Generic temporal features of performance rankings in sports and games

EPJ Data Science

Table 1 Summary of ranking data for each sport and game considered in this study

Sport/game	Data source	Time period	Ranking resolution	#players/teams
Tennis players (male)	Association of Tennis Professionals (ATP) [13]	May 5 2003-Dec 27 2010	Weekly	1,600
Chess players (male)	Fédération Internationale des Échecs (FIDE) [14]	Jul 2012-Apr 2016	Monthly	13,500
Golf players	Official World Golf Ranking (OWGR) [15]	Sept 10 2000-Apr 19 2015	Weekly	1,000
Poker players	Global Poker Index (GPI) [16]	Jul 25 2012-Jun 10 2015	Weekly	1,799
Football teams	Football Club World Ranking (FCWR) [17]	Feb 1 2012-Dec 29 2014	Weekly	850
National football teams	Fédération Internationale de Football Association (FIFA) [18]	Jul 2010-Dec 2015	Monthly	150

Table listing the main properties of the ranking data used here (including data source, time period, ranking resolution, and number of players/teams). In order to have a homogeneous distribution of ranking snapshots and the same number of players/teams in each snapshot for a given activity, we disregard some data for the ATP, FIDE, OWGR, GPI, and FIFA datasets, as explained in the main text.