Statistical Thinking in Sports

by Albert; Jim

• ISBN13: 9781584888680
• ISBN10: 1584888687
• Edition: 1st
• Format: Hardcover
• Copyright: 2007-07-12
• Publisher: Chapman & Hall/

Summary

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 the articles of value and highly useful as starting points for further research.

Table of Contents

Introduction	p. 1
Introduction	p. 1
Patterns of world records in sports (two chapters)	p. 2
Competition, rankings, and betting in soccer (three chapters)	p. 2
An investigation into some popular baseball myths (three chapters)	p. 3
Uncertainty of attendance at sports events (two chapters)	p. 4
Home advantage, myths in tennis, drafting in hockey pools, American football	p. 4
Website	p. 5
Reference	p. 5
Modelling the development of world records in running	p. 7
Introduction	p. 7
Modelling world records	p. 9
Cross-sectional approach	p. 10
Fitting the individual curves	p. 11
Selection of the functional form	p. 12
Candidate functions	p. 12
Theoretical selection of curves	p. 17
Fitting the models	p. 18
The Gompertz curve in more detail	p. 18
Running data	p. 23
Results of fitting the Gompertz curves	p. 23
Limit values of time and distance	p. 26
Summary and conclusions	p. 28
References	p. 29
The physics and evolution of Olympic winning performances	p. 33
Introduction	p. 33
Running events	p. 34
The physics of running	p. 34
Measuring the rate of improvement in running	p. 37
Periods of summer Olympic history	p. 38
The future of running	p. 40
Jumping events	p. 40
The physics of jumping	p. 40
Measuring the rate of improvement in jumping	p. 43
The future of jumping	p. 44
Swimming events	p. 46
The physics of swimming	p. 46
Measuring the rate of improvement in swimming	p. 47
The future of swimming	p. 49
Rowing	p. 49
The physics of rowing	p. 49
Measuring the rate of improvement in rowing	p. 50
The future of rowing	p. 52
Speed skating	p. 53
The physics of speed skating	p. 53
Measuring the rate of improvement in speed skating	p. 54
Periods of winter Olympic history	p. 55
The future of speed skating	p. 57
A summary of what we have learned	p. 57
References	p. 59
Competitive balance in national European soccer competitions	p. 63
Introduction	p. 63
Measurement of competitive balance	p. 64
Empirical results	p. 67
Can national competitive balance measures be condensed?	p. 72
Conclusion	p. 74
References	p. 74
Statistical analysis of the effectiveness of the FIFA World Rankings	p. 77
Introduction	p. 77
FIFA's ranking procedure	p. 78
Implications of the FIFA World Rankings	p. 79
The data	p. 80
Preliminary analysis	p. 80
Team win percentage, in and out of own confederation	p. 80
International soccer versus domestic soccer	p. 82
Forecasting soccer matches	p. 84
Using the FIFA World Rankings to forecast match results	p. 84
Reaction to new information	p. 85
A forecasting model for match result using past results	p. 86
Conclusion	p. 89
References	p. 89
Forecasting scores and results and testing the efficiency of the fixed-odds betting market in Scottish league football	p. 91
Introduction	p. 91
Literature review	p. 92
Regression models for goal scoring and match results	p. 95
Data and estimation results	p. 97
The efficiency of the market for fixed-odds betting on Scottish league football	p. 102
Conclusion	p. 107
References	p. 107
Hitting in the pinch	p. 111
Introduction	p. 111
A breakdown of a plate appearance: four hitting rates	p. 112
Predicting runs scored by the four rates	p. 114
Separating luck from ability	p. 114
Situational biases	p. 117
A model for clutch hitting	p. 124
Clutch stars?	p. 125
Related work and concluding comments	p. 127
References	p. 133
Does momentum exist in a baseball game?	p. 135
Introduction	p. 135
Models for baseball play	p. 136
Situational and momentum effects	p. 138
Does momentum exist?	p. 140
Modeling transition probabilities	p. 140
Modeling runs scored	p. 144
Rally starters and rally killers	p. 149
Conclusions	p. 150
References	p. 151
Inference about batter-pitcher matchups in baseball from small samples	p. 153
Introduction	p. 153
The batter-pitcher matchup: a binomial view	p. 154
A hierarchical model for batter-pitcher matchup data	p. 155
Data for a single player	p. 155
A probability model for batter-pitcher matchups	p. 156
Results - Derek Jeter	p. 158
Results - multiple players	p. 160
Batter-pitcher data from the pitcher's perspective	p. 160
Results - a single pitcher	p. 161
Results - multiple players	p. 163
Towards a more realistic model	p. 163
Discussion	p. 164
References	p. 165
Outcome uncertainty measures: how closely do they predict a close game?	p. 167
Introduction	p. 167
Measures of outcome uncertainty	p. 169
Data	p. 171
Preliminary analysis of the betting market	p. 172
Model	p. 173
Out-of-sample testing	p. 175
Concluding remarks	p. 176
References	p. 177
The impact of post-season play-off systems on the attendance at regular season games	p. 179
Introduction	p. 179
Theoretical model of the demand for attendance and the impact of play-off design	p. 181
Measuring the probability of end-of-season outcomes and game significance	p. 183
The data: the 2000/01 English Football League second tier	p. 185
Statistical issues in the measurement of the determinants of attendance	p. 190
Skewed, non-negative heteroscedastic data	p. 190
Clustering of attendance within teams and unobserved heterogeneity	p. 192
Multicollinearity	p. 192
Final statistical model	p. 193
Model estimation	p. 194
Choice of explanatory variables	p. 194
Regression results	p. 195
The impact of the play-off system on regular league attendances	p. 197
Conclusions	p. 199
References	p. 201
Measurement and interpretation of home advantage	p. 203
Introduction	p. 203
Measuring home advantage	p. 204
Rugby union, soccer, NBA	p. 207
Australian rules football, NFL, and college football	p. 211
NHL hockey and MLB baseball	p. 212
Can home advantage become unfair?	p. 214
Summary	p. 214
References	p. 215
Myths in Tennis	p. 217
Introduction	p. 217
The data and two selection problems	p. 218
Service myths	p. 221
A player is as good as his or her second service	p. 223
Serving first	p. 224
New balls	p. 226
Winning mood	p. 229
At the beginning of a final set, both players have the same chance of winning the match	p. 230
In the final set the player who has won the previous set has the advantage	p. 231
After breaking your opponent's service there is an increased chance that you will lose your own service	p. 232
After missing break points in the previous game there is an increased chance that you will lose your own service	p. 233
Big points	p. 234
The seventh game	p. 234
Do big points exist?	p. 235
Real champions	p. 237
Conclusion	p. 238
References	p. 239
Back to back evaluations on the gridiron	p. 241
Why do professional team sports track player statistics?	p. 241
The NFL's quarterback rating measure	p. 242
The Scully approach	p. 243
Modeling team offense and defense	p. 244
Net Points, QB Score, and RB Score	p. 252
Who is the best?	p. 253
Forecasting performance in the NFL	p. 254
Do different metrics tell a different story?	p. 259
Do we have marginal physical product in the NFL?	p. 260
References	p. 261
Optimal drafting in hockey pools	p. 263
Introduction	p. 263
Statistical modelling	p. 264
Distribution of points	p. 264
Distribution of games	p. 266
An optimality criterion	p. 268
A simulation study	p. 269
An actual Stanley Cup playoff pool	p. 273
Discussion	p. 276
References	p. 276
References	p. 277
List of authors	p. 291
Index	p. 295
Table of Contents provided by Ingram. All Rights Reserved.