Clustering and Analyzing 5v5 NHL Shot Location Data Twitter: @BrendanKumagai Website: kumihockey.com Brendan Kumagai, McMaster University / University of Toronto Email:
[email protected] Results Introduction Key Facts Problem: Heatmap-style visualizations are a common and intuitive way to display patterns How do you read this? 10 clusters in shot location data for individual players or teams. However, analyzing these shot maps The tree diagram at the top becomes exponentially more difficult as the number of players/teams increases. For represents how the players 3 subgroups example of this issue, zoom in on the shot heatmaps on the right, now try imagine were split with Ward's linkage Home plate forwards analyzing these and about 360 more players at once. That will get difficult really quickly. hierarchical clustering, resulting Perimeter forwards in 10 colour coded clusters. Defencemen Solution: I propose a framework for grouping NHL players by 5v5 shot location data The heatmap below is a Results separate: 388x388 comparison of the through fitting a shot density polygrid for each player and performing Ward's linkage Perimeter and home plate shooters players' shot maps by Euclidean hierarchical clustering [1] as outlined in the Methodology section. "Dynamic" D-men who produce more shots distance, where dark blue deep in the zone from "static" D-men who Once I have the hierarchical clustering results, I will analyze the composition of each represents very similar while are very highly concentrated in their corner. cluster and compare differences between clusters to search for any interesting skyblue/white represents very different. See "Cluster Composition Observations" observations and patterns in NHL 5v5 shot maps.