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Analyzing Wimbledon: The Power of Statistics [Mīkstie vāki]

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(Professor of International Economics, University of Amsterdam), (Emeritus Professor, Tilburg University and Visiting Professor of Econometrics,, the Vrije Universiteit Amsterdam.)
  • Formāts: Paperback / softback, 272 pages, height x width x depth: 157x231x18 mm, weight: 431 g
  • Izdošanas datums: 30-Jan-2014
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0199355967
  • ISBN-13: 9780199355969
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  • Cena: 59,25 €
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Analyzing Wimbledon: The Power of StatisticsPalielināt
  • Formāts: Paperback / softback, 272 pages, height x width x depth: 157x231x18 mm, weight: 431 g
  • Izdošanas datums: 30-Jan-2014
  • Izdevniecība: Oxford University Press Inc
  • ISBN-10: 0199355967
  • ISBN-13: 9780199355969
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780199355969.html
Keywords:
The game of tennis raises many questions that are of interest to a statistician. Is it true that beginning to serve in a set gives an advantage? Are new balls an advantage? Is the seventh game in a set particularly important? Are top players more stable than other players? Do real champions win the big points? These and many other questions are formulated as "hypotheses" and tested statistically.

Analyzing Wimbledon also discusses how the outcome of a match can be predicted (even while the match is in progress), which points are important and which are not, how to choose an optimal service strategy, and whether "winning mood" actually exists in tennis. Aimed at readers with some knowledge of mathematics and statistics, the book uses tennis (Wimbledon in particular) as a vehicle to illustrate the power and beauty of statistical reasoning.
Preface xiii
Acknowledgements xv
1 Warming up
1(12)
Wimbledon
1(1)
Commentators
2(1)
An example
3(1)
Correlation and causality
4(1)
Why statistics?
5(1)
Sports data and human behavior
6(2)
Why tennis?
8(1)
Structure of the book
9(1)
Further reading
10(3)
2 Richard
13(20)
Meeting Richard
13(2)
From point to game
15(2)
The tiebreak
17(1)
Serving first in a set
18(2)
During the set
20(1)
Best-of-three versus best-of-five
21(2)
Upsets
23(1)
Long matches: Isner-Mahut 2010
24(3)
Rule changes: the no-ad rule
27(1)
Abolishing the second service
28(2)
Further reading
30(3)
3 Forecasting
33(16)
Forecasting with Richard
34(2)
Federer-Nadal, Wimbledon final 2008
36(2)
Effect of smaller p
38(2)
Kim Clijsters defeats Venus Williams, US Open 2010
40(1)
Effect of larger p
41(1)
Djokovic-Nadal, Australian Open 2012
42(2)
In-play betting
44(2)
Further reading
46(3)
4 Importance
49(16)
What is importance?
49(1)
Big points in a game
50(2)
Big games in a set
52(2)
The vital seventh game
54(2)
Big sets
56(1)
Are all points equally important?
57(1)
The most important point
58(1)
Three importance profiles
59(3)
Further reading
62(3)
5 Point data
65(20)
The Wimbledon data set
65(2)
Two selection problems
67(3)
Estimators, estimates, and accuracy
70(2)
Development of tennis over time
72(2)
Winning a point on service unraveled
74(2)
Testing a hypothesis: men versus women
76(2)
Aces and double faults
78(2)
Breaks and rebreaks
80(2)
Are our summary statistics too simple?
82(1)
Further reading
82(3)
6 The method of moments
85(20)
Our summary statistics are too simple
85(3)
The method of moments
88(2)
Enter Miss Marple
90(1)
Re-estimating p by the method of moments
90(1)
Men versus women revisited
91(1)
Beyond the mean: variation over players
92(2)
Reliability of summary statistics: a rule of thumb
94(3)
Filtering out the noise
97(2)
Noise-free variation over players
99(1)
Correlation between opponents
100(2)
Why bother?
102(1)
Further reading
102(3)
7 Quality
105(22)
Observable variation over players
105(2)
Ranking
107(5)
Round, bonus, and malus
112(2)
Significance, relevance, and sensitivity
114(1)
The complete model
115(1)
Winning a point on service
116(3)
Other service characteristics
119(2)
Aces and double faults
121(2)
Further reading
123(4)
8 First and second service
127(10)
Is the second service more important than the first?
127(3)
Differences in service probabilities explained
130(2)
Joint analysis: bivariate GMM
132(2)
Four service dimensions
134(1)
Four-variate GMM
134(2)
Further reading
136(1)
9 Service strategy
137(24)
The server's trade-off
137(2)
The y-curve
139(1)
Optimal strategy: one service
140(1)
Optimal strategy: two services
141(1)
Existence and uniqueness
142(1)
Four regularity conditions for the optimal strategy
143(2)
Functional form of y-curve
145(1)
Efficiency defined
146(1)
Efficiency of the average player
147(1)
Observations for the key probabilities: Monte Carlo
148(1)
Efficiency estimates
149(1)
Mean match efficiency gains
150(1)
Efficiency gains across matches
151(1)
Impact on the paycheck
152(1)
Why are players inefficient?
153(1)
Rule changes
154(1)
Serving in volleyball
155(2)
Further reading
157(4)
10 Within a match
161(22)
The idea behind the point model
161(1)
From matches to points
162(2)
First results at point level
164(1)
Simple dynamics
165(6)
The baseline model
171(2)
Top players and mental stability
173(4)
Lessons from the baseline model
177(1)
New balls
177(3)
Further reading
180(3)
11 Special points and games
183(10)
Big points
183(3)
Big points and the baseline model
186(1)
Serving first revisited
187(3)
The toss
190(2)
Further reading
192(1)
12 Momentum
193(14)
Streaks, the hot hand, and winning mood
193(2)
Why study tennis?
195(1)
Winning mood in tennis
196(2)
Breaks and rebreaks
198(3)
Missed breakpoints
201(2)
The encompassing model
203(1)
The power of statistics
204(1)
Further reading
205(2)
13 The hypotheses revisited
207(16)
1 Winning a point on service is an iid process
207(1)
2 It is an advantage to serve first in a set
208(1)
3 Every point (game, set) is equally important to both players
209(1)
4 The seventh game is the most important game in the set
210(1)
5 All points are equally important
210(1)
6 The probability that the service is in is the same in the men's singles as in the women's singles
211(1)
7 The probability of a double fault is the same in the men's singles as in the women's singles
211(1)
8 After a break the probability of being broken back increases
212(1)
9 Summary statistics give a precise impression of a player's performance
213(1)
10 Quality is a pyramid
213(2)
11 Top players must grow into the tournament
215(1)
12 Men's tennis is more competitive than women's tennis
215(1)
13 A player is as good as his or her second service
216(1)
14 Players have an efficient service strategy
217(1)
15 Players play safer at important points
217(1)
16 Players take more risks when they are in a winning mood
218(1)
17 Top players are more stable than others
218(1)
18 New balls are an advantage to the server
219(1)
19 Real champions win the big points
220(1)
20 The winner of the toss should elect to serve
220(1)
21 Winning mood exists
220(1)
22 After missing breakpoint(s) there is an increased probability of being broken in the next game
221(2)
Appendix A Tennis rules and terms
223(4)
Tennis rules
223(1)
Tennis terms
224(3)
Appendix B List of symbols
227(4)
Winning probabilities
227(1)
Score probabilities and importance
228(1)
Service probabilities
228(1)
Quality
228(1)
Operators
229(1)
Miscellaneous variables
229(1)
Random/unexplained parts
229(1)
Parameters
229(1)
Miscellaneous symbols
230(1)
Appendix C Data, software, and mathematical derivations
231(6)
Data
231(1)
Software: program Richard
232(2)
Mathematical derivations
234(3)
Bibliography 237(10)
Index 247
Franc Klaassen is Professor of International Economics at University of Amsterdam. Jan R. Magnus is Emeritus Professor at Tilburg University and Visiting Professor of Econometrics at the Vrije Universiteit Amsterdam.