Selected Publications

This project investigates the fouling time distribution of players in the National Basketball Association. A Bayesian analysis is presented based on the assumption that fouling times follow a Gamma distribution. Various insights are obtained including the observation that players accumulate their nth foul more quickly for increasing n. Methods are developed that will allow coaches to better manage playing time in the presence of fouls such that key players are available in the latter stages of matches.
SFU Library, 2020

Tracking data in the National Football League (NFL) is a sequence of spatial-temporal measurements that varies in length depending on the duration of the play. In this paper, we demonstrate how model-based curve clustering of observed player trajectories can be used to identify the routes run by eligible receivers on offensive passing plays. We use a Bernstein polynomial basis function to represent cluster centers, and the Expectation Maximization algorithm to learn the route labels for each of the 33,967 routes run on the 6,963 passing plays in the data set. With few assumptions and no pre-existing labels, we are able to closely recreate the standard route tree from our algorithm. We go on to suggest ideas for new potential receiver metrics that account for receiver deployment and movement common throughout the league. The resulting route labels can also be paired with film to enable streamlined queries of game film.
JQAS, 2019

This paper considers an extension of the Kelly criterion used in sports wagering. By recognizing that the probability p of placing a correct wager is unknown, modified Kelly criteria are obtained that take the uncertainty into account. Estimators are proposed that are developed from a decision theoretic framework. We observe that the resultant betting fractions can differ markedly based on the choice of loss function. In the cases that we study, the modified Kelly fractions are smaller than original Kelly.
JQAS, 2018

Experience

Vancouver Sports Analytics Symposium & Hackathon VanSASH_Logo

VanSASH is a student run event geared towards students with a goal of giving them the tools to grow and succeed in the sports analytics industry. It is hosted by the SFU Sports Analytics Club.

2017

VanSASH 2017 was the inaugural event where we partnered with the Vancouver Whitecaps FC and Vancouver Canucks to bring challenging datasets to our 75 hackathon participants. Additionally, 6 prominent researchers from industry and academia gave speeches about their topic of interest.

2018

VanSASH 2018 was expanded to bring business and soccer analytics problems to our 100 student participants in partnership with the Vancouver Whitecaps FC. This iteration included 2 divisions, soccer and business, each divided into 2 streams, data beginners and data experienced. Judges were from the Executive Team and Coaches of the Vancouver Whitecaps, the Vice President of Data and Analytics at Major League Soccer, and managers and data analysts from companies such as Best Buy, EA Sports, Boeing, Cardinal Path among others.