
Statistical Thinking in Sports
By: Jim Albert (Editor), Ruud H. Koning (Editor)
Hardcover | 1 August 2007 | Edition Number 1
At a Glance
312 Pages
23.4 x 15.6 x 1.91
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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-
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
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| 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. |
ISBN: 9781584888680
ISBN-10: 1584888687
Published: 1st August 2007
Format: Hardcover
Language: English
Number of Pages: 312
Audience: Professional and Scholarly
Publisher: Taylor & Francis Ltd
Country of Publication: GB
Edition Number: 1
Dimensions (cm): 23.4 x 15.6 x 1.91
Weight (kg): 0.59
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