Hockey and Euclid: Predicting AAV With K-Nearest Neighbours

EP: The contract data used in this analysis was graciously provided by Tom Poraszka, the creator of the now-defunct General Fanager. While the hockey community suffers the loss of yet another tremendous resource, I wish Tom the best of luck with his new venture!

Not a year goes by without¬†at least one NHL contract signing bewildering the hockey world. With healthy scratches making $5MM or more per year, it may seem as though the signing process is just one big roulette spun by managers, players and agents. In reality, though, the NHL player market is remarkably consistent as a whole. We can prove and exploit this fact by leveraging available information to try to predict how much an impending Free Agent will be paid. Continue reading “Hockey and Euclid: Predicting AAV With K-Nearest Neighbours”

A Brief Introduction to K

I recently shared a paper of mine entitled “Composite Tailored Regression Modeling For Evaluative Ratings in Professional Hockey” after it had unfortunately been rejected by a sports analytics journal. In it, I introduce a metric I’ve developed called K, explore the underlying math and discuss applications. It is now available here¬†and I strongly urge you to read it in its entirety for a fuller understanding of the model. For the less mathematically-inclined, this post will serve as an introductory explanation of the fundamentals. Continue reading “A Brief Introduction to K”