NCHC Prediction 2016-17

nchc_2017_pre

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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NCHC 2016-17: Returning Defense

2016_nchc_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

2016_nchc_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

2016_cam_johnson_hockey

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:

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When Corsi Bites Back

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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:

Team
Year
W
L
T
SH%
SV%
PDO
CF%
PW
RPI
ELO
Tourney?
FF?
Conf
Air Force14-15162149.00%89.20%98.2153.6490.4521391NONOAHA
Air Force15-16181158.70%91.40%100.1853.8280.5091514NONOAHA
Alabama-Huntsville14-1582646.00%92.60%98.6339.1530.4411302NONOWCHA
Alabama-Huntsville15-1672168.40%91.20%99.6444.2570.4331352NONOWCHA
Alaska15-16102047.30%91.30%98.5947.6550.4461393NONOWCHA
Alaska 14-15191327.30%91.10%98.4154.6240.5201548NONOWCHA
Alaska-Anchorage14-1582247.60%90.70%98.2444.9520.4431357NONOWCHA
Alaska-Anchorage15-16112039.70%90.90%100.5944.6520.4481345NONOWCHA
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
Army14-1582246.10%91.30%97.3449.4570.4141284NONOAHA
Army15-16101396.00%94.30%100.2748.1450.4691426NONOAHA
Bemidji State14-15161758.60%92.60%101.2249.3270.5141504NONOWCHA
Bemidji State15-16161466.40%91.20%97.6951.6320.4981512NONOWCHA
Bentley14-15171557.30%92.80%100.1148.9410.4791443NONOAHA
Bentley15-16111768.00%92.00%99.9849.4500.4611387NONOAHA
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
Brown14-1582037.70%90.30%98.0242.4470.4561413NONOECAC
Brown15-1651777.30%89.50%96.7849.0470.4521375NONOECAC
Canisius14-15181276.40%92.70%99.1249.4400.4841498NONOAHA
Canisius15-16101958.30%92.30%100.5443.4540.4441371NONOAHA
Clarkson14-15122056.70%90.70%97.3456.1440.4721406NONOECAC
Clarkson15-16181337.30%91.30%98.6153.5230.5191534NONOECAC
Colgate14-15221247.70%92.40%100.1254.1170.5391604NONOECAC
Colgate15-16102227.90%89.40%97.3047.9440.4671434NONOECAC
Colorado College14-1562636.20%89.30%95.5843.2510.4491373NONONCHC
Colorado College15-1662716.10%89.30%95.4642.8530.4481339NONONCHC
Connecticut14-15101976.80%92.00%98.8842.7430.4771430NONOHE
Connecticut15-16111947.00%91.30%98.3044.6420.4631413NONOHE
Cornell14-15111465.30%92.40%97.7448.8360.4911485NONOECAC
Cornell15-1613978.10%92.60%100.6947.8170.5351552NONOECAC
Dartmouth14-15171248.30%92.50%100.8053.7220.5211548NONOECAC
Dartmouth15-16141418.30%90.60%98.9450.8270.5221555NONOECAC
Denver14-15241428.90%91.90%100.8553.250.5681628YESNONCHC
Denver15-1621859.00%92.50%101.4953.760.5791704YESYESNCHC
Ferris State14-15182026.50%92.40%98.9450.9340.4991510NONOWCHA
Ferris State15-16151467.60%92.10%99.7150.4330.5111563YESNOWCHA
Harvard14-15211339.60%92.30%101.8348.290.5501576YESNOECAC
Harvard15-1614948.60%92.80%101.4351.5130.5841591YESNOECAC
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
Maine14-15142238.60%89.90%98.4350.1420.4781478NONOHE
Maine15-1682265.30%90.50%95.7949.6510.4441387NONOHE
Massachusetts14-15112328.40%89.00%97.3743.5450.4641418NONOHE
Massachusetts15-1682247.10%89.90%97.0243.8480.4511341NONOHE
Mass-Lowell14-15211269.50%92.00%101.5353.9180.5371592NONOHE
Mass-Lowell15-1621858.50%94.20%102.6751.990.5561654YESNOHE
Mercyhurst14-15191649.20%92.40%101.6245.5390.4841459NONOAHA
Mercyhurst15-16171348.60%91.60%100.2742.9350.4841447NONOAHA
Merrimack14-15161846.90%92.60%99.4848.2300.5071459NONOHE
Merrimack15-16111677.50%91.00%98.5052.8360.4781460NONOHE
Miami14-15251418.20%91.90%100.0454.240.5691625YESNONCHC
Miami15-16151637.20%90.30%97.4948.5210.5161527NONONCHC
Michigan14-15221509.90%90.90%100.7953.6190.5291568NONOBIG10
Michigan15-16227510.80%91.30%102.0651.670.5681649YESNOBIG10
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
Minnesota14-15231338.50%91.90%100.4753.4100.5501622YESNOBIG10
Minnesota15-16191609.40%90.50%99.8752.5180.5281568NONOBIG10
Minnesota State14-1529839.20%91.50%100.7358.010.5921659YESNOWCHA
Minnesota State15-16181176.70%92.00%98.6360.2220.5191577NONOWCHA
Minnesota-Duluth14-15211637.40%91.80%99.2055.860.5661571YESNONCHC
Minnesota-Duluth15-16151456.80%92.50%99.3658.1120.5471624YESNONCHC
Nebraska-Omaha14-15201367.30%93.50%100.7447.580.5501565YESYESNCHC
Nebraska-Omaha15-16181518.20%90.70%98.8649.9150.5291517NONONCHC
New Hampshire14-15191928.20%91.10%99.2649.7290.5121567NONOHE
New Hampshire15-16101868.50%91.80%100.3044.0390.4721434NONOHE
Niagara14-1572846.80%89.30%96.1347.1580.4031319NONOAHA
Niagara15-1652366.60%89.60%96.1950.1580.4121270NONOAHA
North Dakota14-15291038.50%93.50%102.0051.520.5801681YESYESNCHC
North Dakota15-16285310.40%93.40%103.8756.510.6081769YESYESNCHC
Northeastern14-15161647.90%92.70%100.5750.3230.5201549NONOHE
Northeastern15-16161359.10%91.00%100.0451.2190.5431671YESNOHE
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
Princeton14-1542334.20%91.30%95.5044.7560.4171299NONOECAC
Princeton15-1652136.00%92.20%98.2145.1560.4301304NONOECAC
Providence14-15261327.50%93.80%101.3654.3150.5411655YESYESHE
Providence15-1625548.00%94.50%102.4455.150.5751683YESNOHE
Quinnipiac14-15231247.30%91.30%98.6556.5140.5441598YESNOECAC
Quinnipiac15-1625278.90%92.50%101.4255.620.5991738YESYESECAC
Rensselaer14-15122637.00%90.20%97.2348.0460.4631407NONOECAC
Rensselaer15-16151277.70%92.90%100.6045.9250.5191498NONOECAC
RIT14-15201558.20%91.50%99.6853.9380.4871528YESNOAHA
RIT15-16141467.80%89.40%97.2755.4410.4871505YESNOAHA
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
Union14-15191828.10%92.00%100.0950.5280.5101553NONOECAC
Union15-16131296.70%92.50%99.1651.3290.5001499NONOECAC
Vermont14-15221547.40%91.90%99.3452.4200.5361560NONOHE
Vermont15-16122036.10%92.60%98.7653.2370.4921501NONOHE
Western Michigan14-15141857.20%91.40%98.6051.0260.5191515NONONCHC
Western Michigan15-1682136.90%90.50%97.4144.7380.4681413NONONCHC
Wisconsin14-1542656.00%90.60%96.5742.8550.4251380NONOBIG10
Wisconsin15-1681887.90%89.40%97.2749.7400.4711408NONOBIG10
Yale14-15181056.80%94.10%100.9152.6130.5411577YESNOECAC
Yale15-1619646.80%92.90%99.7954.2100.5451602YESNOECAC

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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2016 NCHC Prediction

NCHC_model2016

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

2015_CC_NCHC

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

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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

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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

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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).

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