In this article, we explore the decision-making process of chess players by leveraging a chess engine to evaluate moves across various pools of games. To measure the decisiveness of each move throughout the games, we introduce a metric called Δ, derived from the engine's evaluation of board positions. This metric serves as a proxy for the complexity and criticality of each decision. We then conduct a comparative analysis of players at different competitive levels to understand how they navigate these decision points.

Our first observation reveals that players encounter a broad spectrum of Δ values, reflecting the intricate nature of the decision-making process in chess. By analyzing both winning and losing players, we identify patterns suggesting that a reduction in decision complexity —indicated by lower Δ values— may correlate with a decline in player performance. This suggests that maintaining a certain level of complexity might be integral to strong performance.

Secondly, we find that players' accuracy tends to improve when faced with positions characterized by high Δ values, irrespective of their competitive ranking. This indicates that more decisive moments in the game prompt players to perform better, regardless of their skill level. To complement these findings, we employed a null model where players make completely random legal moves, which allowed us to further delineate the decision-making process. This comparison helped illustrate how players typically opt for moves that minimize Δ, aligning with a basic strategy aimed at simplifying the game.

Finally, building on these insights, we developed a simple yet effective model that replicates the emergent global properties of the system. This model, based on the principle of minimizing Δ values, offers a new perspective on how chess players approach decision-making, highlighting strategic tendencies that emerge throughout the game.