### Decoding the Draft: The Effect of Height on Draft Position

#### by Megan Richardson

I have something of a vested interest in researching how NHL teams view short players, and to what extent any height bias is warranted. In recent years, a number of the Winterhawks’ most prolific players have been on the shorter end of things when drafted. Sven Baertschi (5’10”), Brendan Leipsic (5’8″), Nic Petan (5’8″), Oliver Bjorkstrand (5’10”), and Derrick Pouliot (defenseman, 5’11”) all come to mind.

There are a number of intriguing perspectives on teams’ motivations for preferring tall players, and whether they are justified. This Arctic Ice Hockey question post aggregates a number of observations from members of the analytics community.

(When I was introducing my dad to this project (he’s an NBA fan), he pointed out, “Teams like to draft the player with the most potential. If you draft the small guy and he’s a bust, there’s not much you can say. If you pick the big player, at the end of the day you can still say you drafted the most athletic guy.” So at the very least, there’s the cover-your-ass perspective to be considered.)

**Purpose of study and hypothesis**

But for the purposes of this post, I put the question of motivation and true value aside. I’m not looking at how good short players are compared to tall players. I want to know how valuable teams *think *short players are compared to tall players. I begin with the null hypothesis that all teams value players equally, regardless of height. Given a 5’3″ prospect and a 6’3″ prospect who both scored at a point per game pace, a GM would flip a coin to decide who to draft. If short players are consistently picked far lower than their point production would suggest, while tall players are picked higher, there may be indications of a height bias in drafting.

**Procedure**

I began with Justin Fisher’s NHL Draftbook, a meticulous Excel spreadsheet including draft position, height, and weight for every drafted player from 1963 to 2013. I limited my study to 17 year old CHL players drafted from 2004-2013, which gives a nice sample size without the complications of European or NCAA equivalencies.

**What is short? What is tall?**

Because I later wanted to compare short and tall players, I needed to decide who fell into each category. Plus, I appreciate this kind of visualization when starting a project. I began by graphing height distributions for forwards and defensemen.

Forwards have a right-tailed distribution: the data is clustered on the left side but drawn out on the right side. Anecdotally, this suggests that while there’s a fairly definitive lower bound to the heights of drafted players, teams are more open to drafting exceedingly tall players. The bars in orange represent what naturally falls into a short and tall category.

Here’s the same distribution for defensemen, who have a similar right-tailed distribution.

**The Sham Model, Revisited**

I adapted Rhys Jessop (Thats_Offside)’s Sham Sharron thought experiment to fit this project. In his original post, Rhys replaced every Canucks draft pick since 2000 with the highest point-getting 17 year old CHL player available at that time. Here, I wanted to be somewhat more precise about approximating point production, so I used points per game.

Let me take a moment to add the same disclaimer Rhys uses, although here it’s obviously for a different kind of thought experiment:

We did not design Sham Sharron to be a good or intelligent way to draft. Sham is a benchmark we have created to test each team’s scouting department against publicly available information. We do not for a second believe that any NHL team should draft in the same manner as Sham.

I converted the overall draft order of my selected sample to an ordinal variable (1, 2, 3, etc.). Then, I sorted each draft class from most to least points per game. I assigned the top PPG player “expected draft position” 1, and so on down the line. Here’s a snippet of how this looks for the 2013 draft:

The difference between the expected and overall ordinal variables became a new variable Diff. A negative value indicates a player was drafted lower than anticipated, while a positive value suggests a player was drafted above where his point production would lead us to expect.

**Results**

*What is the effect of height on draft position?*

Remember, I said initially that given the choice between a 5’3″ and 6’3″ forward of identical point production, a GM without height bias would flip a coin to choose his draft pick. This graph pretty clearly demonstrates that shorter forwards systematically experience a decline in draft position relative to players of typical height, while tall players are given a boost above what their point production would suggest. On average a one inch increase in height, all else equal, leads to a 2.758 spot boost in ordinal drafting position. When you consider all the goalies, defensemen, non-CHL players, and overagers being drafted, this could well correspond to a far greater decline overall. With an r^2 of .1576 (.1795 if you adjust for CHL league/era using Rhys’s method), there are missing pieces for sure, but the relationship is definite.

Here’s the damning graph I posted on Twitter a couple of weeks ago.

Let’s also take a brief look at defensemen. They’re mostly ignored in the Sham method, and for good reason–while we can be sure the primary responsibility of most forwards is point production, we can’t say that about defensemen. But I did the work anyway, just for fun. Here’s the graph:

A higher r^2 here suggests that although an increase in height causes draft position to rise by less than among forwards, the overall relationship is stronger. Height explains slightly more of why high-scoring short defensemen are passed over than it does for forwards.

Comparing the two condensed graphs, it looks like within a wide parameter (5’11” to 6’6″), defensemen experience non-trivial but relatively typical shifts in draft position. The extremes (5’9″ – 5’10”, 6’7″ – 6’8″) do interesting things, but I’m inclined to disregard that for lack of sample size.

*Results by round*

To get even more specific, we can take a look at results by round to see in which rounds players tend to be drafted in line with their Sham estimates and when they’re boosted/lowered.

It appears that the effect I noted earlier of tall players being boosted (whether deservedly or not) occurs only in the first three rounds–primarily the second round. Conversely, the perceived value of drafted short players seems to reach a steady low in the fourth round.

Once again, the perceived value of tall players is most exaggerated in the first three rounds, while short defensemen have a steadier decline around the fourth round. You’ll notice, too, that for forwards it bottoms out around -15 or -20, while at -10 the difference for defensemen isn’t quite as striking. I’m interested to hear that.

*When are short/tall forwards drafted?*

The data suggests that selection of tall forwards peaks in rounds 2 and 3, while there’s a noticeable spike in the 5th round for short forwards. This may be when teams take a flyer on a short guy with high point production. You’ll recall I pointed out that the market for short forwards bottoms out in the fourth round. In a world where height made no difference to NHL success, we would likely call short forwards drafted in the fourth round ‘steals.’

*Results by team*

I have results by team as well, which aren’t noticeably groundbreaking or enlightening ; I won’t clutter up this post with 30 charts, but I will include that data in the spreadsheet at the end of this post.

**Discussion
**

There are numerous potential pitfalls related to my approach, so I’ll go ahead and address some of them here. First, I should reiterate that this method looks not at the value of prospects themselves, but how teams *perceive* the value of those players. I also fully concede that there are lots of areas in which old-school scouting matters, and for which there simply isn’t the data to create a more fleshed-out model. For example, Alex Galchenyuk isn’t even in this data set because he played only two games during his draft year due to injury. But lingering injuries that drag down success matter as well, and accounting for those is better done with a human touch. I also confess that while I initially intended to get rid of penalty minute outliers to reduce the influence of enforcers with zero point production, I just didn’t get around to it. Sorry, Marc-Andre Roy.

There’s also a problem with the fact that I converted draft position to an ordinal variable, removing the ability to discern value difference between picks. Picks 4 and 5 in my ordinal scale could be 7 and 10, while 30 and 33 could be 100 and 125. However, I think that effect is somewhat lessened. The numerical differences between picks in this model tend to be greater in later rounds, but given the value of a pick, the difference between 7th and 10th overall may in fact matter more than between 100 and 125. They come close to cancelling one another out.

I could do a few things in the future to increase the explanatory value of this model, if I wanted to take on the grueling, masochistic task of approximating a model for the draft. I would adjust for team success (does Nic Petan’s point total matter less in light of the Winterhawks’ massive scoring?), linemate success (avoiding the Mackinnon/Drouin, Kane/Gagner trouble), and isolating even strength points.

Most importantly, which I glossed over in the introduction, we can’t say for certain that all teams primarily value point production. As I already noted, enforcers are certainly desired by some teams, and they influence this model. Some teams pick for speed, for a ‘two-way player’ or ‘shutdown defenseman’, for speed, for pedigree, for the all-important and yet altogether unknowable “grit”. Whether or not those qualities contribute to NHL success, teams certainly think they do.

**Conclusions**

There is a statistically significant relationship between a player’s height and the difference between his ‘expected’ and actual draft position. This relationship exists for both forwards and defensemen, though a larger coefficient for forwards suggests that they are more advantaged/disadvantaged by teams’ perceived value based on their height. However, this relationship is not wholly predictive. There are other factors at work that it would be interesting to add: plus/minus, linemate success, team success, etc.

The logical next step is to look at the success of these drafted players, to confront the notion that short players struggle more to produce than tall players. My suspicion is that while height does play some role in NHL success, a) it is less important than hockey minds seem to think (market inefficiency?) and b) it is somewhat fed by an opportunity bias of tall players getting more chances to succeed at the NHL level/short players lacking those opportunities.

To step back, it sometimes seems odd how the mythology of height bias comes into play. An example I keep coming back to lately is 5’9″ Max Domi, 1.359 points per game, versus 5’8″ Nic Petan, 1.69 points per game. I struggle to grasp how that extra third of a point per game gets lost in the battle over an inch (and possibly a pedigree), and I welcome that potentially pedantic discussion.

**Data**

Here’s a Dropbox link to download most of the spreadsheet I used. Feel free to use any of it with credit (and hopefully credit to Justin Fisher, who created the Draftbook and deserves many accolades).

Brayden Point this year is his 91 points were over looked as he went 3rd round Sam Bennett same points on a much stronger team went 4th

[…] How does height and weight affect draft position? […]

Very interesting. I am a big NHL fan and its cool to see someone do some deep analysis on the game/players.

Wow. Interesting and in-depth analysis. The only issue I can see in this graph is that there’s no true way to measure actual production in Hockey when it comes to things that height might effect. For example, could it be that taller players, with a larger reach, are more valuable defensively for their ability to stick and poke check? That longer reach seems like it would be more effective defensively than it wold offensively unless you’re looking at their reach near the net on redirects or wrap arounds.

Plus/minus would be an interesting stat to incorporate because it paints a bit of a picture of what they do defensively. Just an idea. Nice work.

I’d like to incorporate plus/minus, though there are a lot of comorbid factors that could create even more noise than my original model, and that kind of worries me. It’s generally harder to measure defensive abilities, and although that’s probably the best method it’s affected by things like on-ice shooting and save percentage, which can fluctuate wildly season to season. There are also things like quality of linemates or defensive pairings–there could be (and often are, I would argue) four other great defensive players on the ice to make up for one’s defensive lapses. Or on the other hand, you could be the best defensive player on the ice but consistently be let down by the other guys. In an ideal world, we’d get NHL-type data from the CHL, but alas, that seems very, very far away.

How long did it take you to research all this 🙂 Good read!

Only a few weeks.

Very well done. However, I see a major problem with this analysis: it involves the Winterhawks. Go Tips! 😉

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