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What is included with this book?
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 |
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