Randomization and Serial Dependence in Professional Tennis Matches: Do Strategic Considerations, Player Rankings and Match Characteristics Matter?
Spiliopoulos, L. (2018). Randomization and serial dependence in professional tennis matches: Do strategic considerations, player rankings and match characteristics matter?. Judgment and Decision Making, 13(5), 413-427.
15 Pages Posted: 29 Aug 2016 Last revised: 22 Dec 2019
Date Written: August 27, 2016
In many sports contests, the equilibrium requires players to randomize across repeated rounds, i.e., exhibit no temporal predictability. Such sports data present a window into the (in)efficiency of random sequence generation in a natural competitive environment, where the decision makers (tennis players) are both highly experienced and incentivized compared to laboratory studies. I resolve a long-standing debate about whether professional players’ tennis serve directions are serially independent (Hsu, Huang & Tang, 2007) or not (Walker & Wooders, 2001) using a new dataset that is two orders of magnitude larger than those studies. I examine both between- and within-player determinants of the degree of serial (in)dependence. Evidence of the existence of significant serial dependence across serves is presented, even among players ranked Number 1 in the world. Furthermore, significant heterogeneity was found with respect to the strength of serial dependence and also its sign. A novel finding is that Number 1 and Number 2 ranked players tend to under-alternate on average, whereas in line with previous findings, the lower-ranked the players, the greater their tendency to over-alternate. Within-player analyses show that high-ranked players do not condition their randomization behavior on their opponent’s ranking. However, the under-alternation of top players would be consistent with a best-response to beliefs that the population of opponents over-alternates on average. Finally, the degree of observed serial dependence is not systematically related to other match variables proxying for match difficulty, fatigue, and psychological pressure.
Keywords: Randomization, Mixed Strategy Nash Equilibrium, Minimax, Tennis, Sports Data Analytics
JEL Classification: C73, C93, Z20
Suggested Citation: Suggested Citation