What Draft Round Can Tell Us About a Player’s Expected Long Term Performance and Development (Part 1)

This article is being posted here as well as in parallel as a guest post at Maple Leaf Hotstove. FYI, for those who read my last post – as promised – this article is a succinct summary of my draft analysis ‘report’ shared earlier. 

The hockey analytics community has looked at many aspects when projecting a player’s performance over their career: prior league, prior scoring rate, performance of players with similar characteristics, size, and date of birth – amongst others. One example of such work was an article from earlier this year by ‘moneypuck’ at NHLnumbers.com.

Moneypuck’s analysis derives its foundation from an excellent study by Michael Shuckers in 2011, where Shuckers was one of the first to create a standardized view of ‘draft pick value’. The quality of Schuckers’ analysis drove many other authors to do work that followed suit, building on his approach. However, in his paper, Michael chose to define ‘draft pick value’ entirely based on likelihood to play >200 games in the league. Although that is a reasonable metric – and few would argue that reaching 200 NHL games means a draft pick was NOT successful – there are limits to using only a single metric to define ‘success’.

How ‘successful’ a pick was, and the ensuing value of a draft pick, is highly sensitive to how we choose to define success. Is a pick successful after 40 games, 80, or 200? Are they successful after 30 career points, or 100? How about their points per game? Or their total career points? How would our definition of ‘success’ change on each of these metrics if they are a forward or a defenseman?

As you can imagine – this isn’t a simple thing to answer. Earlier in 2015, Stephen Burtch did some interesting work down this path, where he combined expected GP with expected Pts/Gm to create a new draft pick value figure – which was a big step in the right direction. However, even Pts/Gm has its gaps, given that it only considers players still in the league. As time goes on, the least successful players will leave the league sooner, increasing the average Pts/Gm of those remaining. In a perfect world, we would want a metric that has already been adjusted for a player’s likelihood to succeed in the league, rather than one based on his success if he can stay in the league. (Though – to be fair – Burtch does seek to address this point through multiplying probability of reaching 200 games by expected Pts/Gm).

In order to address these points, I have taken a very detailed look at long term player performance and development based on draft round, incorporating a wide range of metrics into my analysis. Specifically, I have reviewed the five years of players drafted from 2000-2004, as well as the ensuing 9-13 years of NHL season data.

Arguably the biggest factor in whether or not analysis is put into practice is if a team’s front office and coaching staff truly understand it, and believe the results of the analysis enough to buy-in to it – which will often come down to the method by which that analysis is communicated. As such, I have tried to simplify the statistical methodology involved in this work, and display the output visually in a way that is easily understood and hopefully very accessible to stats and non-stats folks alike.

(As a note – this article focuses strictly on metrics related to player performance and development. However, a natural follow on to this is then connecting that information to draft pick value, as mentioned, and after that, how successful teams have been in drafting – both of which are covered in my full report, originally posted here). 

What I Hope To Answer

The objective of this analysis is to investigate ‘typical’ player performance and development trajectory after being drafted in a given round, in order to answer the following three questions:

  • If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?
  • How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round?
  • Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?

So – let’s get into it.

Analysis of Long Term Player Performance and Development by Draft Round

I have split out the upcoming sections of analysis by each type of metric used. I will then revisit the three questions above directly in the final section on conclusions.

Games Played Thresholds

As Michael Shuckers showed very clearly – players drafted in the first 2-3 rounds are much more likely to appear in the NHL; however, the likelihood of a playing one or more full seasons diminishes substantially after the first round

Games Played Pic 1

Games Played Pic 2

In terms of player development, this data suggests that:

  • If a 1st round pick hasn’t played a game by their fourth potential NHL season, they likely will never appear in the NHL
  • 20-30% of successful 2nd and 3rd round players only begin to meaningfully play for their franchise between 5-7 years after being drafted (e.g. the pink shaded area on the ’80 games played’ chart)
  • The gap between the top 10 overall and the rest of the first round is actually relatively small when looking at the likelihood to pass the 150 game threshold (especially in comparison to metrics later in the article)
  • And, as we know – all other rounds after the first three appear to have close to equal likelihoods of producing long term NHL players

Points / GM Data

Forwards taken in the top 10 overall show an unbelievable ability to outperform in P/GM over their careers (which Stephen Burtch has shown is even more distributed within the top 10¸ where the top 1-3 picks overall are meaningfully better than picks 4-10)

Pts per Game - F

  • Interestingly, 2nd and 3rd round forwards tend to increase their per-game output over time, largely converging with players drafted 11th-30th overall
  • However, given this metric is an average of those still playing, there will be a survivorship bias that partially drives this effect
    • E.g. Low producers will leave the league more quickly, increasing the average for those remaining – as shown by the fact that 30% of the players shown are from Rd 1 in season ‘6’, this increases to 40% by season ‘10’
    • This data can more reasonably be said to tell us that, in order to stay in the NHL over the long term, a forward must achieve a minimum of roughly 0.20 points per game

Defensemen naturally display a much more narrow distribution of results, accounting for the fact that a ‘strong’ defenseman will not always play a significant point-scoring role

 Pts per Game - D

  • P/GM data for defensemen is not terribly insightful, but I have included it in order to provide the data for those interested
  • One note – If you look closely, you can see surprisingly strong (and erratic) performance of Round 5 defensemen – starting very weak (no points registered in season ‘2’), but then ultimately being among the highest points per game in seasons ‘5’ through ‘10’
    • This particular point is driven by a small sample size issue: 49 D were drafted in the 5th round, but only a handful played many games – three of whom happen to be John-Michael Liles, Kevin Bieksa, and James Wisniewski

Points Scored Thresholds

A player’s likelihood to surpass the 30-point threshold tends to resemble their likelihood to pass ~150 career games played…

Pts Threshold Pic 1

… However, players drafted in the third round fall behind in terms of likelihood to pass the 100-point career threshold

Pts Threshold Pic 2

  • Where earlier charts show strong similarities between the long term potential of 2nd and 3rd round players, the ability of those taken in the 2nd round to break the 100-point career threshold is a clear differentiator between the two
  • Based on this, teams may do well to target top scorers in rounds 1 and 2, before moving to defensemen, shut down forwards and goalies in the third round and onwards
  • Again, top 10 overall picks differentiate themselves here as well, with over 70% passing 100 career points

Cumulative Career Points

The ideal metric to compare performance by round must be adjusted for players with limited NHL careers – which brings me to Lifetime Production, or Cumulative Career Points Scored

Lifetime Production - F

Lifetime Production - D

(Note – Forwards and Defensemen are shown on different scales)

 

 

  • Here, 1st round picks wildly outperform all others, showing that the combined skill and typical longevity of even a mid-to-late 1st round player (11th-30th) will equate to an average 159 points over 10 seasons for forwards, and 105 points over the same timeframe for defensemen
  • This compares to the significantly lower 68 average career points for second round forwards, and 44 average career points for second round defense
  • Notably, third round forwards also re-assert their value here, showing that – although they will only typically produce a total of 36 points over 10 seasons – they still will consistently outperform rounds 4-9 in career points

Drawing Some Conclusions

Having now walked through each chart and its meaning, I want to summarize my findings from above. To do so, let’s revisit the original list of three questions:

If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?

  • First round players typically make their initial NHL appearance within 1-2 years, and will almost always have played their first full season (~80 games) by their fourth year after being drafted
  • 2nd and 3rd round players take much longer to develop, and many only play a full season by their 5th-7th years after being drafted
  • Players who haven’t played by these general timelines become highly unlikely to ever make serious NHL contributions (>1 season played)

How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round?

  • Most players drafted outside the first round never make the league at all (2nd round players have a 60% likelihood of playing one game, and a 35% likelihood of playing a full season; for 3rd round players, closer to 40% play one game, and only 28% play a full season)
  • Based on their combined likelihood to play 2+ NHL seasons, score 30+ NHL points, and reach 0.4-0.5 or more pts/gm, 1st, 2nd and 3rd round players are the only players with a meaningfully higher likelihood in succeeding in the league
  • However, based on the likelihood to score >100 NHL points, 1st and 2nd round players are able to separate themselves from the 3rd round as well

Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?

  • The top 10 overall picks are significantly more capable than all others, even versus their first round peers
  • Over 70% of top 10 overall picks pass 100 career points, typically after ~6 seasons, versus 50% of those picked 11th-30th, who often take 9-10 years or more
  • Only forwards taken in the top 10 overall can truly be expected to score 0.6-0.7 pts/gm or more over their careers (although there are many examples of players who perform at this level of production that were taken outside the top 10, such as Ryan Getzlaf and Corey Perry)
  • In a hypothetical trade for active players, a ‘typical’ top-10 overall pick should be treated as likely reaching >350 career points as a F, or >170 as a D – thus, one-for-one, a team should be expecting to get a true star player in return if they are giving up a potential top 10 overall pick

In the end…

The long term performance expected of a player based on their draft round is something that is highly relevant to teams throughout their decisions in trades, on draft day, and in supporting a player’s development over his career. Hopefully you have found this analysis to be interesting, and found that the work was also able to build upon what is already out there by expanding the range of metrics that we look at. As mentioned, the PDF I linked to above also begins to apply this to both a revised (and straightforward) metric of draft pick value, as well as to answer the question of ‘Which teams were the most successful?’ in the draft years studied. Keep an eye out for ‘Part 2’ of this article – where I apply the data above to the trades done by the Leafs on Draft Day last summer, in order to see if Hunter, Dubas and friends were winners or losers in their exciting deals…

What is an NHL Draft Pick Really Worth?

A Detailed Analysis of Player Performance and Development by Draft Round

It is hard to describe drafting as anything less than essential to the success of a professional sports franchise. The teams that are able to plant the seeds for long term success on draft day each year will have a clear advantage over the five to ten years that follow – if not sooner. We all know that every NHL franchise has an expert scouting team, and many likely use metrics like league equivalencies to see what they are getting from a pick – but how many teams know what to expect from a player’s long term performance based on the round they drafted him? Further, how would that knowledge impact how a team assigns ‘value’ to its future picks in trades?

In order to answer these, and other questions, I have dug into some of the data available, and summarized into a report that you can find hereWarning – it is long. In this ‘report’, I try to answer the two questions above, as well as a more detailed list of questions that you can find at the bottom of the page.  Quickly, thanks to Hockey-Reference.com for the draft year data, and HockeyAbstract.com for the historical NHL season data.

As the first piece of work I am releasing to the NHL analytics world, the most basic reason that I have focused on draft analysis is because I personally am very curious about it. Like many fans, I often find myself looking at the exciting picks selected each year, but previously I had not quantitatively understood what to expect from those players in the NHL – specifically on a long term, year by year basis, generalized to the round they were drafted in. Before starting to analyze some of the data available, I also didn’t have a full appreciation of how a team should be treating the long term potential value behind those picks versus what else is out there (though some great work has been done on that in the past, by Stephen Burtch and Michael Shuckers, amongst others).

Lastly, I also think draft analysis has a number of strong parallels to the analysis done in value investing (aka the purpose of this blog). When an investor is evaluating a potential company to buy, the major focus is on its growth and earnings potential over a very long term time horizon, e.g. 4-7 years, or more. Much like ‘investing’ in the potential, growth and long term development of a player, the same type of thought process needs to be followed when buying a company. Investors operate with scarce resources and other constraints (e.g. contract limits, salary caps), they must have a clear understanding of their own strategy and needs (e.g. immediate cup contender vs. rebuild scenario), they have to undergo prioritization by evaluating the alternatives against a set of criteria (e.g. prioritizing positions, ranking players in each), and ultimately they have to make a decision on how to move forward – a decision that they will often have to live with for half a decade or more (in the case of private companies).

In investing, as in drafting to help build an NHL organization – the more detailed, understandable, and accurate quantitative information you have to support your decisions, the more likely you are to be successful. Thus, as the NHL enters the months before the trade deadline, I hope the attached analysis can give the online community some food for thought as to who the winners and losers are of trades to come. Please let me know any comments, questions, feedback or areas for further analysis at @OrgSixAnalytics or OriginalSixAnalytics@gmail.com.

 

 

 

For reference, the analysis seeks to answer the following questions:

  • If a player is drafted in round X, and is ultimately able to make the NHL, by when should they be expected to be a contributing NHL player?
  • How well does the typical player perform over the course of his career (on various metrics) after being selected in a given round? Within the first round, how do the top 10 overall picks perform versus those taken 11th-30th?
  • How much more valuable is a pick in the first round versus the other rounds? All things being equal, what should a pick from each round be worth in a trade?
  • Which teams were the most effective at drafting in the period sampled?

 

 

About the author, and this site

Original Six Analytics is (yet another) blog focused on exploring the world of advanced statistics within the NHL and other hockey leagues. My own background is in the field of private equity investing, and before that as a management consultant – both fields heavily focused on summarizing quantitative analysis into easily understood output in order to support decision making. As such, I hope to specifically use this blog to apply the concepts of value investing (and to an extent, corporate strategy) to the world of hockey analysis.

Lastly – as the image of Morgan Rielly and Leo Komarov may suggest – I am a lifelong Leaf fan, with a great deal of patience and support behind the approach of the current front office. The name of ‘Original Six’ Analytics refers to what seems to be the vision of the current Leafs organization – ‘returning an original six franchise to its rightful place in the league’. Which I think we can all get behind.

My inaugural post (with actual analytics content) will be coming soon, on the topic of the value of draft picks, based on typical player performance and development from a given draft round. Please feel free to reach out at any time, with questions, comments, feedback or any requests for a future type of analysis:

  • @OrgSixAnalytics on Twitter
  • OriginalSixAnalytics@gmail.com