NCHC Prediction 2016-17


Welcome back, NaCoHoCo fans. It’s been a long offseason – partying for some of us (North Dakota, Denver), pangs for others (Omaha, Miami), pleading for others still (Arizona State, Mankato). But the days are getting shorter, the lattes are getting pumpkin spicier and the rev of distant zamboni motors grows and grows. Let’s talk college hockey, eh?

It’s destined to be another exciting year in the National Collegiate Hockey Conference. Sure, it’s tough to beat a national championship season. How about another one? And sure, the conference lost a lot of talent to graduation and pro contracts, but there are incredibly talented goaltenders, forwards and defenders returning for another year of play. How will it all shake out?

The writers and experts have had their say, and once again, I’m taking a stab using shot data from last season to predict how the conference race might unfold this year.

We haven’t always agreed, these hockey writers and me (me being “data”, I mean). In the last couple of years I’ve been doing this, the data has uncovered some interesting trends that bucked conventional wisdom. Two years ago, this statistical model helped predict an insurgent UNO Maverick team (that ultimately made the Frozen Four). Last year, it pegged Denver as the conference favorite – wrong, but the Pios did make the Frozen Four.

Sometimes data finds the trends that we don’t normally see. Other times, it simply confirms what everyone already knows. This year, as we’re about to see, is one of those years.

I’ve collected individual-level data on all NCHC players from 2015-16, primarily goals, shots, shot%, save% and a derived possession-share (individual shots/all shots). That data is readily available thanks to better tracking by the NCHC and more in-depth shot statistics compiled by College Hockey News. (P.S. to NCHC’s marketing team: I love the new website and data page – huge improvements. Someone’s been reading?)

As is tradition, 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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2016 NCHC Prediction


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


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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NCHC 2015-16 Returners: Defense


This is part three in a series on returning 2015-16 NCHC talent. Earlier in the week, we created models to evaluate the relative on-ice performance of goaltenders and forwards. Today, let’s tackle the defenders. But before we do, we need to have a quick discussion about the analysis itself, because this category is always more subjective than the other two.

I picked an image of a talented NCHC defensive player (SCSU’s Ethan Prow) making an offensive move for a reason. Prow is a good two-way guy in a lot of ways, and I’ll show you why we think that in a minute. But when we’re evaluating defensive players in hockey, we tend to conflate their actual defending abilities with their offensive contributions.

Defensemen are the most difficult position to assess in hockey, at all levels. You can evaluate them in the same way you do forwards, but that only tells you who the most offensive-oriented guys are. Trying to determine the most defensive defensemen can be difficult, especially with the lack of data we have at the NCAA level. FOr so long, plus/minus was the standard, but the stats community has come to a consensus that +/- is unreliable and useless. At the NHL level, two-way blue line talent can be looked at through ice time, relative Corsi, player usage charts, etc., (see here, here and here) but we simply don’t have that kind of data in college yet. We’re stuck with shots, shot blocks, faceoffs, goals and assists.

We’ve tried to make do with what we have, knowing that we still need a better way. But in working with the extant data, we can do a pretty good job of evaluating who is helping the team score goals from an offensive perspective, and we might be able to infer some things about who is actually playing good preventative defense. We’ll return to this discussion at the end of the article, because there are a few more preferable indicators of good defense (and they’re not that hard to get at), but it would take some investment from the NCAA and the conferences.

For now, let’s play with the data we’ve got.

Top Losses

NCHC teams lose 18 defenseman in 15-16, whether through graduations or defections. No player will likely be as missed as Denver’s Joey LaLeggia, one of the top five point earners in the league. North Dakota’s Jordan Schmaltz leaves with a year of eligibility, and Nick Mattson graduates – both contributed 20+ points. Colorado College will miss sophomore Jaccob Slavin and senior Peter Stokykewich, who combined for 139 blocked shots last year. WMU’s Kenny Morrison leaves a year early after a relatively fruitful 2014-15, but he certainly could have contributed significantly in the upcoming season. SCSU’s losses of Andrew Proncho and Tim Daly will be felt, too – Daly led the league in blocked shots.

UNO, Miami and Duluth remain relatively unscathed, however, losing only four defensive players between them, and only two who played a full season.

Traditional Analysis

There are 48 returning defenders in the NCHC. As we did for the forwards, let’s look at the top d-men in a few of the more traditional ways – points, blocked shots, and blocks per game.

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NCHC 2015-16 Returners: Forwards


We’re still a few weeks away from the 2015-16 NCHC season, and the photo above sums up how I feel. I can’t wait for some ol’ fashioned hashtag college hockey. But in the meantime, let’s continue our look at the talent that will be returning to the ice. A couple of days ago, we took a more advanced analytic approach to goaltending in the conference. Today, let’s consider the offensive production side of things – forwards.

Much like with the goalies, Taylor and I have utilized the new data available from College Hockey News to get away from the usual points-goals-assists assessment. With a more complete picture of the shot statistics available, we can get closer to understanding who’s really changing the game with their ice time, and who stands out as the most effective forwards in the league. It’s not a perfect analysis, but it’s better than what was possible less than even a year ago. Progress is good.

So we’ll get there, I promise. But first, let’s take a look at who is not returning this year, and which teams have holes to fill.

Top Losses

Looking only at guys who played in 50% or more of their team’s games, the NCHC loses 27 players. Most heavily hit is undoubtedly Miami, who loses three of the top four point earners in the conference – Austin Czarnik, Blake Coleman and Riley Barber, who is leaving early. St. Cloud’s Jonny Brodzinski also leaves early for the pros, taking 21 goals and 7.9 shots per game (!) with him. North Dakota (Michael Parks and Mark MacMillan) and Western Michigan (Colton Hargrove and Justin Kovacs) both lose a 50-plus-point pair of offensive leaders. Rounding out the top ten, say goodbye to Denver’s Daniel Doremus and Duluth’s Justin Crandall.

Every team lost a few key pieces, however Colorado College and Omaha escape graduation relatively unscathed in the forward department. Most depleted? Arguably Miami, though I could see a case for North Dakota or Denver, too.

Traditional Analysis

Time to evaluate the returning forwards in the NCHC. For the sake of defining the discussion, and because the metrics we’re using are all based on shots, we’re only going to examine those players that took 50 or more shots last season. That will include pretty much everyone in each team’s top three lines, and it eliminates the regular scratches, cleanup lines, etc. This way, we’re more likely to compare apples-to-apples when we start looking at percentages and average performance.

Let’s first take a look at the returning talent in the traditional sense. We know the NCHC lost some big playmakers, but it wasn’t a total turnover. Some teams return a strong core of their point-producing players. Below, I list two metrics that are historically used to evaluate player contributions – points and goals scored.

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NCHC 2015-16 Returners: Goalies


Oh my god it’s almost college hockey season again. After an exciting second season of the National Collegiate Hockey Conference, it’s time to gear up for Year Three. Once again, it appears to be a pretty wide-open year thanks to the excellent parity of the conference. So I’m sifting through the rubble of last season to find illustrative statistics on returning players. That way, we can start to get an idea of what to expect from each team this season.

This is part one of a three-part series on returning NCHC talent. We’ll start with goaltenders, arguably the most important position on the ice, with the most potential to change a game. Later, we’ll look at forwards, and then we’ll wrap up with blue liners. All of this should hopefully help inform some predictions for NCHC finishes in 2016.

As in years before, unfortunately, there aren’t as many data points recorded in NCAA hockey as there are in the pros, but last year, College Hockey News starting keeping track of various Corsi event. That’s much more than we’ve had before, and while it’s still not enough to do extensive, accurate analysis of player contributions, it can take us a step further in looking at players.

So in this series, we’ll try to take that one step further. But first, let’s start with the guys not coming back:


This might be the easiest analysis I do all year. Departed after last year are the two top netminders in the league and two of the top in the nation, as both led their club to the Frozen Four. Zane McIntyre has foregone his senior year at North Dakota after posting a .929% save percentage and 2.05 GAA. In Omaha, Ryan Massa graduated on top with a .939% save percentage and 1.96 GAA. Massa also had the best penalty kill save percentage in the NCHC at .891%. Both these players will be sorely missed by their respective schools.

Also not returning are UNO’s Brock Crossthwaite, Colorado College’s Chase Perry and Western Michigan’s Frank Slubowski. Considering they were all backups who played a combined 29 games with a combined save percentage of .891%, I won’t waste your time.


NCHC teams will have 12 returning goaltenders this year who saw significant playing time in 2014-15. Six of them could be considered returning starters. Below is a breakdown of each player’s performance in four situations – all icetime, even strength situations, penalty kill situations, and close-game (defined as play when it’s less than a 2-goal game).

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How the NCHC bracket sim works


If you’ve just arrived from my NCHC Tournament Simulator, a brief, non-technical description of how it works follows. If you’ve come here some other way, check out the NCHC Tournament Simulator here!

This simulator predicts two related outcomes – game score between two opponents and probability of win between two opponents. All simulations are built from real game data, including overall season shots for, shots against, shooting percentage and save percentage.

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NCHC shot results


Watching UNO hockey get manhandled for two consecutive games in St. Cloud this weekend, I couldn’t help but notice how few quality scoring chances the team was able to put together. On the flip side, the Huskies seemed to have a high-probability chance every minute or so. Both teams have played difficult schedules, yet the Mavericks have won many more games that St. Cloud. Meanwhile, on the stat sheet, UNO has much higher season shot and save percentages than St. Cloud, although SCSU’s possession is clearly better than UNO.

So… what’s going on? Is it just luck? Is UNO slumping and SCSU streaking? Is youth finally catching up to UNO and experience paying off for SCSU?

To answer these questions, I took a look at all shots taken this season by every NCHC team. Thanks to College Hockey News, I can now do that for multiple situations (5v5 and power play), and I can also see results of each shot.

There are some interesting observations to be made from looking at shot results. The chart below lists each team’s shot results as a percentage of the total shots they take:

 Shot results – All Shots

Team Blocked Wide Hit Post Saved Goal Total % on Goal
St. Cloud 19.6% (315) 23.2% (372) 1.0% (16) 50.9% (817) 5.2% (84) 1604 56.2%
Colorado College 24.1% (335) 19.4% (270) 0.9% (12) 47.8% (715) 4.1% (57) 1389 55.6%
Miami 22.9% (417) 21.1% (384) 1.3% (24) 50.1% (912) 4.6% (83) 1820 54.7%
North Dakota 24.5% (442) 20.0% (362) 1.2% (21) 48.7% (880) 5.7% (103) 1808 54.4%
Minnesota-Duluth 22.6% (435) 22.5% (434) 1.2% (23) 48.9% (943) 4.8% (93) 1928 53.7%
Western Michigan 23.4% (357) 22.4% (342) 0.6% (9) 48.5% (742) 5.2% (79) 1529 53.7%
Denver 23.7% (400) 21.8% (368) 1.4% (24) 47.8% (807) 5.3% (89) 1688 53.1%
Omaha 24.7% (372) 22.5% (339) 1.2% (18) 46.0% (693) 5.7% (86) 1508 51.7%

First, some minor details. More of Omaha’s shots get blocked than any other NCHC team. Meanwhile, St. Cloud has the fewest amount of shots blocked. However, UNO and North Dakota lead the league in percentage of shots that result in goals. The problem for Omaha is that they take fewer shots than anyone in the league save for Colorado College. And, if you add goals plus shots saved, to count all shots that make it on net, UNO is dead last with 51.7%. Who’s first? St. Cloud, with 56.2%. Even the hapless Colorado College Tigers get 55.6% of their shots on net.

So, let’s isolate even strength vs. power play situations.

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NCHC home advantage


Broncos gonna buck. 

Halfway through the NCHC season, nothing is settled, and most teams in the league are very much in the race for first-round conference tournament home ice. But does every team want to play on home ice? That’s the question that crossed my mind watching Western Michigan take five points from a superior Minnesota-Duluth team this weekend.

It seems like a dumb question. Obviously every team wants to play in front of a friendly crowd and avoid the hassle of travel. Sports psychologists have demonstrated that players get a boost from playing at home, and there is even evidence that refs are affected by the home crowd (PDF). But that doesn’t mean that every team is actually playing better at home. I took a look at home performance vs away performance in terms of shot-based stats (which I’m tracking here), and what they mean for the rest of the season.

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