The other day, @Moneypuck_ and @SteveBurtch had a conversation about the Prospect Cohort Success Model:
While the PCS model is interesting in its own right, I found the discussion about the methods we use to analyze players to be interesting as well.
Johan Larsson’s TOI% jumps 8.5% when the Sabres are leading by 2 or more goals (which is never). Photo by Michael Miller, via Wikimedia Commons
Right out the gates, I knew two things: 1) I wanted to take TOI% data from close scores and subtract it from TOI% data from 2+ goal leads, and 2) that it would automatically tell us that perceived poorer players are given more playing time with the lead. Why? Because they tend to play less when the score is close, which increases the likelihood that a differential with 2+ goal time on-ice will show they get to play more with a big lead. That said, I wanted to run a quick study to see just how much of a difference that time swing could be, and which players come out of the woodwork on either end.
But first, I want to whittle away the small sample players, and to do that I’m going to run a quick test to see at what # of games played this TOI 2+ minus TOI Close differential (let’s call it “TOI Lead Diff”) stabilizes.
Hockey Graphs 