Dynamic prediction of the National Hockey League draft with rank-ordered logit models

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 paper, we develop a new modelling framework for this problem using a Bayesian rank-ordered logit model based on draft ranking data from industry experts between 2019 and 2022. This model builds upon previous approaches by incorporating team tendencies, 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 (e.g., North American skaters, European goalies, etc.).