Brendan Kumagai

Title: Dynamic Prediction of the National Hockey League Draft with Rank-Ordered Logit Models
Date: November 28th, 2022
Time: 4:30PM PST
Location: Library Thesis Defence Room 2020

Abstract

The National Hockey League (NHL) Entry Draft has been an active area of research in hockey analytics over the past decade. Prior research has explored predictive modelling for draft results using player information and statistics as well as ranking data from draft experts. In this project, we develop a new modelling framework for this problem using a Bayesian rank-ordered logit model based on draft ranking data obtained from industry experts between 2019 and 2022. This model builds upon previous approaches by incorporating team tendencies and needs, addressing within-ranking dependence between players, and solving various other challenges of working with rank-ordered outcomes such as incorporating both unranked players and rankings that only consider a subset of the available pool of players (i.e., North American skaters, European goalies, etc.).

Keywords: Bayesian Analysis; Sports Analytics; Rank-Ordered Logit; Multinomial Logit; National Hockey League; Entry Draft