Determining Chess Piece Values Using Machine Learning

Abstract

This paper attempts to generate point values for chess pieces, as alternatives to the commonly accepted chess piece values. We use a database of over a million online chess games to heuristically determine the value of a chess piece, by using material imbalances to predict game results. We then explore how piece values change when we analyze material imbalances at various stages of a chess game. As further exploration, we determine what practical values chess pieces and imbalances have at various rating ranges. This creates practical data that players of varying rating can use to aid in chess calculation, as opposed to the rigid values that are typically accepted.