Dynamic Prediction of the NHL Draft with Rank-Ordered Logit Models
We are currently writing a research paper on our latest work. The links that I have provided at the moment describes the initial approach taken by Ryker, Kimberly, and I for our presentation at the Linkoping Hockey Analytics Conference; however, I have provided the abstract for my thesis and will update this website with additional resource when available.
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.).