Draft Analysis: Translating CHL Production to the NHL

Drafting isn’t easy. The majority of picks never make an impact in the NHL, despite having been identified as players who could have.

Considering the difficulty of drafting, any small advantage can make a huge difference.

As a result, I believe to be quite important to understand how the draft works, particularly with regards to CHLers. They typically consume a large portion of the draft–particularly the forwards.

This article takes an in-depth statistical look at draft CHL forwards based on percentages. First, it begins with a quick note on how many of these players will play in the NHL and score in the NHL. Next, it delves into the content with breaking down the first round by draft year CHL production versus NHL career PPG. Most importantly, the article then covers the second to seventh rounds based on the same approach.

Successfully drafting outside of the first round is quite rare, and so my desire is to understand is there any way that teams get an edge? If so, how?

NOTE: “Rate of Translation”: Refers to the percentage of CHL production (PPG [DY]) that turns into NHL production (PPG [Career]). If the rate is 20%, the average prospect brings to 20% of their CHL production to the NHL.

A Quick Note on Drafting Percentages

Before delving into the results, it’s important to keep in mind the actual success rate that CHL forwards have coming into the NHL.

I used a similar method to TSN’s Travis Yost in this section, except used just CHL forwards, added the 2010 draft class, and also expanded on the numbers of game played. The results are strikingly similar, even with the players limited to just CHL forwards.

NHL GP Success Rate

First rounders have a near 80% of playing at least 50 GP, which drops down to roughly 45% in the second round, and then begins a steady descent to around in the seventh round. As the number of games played increases, the percentage of players decreases, showing the well-known inverse relationship.

Next, it’s important to look at the rate at which CHL forwards turn into players with the ability to score at the NHL level. I took the CHL forwards drafted between 2000 and 2010, and organized them by their NHL career PPG.

NHL PPG CHl

The results here are exactly what was expected. A massive drop off in terms of players who produced at just 0.2 PPG after the first ends and then another big drop after the second round. As the NHL PPG increases, the percentage of players decreases.

From this data, it’s clear that elite (>0.8 PPG) NHL forwards aren’t drafted outside the top-three, let alone outside the first round, and that scoring NHL forwards are hard to obtain, particularly after the first round.

So, I asked myself: Is there any simplistic way of figuring out which CHL forwards have the highest chance of becoming scoring forwards at the NHL? If and how does CHL draft year production translate to the NHL? Is it simply random?

First Round

This isn’t surprising or earth-shattering, but it’s quite clear that if you score more in junior than your peers, you typically will in the NHL, too.

CHL First Round

On average, top three picks see their production get roughly cut in half, but first overall picks remain the highest producers by far. As the first round winds down, both draft year and NHL career production decreases rapidly. Most importantly, junior production translates to the NHL at a lower rate as the first round continues on.

I began by deriving creating categories for first round draft picks. I accomplished this through utilizing three different draft pick models: Scott Cullen’s draft pick values, Michael Schuckers’ value pick chart, and Don’t Tell Me About Heart’s draft pick value chart. From there, I broke the first round into tiers based on the value that each pick displays.

Draft SlotJr. PPG DYNHL Career PPGRate of Translation
#11.9970.98549.33%
#2-31.4110.75353.32%
#4-81.3350.46134.55%
#9-121.2360.432.37%
#13-141.1880.3731.14%
#15-211.0010.27827.72%
#22-301.0350.37235.89%

From picks #13-#30 the results get fascinating. Between the 15th and 21st overall selections, the average draft year production decreases, which results in the lowest NHL production. However, #22-30 sees a minor uptick in CHL production and a significant uptick in NHL career PPG. The increases in both CHL and NHL production show that in this group their CHL production translated better to the NHL.

Perhaps the most intriguing suggestion found is: Forwards selected in the first round outside of the top-10, with production over 1.2 PPG, produce in the NHL at a higher rate than those below by +0.132 PPG—a huge difference. These players also see their CHL production decrease by 195% from their draft year to NHL career, compared to the 234% that players under 1.2 PPG. So not only do the players who score less produce less, their rate of translation is also lower.

Also fascinating is that the over 1.2 PPG group has a lower bust rate, producing players who participated in less than 150 games 33% of the time, compared to 49% of the time for those under the 1.2 mark.

Simply put, the numbers suggest that if a first round prospect scores more than their peers in the draft year, they will in the score more in the NHL, even if they are outside of the top-10.

Second Round and Beyond

The same effect prevalent in the first round is shown throughout the remainder of the draft:

Rounds 2-7

As the draft winds down, so does the average prospect’s ability to translate their CHL draft year production to the NHL. The second round’s rate of translation drops, with the average prospect carrying over just 21.72% of their draft year CHL production to the NHL. The third round drops further to 15.40%, but it’s the fourth round that actually sees the lowest rate of translation, at 9.91%. In rounds 5-7 it holds fairly steady.

Rate of Translation

Unsurprisingly, CHL production drops considerably as the rounds tick down. So not only do prospects drafted later on typically produce less in their draft years, they also more often fail to turn that lesser production in NHL production.

ROUNDJr. PPG DYNHL Career PPG
Round 11.2200.451
Round 20.8970.195
Round 30.8090.125
Round 40.8310.082
Round 50.7590.104
Round 60.6530.088
Round 70.6170.069

In order to determine if and how junior scoring translates to the NHL, two methods will be explored. The first takes an arbitrary number and compares the players above and below that mark, while the second takes the average production of a group of prospects based on year and round and compares those over it and those under it. For both of the comparisons, draft re-entries will be excluded (as they will be discussed in another article).

(1) Arbitrary Numbers

The three cut-off points used were 0.8, 1.0, and 1.2 CHL PPG.

Line Chart

Preferably, more cut offs would have been used; however, in rounds six and seven the sample sizes grew incredibly small. Even in rounds four and five, it grows increasing hard to find CHL forwards who exceed the 0.8 PPG threshold.

This chart is skewed by sample size problems; however, it does demonstrate that on average CHL forwards who score more above 0.8 PPG, 1.0 PPG, and 1.2 PPG are more likely to translate that scoring to the NHL. This relationship is especially evident through rounds two and three, where the sample size is large.

However, I was not content with the results. The results were greatly skewed by the a few high-scoring players (I’m looking at you, Brendan Gallagher). Therefore, I believe the next method used is far superior:

(2) Production Relative to Peers

This method takes the average production of every round of each draft and compares the players against it. Those above their average is considered to have “outscored their peers” and those below it didn’t.

As previously shown, the first round has a clear dominance in terms of (1) high-scoring junior forwards, (2) high-scoring NHL forwards, and (3) forwards that translated their production to the NHL at a high rate. As a result, there are bars with first rounders removed. While not quite as a striking, the second has a similar result, so there are bars without the top-60 included.

What is particularly important is to compared the orange and grey bars to their own colour. The percentages are based off the total pool of players picked.

Entire Pool

The second way I calculated success rate was taking the number of players in each category (i.e. outscored peers in draft year & scored min. 0.4 NHL PPG) and divided it by the number of players that were either above or below the average. For example, players that scored above their CHL average were divided by the entire pool of “above” the average players. This shows the success rate of prospects relative to their own group, either “above CHL average” group or “below CHL average” group.

Success Rate Rework Again

First, it’s seems that after the top-60, drafting players who can simply play in the NHL appears to not be connected to whether or not they outscored their peers. However, drafting scoring NHL players after the top-60, while a low-frequency event, requires the player to have outscored their peers in their draft year the vast majority of the time.

The success rate of drafting players later on to simply play in the NHL may be a result of “safe prospects” or drafting for “roles.” It also could be linked to players that had the talent but where unable to put it together in their NHL draft season. Either way, if you want scoring, the percentages say that higher scoring draft year players are a better bet.

Discussion

Drafting is only one part of a player’s journey. This journey consists of many components, which begin far before the draft and end far later. Development isn’t linear.

By no means is this a comprehensive study that firmly displays the relationship between CHL scoring and NHL scoring.

The predictive value, until the datasets are expanded, is highly limited. There are great statistical projection models such as the Prospect Graduation Probabilities Model (PGPS) (and formerly, the Player Cohort Success Model) that utilize a range of data beyond simply production.

This is also not an argument to replace tradition scouting by any means. In fact, when high-scoring players slip in the draft, there are often very good reasons that they do, and are typically identified by scouts and NHL management teams.

However, this was never intended to be a projection tool, but rather to display how CHL forwards’ production has previously translated to the NHL.

The results aren’t earth-shattering, but it does suggest a relationship between draft year CHL scoring and NHL scoring throughout the draft.

The chance of translating production significantly decreasing throughout the draft is partly a result of scouting. Scouts identify players who have a higher chance of: (1) making the NHL and; (2) translating their production to the NHL. I believe scouts are usually successful in this because the percentage of points translating drops far more rapidly than the average CHL draft year production.

Simply put, drafting players to simply play in the NHL between rounds 2-7 appears to have no distinct relationship with draft year CHL scoring; however, drafting players to score in the NHL does.

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