Advanced analytics and predictive statistics in continuous flow sports.

Hockey has long been shown to be among the least predictable of all professional sports. Recent developments in data collection methods have created the demand for more detailed and advanced predictive modelling techniques to extract value from and apply the data to real world problems. This project focuses on predicting important outcomes in hockey at both team and player levels. Game winners and scores will be predicted using Bayesian approaches tailored to accommodate evaluative statistics and relevant pre-game factors. Player level models will predict individual game performances and project career trajectories for draft eligible players.

Intern: 
Abdolnasser Sadeghkhani
Faculty Supervisor: 
Syed Ahmed
Province: 
Ontario
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