Before we get rolling with the post I want to introduce myself. Recently I’ve fan posted a few stats related articles and also spent some time writing for BTN (now AIC). I plan to contribute some advanced stats analysis of the sharks here at FTF, and some material on league wide metrics as well. I’ll try to bring highly relevant, interesting stat related material regarding this year’s team. If you want to see something specific feel free to let me know. I hope you enjoy it!
A few weeks ago I started compiling a few different models that would look at playoff probabilities. In part because I think there is still considerable debate going on in the advanced stats community about what exactly is the best metric for predicting future success (Point% specifically), and also because I wanted to see what the potential playoff match-ups would look like. We’re basically killing 2 birds with 1 stone by setting up an “experiment” for which variable predicts future success the best, while also looking at current team rankings through playoff probabilities. Let’s look at data through the lens of the currently most predictive model, score adjusted fenwick%.
Grab your super nerd goggles and join me at the end of the post for a detailed explanation of this and other models. The tables are sortable, click the table headers to sort for that column.
A full spreadsheet of this model can be found here.
For Sharks fans- San Jose comes out looking pretty good. They make the playoffs in about 98% of the simulations, and win the Pacific division in 79% of sims. They’re pretty much a lock for the 2nd or 3rd seed coming in at 71%, while they probably won’t take the President’s trophy or the Western Conference crown, claiming those titles only 5% and 8% respectively.
The West is boasting some great teams (again) this year, with a sharp drop-off outside of the big 5. It looks like DET, STL, VAN, CHI, and SJS are all but locks for the playoffs this year. After that it gets pretty interesting. LA, NSH, and DAL look like front runners for the remaining spots, but it’s open, with LA and NSH missing the playoffs in 1/4 of the simulations. Of note NSH has a terrible adj-fenwick%, but they’ve won so many games already that they have a significant shot at the playoffs. I’m sure most Sharks fans would love seeing NSH come to the tank this April. LA is a bit the opposite. Despite a strong adj-fenwick% their early season woes have left them fighting for a playoff spot, which may come down to the final week of the season for them. Also it’s interesting to see MIN’s numbers They carry such a low fenwick% that the model predicts them to make the playoffs in only 19% of sims despite clinging to the 8th spot currently. Unfortunately for CLB fans in the 100,000 sims I ran, they never made the playoff, not even once.
The East similarly has 4 top teams that drop-off after that. WPG fans can take notice that in about 1/3 of sims they make the playoffs. Their underlying numbers are really quite good, for whatever reason they just haven’t got the bounces this year (Kinda ironic in Artic Ice Hockey’s first year?).
The race for the President’s trophy should get real heated toward the end of the season as well. The real front runners are DET, STL, and BOS. They take top team in 32%, 20%, and 19% of sims respectively. That’s nearly 3/4 of all sims. What else can you say about STL’s turnaround year? They seem to be only getting better.
I’ll try update this in a week or so, and we can take a look at trends over that time. An obvious disclaimer here is that as a whole hockey just isn’t that predictive of a sport. Either we haven’t found the key to measuring success yet, or it’s just that random of a game. Take all these numbers with a grain of salt, as they will be changing throughout the rest of the season as teams get hot and cold.
***Model Specifics***
A few days ago Eric T over at BroadStreetHockey wrote a great piece about adjusting team fenwick% using score effects. This ignited a lot of articles covering the subject of score effects, and some debate over its validity (here). I decided to use Eric’s data for the model I created. First, I calculated each team’s score-adj fenwick% using Eric’s formula
SAF = 0.089 * Fen_up_2 + 0.200 * Fen_up_1 + 0.424 * Fen_tied + 0.200 * Fen_down_1 + 0.089 * Fen_down_2 + .210
and then regressed that by 0.15 to the mean, as this is about the reliability of that stat over the average amount of games played by teams this year. I then calculated the Goals/Game and Goals Against/Game for each team assuming a 0.7 Fenwick Sh%. I then ran all that through a Monte Carlo Simulation of the rest of the season 100,000 times. At the end of each simulated season I recorded each teams standings so that probabilities could be calculated at the end. Although not shown here, I also created 2 other models much in the same way. One using Fenwick% Road Close, and the other Goal%. In April we can go back and see which of all the models was the most accurate. We could theoretically choose any point in any of the past 4 seasons to do the same exact thing, which if warranted (and time permitting) I may do.
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