  In this article, we study the decision-making process of chess players by
using a chess engine to evaluate the moves across different pools of games. We
quantify the decisiveness of each move during the games using a metric called
$\Delta$, which is derived from the engine's evaluation of the positions. We
then performed a comparative analysis across players of varying competitive
levels. Firstly, we observed that players face a wide spectrum of $\Delta$
values, evidencing the complexity of the process. By examining groups of
winning and losing players, we found evidence where a decrease in complexity
may be associated with a drop in player performance levels. Secondly, we
observed that players' accuracy increases in positions with high $\Delta$
values regardless of competitive level. Complementing this information with a
null model where players make completely random legal moves allowed us to
characterize the decision-making process under the simple strategy of making
moves that minimize $\Delta$ values. Finally, based on this idea, we proposed a
simple model that approximately replicates the global emergent properties of
the system.
