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E-raamat: Statistical Thinking in Sports

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Jul-2007
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781584888697
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Statistical Thinking in SportsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 12-Jul-2007
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781584888697
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Since the first athletic events found a fan base, sports and statistics have always maintained a tight and at times mythical relationship. As a way to relay the telling of a game's drama and attest to the prodigious powers of the heroes involved, those reporting on the games tallied up the numbers that they believe best described the action and best defined the winning edge. However, they may not have always counted the right numbers. Many of our hallowed beliefs about sports statistics have long been fraught with misnomers. Whether it concerns Scottish football or American baseball, the most revered statistics often have little to do with any winning edge.

Covering an international collection of sports, Statistical Thinking in Sports provides an accessible survey of current research in statistics and sports, written by experts from a variety of arenas. Rather than rely on casual observation, they apply the rigorous tools of statistics to re-examine many of those concepts that have gone from belief to fact, based mostly on the repetition of their claims. Leaving assumption behind, these researchers take on a host of tough questions-

Is a tennis player only as good as his or her first serve?

Is there such a thing as home field advantage?

Do concerns over a decline in soccer's competitive balance have any merit?

What of momentum-is its staying power any greater than yesterday's win?

And what of pressure performers? Are there such creatures or ultimately, does every performer fall back to his or her established normative?

Investigating a wide range of international team and individual sports, the book considers the ability to make predictions, define trends, and measure any number of influences. It is full of interesting and useful examples for those teaching introductory statistics. Although the articles are aimed at general readers, the serious researcher in sports statistics will also find t

Arvustused

... For those with an interest in sports and statistics, this book is an interesting and sometimes enlightening read. As a baseball fan, I particularly enjoyed the related chapters on clutch hitting, in-game momentum, and batter-pitcher matchups. I would recommend this text to any statistician contemplating related future research. Toward this end, additional information, data, and appendixes for some chapters are available on the book's website. -Technometrics, February 2009, Vol. 51, No. 1 A serious effort by thoughtful statisticians in the area of sports not only contributes to the general area, but also offers a window to the world on the work of the professional statistician. As a more general audience becomes aware of the work of the statistician, it can only serve to bolster the reputation of our profession in general. ... All in all, I found the volume to be very enjoyable. I would recommend it to anyone looking for an introduction to one of the subjects covered, as the introductions and bibliographies are generally quite good. ... this volume has something for everyone. -Gilbert W. Fellingham, Brigham Young University, The American Statistician, November 2008 ...an international collection of current research in statistics and sports...is full of interesting and useful examples to use when teaching statistics. -Susan Starkings, London South Bank University, International Statistical Review, 2008

1 Introduction
1
Jim Albert and Ruud H. Koning
1.1 Introduction
1
1.1.1 Patterns of world records in sports (two chapters)
2
1.1.2 Competition, rankings, and betting in soccer (three chapters)
2
1.1.3 An investigation into some popular baseball myths (three chapters)
3
1.1.4 Uncertainty of attendance at sports events (two chapters)
4
1.1.5 Home advantage, myths in tennis, drafting in hockey pools, American football
4
1.2 Web site
5
Reference
5
2 Modelling the development of world records in running
7
Gerard H. Kuper and Elmer Sterken
2.1 Introduction
7
2.2 Modelling world records
9
2.2.1 Cross-sectional approach
10
2.2.2 Fitting the individual curves
11
2.3 Selection of the functional form
12
2.3.1 Candidate functions
12
2.3.2 Theoretical selection of curves
17
2.3.3 Fitting the models
18
2.3.4 The Gompertz curve in more detail
18
2.4 Running data
23
2.5 Results of fitting the Gompertz curves
23
2.6 Limit values of time and distance
26
2.7 Summary and conclusions
28
References
29
3 The physics and evolution of Olympic winning performances
33
Ray Stefani
3.1 Introduction
33
3.2 Running events
34
3.2.1 The physics of running
34
3.2.2 Measuring the rate of improvement in running
37
3.2.3 Periods of summer Olympic history
38
3.2.4 The future of running
40
3.3 Jumping events
40
3.3.1 The physics of jumping
40
3.3.2 Measuring the rate of improvement in jumping
43
3.3.3 The future of jumping
44
3.4 Swimming events
46
3.4.1 The physics of swimming
46
3.4.2 Measuring the rate of improvement in swimming
47
3.4.3 The future of swimming
49
3.5 Rowing
49
3.5.1 The physics of rowing
49
3.5.2 Measuring the rate of improvement in rowing
50
3.5.3 The future of rowing
52
3.6 Speed skating
53
3.6.1 The physics of speed skating
53
3.6.2 Measuring the rate of improvement in speed skating
54
3.6.3 Periods of winter Olympic history
55
3.6.4 The future of speed skating
57
3.7 A summary of what we have learned
57
References
59
4 Competitive balance in national European soccer competitions
63
Marco Haan, Ruud H. Koning, and Arjen van Witteloostuijn
4.1 Introduction
63
4.2 Measurement of competitive balance
64
4.3 Empirical results
67
4.4 Can national competitive balance measures be condensed?
72
4.5 Conclusion
74
References
74
5 Statistical analysis of the effectiveness of the FIFA World Rankings
77
Ian McHale and Stephen Davies
5.1 Introduction
77
5.2 FIFA's ranking procedure
78
5.3 Implications of the FIFA World Rankings
79
5.4 The data
80
5.5 Preliminary analysis
80
5.5.1 Team win percentage, in and out of own confederation
80
5.5.2 International soccer versus domestic soccer
82
5.6 Forecasting soccer matches
84
5.7 Using the FIFA World Rankings to forecast match results
84
5.7.1 Reaction to new information
85
5.7.2 A forecasting model for match result using past results
86
5.8 Conclusion
89
References
89
6 Forecasting scores and results and testing the efficiency of the fixed-odds betting market in Scottish league football
91
Stephen Dobson and John Goddard
6.1 Introduction
91
6.2 Literature review
92
6.3 Regression models for goal scoring and match results
95
6.4 Data and estimation results
97
6.5 The efficiency of the market for fixed-odds betting on Scottish league football
102
6.6 Conclusion
107
References
107
7 Hitting in the pinch
111
Jim Albert
7.1 Introduction
111
7.2 A breakdown of a plate appearance: four hitting rates
112
7.3 Predicting runs scored by the four rates
114
7.4 Separating luck from ability
114
7.5 Situational biases
117
7.6 A model for clutch hitting
124
7.7 Clutch stars?
125
7.8 Related work and concluding comments
127
References
133
8 Does momentum exist in a baseball game?
135
Rebecca J. Sela and Jeffrey S. Simonoff
8.1 Introduction
135
8.2 Models for baseball play
136
8.3 Situational and momentum effects
138
8.4 Does momentum exist?
140
8.4.1 Modeling transition probabilities
140
8.4.2 Modeling runs scored
144
8.5 Rally starters and rally killers
149
8.6 Conclusions
150
References
151
9 Inference about batter-pitcher matchups in baseball from small samples
153
Hal S. Stern and Adam Sugano
9.1 Introduction
153
9.2 The batter-pitcher matchup: a binomial view
154
9.3 A hierarchical model for batter-pitcher matchup data
155
9.3.1 Data for a single player
155
9.3.2 A probability model for batter-pitcher matchups
156
9.3.3 Results - Derek Jeter
158
9.3.4 Results - multiple players
160
9.4 Batter-pitcher data from the pitcher's perspective
160
9.4.1 Results - a single pitcher
161
9.4.2 Results - multiple players
163
9.5 Towards a more realistic model
163
9.6 Discussion
164
References
165
10 Outcome uncertainty measures: how closely do they predict a close game? 167
Babatunde Buraimo, David Forrest, and Robert Simmons
10.1 Introduction
167
10.2 Measures of outcome uncertainty
169
10.3 Data
171
10.4 Preliminary analysis of the betting market
172
10.5 Model
173
10.6 Out-of-sample testing
175
10.7 Concluding remarks
176
References
177
11 The impact of post-season play-off systems on the attendance at regular season games 179
Chris Bojke
11.1 Introduction
179
11.2 Theoretical model of the demand for attendance and the impact of play-off design
181
11.3 Measuring the probability of end-of-season outcomes and game significance
183
11.4 The data: the 2000/01 English Football League second tier
185
11.5 Statistical issues in the measurement of the determinants of attendance
190
11.5.1 Skewed, non-negative heteroscedastic data
190
11.5.2 Clustering of attendance within teams and unobserved heterogeneity
192
11.5.3 Multicollinearity
192
11.5.4 Final statistical model
193
11.6 Model estimation
194
11.6.1 Choice of explanatory variables
194
11.6.2 Regression results
195
11.7 The impact of the play-off system on regular league attendances
197
11.8 Conclusions
199
References
201
12 Measurement and interpretation of home advantage 203
Ray Stefani
12.1 Introduction
203
12.2 Measuring home advantage
204
12.3 Rugby union, soccer, NBA
207
12.4 Australian rules football, NFL, and college football
211
12.5 NHL hockey and MLB baseball
212
12.6 Can home advantage become unfair?
214
12.7 Summary
214
References
215
13 Myths in Tennis 217
Jan Magnus and Franc Klaassen
13.1 Introduction
217
13.2 The data and two selection problems
218
13.3 Service myths
221
13.3.1 A player is as good as his or her second service
223
13.3.2 Serving first
224
13.3.3 New balls
226
13.4 Winning mood
229
13.4.1 At the beginning of a final set, both players have the same chance of winning the match
230
13.4.2 In the final set the player who has won the previous set has the advantage
231
13.4.3 After breaking your opponent's service there is an increased chance that you will lose your own service.
232
13.4.4 After missing break points in the previous game there is an increased chance that you will lose your own service . . .
233
13.5 Big points
234
13.5.1 The seventh game
234
13.5.2 Do big points exist?
235
13.5.3 Real champions
237
13.6 Conclusion
238
References
239
14 Back to back evaluations on the gridiron 241
David J. Berri
14.1 Why do professional team sports track player statistics?
241
14.2 The NFL's quarterback rating measure
242
14.3 The Scully approach
243
14.4 Modeling team offense and defense
244
14.5 Net Points, QB Score, and RB Score
252
14.6 Who is the best?
253
14.7 Forecasting performance in the NFL
254
14.8 Do different metrics tell a different story?
259
14.9 Do we have marginal physical product in the NFL?
260
References
261
15 Optimal drafting in hockey pools 263
Amy E. Summers, Tim B. Swartz, and Richard A. Lockhart
15.1 Introduction
263
15.2 Statistical modelling
264
15.2.1 Distribution of points
264
15.2.2 Distribution of games
266
15.3 An optimality criterion
268
15.4 A simulation study
269
15.5 An actual Stanley Cup playoff pool
273
15.6 Discussion
276
References
276
References 277
List of authors 291
Index 295


Jim Albert, Ruud H. Koning
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