Monthly Archives: October 2015

2016 NCHC Prediction

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Here it comes – Year Three of the NCHC. Once again, the eight-team conference looks to be the toughest and most talented in the nation. Most polls put 4-5 teams in the national Top 10, and, having sent 75% of the conference to the NCAA Tournament in March, the NCHC looks poised to return at least half the conference this year.

As the 2016 NCAA hockey season gets underway, it’s time to predict the final 2016 standings of the National Collegiate Hockey Conference. Per usual, I will be doing this using only actual statistical data based on each team’s past performance.

This only gets tougher as nearly every team in the conference has proven a national contender over the last few years. Already the teams are so talented that the marginal differences between each team are so slim – any team could beat any other team on any given night (yes, even CC).

Last year when I did this, the statistical method of predicting did slightly better than all of the NCHC hockey journalists. There’s no guarantee I will do as well this year though, so the better my odds I made a few adjustments to the model to try and get an even more accurate prediction.

I’ve collected individual-level data on all NCHC players from 2014-15, primarily goals, shots, shot%, save% and a derived possession-share (individual shots/all shots). That data was much more readily available thanks to better tracking by the NCHC and more in-depth shot statistics compiled by College Hockey News.

As we did last year, we’re going to adjust sh%, sv% and possession for each team based on what we know about roster changes, particularly about who is returning and who has left. To do this, I have to make some assumptions about players and teams. I’ll try to keep these as safe as possible:

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NCHC Model: Adjustments 2016

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Welcome to a long thorough analysis of my analysis – the NCHC model for predicting final standings. Before last season, I created a model to predict the NCHC final standings. In this post, I’m re-examining my methods and assessing their validity. Honestly, I thought I did OK last year, considering I did better than most of the expert writers and media. However, my model didn’t exactly nail the final standings, so there’s room for improvement. Today I’ll take a look at the components of the model and whether they worked as intended. Later this week, I’ll bring it all together for a 2016 prediction.

Be warned – this post is methods heavy. If you have an interest in NCAA hockey analytics, read on. If not, turn back now, and come back later in the week for my stats-based NCHC predictions. Still here? Ok, here we go.

First of all, kudos to the National Collegiate Hockey Conference for providing full-season and in-conference shot statistics for 2014-15. It’s a good start and a huge improvement over last year.

If you’d like to get familiar with the theory behind this model, I suggest reading my post from last year in which I created the NCHC model. Also, before we get started, the usual disclaimers: this analysis uses NCHC data taken from NCHC official records, and only considers intra-conference play during the regular season. Non-conference games, NCHC tournament games, and NCAA tournament games are not included.

2013-14 vs. 2014-15

Team '14 Sh% '15 Sh% Δ '14 Sv% '15 Sv% Δ '14 Poss. '15 Poss.  Δ
Colorado College 7.74% 6.81% -0.93% 89.39% 88.65% -0.74% 48.73% 42.10% -6.63%
Denver 9.08% 10.49% 1.41% 92.87% 90.65% -2.22% 44.81% 50.90% 6.09%
Miami 7.60% 8.99% 1.39% 88.90% 90.90% 2.00% 50.55% 55.20% 4.65%
Minnesota-Duluth 8.83% 8.95% 0.12% 89.83% 91.26% 1.43% 53.17% 53.00% -0.17%
Omaha 9.87% 10.15% 0.28% 89.22% 92.63% 3.41% 56.49% 46.50% -9.99%
North Dakota 11.06% 9.61% -1.45% 91.41% 92.75% 1.34% 48.38% 49.70% -1.36%
St. Cloud State 12.46% 9.18% -3.28% 91.14% 91.37% 0.23% 49.15% 53.70% 4.55%
Western Michigan 10.06% 7.38% -2.68% 90.11% 89.51% -0.60% 48.45% 49.80% 1.35%
NCHC Total 9.58% 9.02% -0.56% 90.42% 90.98% 0.56% 50.00% 50.00% 0.00%

The adage says the best predictor of future performance is past performance. Looking at this table, that holds. St. Cloud lost some shooting prowess, and Omaha had better goaltending, but not much jumps out otherwise. Performance was fairly steady across the three categories from ’14 to ’15 – except possession. Wild swings there, eh? Omaha lost nearly 10 percentage points in possession share. Meanwhile, Denver and Miami saw great improvements, which was a big reason for their good finishes. I’ll take a look at what might be driving possession swings a little later. For now, when comparing ’14 and ’15 performance, you can group the eight teams into four categories:

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