Hockey analytics are slowly but surely changing the game and we’re seeing a lot of these changes happen on the ice. When it comes to the value of a player’s contract though, it seems that analytics may have a much bigger role to play. There are certain aspects of a player’s past performance that will undoubtedly impact his next contact’s dollar amount. Things like years of experience, the number of Stanley Cups won and where he was selected overall in the draft, all have an impact on his potential salary.
Historically, a rookie’s lack of NHL experience has always been of some benefit to a team in terms of how much he’s going to cost them. Furthermore, once a player’s been drafted, he’ll develop with his draft team and eventually play in their system. This is where accusations of ‘destroying great potential’ and scouting ‘diamonds in the rough’ are often derived. It’s quite a harsh and sometimes sweet reality.
Increased Salary Reflected in Role
Auston Matthews tied it up for the Leafs against the Canucks. #TMLtalk pic.twitter.com/GnpoQxJbIn
— Toronto Maple Leafs (@MapleLeafs) December 4, 2016
Currently, the predominant way of measuring an elite goal-scorer is through his point-per-game played (P/GP) rate. Rookies like Auston Matthews, Mitch Marner and Patrik Laine all have the highest P/GPs because they score the most goals per game of all rookies who’ve debuted the NHL this season. However, this metric does not accurately describe a player’s ability to score goals but rather his efficiency to score goals within the system he plays. Meaning, if he played in a different system, the results would be different.
Let me explain.
Every team in the NHL adheres to a system set forth by the coach, general manager, etc. It’s basically the style of hockey a team plays, which entails their strategy to gain puck possession, as well as preventing the opposition from gaining it. Every player on a team has a role to play within their system and that comes with a list of corresponding responsibilities.
In many systems, wingers are responsible for battling for the puck along the boards and in the corners to gain possession and then pass to the centreman, so he can take a shot on net. In these systems, whoever’s responsible for shooting will be given majority scoring opportunities. Therefore, he’ll have increased goals per game played. Usually, players most talented at scoring are given this role. What the point-per-game played metric doesn’t account for are all the missed and attempted shots blocked in between each goal scored.
If a team has a lot of talented goal scorers, they can only select a few of them to be responsible for the majority of scoring opportunities. Even though others are capable of scoring, their roles require them to focus on something other than that. As a result, their P/GP is less than what it could be.
Top 5 Rookies
One metric that can be used to more accurately measure the efficiency of a player’s scoring ability is his goal per shot on goal (G/SOG) scored rate. This metric not only includes missed and attempted shots blocked between goals scored but it may also shed light onto players best equipped to play the role of goal scorer.
For the months of October and November of the 2016-17 season, here is the list of top five rookies (who’ve debuted the NHL this season) ranked by best G/SOG:
#1 Jimmy Vesey
Selected at No. 66 in the third round of the 2012 Draft by the Nashville Predators, this Harvard graduate ranks first for most goals scored per shot on goal of all NHL rookies for the months of October and November. Even though his P/GP rate is only 0.58, Vesey scored a goal every 4.88 shots on goal in his first 24 NHL games played.
#2 Patrik Laine
Selected at No. 2 in the first round of the 2016 Draft by the Winnipeg Jets, the Finnish phenomenon probably should have been selected No. 1. Expectedly, his P/GP rate is an astounding 0.85 and has scored a goal every 4.92 shots on goal in his first 25 NHL games played.
#3 Matthew Tkachuk
Selected at No. 6 in the first round of the 2016 Draft by the Calgary Flames, the son of retired NHL player Keith Tkachuk is certainly defining his own limits as being limitless. He’s quickly carving a name for himself as being the boss. Although Tkachuk’s P/GP is only 0.48, he’s scored a goal every 6.5 shots on goal in his first 22 NHL games played.
#4 Christian Dvorak
Selected at No. 58 in the second round of the 2014 Draft by the Arizona Coyotes, the 2016 CHL Memorial Cup Championship team Captain is turning into one of those hidden gems every NHL team wishes they had. Although Dvorak’s P/GP is only 0.37, he’s scored a goal every 7.5 shots on goal in his first 17 NHL games played.
#5 Denis Malgin
Selected at No. 102 in the fourth round of the 2015 Draft by the Florida Panthers, the rapidly developing Swiss forward ranks as the fifth most efficient goal-scoring rookie. Although Malgin’s P/GP is only 0.32, he’s scored a goal every eight shots on goal in his first 23 NHL games played.
If you’re wondering where Matthews and Marner rank, they’re within the top 10. Matthews ranks No. 7 on the list and scored every 8.4 shots on goal during his first 23 NHL games played, even though he has a P/GP rate of 0.79. Marner ranks No. 8 and scored a goal every 8.9 shots on goal in his first 23 NHL games played, even though his P/GP rate is 0.79.
As you can see, the number of scoring opportunities a rookie gets has a drastic impact on his point-per-game played rate. Ironically, this is one of the crucial components of determining the dollar amount of the player’s next contract. If a team was in need of an elite scorer but didn’t want to spend an arm and a leg to get one, they could very well go on the hunt for a lower-drafted rookie and propose a trade. Since this player has a less than attractive P/GP rate, his contract could be worth a lot less than what it would have been if his team had placed him in a goal-scoring role.
If a rookie like this were acquired within the first couple years of his NHL career, his development would seemingly skyrocket assuming he was placed in a goal-scoring role upon acquisition.
This then begs the question addressing the elephant in the room. With the increasing use of hockey analytics, how long will it be for such stats to be used in player contract negotiations?
In fact, these metrics are already being used.
Agents Driving Hockey Analytics
Professional sports organizations do place restrictions on which evidence is admissible in contract negotiations and this includes the use of hockey analytics. Interestingly, the NHL’s relatively new 2013 Collective Bargaining Agreement (CBA) responds to this issue quite eloquently. Section 12.9 (h) ‘Statistics’ of the CBA states:
The League shall obtain and provide to the NHLPA any statistics relative to any aspect of Player performance: (i) kept or maintained by the League; or (ii) retained by any club.
Some of the best writers I come across are lawyers. Their ability to communicate is definitive, supported by evidence and leaves no unanswered questions. Agent Allan Walsh is an agent who represents players like Jonathan Drouin, David Perron and Antoine Vermette. In an article from The Hockey News, ‘How Advanced Stats Are Changing NHL Contract Negotiations,’ Walsh described a meeting he attended with a client’s NHL GM and assistant GM, so they could hammer out a deal and told The Hockey News,
…They spent 45 minutes discussing staple statistics like points per game, goals, assists and ice time. Walsh, though, wasn’t satisfied. He told the executives they were omitting a crucial criterion.
It just so happened, Walsh explained to them, the player in question was tops on the team in almost every major possession metric, including Corsi and Fenwick. Walsh had his own advanced stat booklet prepared. He fished out two copies.
“I saw them open the first page, and I saw the GM and the assistant GM lock eyes with each other,” Walsh said. “And the look on their faces was, ‘Oh s—, he knows.’ ”
Over the last two years, the NHL has been maintaining enhanced statistics, which can be easily found on their website. As a result, they’ve become admissible in the contract-negotiation process and now teams and agents are placing greater focus on advanced stats when negotiating their client’s and player’s contracts.
Change can sometimes be quite challenging, especially if we’re used to doing something a certain way for a long time. However, just as social and digital media took precedence in the world of business over the last decade, so too will advanced stats and analytics in the world of hockey. If the driving force behind hockey analytics is salary and profit increases, then it’s simply a matter of time before every crack and crevasse of player performance are measured, tracked and evaluated.