1. Baseball’s Pythagorean Theorem

Chapters in this book

1. Frontmatter i
2. Contents vii
3. Preface xi
4. Acknowledgments xv
5. Abbreviations xvii
6. Part I. Baseball
7. 1. Baseball’s Pythagorean Theorem 3
8. 2. Who Had a Better Year: Mike Trout or Kris Bryant? 12
9. 3. Evaluating Hitters by Linear Weights 18
10. 4. Evaluating Hitters by Monte Carlo Simulation 31
11. 5. Evaluating Baseball Pitchers, Forecasting Future Pitcher Performance, and an Introduction to Statcast 44
12. 6. Baseball Decision Making 60
13. 7. Evaluating Fielders 73
14. 8. Win Probability Added (WPA) 84
15. 9. Wins Above Replacement (WAR) and Player Salaries 92
16. 10. Park Factors 101
17. 11. Streakiness in Sports 105
18. 12. The Platoon Effect 124
19. 13. Was Tony Perez a Great Clutch Hitter? 127
20. 14. Pitch Count, Pitcher Effectiveness, and PITCHf/x Data 133
21. 15. Would Ted Williams Hit .406 today? 139
22. 16. Was Joe DiMaggio’s 56-Game Hitting Streak the Greatest Sports Record of All Time? 142
23. 17. Projecting Major League Performance 151
24. Part II. Football
25. 18. What Makes NFL Teams Win? 159
26. 19. Who’s Better: Brady or Rodgers? 164
27. 20. Football States and Values 170
28. 21. Football Decision Making 101 178
29. 22. If Passing Is Better than Running, Why Don’t Teams Always Pass? 186
30. 23. Should We Go for a One-Point or a Two-Point Conversion? 195
31. 24. To Give Up the Ball Is Better than to Receive: The Case of College Football Overtime 207
32. 25. Has the NFL Finally Gotten the OT Rules Right? 211
33. 26. How Valuable Are NFL Draft Picks? 222
34. 27. Player Tracking Data in the NFL 229
35. Part III. Basketball
36. 28. Basketball Statistics 101: The Four Factor Model 249
37. 29. Linear Weights for Evaluating NBA Players 259
38. 30. Adjusted +/− Player Ratings 265
39. 31. ESPN RPM and FiveThirtyEight RAPTOR Ratings 282
40. 32. NBA Lineup Analysis 289
41. 33. Analyzing Team and Individual Matchups 296
42. 34. NBA Salaries and the Value of a Draft Pick 303
43. 35. Are NBA Officials Prejudiced? 307
44. 36. Pick-n- Rolling to Win, the Death of Post Ups and Isos 313
45. 37. SportVU, Second Spectrum, and the Spatial Basketball Data Revolution 321
46. 38. In-Game Basketball Decision Making 341
47. Part IV. Other Sports
48. 39. Soccer Analytics 355
49. 40. Hockey Analytics 373
50. 41. Volleyball Analytics 385
51. 42. Golf Analytics 391
52. 43. Analytics and Cyber Athletes: The Era of e-Sports 398
53. Part V. Sports Gambling
54. 44. Sports Gambling 101 409
55. 45. Freakonomics Meets the Bookmaker 420
56. 46. Rating Sports Teams 423
57. 47. From Point Ratings to Probabilities 447
58. 48. The NCAA Evaluation Tool (NET) 464
59. 49. Optimal Money Management: The Kelley Growth Criterion 468
60. 50. Calcuttas 474
61. Part VI. Methods and Miscellaneous
62. 51. How to Work with Data Sources: Collecting and Visualizing Data 479
63. 52. Assessing Players with Limited Data: The Bayesian Approach 490
64. 53. Finding Latent Patterns through Matrix Factorization 499
65. 54. Network Analysis in Sports 508
66. 55. Elo Ratings 524
67. 56. Comparing Players from Different Eras 531
68. 57. Does Fatigue Make Cowards of Us All? The Case of NBA Back-to- Back Games and NFL Bye Weeks 538
69. 58. The College Football Playoff 543
70. 59. Quantifying Sports Collapses 551
71. 60. Daily Fantasy Sports 559
72. Bibliography 569
73. Index 579