Building the game projection model

Abstract: In the previous article I laid the groundwork for my game projection model. Now I will use the actual starting lineups and add in season adjustments using the ELO ranking principle. The data used in this article is from the 2016/2017 season to the 2019/2020 season. Finding the starting lineups: I used Evolving-Hockey’s ShiftContinue reading “Building the game projection model”

Converting season projections to game Projections

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”

Evaluating the projection model

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”

What is hockey IQ?

Abstract: This is me taking off my statistical glasses and putting on my coaching glasses. With the NHL draft behind us, I want to share my view on non-statistical player evaluation. Meghan Chayka declared the term “Hockey IQ” banished yesterday, so I figured I would give you my interpretation of on-ice decision making. Talent vs.Continue reading “What is hockey IQ?”

Projecting future results – Goaltender edition

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”

Projecting future results – Skater edition

Abstract: I have previously introduced the sGAA model (See here), which is primarily a descriptive model. Now I will introduce a predictive counterpart – projected sGAA or p-sGAA. It’s based on 3 years of weighted data. sGAA describes past performances while p-sGAA projects future performances. The projection model: The model is basically based on 3Continue reading “Projecting future results – Skater edition”

Is NHL a strong link league?

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?”

Applying Arena Effects to goalies and skaters

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”

Indications that shot location data is flawed – Depends on where games are being played

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 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”