A model for competitiveness level analysis in sports competitions: Application to basketball

Abstract

The degree of overall competitiveness of a sport league is a complex phenomenon. It is difficult to assess and quantify all elements that yield the final standing. In this paper, we analyze the general behavior of the result matrices of each season and we use the corresponding results as a probably density. Thus, the results of previous seasons are a way to investigate the probability that each team has to reach a certain number of victories. We developed a model based on Shannon entropy using two extreme competitive structures (a hierarchical structure and a random structure), and applied this model to investigate the competitiveness of two of the best professional basketball leagues: the NBA (USA) and the ACB (Spain). Both leagues' entropy levels are high (NBA mean 0.983; ACB mean 0.980), indicating high competitiveness, although the entropy of the ACB (from 0.986 to 0.972) demonstrated more seasonal variability than that of the NBA (from 0.985 to 0.990), a possible result of greater sporting gradients in the ACB. The use of this methodology has proven useful for investigating the competitiveness of sports leagues as well as their underlying variability across time.