Models for Third Down Conversion in the National Football League
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Ryan Cafarelli
Abstract
Several models are proposed for the probability of converting a third down attempt in the National Football League. The probability, which can depend on the number of yards to go, the strength of the offense, and the strength of the defense, leads to a logistic regression. We approach the problem through a hierarchical Bayes model and estimate parameters by using Markov chain Monte Carlo (MCMC). This MCMC estimation in the context of a hierarchical Bayes model may be relevant in other sports situations where a probability depends on the difference of strengths of the two teams. We find that the statistic "third-down conversion rate" to be a nearly meaningless measure of the efficiency of an offense. Even when this is adjusted for yards to go for a first down, there is little evidence that teams differ in their ability to achieve a first down on a third down conversion.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Article
- Models for Third Down Conversion in the National Football League
- A Comparison of the Autocorrelation and Variance of NFL Team Strengths Over Time using a Bayesian State-Space Model
- The Sensitivity of College Football Rankings to Several Modeling Choices
- The Individual Factors of Successful Free Throw Shooting
- New Insights on the Tendency of NCAA Basketball Officials to Even Out Foul Calls
- Was There a Structural Break in Barry Bonds's Bat?
- Estimating Fielding Ability in Baseball Players Over Time
- Adjusted Plus-Minus for NHL Players using Ridge Regression with Goals, Shots, Fenwick, and Corsi
- An Analysis of Curling Strategy
