Evaluating trade-offs in soccer performance with tools from evolutionary ecology

Abstract

Swimming from nook to cranny, a bluehead wrasse can dart with great speed through a coral reef. The fish is not big, nor does it posses any predatory defenses, but it is able to move quickly, turn on a dime and escape a predator. An ecologist can easily quantify these behaviors to, for instance, ask whether the wrasse’s speed and length are correlated, or whether fatigue significantly affects top speed. If the increased ability of one of these traits showed a negative correlation to the ability of the other, it would give evidence to a trade off. Defined as two traits that are negatively correlated such that the the efficiency of one thing may not increase without the other diminishing. These would be pretty standard studies in ecology or evolutionary biology. Therefore, I used these multivariate measurement techniques and applied them to soccer players to analyze trade offs. Trade offs are commonly misinterpreted in both sports science and evolutionary biology because it is difficult to account for quality when measuring at the among-individual level. In order to accomplish this, I used an empirical dataset (n =10) of NCAA Division III soccer players, and an additional larger database from EA Sports FIFA 19 (n =18207). Using their multivariate skills attribute scores I found evidence of trade offs in multiple attributes across both the empirical and the addition datasets. Furthermore, evidence of fatigue altering the correlation of these trade offs in NCAA athletes was found in the empirical dataset. The results from this research can be used as a model for future trade offs studies in evolutionary biology and can be used by talent analysts in the soccer world to more accurately measure the output of a players performance.