A statistical investigation of data from the NHL Combine

This project seeks to discover useful information from the NHL Combine results by comparing NHL Central Scouting Service rankings, NHL Draft results and measures of player evaluation. Data management is central to this project and we describe the details of handling datasets including the large and proprietary Combine dataset. Many data management decisions are made based on knowledge from the sport of hockey. The investigation of three questions of interest are carried out utilizing modern machine learning techniques such as random forests. Investigation 1 determines whether the Combine serves any purpose in terms of modifying the opinion of Central Scouting. Investigation 2 focuses on which test results of the Combine are important in predicting prospects’ future development. Investigation 3 considers how the Combine results revise Central Scouting’s beliefs.