IntroductionCB and MUResultsConclusions IASE Annual conference. Gijón May 9-10, 2008 Data Measuring Competitive Balance and Match Uncertainty in Professional Tennis. Are There Differences Among Gender and Court Surfaces? Julio del Corral

IntroductionCB and MUResultsConclusions IASE Annual conference. Gijón May 9-10, 2008 Data 1.Introduction 2.Data 3.Competitive balance (CB) and match uncertainty (MU) in elimination tournaments 4.Results 5.Conclusions

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData Competitive balance and match uncertainty has been analyzed widely in leagues of team sports (American football, baseball, football). However there is almost no paper that analyze those concepts in individual sports. Some exceptions are: –Rhom et al. (2004) investigated the degree of competition at a major tennis championship (Wimbledon) from 1968 to 2001 using the degree of dominance among the four top seeds –Du Bois and Heyndels (2007) studied match-specific uncertainty, inter-seasonal uncertainty as well as indicators for long-term uncertainty in men´s and women´s tennis But they have not compared neither gender nor court surfaces differences using match data

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData Women grand slam matches are played to the best of 3 sets while men matches are played to the best of 5 sets. Hence, the favorite is more likely to win in men than in women matches by probabilistic theory On the contrary, Magnus and Klaasen (1999) argued that men are more equal in quality than are women Therefore, it is very interesting to test the difference in match uncertainty and competitive balance among gender since it remains unclear from a theoretical point of view

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData Tennis circuits are divided into Grand Slam Tournaments and other tournaments such as Master Series, Fed Cup, Davis Cup... There are four tennis Grand Slam tournaments which are played in different court surfaces (i.e., grass in Wimbledon, hard in US and Australian Opens and clay in the French Open) The court surface could influence the balance. In particular, it is expected that the match uncertainty will be higher on grass than on clay and hard courts

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData The objectives of the paper are: –Propose two alternative measures of competitive balance in elimination tournaments based on the performance of all seeded players –To use such measures to analyze the competitive balance in tennis Grand Slam tournaments –Measure the level of match uncertainty in tennis as well as analyzing its determinants. Especially, we are interested in the effect of gender and soil court on match uncertainty

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData In order to calculate the competitive balance in elimination tournaments, we propose to use two measures based on the seed players’ performance: –the percentage of the seeded players who should achieve some round over the total players in that round –The percentage of some points given according to the ranking over the points that should achieve some round n- number of seeded players

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData RankingPoints

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData %

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData % 50%

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData No seeded player %75%50%

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData % 86%

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData No seeded player %80%57%

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData We have used data from the tennis Grand Slams tournaments from Wimbledon 2005 to Roland Garros 2007 (that is 8 tournaments) Each draw is composed by 128 players. In total we gathered data on 2,032 matches

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData Pretty similar In first rounds Wimbledon has the highest CB

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData Women has lower competitive balance with the only exception of the final (Federer vs. Nadal) In first rounds Wimbledon has the highest CB

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData MATCH UNCERTAINTY DETERMINATS-It is used several non-nested probit estimations where the dependent variable takes the value of one whether there is an upset and zero otherwise. Therefore, positive coefficients  higher match uncertainty –Independent variables:  Gender dummy variables (DMALE, DFEMALE)  Court surface dummies (HARD, CLAY, GRASS)  Difference in absolute ranking (DIFRANK, DIFRANK2)  DIFRANKM=8-log(rank) following Klaasen and Magnus (2001)  Other variables: Player from qualifying round (DQUALIFICATION), Local player dummies (DLOCALF, DLOCALU) and differences in height and weight (DIFHEIGHT, DIFHEIGHT2, DIFWEIGHT, DIFWEIGHT2)

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData GRASS*DFEMALE is the base Lower MU in men than in women Higher MU in grass Higher rank difference  lower MU

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData We proposed to measure the tournament competitive balance using the performance of all seeded players Women have lower competitive balance than men except in the final round It was found some support for the notion that grass courts have the highest competitive balance The upset probability is significantly greater in women´s than in men´s mathches. Moreover, the highest upset probability occurs on the quickest surface (i.e., grass)

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData THANKS FOR YOUR ATTENTION!!!

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData In order to calculate the match uncertainty we use the percentages of the matches in which the underdog beats the favorite (upset)

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData TABLE 3: Percentage of Upsets Tournament All Gender WomenMenBoth 1 st Round nd Round rd Round /8 Final ¼ Final ½ Final Final Total 2827

IASE Annual conference. Gijón May 9-10, 2008 IntroductionCB and MUResultsConclusionsData TABLE 3: Percentage of Upsets Tournament Australian OpenFrench OpenWimbledonUS Open Gender WomenMenBothWomenMenBothWomenMenBothWomenMenBoth 1 st Round nd Round rd Round /8 Final ¼ Final ½ Final Final Total