So there I was on draft day listening to commentary about which players currently being chosen may see their way onto their NHL club this season and what they could bring. The interesting thing I caught is as one breath mentioned they could make that difference on their new team, it was logically bolstered in the next gasp by their stats from their previous years in a league decidedly not the NHL. Is that logic flawed? Were we led astray?
Yes and yes. But only if we do not understand how the previous years in college, juniors, the AHL, Europe, etc translate into NHL Year 1 will we be confused. Since nobody gave us a translation, OGA provides that here in order to manage your expectations with rookies this season.
In this analysis, we looked at 30 players, both past and present, and what they did in their last year prior to the pros versus NHL Year 1. We then compare the average results to the top five draft picks from the 2009 Entry Draft.
The Data Group
For this study, we studied players at every position. They came from out of NHL history, Europe, college, juniors and the AHL in their last year before turning pro. For skaters, we only compared goals, assists and PIMs; for goalies, just wins and goals against. In that comparison, we looked for, and provide here, a percentage of change in statistics. An admittedly uncomplicated study, this one still to an extent gauges the rough difference in output from the last, pre-Pro, and first NHL, seasons.
In order to acquire that percentage of difference when a player often times did not play the same number of games between years, we extrapolated. We love the term ‘extrapolate’ because it implies that critics are going to tear off a strap of your behind for your interpretation of something not so straightforward. To get our magic numbers though, we simply:
1. Divided the largest number of GP by the smallest to get a percentage of GP; then we
2. Multiplied all of the statistics from the lowest GP season by the percentage to see if the player played an equal number of games in both leagues, how much those stats would be worth
(Email us for a copy of the raw data at email@example.com .)
Here is a rundown of the rather interesting findings for NHL Year 1:
• All but five players displayed a drop in output in NHL Year 1 and two of those ‘gainers’ held a 0% difference
• Best average change: Maurice “Rocket” Richard with a +75.28%; this stat is a little different from all of the others because there are no juniors/college/etc. stats prior to NHL Year 1 for The Rocket; so we compared his 16 games on the ‘42-’43 Canadiens with the 46 games played for Les Habs the following season.
• Worst average change: David Booth with a –55.98%
• Average change in output for NHL Year 1 for ALL players: –19.46%
• Average change in output for NHL Year 1 for ALL players minus the highest and lowest averages: –21.54%
• Average change in output for NHL Year 1 for goalies: –6.99%
The first bullet above is interesting for a couple of reasons. The five players not displaying a drop off in numbers are The Rocket, Wayne Gretzky and three goalies. The Rocket’s math indicated simply what most people always said about him – he got better as time went on. For instance, his ’42-’43 totals of 5 Gs, 6 As, and 4 PIMs when extrapolated out from 16 games to the same 46 he played the next season become 14-17-12. The following season’s 46 games, however, saw him go 32-33-45, a positive change of just over +75% in a year. After The Rocket: Billy Smith of legendary crease defending fame with the Islanders improved +22.44%; Wayne Gretzky was a +20.6%; and Scott Clemmensen and Roberto Luongo broke even. More on the goalies below.
Panthers fans should not hurl warm beer at David Booth (or us for that matter) and call for his immediate trade for the almost-negative-56% change. He simply has the worst percentage of change out of these 30 players – there have been more than 5000 professional Hockey players in the history of the NHL and chances are somebody beats him in this category.
For all 30 players in our data sample, the average change was almost –19.5%. Some notable drops:
Mario Lemieux –51.5% (but still with 100 points in his rookie season)
Steven Stamkos –50.15% (last year’s #1 entry draftee)
Bobby Orr –32.45% (“What?” you ask, but you know how he turned out)
Todd Bertuzzi –32.4% (A scoring machine in juniors)
Sidney Crosby –28.5% (Youngest NHL team captain in history and Cup winner)
Many data samples throw out your highest and lowest numbers because they often times skew the results. When we did so, the 28 players left gave us an average change that was a –21.45%. You might like to average the two and say something like “…The average difference a player’s statistics will display in NHL Year 1 is 20.5 percent less than his last year before turning pro….” For the purposes of the rest of this study, and in our heads when we look at somebody coming up to the NHL, we will round it off to a manageable –20% under the assumption a study of hundreds of more players would yield more positive differences.
And goalies took special note here because of the one positive, two zeros, and others that were relatively low in change when coming up for NHL Year 1. We looked at Roberto Luongo, Scott Clemmensen, Martin Biron, Martin Brodeur, Billy Smith, Marty Turco, Dominik Hasek, Jacques Plante and Gump Worsley. Some really great netminders here. Their overall average difference when coming up was only a –6.99% in Wins and Goals Against. This suggests that for the really good goalies, they are already well seasoned when brought forward. This only makes sense because, as we have always said, the only player who can single-handedly win a game in the goalie. So for any new goalies arriving on the roster, we would go with expectations about 7% less than before they got there.
There are many who think the top five players chosen in the 2009 Entry Draft stand a better chance of making their club than not. Let’s assume they do and you wanted to see how they would do if this were NHL Year 1 compared to last year based on a –20% rule. Here’s what you would expect:
John Tavares = 46 Goals; 37 Assists; 43 PIMs. Really? That high, you say? It could happen depending on a lot of factors, but, based on the team as a whole, is probably more like 30 Goals and 35 Assists this next season.
Victor Hedman = 13 Goals; 29 Assists; 113 PIMs. For this to happen, he has a farther stretch to go coming off the bigger ice surface and adjusting to North American hockey. These numbers would also require the team to catch fire and Hedman to play on the PP’s first unit. But somewhere between 30 and 40 points is within the realm of the possible.
Matt Duchene = 25 Goals; 38 Assists; 34 PIMs. This is possible, but he would need to be one of the top three centers and get some PP time. Look for him to score more like 40 total points with good ice time.
Evander Kane = 38 Goals; 38 Assists; 77 PIMs. If he can do this on the third of fourth line, Atlanta will be very happy, both on the scoreboard and at the turnstiles. Our hedge, however, only gives him about 30 Goals and 25 Assists if he plays all season long.
Brayden Schenn = 26 Goals; 45 Assists; 70 PIMs. Not as likely to be that high for the total. For our part, we think the Kings are an improved team this season. But Schenn would likely be a third or fourth liner if he makes the club out of camp. OGA says 20 Goals and 25 Assists is a more reasonable figure.
In our study, we found that fans can count on a Rookie in NHL Year 1 to provide about 80% of the scoring and PIMs that they produced in their final year of the previous league. Maybe more importantly, teams drafting players should be exceedingly happy if that prospect comes up for his turn with the big club and produces numbers that high given some greats and the fact they couldn’t hit the 80% plateau their first season. We also found it is wise to expect goalies coming up do so at a high caliber and are only off on Wins and Goals Against by about 7%.
Players who meet these marks are the average of the best talent to lace up skates and lug a stick onto the ice. To expect more than that is to overestimate what is most likely to happen with this young talent.