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The fast-paced, volatile nature of hockey makes it a challenging sport to analyze and predict the final outcome. This paper presents two continuous-time Markov process models that predict the probability that a team will win a hockey game given particular states during the game. These states incorporate shot and goal differential relative to the opposing team and are used to approximate the probability that the home team would win depending on the state they are currently in at a given time in the game. We also provide several examples of using this model to predict National Hockey League games.
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Predictive Modeling and Analysis of Hockey Using Markov Chains
The fast-paced, volatile nature of hockey makes it a challenging sport to analyze and predict the final outcome. This paper presents two continuous-time Markov process models that predict the probability that a team will win a hockey game given particular states during the game. These states incorporate shot and goal differential relative to the opposing team and are used to approximate the probability that the home team would win depending on the state they are currently
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- Award ID(s):
- 1722563
- PAR ID:
- 10482208
- Publisher / Repository:
- Mathematics and Sports
- Date Published:
- Journal Name:
- Mathematics and Sports
- ISSN:
- 0000-0000
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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