Models for Third Down Conversion in the National Football League
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.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 8 (2012)
Issue (Month): 3 (October)
|Contact details of provider:|| Web page: https://www.degruyter.com|
|Order Information:||Web: https://www.degruyter.com/view/j/jqas|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Steven Rigdon & Robert Tsutakawa, 1983. "Parameter estimation in latent trait models," Psychometrika, Springer;The Psychometric Society, vol. 48(4), pages 567-574, December.
- R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:8:y:2012:i:3:n:1. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Peter Golla)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.