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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Sports Analytics Methods – Mixed-Integer Linear Programming Model

This is a review of the NFL schedules research applying mixed-integer linear programming titled “Alleviating Competitive Imbalance in NFL Schedules:  An Integer-Programming Approach“. The National Football League has a strong fan base and generates more revenue than any other sports league in the world.  During a regular season, NFL games are scheduled primarily on Sundays…

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Sports Analytics Methods – Probabilistic Graphical Models

This is a review of the basketball research conducted by Min-hwan Oh, Suraj Keshri, and Garud Iyengar applying probabilistic graphical models for basketball match simulation. With any sporting event, it is natural for analysts, bettors, and fans to make predictions regarding the outcome.  This is certainly true within the National Basketball Association. A simulation infrastructure…

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Sports Analytics Methods – Convex Hull Metrics

This is a review of the home plate umpire research conducted by David J. Hunter, applying convex hull metrics. Boos have been ringing out across baseball diamonds, criticizing umpire’s call judgments since the beginning of the game.  Pitch-tracking data provides a method to evaluate and train Major League Baseball umpires and evidence suggests that since…

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