving: First Results from Observing Chess
I. Palacios-Huerta, “Cognitive performance in competitive environments: Evidence from a natural experiment,” J. Public Econ., vol. 139, pp. 40–52, 2016. Link: Cognitive performance in competitive environments: Evidence from a natural experiment - ScienceDirect
J. Schrittwieser et al., “Mastering Atari, Go, chess and shogi by planning with a learned model,” Nature, vol. 588, no. 7839, pp. 604–609, 2020. Link: Mastering Atari, Go, chess and shogi by planning with a learned model | Nature
D. Silver et al., “Mastering chess and shogi by self-play with a general reinforcement learning algorithm,” arXiv preprint arXiv:1712.01815, 2017. Link: [1712.01815] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
N. Tomašev, U. Paquet, D. Hassabis, and V. Kramnik, “Assessing game balance with AlphaZero: Exploring alternative rule sets in chess,” arXiv preprint arXiv:2009.04374, 2020. Link: Assessing Game Balance with AlphaZero: Exploring Alternative Rule Sets in Chess
H. L. J. van der Maas and E.-J. Wagenmakers, “A psychometric analysis of chess expertise,” Amer. J. Psychol., vol. 118, no. 1, pp. 29–60, 2005. Link: A psychometric analysis of chess expertise - PubMed
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