Draft by Numbers: Using Data and Analytics to Improve National Hockey League (NHL) Player Selection

Michael Shuckers, Professor, St. Lawrence University


Abstract: One of the most important tasks for the general manager of any sports team is the efficient acquisition of player talent. Often one relatively inexpensive ways to accomplish this is through a league draft. In this paper we use historical data available when players were eligible to be selected in the National Hockey League (NHL) Player Entry Draft to build a statistical prediction model for their performance in the NHL. The data that we use is demographic (e.g, heights and weights), pre-draft performance (e.g., points per game and goals against average) and scouting (rankings from the NHL’s own Central Scouting Service (CSS)). We focused on two cohorts of players: those drafted in the 1998 to 2002 drafts and those eligible to be taken in the 2004 to 2008 drafts. In both cohorts, we train our model on the first three draft years and test our model’s performance on the remaining (out of sample) two years. We find that in both cohorts our statistical model consistently orders players for selection in a way that is more highly correlated with how they eventually perform in the NHL. Simply stated, our statistical model is better at ordering players for the NHL draft than NHL teams using only data available when players were selected.

Back to Videos