Statistical Modeling to Inform Optimal Game Strategy: Markov Plays H-O-R-S-E

Thaddeus Tarpey and R. Todd Ogden

The American Statistician, 2016, vol. 70, issue 2, 181-186

Abstract: We illustrate practical uses of logistic regression and Markov chains by applying these concepts to the problem of developing optimal strategy in the popular basketball game of H-O-R-S-E. Based on data collected by the authors, we estimate model parameters for each author, describe strategies of optimizing each author’s probability of winning, and calculate the stationary distribution of a Markov chain that arises from the game.

Date: 2016
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00031305.2016.1148629 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:amstat:v:70:y:2016:i:2:p:181-186

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UTAS20

DOI: 10.1080/00031305.2016.1148629

Access Statistics for this article

The American Statistician is currently edited by Eric Sampson

More articles in The American Statistician from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().