American Football : A Markovian Approach
This bachelor's thesis in applied mathematics & industrial economics is an attempt to model drives in American football using Markov chains. The transition matrix is obtained through logit regression analysis on historical data from the NFL. Different outcomes of drives are modelled as separate absorbing states in the Markov chain. Absorption probabilities are calculated representing the probabilities of each outcome. Results are tested against a Markov chain with the transition matrix based on frequency analysis. Three scoring rules unanimously declare the regression based model to be superior.
The application of the model pertains to live sports betting. With the insight provided by the Markovian model, a bettor should be able to make statistically informed betting decisions. The prospect of creating a start-up based on the Markovian betting model is discussed.
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