Who's #1?: The Science of Rating and Ranking

Who's #1?: The Science of Rating and Ranking


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Who's #1?: The Science of Rating and Ranking by Amy N. Langville, Carl D. Meyer

A website's ranking on Google can spell the difference between success and failure for a new business. NCAA football ratings determine which schools get to play for the big money in postseason bowl games. Product ratings influence everything from the clothes we wear to the movies we select on Netflix. Ratings and rankings are everywhere, but how exactly do they work? Who's #1? offers an engaging and accessible account of how scientific rating and ranking methods are created and applied to a variety of uses.

Amy Langville and Carl Meyer provide the first comprehensive overview of the mathematical algorithms and methods used to rate and rank sports teams, political candidates, products, Web pages, and more. In a series of interesting asides, Langville and Meyer provide fascinating insights into the ingenious contributions of many of the field's pioneers. They survey and compare the different methods employed today, showing why their strengths and weaknesses depend on the underlying goal, and explaining why and when a given method should be considered. Langville and Meyer also describe what can and can't be expected from the most widely used systems.

The science of rating and ranking touches virtually every facet of our lives, and now you don't need to be an expert to understand how it really works. Who's #1? is the definitive introduction to the subject. It features easy-to-understand examples and interesting trivia and historical facts, and much of the required mathematics is included.

Product Details

ISBN-13: 9780691162317
Publisher: Princeton University Press
Publication date: 12/01/2013
Pages: 272
Sales rank: 645,358
Product dimensions: 7.00(w) x 9.90(h) x 0.70(d)

About the Author

Amy N. Langville is associate professor of mathematics at the College of Charleston. Carl D. Meyer is professor of mathematics at North Carolina State University. They are the authors of Google's PageRank and Beyond: The Science of Search Engine Rankings (Princeton).

Table of Contents

Preface xiii
Purpose xiii

Audience xiii
Prerequisites xiii
Teaching from This Book xiv
Acknowledgments xiv

Chapter 1. Introduction to Ranking 1
Social Choice and Arrow’s Impossibility Theorem 3
Arrow’s Impossibility Theorem 4
Small Running Example 4

Chapter 2. Massey’s Method 9
Initial Massey Rating Method 9
Massey’s Main Idea 9
The Running Example Using the Massey Rating Method 11
Advanced Features of the Massey Rating Method 11
The Running Example: Advanced Massey Rating Method 12
Summary of the Massey Rating Method 13

Chapter 3. Colley’s Method 21
The Running Example 23
Summary of the Colley Rating Method 24
Connection between Massey and Colley Methods 24

Chapter 4. Keener’s Method 29
Strength and Rating Stipulations 29
Selecting Strength Attributes 29
Laplace’s Rule of Succession 30
To Skew or Not to Skew? 31
Normalization 32
Chicken or Egg? 33
Ratings 33
Strength 33
The Keystone Equation 34
Constraints 35
Perron-Frobenius 36
Important Properties 37
Computing the Ratings Vector 37
Forcing Irreducibility and Primitivity 39
Summary 40
The 2009-2010 NFL Season 42
Jim Keener vs. Bill James 45
Back to the Future 48
Can Keener Make You Rich? 49
Conclusion 50

Chapter 5. Elo’s System 53
Elegant Wisdom 55
The K-Factor 55
The Logistic Parameter ξ 56
Constant Sums 56
Elo in the NFL 57
Hindsight Accuracy 58
Foresight Accuracy 59
Incorporating Game Scores 59
Hindsight and Foresight with ε = 1000, K = 32, H = 15 60
Using Variable K-Factors with NFL Scores 60
Hindsight and Foresight Using Scores and Variable K-Factors 62
Game-by-Game Analysis 62
Conclusion 64

Chapter 6. The Markov Method 67
The Markov Method 67
Voting with Losses 68
Losers Vote with Point Differentials 69
Winners and Losers Vote with Points 70
Beyond Game Scores 71
Handling Undefeated Teams 73
Summary of the Markov Rating Method 75
Connection between the Markov and Massey Methods 76

Chapter 7. The Offense-Defense Rating Method 79
OD Objective 79
OD Premise 79
But Which Comes First? 80
Alternating Refinement Process 81
The Divorce 81
Combining the OD Ratings 82
Our Recurring Example 82
Scoring vs. Yardage 83
The 2009-2010 NFL OD Ratings 84
Mathematical Analysis of the OD Method 87
Diagonals 88
Sinkhorn-Knopp 89
OD Matrices 89
The OD Ratings and Sinkhorn-Knopp 90
Cheating a Bit 91

Chapter 8. Ranking by Reordering Methods 97
Rank Differentials 98
The Running Example 99
Solving the Optimization Problem 101
The Relaxed Problem 103
An Evolutionary Approach 103
Advanced Rank-Differential Models 105
Summary of the Rank-Differential Method 106
Properties of the Rank-Differential Method 106
Rating Differentials 107
The Running Example 109
Solving the Reordering Problem 110
Summary of the Rating-Differential Method 111

Chapter 9. Point Spreads 113
What It Is (and Isn’t) 113
The Vig (or Juice) 114
Why Not Just Offer Odds? 114
How Spread Betting Works 114
Beating the Spread 115
Over/Under Betting 115
Why Is It Difficult for Ratings to Predict Spreads? 116
Using Spreads to Build Ratings (to Predict Spreads?) 117
NFL 2009-2010 Spread Ratings 120
Some Shootouts 121
Other Pair-wise Comparisons 124
Conclusion 125

Chapter 10. User Preference Ratings 127
Direct Comparisons 129
Direct Comparisons, Preference Graphs, and Markov Chains 130
Centroids vs. Markov Chains 132
Conclusion 133

Chapter 11. Handling Ties 135
Input Ties vs. Output Ties 136
Incorporating Ties 136
The Colley Method 136
The Massey Method 137
The Markov Method 137
The OD, Keener, and Elo Methods 138
Theoretical Results from Perturbation Analysis 139
Results from Real Datasets 140
Ranking Movies 140
Ranking NHL Hockey Teams 141
Induced Ties 142
Summary 144

Chapter 12. Incorporating Weights 147
Four Basic Weighting Schemes 147
Weighted Massey 149
Weighted Colley 150
Weighted Keener 150
Weighted Elo 150
Weighted Markov 150
Weighted OD 151
Weighted Differential Methods 151

Chapter 13. "What If . . ." Scenarios and Sensitivity 155
The Impact of a Rank-One Update 155
Sensitivity 156

Chapter 14. Rank Aggregation-Part 1 159
Arrow’s Criteria Revisited 160
Rank-Aggregation Methods 163
Borda Count 165
Average Rank 166
Simulated Game Data 167
Graph Theory Method of Rank Aggregation 172
A Refinement Step after Rank Aggregation 175
Rating Aggregation 176
Producing Rating Vectors from Rating Aggregation-Matrices 178
Summary of Aggregation Methods 181

Chapter 15. Rank Aggregation-Part 2 183
The Running Example 185
Solving the BILP 186
Multiple Optimal Solutions for the BILP 187
The LP Relaxation of the BILP 188
Constraint Relaxation 190
Sensitivity Analysis 191
Bounding 191
Summary of the Rank-Aggregation (by Optimization) Method 193
Revisiting the Rating-Differential Method 194
Rating Differential vs. Rank Aggregation 194
The Running Example 196

Chapter 16. Methods of Comparison 201
Qualitative Deviation between Two Ranked Lists 201
Kendall’s Tau 203
Kendall’s Tau on Full Lists 204
Kendall’s Tau on Partial Lists 205
Spearman’s Weighted Footrule on Full Lists 206
Spearman’s Weighted Footrule on Partial Lists 207
Partial Lists of Varying Length 210
Yardsticks: Comparing to a Known Standard 211
Yardsticks: Comparing to an Aggregated List 211
Retroactive Scoring 212
Future Predictions 212
Learning Curve 214
Distance to Hillside Form 214

Chapter 17. Data 217
Massey’s Sports Data Server 217
Pomeroy’s College Basketball Data 218
Scraping Your Own Data 218
Creating Pair-wise Comparison Matrices 220

Chapter 18. Epilogue 223
Analytic Hierarchy Process (AHP) 223
The Redmond Method 223
The Park-Newman Method 224
Logistic Regression/Markov Chain Method (LRMC) 224
Hochbaum Methods 224
Monte Carlo Simulations 224
Hard Core Statistical Analysis 225
And So Many Others 225

Glossary 231
Bibliography 235
Index 241

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