Sports Analytics Methods – Poisson Factorization

This is a review of the NCAA basketball research conducted by Francisco J. R. Ruiz and Fernando Perez-Cruz, applying Poisson factorization. Predicting probabilities regarding outcomes of sporting events is difficult as it is often not clear which variables actually affect the outcome and what information is known before the event begins.  Predicting outcomes for team…

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Sports Analytics – Multiple Least Squares Regression w a Lagged Dependent Variable

This is a review of the NBA research conducted by James Tarlow, applying multiple least square regression, including a lagged dependent variable. Within sports, it is always assumed that experience improves a team and leads to championships. This is especially true in the NBA. Teams made up of younger players are viewed negatively for their…

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Sports Analytics Methods – Negative Binomial Regression

This is a review of the MLB research conducted by Joseph Xu, Peter Fader, and Senthil Veeraraghavan, applying negative binomial regression. As much as sports teams are about winning championships, the aim of management and stakeholders is t o maximize revenue.  Many teams across various sporting leagues have adopted a dynamic pricing strategy for single-game…

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Sports Analytics Methods – Multinomial Logistic Regression

This is a review of the NBA research conducted by Alexander Franks, Andrew Miller, Luke Bornn, and Kirk Goldsberry applying multinomial logistic regression. Basketball is a game based on both offensive and defensive skill.  However, to date most basketball statistics deal with the offensive side of the game.  This limits the ability to evaluate teams…

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