IntroductionCB and MUResultsConclusions IASE Annual conference. Gijón May 9-10, 2008 Data Measuring Competitive Balance and Match Uncertainty in Professional

Introduction

CB and MU Results Conclusions

IASE Annual conference. Gijón May 9-10, 2008

DataMeasuring Competitive Balance and Match

Uncertainty in Professional Tennis. Are There

Differences Among Gender and Court Surfaces?

Julio del Corral

Introduction



Data

1. Introduction

2. Data

3. Competitive balance (CB) and match uncertainty (MU) in elimination tournaments

4. Results

5. Conclusions


Introduction


Data

• 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


Introduction


Data

• 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


Introduction


Data

• 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


Introduction


Data

• 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


Introduction


Data

• 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

iijt

iijp

j seedn

seednCB

1

1

n- number of seeded players


Introduction


Data

Ranking Points1 322 313 304 295 286 277 26... ...32 1


Introduction


Data

1

1

1

4

4

3

3

2

2

2

100% 100% 100%


Introduction


Data

1

1

1

4

4

3

3

3

2

2

100% 100% 50%


Introduction


Data

1

1

1

4

4

3

No seeded player

No seeded player

2

2

100% 75% 50%


Introduction


Data

1

1

1

4

4

3

3

3

2

2

100% 100% 86%


Introduction


Data

1

1

1

4

4

3

No seeded player

No seeded player

2

2

100% 80% 57%


Introduction


Data

• 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


Introduction


Data

Pretty similarIn first rounds Wimbledon has the highest CB


Introduction


Data

Women has lower competitive balance with the only exception of the final (Federer vs. Nadal)

In first rounds Wimbledon has the highest CB


Introduction


Data

• 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)


Introduction


Data

GRASS*DFEMALE is the baseLower MU in men than in womenHigher MU in grassHigher rank difference lower MU


Introduction


Data


Introduction


Data

• 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)


Introduction


Data

THANKS FOR YOUR ATTENTION!!!


Introduction


Data

• In order to calculate the match uncertainty we use the percentages of the matches in which the underdog beats the favorite (upset)


Introduction


Data

TABLE 3: Percentage of Upsets

Tournament All

Gender Women Men Both

1st Round 31 30 31

2nd Round 22 23 23

3rd Round 23 26 25

1/8 Final 27 25 26

¼ Final 25 22 23

½ Final 67 6 35

Final 38 25 31

Total 28 27 27


Introduction


Data

TABLE 3: Percentage of Upsets

Tournament Australian Open French Open Wimbledon US Open

Gender Women Men Both Women Men Both Women Men Both Women Men Both

1st Round 32 26 29 31 32 32 38 32 35 22 31 27

2nd Round 31 18 25 17 27 22 20 21 20 19 27 23

3rd Round 22 9 16 16 29 22 25 32 29 31 34 33

1/8 Final 38 31 34 19 31 25 19 13 16 31 25 28

¼ Final 25 38 31 25 0 13 13 25 19 38 25 31

½ Final 75 25 50 50 0 25 75 0 38 67 0 33

Final 50 0 25 0 100 50 50 0 25 50 0 25

Total 31 23 27 25 29 27 31 27 29 24 29 27

Documents

IntroductionCB and MUResultsConclusions IASE Annual conference. Gijón May 9-10, 2008 Data Measuring Competitive Balance and Match Uncertainty in Professional