With the National Hockey League season ending a couple weeks ago, it would be a good time to estimate how teammates influence scoring by using network analysis. I grew up watching hockey and I have always wondered how we could use standard, readily available metrics in such a way to accurately see a players ability to perform. The curiosity has only grown bigger as I have taken more practical courses for network analytics like CSCC46.
It is known that box scores in any sport can never tell the whole story of the game and so deeper analytics should always be done to truly know how the team and players performed. There has also always been a place for advanced analytics in hockey where they use standard metrics from the boxscores to calculate advanced analytics such as Corsi. However, it gets really interesting when you can use standard metrics from these boxscores and start to apply network analysis to it, directly relating to CSCC46 course material. This is precisely what Oppenheimer attempted to do, using standard metrics to calculate a single analytic that can give you a good understanding of the players performance on the team.
Oppenheimer was able to calculate betweenness and make it meaningful for hockey by visualizing scoring as a directed network where the nodes represent players and the edges represent primary assists to and from each players’ goals. The above diagram depicts this but the edges reflect shot and passing instead of primary assists. This calculation will show us that if the player has a high betweenness, it means their production is really high as they score and assist frequently and their assists are sometimes distinct too but you also start to see if some players rely on other players for their scoring. Sometimes however, this analytic gets meaningless if the team is stacked in the sense that they have a lot of players who can produce. This is because, betweenness is a relative metric hence it is relative to the players’ team and so being on an offensive team, the player can be deemed less influential than they really are.
With all that said, I believe this is the start of applying network analysis to the game of hockey and there is a lot to explore in this field and further analyze a players production to see if they would be a good fit for another team. The really cool thing about betweenness is that it can also be applied to other sports in virtually the same way and you can play around with different types of edges and nodes. I hope to see more research in this as I would love to see the world of analytics in hockey and possibly alter how teams are built forever.