Nathan MacKinnon

How age, shooting percentage and 'puck luck' factor into future performance

Published Jun. 30, 2017 6:28 p.m. ET

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This work was completed as part of a larger project on age profiles and could not have been done without the valuable assistance, data and support of HockeyTech

At the beginning of the 2012-13 season, Kris Versteeg was a highly respected two-way forward whose resume included four 20-goal seasons and a Stanley Cup championship. There was little reason to think the 26-year-old didn’t have more great years ahead of him. As a result, the Florida Panthers rewarded him with a 4-year contract extension at an average annual value of $4.4 million.

The decision may have seemed reasonable to most pundits at the time, but then age and injuries took their toll and Versteeg bounced around among four different teams over the next four seasons.

Now 30, the former star came back to the NHL via a tryout contract in September after failing a physical with SC Bern in the Swiss League.

While injuries are difficult to predict, age comes for us all. Exactly when can vary among players, but there are some patterns to how players age, and it’s a mistake to believe there’s a “one size fits all” approach.

More skilled players, for example, tend to peak later and decline more slowly.

What complicates these patterns are the now widely recognized fluctuations in shooting percentages that can lead to players having abnormally good or bad seasons just due to "puck luck." For example, many analysts will often take note if a player with a career average shooting percentage of 10% suddenly scores 30 goals one season based on 20% shooting. Chances are his shooting percentage will drop the following year and he won’t repeat those statistics.

This concept is what statisticians call “mean reversion”, which is a fancy way of saying that, in the long run, everything tends toward an average number even if there are short-term ups and downs.

Moreover, it’s not just a player’s own shooting percentage that can influence his point totals. Those of his linemates also have a big impact on the number of assists he gets. If you make a bunch of outlet passes that end up being second assists on your linemates’ lucky goals, you’re likely a good candidate for a disappointment the next season.

It’s all well and good to acknowledge these effects, but it’s another to quantify their impact accurately.

In order to do that, we looked at every single forward who played more than half a season since 2005-06 and tracked how his offensive production changed the following season.

We then considered how these changes were influenced by things such as age and mean reversion in individual and linemate shooting percentages (as well as a lot of other variables).

As it turns out, analysts who assume a player’s shooting percentage will always revert to his career average are missing a very important detail, namely that a player’s shooting percentage follows its own age curve.

Younger players tend to have lower shooting percentages that improve as they gain pro experience. Conversely, as players start to decline overall, one of the things that also goes is their shooting ability.

This means that a 23-year-old who shows a sudden jump in his shooting percentage may in fact be able to sustain that higher level in subsequent years. Equally, a 29-year-old who is performing at below his career average shouldn’t automatically be expected to rebound next season.

Now to the fun part.

We took every forward who played more than 41 games in 2015-16 and used our model to make predictions of what we could expect if he played a full 82-game season in 2016-17 (and yes we’re aware that guys will get injured, but good luck predicting that).

Below are predictions for four kinds of players:

(i) Stagnant Youth - Young players who won’t improve because their performance has been unsustainable

(ii) Breakout Players - young players who had bad luck previously and so will get the double kick of age and mean reversion

(iii) Veterans With Upside - older stars who will bounce back

(iv) Faded Stars - veterans who are in full decline mode

(Note that, because our model excludes guys with only one season under their belt, we’re not making predictions for last year’s rookie crop, which included some standout talents. So while we don’t doubt that Connor McDavid will contend for the scoring title while resurrecting the Oiler franchise, we’re not predicting an actual number for him).

Because age is so critical in this analysis, our model is kinder to younger players. So, for example, all of the players on our “Stagnant Youth” list are still going to have very good, and in some cases excellent, seasons.

For example, Artemi Panarin and Johnny Gaudreau will remain great; however, all signs point to last season’s performance having benefitted from some “puck luck”. So while they may improve simply due to additional experience, that effect will be counteracted by some mean reversion.

THE STAGNANT YOUTH

Name	2015-16 Age	2015-16 GP	2015-16 Pts	2016-17 Predicted points
A. Barkov	20	66	59	61
E. Kuznetsov	23	82	77	66
A. Panarin	23	80	77	70
J. Gaudreau	22	79	78	74
A. Wennberg	20	69	41	42
V. Trocheck	22	76	53	52
J.T. Miller	22	82	43	38
M. Scheifele	22	71	61	66
A. Duclair	20	81	44	40
N. Kucherov	22	77	66	66

The flipside of this is that younger players who underperformed last season – guys like Elias Lindholm, David Pastrnak, Nathan MacKinnon and Ryan Nugent-Hopkins – are poised for big jumps. They won’t necessarily surpass their “stagnant” peers, but just as most pundits are overestimating the previous group’s performance for this season, the “Breakout Youth” are heading in the opposite direction and so are generally being underrated.

THE BREAKOUT YOUTH

Name	2015-16 Age	2015-16 GP	2015-16 Pts	2016-17 Predicted points
E. Lindholm	20	82	39	56
D. Pastrnak	19	51	26	51
N. MacKinnon	20	72	52	68
R. Nugent-Hopkins	22	55	34	59
S. Bennett	19	77	36	44
T. Toffoli	23	82	58	63
S. Monahan	20	81	63	69
T. Hertl	21	81	46	51
R. Fabbri	19	72	37	47
N. Ehlers	19	72	38	48

Meanwhile, older players will live out a simple fact that everyone should know: age is cruel, punishing, and rarely forgiving.

Finding vets with upside isn’t easy. And when you do, the reason usually is that a former elite talent like Eric Staal had a horrifically bad 39 point season that is expected to translate into an adequate 44 point campaign this year. Staal is never going to be the 30 goal 70 point talent he once was, but he’s not ready for his next career just yet.

In order to get a top 10 list for this group, we ended up mostly with guys who were expected to hold the line rather than improve. So “upside” for a vet means simply staving off his inevitable decline for one more season.

VETERANS WITH UPSIDE

Name	2015-16 Age	2015-16 GP	2015-16 Pts	2016-17 Predicted points
E. Staal	30	83	39	44
D. Brown	30	82	28	32
M. Perreault	27	71	41	51
J. Pominville	32	75	36	42
D. Perron	27	71	36	43
N. Foligno	27	72	37	43
P. Hornqvist	28	82	51	52
P. Kessel	27	82	59	60
T.J. Oshie	28	80	51	53
P. Berglund	27	42	15	30

Last of all, our Faded Star group is made up of the types of players we might expect to see on a team’s unprotected list for June’s expansion draft. These are exactly the kinds of players their current teams would know are done and who an expansion team might optimistically imagine will have a renaissance in Las Vegas.

THE FADED STARS

Name	2015-16 Age	2015-16 GP	2015-16 Pts	2016-17 Predicted points
H. Sedin	34	74	55	48
J. Ward	34	79	43	34
D. Sedin	34	82	61	51
C. Soderberg	29	82	51	42
L. Stempniak	32	82	51	42
B. Wheeler	29	82	78	70
J. Spezza	32	75	63	61
T. Vanek	31	74	41	37
S. Hartnell	33	79	49	43

Predicting the future isn’t an easy business. But if you’re making player personnel decisions for any NHL team - or assembling an expansion team in Las Vegas - understanding the complex interaction between the dual phenomena of age and mean reversion is one way of making better predictions.

The Department of Hockey Analytics employs advanced statistical methods and innovative approaches to better understand the game of hockey. Its three founders are Ian Cooper, a lawyer, former player agent and Wharton Business School graduate; Dr. Phil Curry, a professor of economics at the University of Waterloo; and IJay Palansky, a partner at the law firm of Armstrong Teasdale, former high-stakes professional poker player, and Harvard Law School graduate. Please visit us online at www.depthockeyanalytics.com

Jordan Hill is a masters student in the economics program at the University of Waterloo and a former Jr. A hockey player.

HockeyTech (www.hockeytech.com) is a worldwide leader in providing hockey-related technologies, analytics and information services. HockeyTech was founded in 2013 by Stu Siegel (technology entrepreneur and former Florida Panthers Managing Partner/CEO) through a series of acquisitions. While HockeyTech is a new corporate identity, its brands have been providing cutting-edge solutions to the hockey world since 1998. HockeyTech brands include RinkNet, ISS Hockey, HockeyTV Website, LeagueStat, and NEXT Testing.

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