The information in the table is pretty straightforward - it simply shows each team's probability of advancing as well as the probability of winning in a particular number of games. So, for example, Vancouver has a 61.8% chance of advancing and a 9% chance of doing so in a sweep.

The manner in which the odds were computed, however, requires some explanation. The method I used was similar to the "underlying numbers" method I employed last year, but some changes have been made. They include:

- Each team's expected shot differential was calculated on the basis of adjusted corsi - that is, overall corsi adjusted for how often each team played with the lead and trailed during the regular season. This is in contrast to last year's method, which used score tied corsi for this purpose. I elected to switch to adjusted corsi because it has more predictive power in relation to future results than both score tied corsi and overall corsi.
- I regressed each team's EV shooting and save percentage based on the extent to which the seasonal variation for each statistic can be attributed to non-luck. This differs from last year's method, in which each team was assigned a league average EV shooting percentage, with team EV save percentage computed on the basis of the overall career EV save percentage of the starting goaltender
- I regressed each team's PP and PK shot rates and percentages on the same basis as above when calculating each team's expected special teams scoring rates. Last year, I (erroneously) assumed no skill component for the percentages and elected not to regress shooting rates.
- Each team's expected PP and PK time on ice was calculated on the basis of it's predicted powerplay differential as well as its expected special teams scoring rates ( the latter adjustment is necessary given that a more efficient powerplay, as well as a less efficient penalty kill, will lead to fewer powerplay and penalty kill minutes, respectively). I actually performed the exact same calculation last year, with the only difference being the manner in which I determined predicted powerplay differential. Last year, raw powerplay differential was used. This year, powerplay differential was adjusted to reflect the percentage of team variation attributable to luck.

I then simulated each series 10000 times based on each team's expected winning percentage, which produced the odds displayed in the table at the top of the post.

In terms of predictive power, I have adjusted goal differential data for every season since the lockout (up to 2009-10), in which the adjustment was made using a similar but slightly different method than the one described in this post. I found that the adjusted goal differentials proved to be a superior predictor of the results of individual playoff games during that timeframe when compared to raw goal differential (empty netters removed).

I also found that the adjusted goal differentials better predicted how a team performed in the following regular season relative to raw goal differential.

I wanted to get this post up before the puck drop for Wednesday's games, so I wasn't able to include everything I wanted to content-wise. I plan to post cup probabilities and some more information relating to the method used to calculated the above odds.

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## 3 comments:

I'd be very interested in seeing the results your new model generates for an absurd shooting percentage abberation, like last year's Capitals.

Very informative stuff here. I'm curious though as to how you get to the probabilities. Are you creating expected goals, and then using something like a poisson distribution?

It is quite hard for Vancouver to sweep over but it is impossible and as a fan it is something that you wish for.

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