# 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:

# 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.

### Returners

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:

# 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.

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.

# 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:

### Losses

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.

### Returners

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).

# 2015 NCHC Prediction: Testing the Model

In Part One earlier this week, I attempted to devise a model to predict last season’s NCHC standings, using puck possession (shots) and bounces (shot and save percentage) as the input variables. Not only did the formula reasonably predict the regular-season standings, but it appeared to express a strong fit to the data. Once again, that model is:

$\text{POINTS} = b + b_{1}(\frac{g_{f}}{sh_{f}})+b_{2}(\frac{sh_{a}-g_{a}}{sh_{a}})+b_{3}(\frac{sh_{f}}{sh_{a}})+e$

However, I only have one season of NCHC data to work with. It’s possible this is an anomaly, or it’s possible I fashioned a model to fit the standings that we already knew. What I really need is another couple of data sets (aka a few more NCHC seasons) to test this exact model. I don’t have that for the NCHC because there has only been one season so far, but I have something close.

Six of the eight NCHC teams played at least three seasons in the WHCA, so I applied this framework to those three previous seasons, building a regression model that considers all three seasons at once to get a model that is significant beyond the .0001 level. The results:

# A mathematical 2015 NCHC prediction

It’s that time of year when NCAA hockey writers are prognosticating about the upcoming season. Naturally, NCHC fans are all abuzz about their 2015 NCHC prediction. I’m not a hockey writer, but I do enjoy the advanced analytics that are swiftly entering the hockey universe. I also love NCHC hockey.

It’s difficult to do any fancy stat work for NCAA hockey (mostly because of a lack of data), but on a team level, there’s enough to work with. Therefore, I’m going to attempt to make an NCHC prediction that’s based purely the numbers (aka the actual expected performances of the teams) instead of qualitative assessment.

(Also I’m going to rely on my wife Taylor, a PhD student who is much more skilled with statistics and regression than I ever will be. While I had an ok handle on the theoretical framework, I was coming up with some pretty wacky-but-reliable models, and she destroyed them and found models that were both statistically sound and even better fits than mine, so I owe this whole thing to her.)

The first step in this process will be to design the simplest-possible model that accurately predicts last year’s NCHC final standings. What a season, right? Miami, picked by writers to win the league, finished dead last. UNO, picked last, finished third. Denver, who finished sixth in the league, won the conference tournament! Surprise!

But were these results all that surprising? Let’s find out, and let’s use some actual, publicly available game data to do so.