Empirical Evaluation of Real World Tournaments
This provides empirical insights for researchers in computational social choice and sports analytics, though it is incremental as it applies existing methods to new data.
The paper tackled the gap between theoretical worst-case results and practical performance in computational social choice by analyzing real-world soccer and tennis tournament data, finding that NP-hard seeding problems are easily solvable in practice and that the Condorcet Random model fails to generate realistic data.
Computational Social Choice (ComSoc) is a rapidly developing field at the intersection of computer science, economics, social choice, and political science. The study of tournaments is fundamental to ComSoc and many results have been published about tournament solution sets and reasoning in tournaments. Theoretical results in ComSoc tend to be worst case and tell us little about performance in practice. To this end we detail some experiments on tournaments using real wold data from soccer and tennis. We make three main contributions to the understanding of tournaments using real world data from English Premier League, the German Bundesliga, and the ATP World Tour: (1) we find that the NP-hard question of finding a seeding for which a given team can win a tournament is easily solvable in real world instances, (2) using detailed and principled methodology from statistical physics we show that our real world data obeys a log-normal distribution; and (3) leveraging our log-normal distribution result and using robust statistical methods, we show that the popular Condorcet Random (CR) tournament model does not generate realistic tournament data.