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The notion that Sidney Crosby is no longer the league's best player exists in some capacity and I'm not sure why... pic.twitter.com/Lu6QVaSq2J
— Domenic Galamini (@MimicoHero) July 29, 2015
If you're wondering what rGF% is you can read about it here http://t.co/23B9oUq7vf
— Domenic Galamini (@MimicoHero) July 29, 2015
A common goal in modern hockey statistics is to find repeating patterns that best predict future results. In other words, you want two things: repeatability and predictive ability. Understanding the importance of these two concepts has led to the popularization of shot attempt based measures like Corsi and Fenwick. Consequently, on-ice percentages have often times been deemed useless at the player level due to their lack of repeatability. Therein lies the inefficiency. Just because a metric is susceptible to high levels of variance doesn’t necessarily warrant its complete dismissal. In the case of shooting percentage and save percentage, it comes down to regressing and extracting whatever useful information we can. The question is, how far do we regress? The point of this piece is to answer that exact question and show how we can regress percentages to markedly improve on the predictive ability of Corsi at the player level.
Brandon Sutter vs. Nick Bonino on the #GlassToCrosbyScale pic.twitter.com/ItFJCFJYaS
— Domenic Galamini (@MimicoHero) July 30, 2015
Nick Spaling vs. Phil Kessel @matt_darnley pic.twitter.com/r65QM1Sosc
— Domenic Galamini (@MimicoHero) July 30, 2015
Anze Kopitar vs. Ryan Getzlaf vs. Joe Thornton vs. Evgeni Malkin pic.twitter.com/cEjF8yGcNQ
— Domenic Galamini (@MimicoHero) July 30, 2015