NCHC 2016-17: Returning Defense


This week I’ve been looking at returning 2016-17 NCHC talent. I’ve evaluated the goaltenders and forwards. Today, let’s tackle the defenders.

In the forwards article, I mentioned just how much offensive talent has left the conference this year. But with the exception of a trio of North Dakota juniors, defensive talent largely sticks around. Coupled with the solid goaltenders who remain, this could be a year of defensive chess matches in the NCHC – very exciting stuff.

Before looking at any data, however, we need to have a quick discussion about the analysis itself, because this category is always more subjective than the other two. 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 17 defenseman in 16-17, just one less than last year. Let’s take a quick look at the top departures before getting to returning players:

Team Player Year Total Shots Blocks Expected Points Actual Points Rating
SCSU Ethan Prow Sr 136 73 29.19 38 1.30
NDAK Troy Stetcher Jr 253 54 31.28 29 0.93
DEN Nolan Zajac Sr 198 77 18.13 20 1.10
UMD Andy Welinski Sr 214 49 20.52 19 0.93
NDAK Paul LaDue Jr 216 51 21.60 19 0.88
NDAK Keaton Thompson Jr 134 34 16.38 17 1.04
UNO Brian Cooper Sr 133 64 16.16 16 0.99
MIA Matthew Caito Sr 119 40 18.17 11 0.61
UMD Willie Corrin Sr 106 33 6.20 10 1.61
MIA Chris Joyaux Sr 58 40 10.85 6 0.55

Last year, I suspected Ethan Prow would be the best defender in the conference. That held up pretty well – Prow led blue liners in points, and overperformed statistical expectations by about 30%. Similarly, Nolan Zajac and Troy Stetcher – also in my top five – had good years. Matthew Caito was someone of a miss, though. Perhaps he had an off year, but he earned about 40% fewer points than I would have expected. Though quite a few top Miami defenders had poor years – perhaps something about their systems? Could be an artifact, too.

The Fighting Hawks lose the most at defense, as three juniors depart. Interesting, Western Michigan has zero returning defenders, a good sign for them in a year they’ll be seeking a new goalie. Also interesting is that some of the “best” defenders have very even ratings (close to 1.00), which differs from top forwards, who tend to have high ratings. This supports my theory posed last year, which suggests the best defenders cluster around 1.00, or “at offensive expectations.”

Traditional Analysis

Let’s get to it – who are the top returning defenders in the NCHC? There are 47 returning this year, just one less than last year. 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 2016-17: Returning Forwards


Last year turned into a banner year for the National Collegiate Hockey Conference’s offensive players. Two seasons ago, the top 20 NCHC forwards combined for 703 points. Last year? 808 – nearly 15% more scoring. High-powered offense certainly made for more than a few exhilarating tilts.

Now as 2016-17 approaches, NCHC hockey returns but without 14 of the leagues 20 top forwards, six of whom departed early. Adios, Kalle Kossila. See ya, Danton Heinen. Peace out, Nick Schmaltz. Jack Roslovic? We hardly knew ye.

A huge vacuum of talent waits to be filled, but in this league the wait never lasts long. Who’s going to step in for all that lost offensive production? My goal here is to figure that out.

Earlier this week I looked at returning NCHC goaltenders, finding few surprises. That’s not so with the NCHC forwards. Much like with the goalie model Taylor and I developed, we have utilized the new data available from College Hockey News to look beyond the traditional scouting reports. 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. I’ll spare you the gory methodology since it’s about the same as last year’s analysis.

Let’s get warmed up by applying that analysis to those NCHC forwards not returning in 2016-17.

Top Losses

Looking only at guys who played in 50% or more of their team’s games, the NCHC loses 31 forwards, just a few more than last year. As I mentioned above, though, the list is top heavy, and some teams get hit harder than others.

St. Cloud State loses its top five point earners, for starters. The represents 61% of their forward’s scoring from 2015-16, and even for a strong program like SCSU, that’s a tough roster to reload. Denver and North Dakota each lose three of their top five scorers, though for Denver that includes underclassmen Heinen and Trevor Moore. For North Dakota, Nick Schmaltz leaves early, as does Luke Johnson. Most unscathed is probably Western Michigan – losing only 15% of their scoring from last season.

Teams losing their top-scoring forward include Colorado College (Hunter Fejes), Denver (Heinen), Miami (Roslovic), St. Cloud (Kossila), Duluth (Tony Cameranisi), and Omaha (Jake Guentzel). Only Western Michigan and North Dakota return their top forward. Woof.

Let’s warm up by applying the advanced model to the top 10 departing NCHC forwards by points earned:

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NCHC 2016-17: Returning Goalies


Who’s ready for year four of the National Collegiate Hockey Conference? So much off-season intrigue. A national champion among our ranks. A bunch of big names going pro. A will-they-won’t-they melodrama of expansion talk. Thank Gretzky the summer is almost over and college hockey is nearly here again.

It should be a very interesting year in the NCHC. The parity of the first two years seemed to dissipate in year three, with contenders separating themselves from teams not quite there. Also, the incredibly loaded rosters of last year have… uh, unloaded. This year, 15 of the top 20 point scorers from last season will not return, and six of those are early departures. In the year prior, the league lost only eight of the top 20 point scorers. From a qualitative perspective, that makes it tough to know what to expect, and even tougher to know who will emerge as the conference’s top talent.

Lucky for us, we can get quantitative! Last year, Taylor and I developed a series of models to assess returning players’ contributions. The model constructed an average NCHC position player and compared each individual real player to that standard, determining if they were overperfoming or underperfoming expectations. This helped (successfully, I might add) identify who some of the key players would be in the new season.

In this post, I’m revisiting that model and applying it for 2016-17. 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 defenders. All of this should hopefully help inform some predictions for NCHC finishes in 2017.

Once again, my data comes from the invaluable College Hockey News database of Corsi events, which tracks every NCAA player throughout the year. I’ll try not to get to into the methodology in this post. If you’re really interested in that, I’ll have you check out last year’s installment, which explains everything in detail. This time around, let’s get to the good stuff.

Players left behind

The NCHC collectively loses nine goaltenders for 16-17, seven of whom saw significant playing time. With no disrespect to Duluth’s Matt McNeely or St. Cloud’s Rasmus Reijola, who combined saw less than 100 shots and appeared in two and five games respectively, let’s take a look at those seven significant contributors:

Team Player Year GP GAA Saves GA Sv%
UMD Kasimir Kaskisuo Jr 39 1.92 904 75 92.3%
SCSU Charlie Lindgren Jr 40 2.13 1019 83 92.5%
MIA Ryan McKay Sr 17 2.57 371 39 90.5%
MIA Jay Williams Sr 22 2.58 491 53 90.3%
CC Tyler Marble Jr 13 3.66 323 39 89.2%
WMICH Lukas Hafner Sr 28 3.67 804 96 89.3%
UNO Kirk Thompson Jr 15 3.27 294 42 87.5%

Duluth’s Kaskisuo and St. Cloud’s Lindgren leave the biggest holes to fill. Both performed well above average and appeared in nearly every game for their team. Our model shows both of these netminders let in 11% fewer goals than expected from an NCHC goalie, which puts them in “very good” but not “great” territory. The rest on this list, frankly, underperformed expectations. Nevertheless, this particularly leaves Miami and Western Michigan in a bind, because neither returns an heir apparent between the pipes. All in all, it looks like at least four NCHC teams will open the season with a fresh face in goal.

Let’s move on to the returning talent.


NCHC teams will have 10 returning goaltenders this year who saw significant playing time in 2015-16. I would consider five of them 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). These players are sorted by total save percentage:

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When Corsi Bites Back

For the records, the above does not count as puck possession.

For an Omaha hockey fan, last season was rough. In late February and March, I watched in despair as my UNO Mavericks lost the last eight games of the season and slid out of the NCAA tournament conversation. Six of those losses came against eventual Frozen Four teams, but it still stung. How could a team that reached the Frozen Four just a year prior – and was a #1 seed as late as January – fall so far, so fast?

Honestly, I checked out of college hockey for a little while. I whistled past the further carnage of losing UNO’s top player and two assistant coaches. I enjoyed my spring and summer, cautiously avoiding dealing with the emotional rollercoaster that was 2015-16.

Now we’re less than a month away from a new season. New year, clean slate. But… what to make of this year’s Mavericks? I can’t get 100% pumped for this season without coming to terms with how last season ended. I need closure. As a UNO fan, I need to process this unthinkable turn of events.

So I’m going to do that in the only way I know how: hockey stats.

First, a disclosure: I am not a hockey expert. I am not a hockey coach, nor a hockey player. I offer no prescriptions, indictments or playbooks. I simply intend to lay out the data and share the story it tells. Speaking as a UNO fan, I will warn that this might be painful, but it needs to be done.

Second, another disclosure: I’m going to analyze primarily by looking at two “advanced” stats – Even Strength Corsi For, and Even Strength PDO. Thanks to College Hockey News, we now have two seasons of these advanced stats available for every NCAA team. I’ve compiled this data for the last two seasons. For comparison, I also included RPI and Pairwise rankings from regular-season end (via siouxsports) and season-end ELO rankings (via Ebscer). I consider these three “rankings” more or less objective measures of team quality, and none of them directly factor in on-ice statistics.

This is the database I’ll be using. Feel free to play around and do your own digging:

Air Force14-15162149.00%89.20%98.2153.6490.4521391NONOAHA
Air Force15-16181158.70%91.40%100.1853.8280.5091514NONOAHA
Alaska 14-15191327.30%91.10%98.4154.6240.5201548NONOWCHA
American Int'l14-1542578.60%89.40%98.1038.8590.3951230NONOAHA
American Int'l15-1662737.70%89.80%97.5240.3600.4011235NONOAHA
Arizona State15-1632204.30%91.40%95.6836.7590.4011297NONOIND
Bemidji State14-15161758.60%92.60%101.2249.3270.5141504NONOWCHA
Bemidji State15-16161466.40%91.20%97.6951.6320.4981512NONOWCHA
Boston College14-15211438.20%92.20%100.3352.9110.5461591YESNOHE
Boston College15-16245510.00%93.70%103.6952.440.5801676YESYESHE
Boston University14-1528858.60%93.80%102.3455.030.5721683YESYESHE
Boston University15-16191058.40%91.60%99.9556.2110.5531604YESNOHE
Bowling Green14-15231159.40%91.40%100.8152.6160.5411540NONOWCHA
Bowling Green15-16201267.60%93.40%100.9852.5260.5131538NONOWCHA
Colorado College14-1562636.20%89.30%95.5843.2510.4491373NONONCHC
Colorado College15-1662716.10%89.30%95.4642.8530.4481339NONONCHC
Ferris State14-15182026.50%92.40%98.9450.9340.4991510NONOWCHA
Ferris State15-16151467.60%92.10%99.7150.4330.5111563YESNOWCHA
Holy Cross14-15141856.20%93.20%99.4249.7480.4541379NONOAHA
Holy Cross15-16181159.00%92.10%101.1051.9300.4941458NONOAHA
Lake Superior14-1582826.10%91.40%97.4743.9540.4371345NONOWCHA
Lake Superior15-16132055.00%92.70%97.6744.7460.4651439NONOWCHA
Michigan St14-15171626.90%93.40%100.3149.0310.5051542NONOBIG10
Michigan St15-16102247.70%89.50%97.2346.8430.4621439NONOBIG10
Michigan Tech14-15291029.20%94.20%103.4155.670.5651616YESNOWCHA
Michigan Tech15-1621858.90%92.70%101.5657.0140.5361630NONOWCHA
Minnesota State14-1529839.20%91.50%100.7358.010.5921659YESNOWCHA
Minnesota State15-16181176.70%92.00%98.6360.2220.5191577NONOWCHA
New Hampshire14-15191928.20%91.10%99.2649.7290.5121567NONOHE
New Hampshire15-16101868.50%91.80%100.3044.0390.4721434NONOHE
North Dakota14-15291038.50%93.50%102.0051.520.5801681YESYESNCHC
North Dakota15-16285310.40%93.40%103.8756.510.6081769YESYESNCHC
Northern Michigan14-15141867.30%92.20%99.5046.4350.4951417NONOWCHA
Northern Michigan15-16151478.10%92.80%100.9546.2340.4921452NONOWCHA
Notre Dame14-15181959.30%92.20%101.5048.7330.5041537NONOHE
Notre Dame15-1618778.80%93.30%102.0850.080.5461583YESNOHE
Ohio State14-15141938.80%90.50%99.3947.2370.4891506NONOBIG10
Ohio State15-16131749.60%90.50%100.0547.6310.4981531NONOBIG10
Penn State14-15181547.00%91.00%97.9955.1320.5041496NONOBIG10
Penn State15-16201247.20%91.50%98.7056.2200.5221516NONOBIG10
Robert Morris14-1524858.60%93.30%101.9248.5250.5201546NONOAHA
Robert Morris15-1621949.10%93.10%102.1648.9160.5261553NONOAHA
Sacred Heart14-15131967.90%91.20%99.1750.7500.4521401NONOAHA
Sacred Heart15-16121847.10%91.00%98.1051.0490.4541368NONOAHA
St. Cloud State14-15201917.10%92.30%99.4149.6120.5461615YESNONCHC
St. Cloud State15-16278111.30%93.10%104.3450.830.5961696YESNONCHC
St. Lawrence14-15201439.80%94.20%104.0548.0210.5271556NONOECAC
St. Lawrence15-16171349.00%93.50%102.5351.0240.5221563NONOECAC
Western Michigan14-15141857.20%91.40%98.6051.0260.5191515NONONCHC
Western Michigan15-1682136.90%90.50%97.4144.7380.4681413NONONCHC

Click here to open in Google Sheets.

Now, we can argue all day about what shot stats mean (I did some of that myself in this post last year). Certainly the usefulness of advanced stats has not yet been demonstrated to the level of sophistication in the NHL. There are certainly reasons to be skeptical. Alone, the data suggests Corsi and PDO aren’t particularly indicative of anything. But in combination, I believe these stats do illustrate larger trends. More on that in a minute.

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House Painting: Before and After

We had the house painted this summer. House painting makes you sweat a little bit – you’re never sure how it will turn out. After some research, we settled on navy because we thought it would contrast well with the brick facade. The painters gave us a funny look when we gave them the color. However, I think we got lucky.

Naturally I took before and after photos to compare. Sadly I didn’t get “before” photos with our broken, striped cloth window awnings and the ugly satellite cables running down the front of the house. Well, maybe not sadly.

Honestly I think the back looks even better than the front, probably because there’s more depth. But it all looks good! If you look really closely, we also trimmed the hedges and did some minimal landscaping, too. Exciting!

It’s been a busy summer, but I’m glad we got this done. The house looks updated and a lot less dingy. It feels like home now. Plus, the new color really makes the cactus garden pop. On to the next project, I guess…


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