Characterizing Chess Player Styles with Neural Network Embeddings from Stockfish

Details

Supervisors

Delvenne, Jean-Charles

Faculty

Ecole polytechnique de Louvain

Degree label

Master [120] : ingénieur civil en mathématiques appliquées, à finalité spécialisée

Abstract

enThis thesis investigates how neural networks can be used to analyze and compare chess player styles. Using the last layer of Stockfish’s neural network, we process positions from historical World Chess Championships (1886–2024), as well as the 2024 World Blitz and Rapid Championships. We apply dimensionality reduction (PCA, t-SNE, MDS) and clustering (K-means) to build style-based player maps, measuring similarities through Jensen–Shannon divergence. Results show consistent stylistic signatures for some players—such as Firouzja and Dubov but may fail with others e.g. Nepom niachtchi. We also confirmed the evolution of chess player’s still accros time. Though the approach is promising, limitations remain, especially in data size and the influence of forced lines. Still, this work lays a foundation for future stylometry studies and applications in AI driven chess training