Wentworth
Institute of Technology
Improving your NCAA bracket with statistics /
Twenty-four million people wager nearly $3 billion on college basketball pools each year, but few are aware that winning strategies have been developed by researchers at Harvard, Yale, and other universities over the past two decades. Bad advice from media sources and even our own psychological incl...
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| Format: | Electronic eBook |
| Language: | English |
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Boca Raton, Florida :
CRC Press,
[2019]
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| Series: | ASA-CRC series on statistical reasoning in science and society.
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| Subjects: | |
| Online Access: |
OCLC metadata license agreement Full text (WIT users only) |
Table of Contents:
- Cover; Half Title; Series Page; Title Page; Copyright Page; Contents; Preface; Acknowledgments; Chapter 1: The Birth of the Pool; 1.1 The Tournament; 1.2 The Bracket Pool Emerges; 1.3 A Natural Experiment in Economics; 1.3.1 Simplify the Problem; 1.3.2 A Team's Chances of Winning; 1.3.3 Are There Favorable Strategies?; 1.3.4 Pooled Betting; 1.3.5 Some Pool Players Already Knew; 1.3.6 What a Competitive Pool Would Look Like; 1.3.7 Are Most Pool Players Irrational?; 1.3.8 Was the Simplified Problem Good Enough?; 1.3.9 Down-Bracket Chalk; 1.4 Obama's Brackets; 1.5 Metrick's Impact
- Chapter 2: Predicting the Tournament Outcome2.1 An Example of a Tournament Outcome Model; 2.2 Why Use a Probability Model?; 2.3 Estimating Game Outcome Probabilities; 2.4 Converting a Point Spread to a Probability; 2.5 Using Rating-based Spreads; 2.6 Improving the Tournament Outcome Model; 2.7 Precision versus Accuracy; 2.8 Will a 16 Seed Ever Beat a 1 Seed?; 2.9 Judging Models based on the Tournament Outcome; 2.10 Judging a Model-Generating Method; 2.11 Using a Tournament Outcome Model in Bracket Pool Strategy; 2.12 The Team Advancement Table; Chapter 3: Ratings versus Seedings
- Chapter 4: The Conquest of Pools with Upset Incentives4.1 How Upset Incentives Work; 4.2 The Expected-Point-Maximizing Bracket; 4.3 Monte Carlo Computer Simulations; 4.4 Finding the EPM Bracket; 4.5 Direct Calculation of the EPM Bracket; 4.6 EPM for the Public; 4.7 EPM Results; 4.8 Strengths and Weaknesses of the EPM Bracket; 4.9 Surprising Reactions to Expected-Point Maximizers; Chapter 5: Predicting Your Opponent's Brackets; 5.1 Data Sources for an Opponent Model; 5.2 Advancement Table Bias; 5.3 A Pick Advancement Table Example; 5.4 Some Laws of Probability
- 5.5 Pick Advancement Table Characteristics5.6 Converting an Advancement Table into an Opponent Model; 5.6.1 The mRchmadness Method; 5.6.1.1 Using the Categorical Distributions; 5.6.1.2 Simulating Categorical Distributions; 5.6.2 Reverse Engineering a Markov Model; 5.7 Summary; Chapter 6: Parametric Whole-Bracket Optimization; 6.1 Inputs to the Strategy; 6.2 The Goal: Maximizing Expected Return; 6.3 Assumptions of the Method; 6.4 The Opponent Score Probability Distribution; 6.5 Your Bracket is the Decision Variable; 6.6 Distinguishing Your Bracket from the Competition
- 6.7 Estimating the Return of a Candidate Bracket6.8 Searching for the Optimal Bracket; 6.9 The Optimal Bracket for a Pool with a Million Opponents; 6.10 The Normality Assumptions Evaluated; 6.11 Variations in Tournament Outcome Models; 6.12 Sources for the Opponent Model; 6.13 Conclusion; Chapter 7: A Practical Contrarian Strategy; 7.1 Pool Betting Behavior; 7.2 Defining Return on Investment; 7.3 Tournament Outcome Models; 7.4 Estimating ROIs Using Simulations; 7.5 Most Players Make Bad Bets; 7.6 A Similarity Metric; 7.7 Identifying Contrarian Champs; 7.8 Improving Your Bracket