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.

I’m also going to throw in shots/game, so we can start to suss out the offensive-oriented guys vs. the true defensive defenders. Here are those four categories:

Points   Blocks Blocks per Game Shots per Game
Will Butcher (DEN) 32 Jimmy Schuldt (SCSU) 95 Andrew Farny (CC) 2.44 Will Butcher (DEN) 5.79
Jimmy Schuldt (SCSU) 26 Gage Ausmus (UND) 91 Jimmy Schuldt (SCSU) 2.32 Luc Snuggerud (UNO) 5.66
Tucker Poolman (UND) 24 Andrew Farny (CC) 83 Nate Kwiecinski (CC) 2.20 Teemu Kivihalme (CC) 5.64
Niklas Nevaleinen (SCSU) 23 Nathan Widman (SCSU) 75 Grant Gallo (UNO) 2.18 Willie Raskob (UMD) 5.31
Luc Snuggerud (UNO) 18 Adam Plant (DEN) 66 Gage Ausmus (UND) 2.17 Tucker Poolman (UND) 5.30
Neal Pionk (UMD) 17 Nate Kwiecinski (CC) 66 Nathan Widman (SCSU) 1.83 Corey Schueneman (WMU) 5.17
Louie Belpedio (MIA) 17 Neal Goff (WMU) 65 Neal Goff (WMU) 1.81 Neal Pionk (UMD) 4.75
Christian Wolanin (UND) 15 Will Butcher (DEN) 62 Ian Brady (UNO) 1.70 Clark Kuster (SCSU) 4.50
Teemu Kivihalme (CC) 15 Neal Pionk (UMD) 59 Adam Plant (DEN) 1.69 Louie Belpedio (MIA) 4.47
Three-way tie 14 Luc Snuggerud (UNO) 58 Luc Snuggerud (UNO) 1.66 Carson Soucy (UMD) 3.92

Offensive performance mostly stands out in this table. We can see which d-men earned the most points, and there’s a lot of overlap with those who shoot the most. Guys like Butcher, Snuggerud, Kivihalme and Poolman contribute significantly to their team’s offense. Does this translate into their primary job of preventing opposing shots?

Well, let’s look at the blocks. The top shot blockers overlap little with the top offensive defenders. St. Cloud’s Jimmy Schuldt and Omaha’s Luc Snuggerud are the noticeable exceptions, but I’m still not sure that means they’re top defenders. I’m ambivalent about blocked shots. Sure, they prevent pucks from reaching the crease, but if you’re leading the league in blocked shots, it probably means teams are shooting a lot against you. This is why Colorado College and Omaha players appear frequently among the top shot blockers – both teams struggled with puck possession last year. On the flip side of that coin, I wouldn’t ding Duluth’s defensemen too much – the Bulldogs led the conference in possession last year, and clearly those players operate in a system that encourages possession and shooting from all players.

So… what have we learned here? Not much yet (hey, I said defensive assessment is hard). Let’s apply the statistical model to get a bit closer to the truth.

New Model

We’ll use the model we used with forwards – comparing returning defenders to the Average NCHC Defender. As I explained in that article, this method helps us understand who is making the most of their time on the ice, whether through skill or luck.

The Average NCHC Skater model has allowed us to put a point value on each kind of shot a player takes – wide, blocked, post, saved shot and goal. It also allows us to do a fair comparison of guys who took 250 shots against a guy who only took, say, 50. We run each player in the league through this model to get an expected points output. From there, we can compare each individual player’s efforts last year to the Average NCHC Defender and give them a rating based on how they compare to expectations.

What this method does is allows us to see the extent to which each defender took advantage of the opportunities they were given. That is, on average, each time Player X had the puck, how likely was he to make a decision that led to his team scoring compared with the other players in the league.

This will not give us the top goal scorers, or the most accurate shooters, or the best possession guys. It will simply tell us who are the smartest, most effective or just plain lucky hockey players.

We’re also looking at only those players who took more than 50 shots. That roughly equates to players who got significant ice time in the majority of their team’s last season (aka cutting out the typical scratches).

Based off the hypothesis we posed last year, I’m going to look at two sets of defensemen – those who outperformed expectations, and those who were right at expectations. More on this in a minute.

First, here are the over-performers:

Team Player Year Total Shots Expected Points Actual Points Rating
SCSU Niklas Nevaleinen Sr 81 7.37 23 3.12
SCSU Nathan Widman Jr 82 4.14 11 2.66
WMU Taylor Fleming Sr 51 2.43 6 2.47
SCSU John Lizotte So 62 4.32 10 2.32
UNO Joel Messner Jr 62 2.50 5 2.00
NDAK Gage Ausmus Sr 115 5.98 11 1.84
MIA Scott Dornbrock Jr 73 3.71 6 1.62
WMU Neal Goff Jr 51 3.24 5 1.54
SCSU Will Borgen So 90 9.80 14 1.43
WMU Chris Dienes Sr 101 10.06 14 1.39

This makes for an interesting list, and likely less valid than the forwards, because of the smaller amount of shots taken and points earned. This results in more variability among the ratings.

Nevaleinen takes the top spot here. He earned 23 points where the model only expected him to earn seven. Interestingly, St. Cloud dominates this list. Why is that so? The Huskies scored goals in droves, netting 104 in league alone. That means these defenders earned a disproportionate amount of assists, which drives up their rating. I’m not saying they’re not good, but statistical artifacts like this need to be taken into account for a fair analysis. The overperformance is more impressive from players on teams that didn’t score many goals last year – like Western Michigan and Miami. Surprisingly though, there’s very little overlap between these players and the top point scorers – a good sign. Have we truly discovered the top defensive-minded blue liners?

Not yet. We’ve only really identified those guys who are the most offensively oriented. Should we look at those players who rank low against the Average NCHC Skater? No, those will be the players who are just plain not very good. What about those defenders who are smack in the middle – performing exactly as expected given the shots they take? Here are the players rated most closely to 1.00:

Team Player Year Total Shots Expected Points Actual Points Rating Blocked
CC Nate Kwiecinski So 58 2.05 2 0.98 66
CC Ben Israel So 80 7.17 7 0.98 34
CC Andrew Farny So 88 11.57 12 1.04 83
CC Cole McCaskill So 90 8.32 8 0.96 25
WMU Oliwer Kaski So 100 12.63 12 0.95 15
MIA Louie Belpedio Jr 152 16.00 17 1.06 29
DEN Will Butcher Sr 226 30.07 32 1.06 62
UMD Willie Raskob Sr 191 14.24 13 0.91 41
UNO Luc Snuggerud Jr 198 19.83 18 0.91 58
UNO Jordan Klehr So 82 5.48 6 1.09 20

Huh! That’s interesting! Good news, Colorado College fans – if our theory is correct you’ve got a quartet of young, reliable up-and-coming defenders. Andrew Farny in particular shows promise, earning an above-average point tally and among the leading shot blockers in the NCHC. To see four Tigers lead a list that also includes the like of Louie Belpedio, Willie Raskob, Will Butcher and Luc Snuggerud would seem to suggest, defensively at least, their recent recruits show great promise.

Back to those other guys, though. Outside of the surprising CC inclusions, this evidence supports my theory that the best defensemen put in a “expected” offensive performance, allowing them to play hard on both ends of the ice. Many of these names I would expect on any short list of best defenders (Belpedio and Butcher are pre-season all conference selections). I think CC fans have good reason for optimism on the blue line.

Evaluating Good Defense?

Defenders will always be difficult to analyze until more game data becomes available for NCAA games, particularly:

  1. Zone entries – Track each time a player defends an opposing team’s entry into the offensive zone, and what happens. Does the offensive player carry the puck across the blue line or dump it in? Does the defender successfully break up the play either through a check, steal, pass break up or something else? This data could give us a great idea of which players are doing the best defending.
  2. Zone starts – For which faceoffs does a defender get put into the game? When the drop is near his goalie? Or when his team is in the offensive zone, ready to attack? Theoretically, the best defenders will get more defensive zone starts, because they’re the best at clearing the puck.
  3. Player-level possession – If teams simply published the clock time for the start and end of each player shift, we could do all kinds of wondrous things with NCAA analytics. Among them, we could calculate a player-level possession stat for every player. For defenders, this might mean the most, because if we notice a team’s possession goes up when a certain defender is on the ice, well… there’s really no better indicator of their effectiveness.

These would all be major improvements, though I don’t know if it will be available any time soon. In the meantime, we have what we have.

Given the limited analyses we are able to perform:

Top Offensive Defensemen:

  1. Niklas Nevaleinen, St. Cloud (Sr.)
  2. Tucker Poolman, North Dakota (Jr.)
  3. Chris Dienes, Western Michigan (Sr.)
  4. Will Borgen, St. Cloud (So.)
  5. Jimmy Schuldt, St. Cloud (So.)

Honorable mention: Gage Ausmus, North Dakota (Sr.); Christian Wolanin, North Dakota (So.); Neal Pionk, Minnesota-Duluth (So.); Nathan Widman, St. Cloud (Jr.)

Top Defensive Defensemen:

  1. Will Butcher, Denver (Sr.)
  2. Louie Belpedio, Miami (Jr.)
  3. Andrew Farny, Colorado College (So.)
  4. Willie Raskob, Minnesota-Duluth (Sr.)
  5. Luc Snuggerud, Omaha (Jr.)

Honorable mention: Ben Israel, Colorado College (So.); Cole McCaskill, Colorado College (So.); Oliwer Kaski, Western Michigan (So.)

There you have it – top goalies, top forwards, and now, top defenders. St. Cloud appears to have a very offensive-minded defense, and we’ll see if that keeps up this year. Honestly, every team has a bright spot on defense this year – though some teams are deeper than others. With the expectation of a down-year for offenses, it’ll be interesting to see who emerges on the blue-line. Colorado College, here’s your chance.

I’d love to see your rebuttals (find me on Twitter at @joel_gehringer). In the meantime, good luck to all teams on the ice starting this weekend. Prove me wrong, guys!

See top returning NCHC goaltenders.

See top returning NCHC forwards.

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