Picking NFL Winners: A Statistical Handicapping Primer

by Masaru Kanemoto

Every year, sports bettors around the world wager billions of dollars on the NFL. But only a small fraction win. The decades of experience the sportsbooks have when creating the lines combined with the vigorish charged on each bet mean that without a battle-tested, mathematically sound handicapping approach, the typical recreational sports bettor has no chance. Picking NFL A Statistical Handicapping Primer introduces a powerful handicapping methodology grounded in predictive analytics to give the reader the upper hand over the sportsbooks. In this book, we will explore which statistics, ratings, and other information have had the biggest influence on the NFL point spread. Using this knowledge, we then introduce predictive models which the reader can use themselves, leveraging readily-available information, to beat the NFL.Written for intermediate to advanced sports bettors, or anyone else who wants to win, Picking NFL A Statistical Handicapping Primer addresses many key questions about NFL handicapping, How to account for injuries-- Which statistics have the most value in predicting the point spread-- How one should weigh recent results versus past results when handicapping-- Why early-season and late-season handicapping are very different-- How big of an influence do short-rest weeks, the bye week, and travel have on the point spread-- Whether time-zone effects / circadian advantage exists in the NFL-- How to use teasers, halftime lines, and line moves to improve your NFL betting resultsAnd much more. Picking NFL A Statistical Handicapping Primer is an invaluable resource to all serious NFL bettors, and anyone else looking to understand how to beat sports betting, regardless of what sport you wager.

Genres: Sports

133 pages, Paperback

Published November 20, 2018

Reviews

[Felix]

Great book on handicapping the NFL. It's heavy on statistical model theory and data-related analysis. This book has been one of my foundational texts of study.