This method, while more complex than just optimizing a player’s CF%, does allow a more diverse lineup with respect to team selection instead of just a roster filled with Golden Knights, Canadiens, and Hurricanes. The more they exceed their expectation, the higher of the Corsi impact. This is compared with their actual Corsi. The general idea with RAPM is that it takes into account how a player is deployed (ice time), the game situation (score, venue) and comes with an expected value for the stat. Rather than using CF% directly, CJ opted to use Corsi Impact, which is generated from Relative Adjusted Plus Minus (RAPM) Corsi for and against. This way we filter out called up players who only played a little bit and therefore have an extraordinarily high or low Corsi value. The Approach & The Confounding FactorsĪs CJ put together a linear optimization algorithm to create an optimized roster with WAR, I asked him to do so for Corsi for all NHL players who played at least 500 minutes in the NHL in 2019-20. Play through the 2019-20 season with this Corsi-optimized roster. Replace the 2019-20 Devils roster with a NHL roster optimized for Corsi based on the 2019-20 season. I did indicate in last week’s post that I would be using 2019-20 data instead of 2018-19 to be consistent with the WAR-optimized team. This comment came in response to the WAR-optimized Team post, where they steamrolled most of the NHL in the regular season and went 16-1 in the Stanley Cup Playoffs. How about utilizing the highest ranked Corsi players from the 2018-19 season on a team together to see how they perform when compared to a WAR optimized team? As suggested by MedicSBK from a few weeks back: This week’s experiment is another exercise in optimizing a roster for a given stat. In general, he crushed it in the other leagues before having a tougher time in the best league in the world. Last week’s experiment saw Jack Hughes play his 2019-20 season in several other leagues before playing 2020-21 in the NHL and the results of how he did. Welcome to the fourth experiment in the Let’s Experiment in Eastside Hockey Manager (EHM) series.
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