Abstract: The goal in this article is to convert my projection model (Read more here and here) from season projections into game projections. From season projections to game projections: The model as it stands right now uses p-sGAA to project team strength. I want to convert that into Win%, which is the projected number ofContinue reading “Converting season projections to game Projections”
Abstract: The goal here is to compare my projection model (read more here and here) to other models out there. All of my projections below are made well after the games were played, so the true test of the model will be its performance this upcoming season. The model is the same for all tenContinue reading “Evaluating the projection model”
Abstract: In the last article, I tried to project future performances of the NHL skaters. In this piece I will add goaltender projections, so we can see how the first draft of the projection model performs. What if all goaltenders were average? Before we get to the goaltender projections, let’s try to assume every goaltenderContinue reading “Projecting future results – Goaltender edition”
Abstract: The goal of this piece is to discuss if it’s more important to have elite players (strong link) or great depth (weak link). To do so, I will look at talent distributions. How is the talent/impact spread out across the players? Do the elite players have a relatively larger impact on the game? I’mContinue reading “Is NHL a strong link league?”
Abstract: In the previous article, I found some differences in the shot tracking data depending on the arena. I therefore defined the Arena Effect as the difference between shot quality at home and on the road. Those Arena Effects can be downloaded here. Now, I want to add these effects to my sGAA model, whichContinue reading “Applying Arena Effects to goalies and skaters”
Abstract The goal in this article is to determine whether xG data is impacted by where the games are being played. Do certain arenas impact the shot location data one way or the other? All data for this article is 5v5 data from http://www.evolving-hockey.com Henrik Lundqvist vs. Tuukka Rask A big part of the inspirationContinue reading “Indications that shot location data is flawed – Depends on where games are being played”
Redefining evolving-hockey’s GAR and xGAR models, so they correlate directly with goal differential.
The first draft of an actual projection model
How predictable are on-ice stats like corsi and expected goals? And how does that compare to the predictability of LS-GAA?
Is it possible to project future LS-GAA values? How predictable is the model?