Quote (Nihility @ 22 Oct 2020 07:07)
0.5 wins bought for the habs? Just with Allen alone id say we got 7-10 more wins in a full season calendar
It's not literal "wins"
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As always, there are other variables that will decide how much better or worse a team is next season — team-wide regression, differing usage, breakouts, declines, lineups, rookies, luck, coaching — but this should still provide a solid framework for how much each team has changed on paper. For better or worse.
That’s what we’ll be looking at here as we measure how many wins a team added or subtracted from their roster to date based on their current lineup, while also paying attention to how much salary has been added or subtracted (though that figure will only take into account the players who were brought in or taken out, nothing else). As usual, value is determined using my model, Game Score Value Added.
his model explanation, although he improved it further by using expected goals for
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How the model works
It’s mostly outlined here in this FAQ posted before our 2017-18 projections, but basically it’s built at the player level using Game Score – a stat I adapted from basketball a few years ago. Working at the player level rather than the team level is one way that my model differs from others that are scaled via team performance only. It offers some challenges in terms of allocating proper credit, but has the advantage of being able to instantly factor for injuries and trades in ways a team-level model cannot.
Game Score is a linear weight model with the weights for each stat within it being derived according to the frequency of goals occurring from them and are as such:
Goals: 0.75
Primary Assists: 0.7
Secondary Assists: 0.55
Shots: 0.075
Blocks: 0.05
Penalty Differential: 0.15
Faceoff Differential: 0.01
5-on-5 Corsi Differential: 0.05
5-on-5 Goal Differential: 0.15
It uses data from each player’s last three seasons, with each component weighted by recency and regressed to the mean individually. That means that the weight for each prior season is different for goals than it is for shots or blocks (and different for forwards and defencemen), as is the regression factor. On top of that, there’s an age adjustment (using methods outlined here) performed at the start of each year that slowly lessens until the end of the season, as well as a small usage adjustment that factors in a player’s teammates and competition based on 5-on-5 Game Score.
From there, each player has a projection for each component going forward and that’s plugged into the Game Score formula to get a projected Game Score going forward. That’s then transformed into a wins above replacement rate (with replacement level being the 372nd forward and 186th defenceman) to create Game Score Value Added, or GSVA. That value is added up for each team based on the players in their starting lineup, and voila: team strength projections.
Here is his reason for lolhabs
Wins Added: 0.5 wins
Salary Added: $12.3 million
In: Josh Anderson, Jake Allen, Tyler Toffoli, Joel Edmundson
Out: Max Domi
This may feel like a shockingly low spot for the Canadiens, so it’s worth explaining what the model sees here for a team that is spending $12.3 million to earn an extra 0.5 wins.
For starters, the Jake Allen and Joel Edmundson additions don’t move the needle. In Allen’s case, the model doesn’t account for the fact that Carey Price will likely perform better from being more well-rested. As for Edmundson, he’s not viewed fondly by the model and pushes arguably stronger options like Brett Kulak and Victor Mete down the depth chart.
Up front, both Tyler Toffoli and Josh Anderson grade out as top-six calibre forwards, but neither project to be as good as Max Domi. Together they are, but the Canadiens were already relatively deep at wing meaning the two moves offer diminishing returns. In any sense, the forward group is indeed stronger and that’s where the 0.5 extra wins comes from.