Ryker Moreau

Title: Valuation of NHL Draft Picks using Functional Data Analysis
Date: November 28th, 2022
Time: 2:30PM
Location: Library Thesis Defence Room 2020

Abstract

Evaluation of player value in sport can be measured in several ways. These measures, when captured over an entire career, provide insights concerning player contributions. Professional sports teams select young talent through a draft process with the goal of acquiring a player that will provide maximum value, but these expectations diminish as the pool of players grows smaller. In this project, we develop valuation measures for draft picks in the National Hockey League (NHL) and analyze the value of each pick with these measures. Specifically, we use different measures of player value to provide an expected value of that measure to each pick number in the draft. Our approach uses functional data analysis (FDA) to find a mean value curve from many observed functions in a nonparametric fashion. These functions are defined by each separate year of draft data. The resulting FDA model follows the assumption of monotonicity, ensuring that a smaller pick number always provides more value than any larger pick number. The resultant pick-value-chart is validated against future drafts. The proposed approach can be extended to sports in which an entry draft occurs and player career data is available.