Quantifying the probability of a shot in women’s collegiate soccer through absorbing Markov chains
Woodfield Devyn Norman () and
Fellingham Gilbert W. ()
Additional contact information
Woodfield Devyn Norman: Department of Statistics, Brigham Young University, 223 TMCB, Provo, UT 84602, USA
Fellingham Gilbert W.: Department of Statistics, Brigham Young University, 223 TMCB, Provo, UT 84602, USA
Journal of Quantitative Analysis in Sports, 2018, vol. 14, issue 3, 103-115
Abstract:
A Bayesian model is used to evaluate the probability that a given skill performed in a specified area of the field will lead to a predetermined outcome by using discrete absorbing Markov chains. The transient states of the Markov process are defined by unique skill-area combinations. The absorbing states of the Markov process are defined by a shot, turnover, or bad turnover. Defining the states in this manner allows the probability of a transient state leading to an absorbing state to be derived. A non-informative prior specification of transition counts is used to permit the data to define the posterior distribution. A web application was created to collect play-by-play data from 34 Division 1 NCAA Women’s soccer matches for the 2013–2014 seasons. A prudent construction of updated transition probabilities facilitates a transformation through Monte Carlo simulation to obtain marginal probability estimates of each unique skill-area combination leading to an absorbing state. For each season, marginal probability estimates for given skills are compared both across and within areas to determine which skills and areas of the field are most advantageous.
Keywords: Bayesian; Markov chain; soccer (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1515/jqas-2015-0076 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.
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:bpj:jqsprt:v:14:y:2018:i:3:p:103-115:n:1
Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jqas/html
DOI: 10.1515/jqas-2015-0076
Access Statistics for this article
Journal of Quantitative Analysis in Sports is currently edited by Mark Glickman
More articles in Journal of Quantitative Analysis in Sports from De Gruyter
Bibliographic data for series maintained by Peter Golla ().