Statistical Thinking in Sports

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

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

Table of Contents

Introductionp. 1
Introductionp. 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 footballp. 4
Websitep. 5
Referencep. 5
Modelling the development of world records in runningp. 7
Introductionp. 7
Modelling world recordsp. 9
Cross-sectional approachp. 10
Fitting the individual curvesp. 11
Selection of the functional formp. 12
Candidate functionsp. 12
Theoretical selection of curvesp. 17
Fitting the modelsp. 18
The Gompertz curve in more detailp. 18
Running datap. 23
Results of fitting the Gompertz curvesp. 23
Limit values of time and distancep. 26
Summary and conclusionsp. 28
Referencesp. 29
The physics and evolution of Olympic winning performancesp. 33
Introductionp. 33
Running eventsp. 34
The physics of runningp. 34
Measuring the rate of improvement in runningp. 37
Periods of summer Olympic historyp. 38
The future of runningp. 40
Jumping eventsp. 40
The physics of jumpingp. 40
Measuring the rate of improvement in jumpingp. 43
The future of jumpingp. 44
Swimming eventsp. 46
The physics of swimmingp. 46
Measuring the rate of improvement in swimmingp. 47
The future of swimmingp. 49
Rowingp. 49
The physics of rowingp. 49
Measuring the rate of improvement in rowingp. 50
The future of rowingp. 52
Speed skatingp. 53
The physics of speed skatingp. 53
Measuring the rate of improvement in speed skatingp. 54
Periods of winter Olympic historyp. 55
The future of speed skatingp. 57
A summary of what we have learnedp. 57
Referencesp. 59
Competitive balance in national European soccer competitionsp. 63
Introductionp. 63
Measurement of competitive balancep. 64
Empirical resultsp. 67
Can national competitive balance measures be condensed?p. 72
Conclusionp. 74
Referencesp. 74
Statistical analysis of the effectiveness of the FIFA World Rankingsp. 77
Introductionp. 77
FIFA's ranking procedurep. 78
Implications of the FIFA World Rankingsp. 79
The datap. 80
Preliminary analysisp. 80
Team win percentage, in and out of own confederationp. 80
International soccer versus domestic soccerp. 82
Forecasting soccer matchesp. 84
Using the FIFA World Rankings to forecast match resultsp. 84
Reaction to new informationp. 85
A forecasting model for match result using past resultsp. 86
Conclusionp. 89
Referencesp. 89
Forecasting scores and results and testing the efficiency of the fixed-odds betting market in Scottish league footballp. 91
Introductionp. 91
Literature reviewp. 92
Regression models for goal scoring and match resultsp. 95
Data and estimation resultsp. 97
The efficiency of the market for fixed-odds betting on Scottish league footballp. 102
Conclusionp. 107
Referencesp. 107
Hitting in the pinchp. 111
Introductionp. 111
A breakdown of a plate appearance: four hitting ratesp. 112
Predicting runs scored by the four ratesp. 114
Separating luck from abilityp. 114
Situational biasesp. 117
A model for clutch hittingp. 124
Clutch stars?p. 125
Related work and concluding commentsp. 127
Referencesp. 133
Does momentum exist in a baseball game?p. 135
Introductionp. 135
Models for baseball playp. 136
Situational and momentum effectsp. 138
Does momentum exist?p. 140
Modeling transition probabilitiesp. 140
Modeling runs scoredp. 144
Rally starters and rally killersp. 149
Conclusionsp. 150
Referencesp. 151
Inference about batter-pitcher matchups in baseball from small samplesp. 153
Introductionp. 153
The batter-pitcher matchup: a binomial viewp. 154
A hierarchical model for batter-pitcher matchup datap. 155
Data for a single playerp. 155
A probability model for batter-pitcher matchupsp. 156
Results - Derek Jeterp. 158
Results - multiple playersp. 160
Batter-pitcher data from the pitcher's perspectivep. 160
Results - a single pitcherp. 161
Results - multiple playersp. 163
Towards a more realistic modelp. 163
Discussionp. 164
Referencesp. 165
Outcome uncertainty measures: how closely do they predict a close game?p. 167
Introductionp. 167
Measures of outcome uncertaintyp. 169
Datap. 171
Preliminary analysis of the betting marketp. 172
Modelp. 173
Out-of-sample testingp. 175
Concluding remarksp. 176
Referencesp. 177
The impact of post-season play-off systems on the attendance at regular season gamesp. 179
Introductionp. 179
Theoretical model of the demand for attendance and the impact of play-off designp. 181
Measuring the probability of end-of-season outcomes and game significancep. 183
The data: the 2000/01 English Football League second tierp. 185
Statistical issues in the measurement of the determinants of attendancep. 190
Skewed, non-negative heteroscedastic datap. 190
Clustering of attendance within teams and unobserved heterogeneityp. 192
Multicollinearityp. 192
Final statistical modelp. 193
Model estimationp. 194
Choice of explanatory variablesp. 194
Regression resultsp. 195
The impact of the play-off system on regular league attendancesp. 197
Conclusionsp. 199
Referencesp. 201
Measurement and interpretation of home advantagep. 203
Introductionp. 203
Measuring home advantagep. 204
Rugby union, soccer, NBAp. 207
Australian rules football, NFL, and college footballp. 211
NHL hockey and MLB baseballp. 212
Can home advantage become unfair?p. 214
Summaryp. 214
Referencesp. 215
Myths in Tennisp. 217
Introductionp. 217
The data and two selection problemsp. 218
Service mythsp. 221
A player is as good as his or her second servicep. 223
Serving firstp. 224
New ballsp. 226
Winning moodp. 229
At the beginning of a final set, both players have the same chance of winning the matchp. 230
In the final set the player who has won the previous set has the advantagep. 231
After breaking your opponent's service there is an increased chance that you will lose your own servicep. 232
After missing break points in the previous game there is an increased chance that you will lose your own servicep. 233
Big pointsp. 234
The seventh gamep. 234
Do big points exist?p. 235
Real championsp. 237
Conclusionp. 238
Referencesp. 239
Back to back evaluations on the gridironp. 241
Why do professional team sports track player statistics?p. 241
The NFL's quarterback rating measurep. 242
The Scully approachp. 243
Modeling team offense and defensep. 244
Net Points, QB Score, and RB Scorep. 252
Who is the best?p. 253
Forecasting performance in the NFLp. 254
Do different metrics tell a different story?p. 259
Do we have marginal physical product in the NFL?p. 260
Referencesp. 261
Optimal drafting in hockey poolsp. 263
Introductionp. 263
Statistical modellingp. 264
Distribution of pointsp. 264
Distribution of gamesp. 266
An optimality criterionp. 268
A simulation studyp. 269
An actual Stanley Cup playoff poolp. 273
Discussionp. 276
Referencesp. 276
Referencesp. 277
List of authorsp. 291
Indexp. 295
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