Fighting the Data Gap
What current & future NHLers need to understand
Recently a newsletter subscriber writes me with a series of questions as part of an assignment for his Sports Law & Data Analytics college course.
Question 5 is the following:
Do you feel data will impact the future players in the NHL, specifically related to longevity and contract value?
This is a multi-million dollar question for a specific segment of newsletter subscribers: current and future professional hockey players.
For that group of people, below is a crash course on data and you.
Broadly speaking there are three types of data you should know something about:
Public output data
Private output data
Private input data
1) Public Output Data
Think about your HockeyDB or EliteProspects page.
On the one hand there’s not that much information there.
But on the other hand there is enough on a single page for a motivated amateur or professional analyst to construct a statistical model that can contextualize your past and predict your future as a player.
An example: Nikita Kucherov’s stats leading up to the 2011 NHL draft.
To make an apples-to-apples comparison with a top North-American prospect, one would need to adjust Kucherov’s production for a variety of factors:
League (the MHL is a lower-scoring league than the CHL)
Position (Fs score more than Ds)
Relative age (factoring in month and date of birth for players in the same draft class)
Similarity to past NHL draft picks (certain statistical profiles tend to translate better to professional level)
Adjusting for those factors, Byron Bader’s Hockey Prospecting model has Kucherov as a top-10 pick in the 2011 draft, a player who is not only a slam-dunk to make the NHL, but has an excellent chance of becoming a star player.
Lesson for players: Before setting your expectations based on what your agent, team or parent tells you, do your own research and know how you stack up.
2) Private Output Data
As an analyst, then assistant coach in the Toronto Maple Leafs organization between 2017 and 2020, I had access to a bevy of statistical information in addition to the publicly-available stuff.
This access allowed my colleagues and I to go much deeper in quantifying player value and, therefore, assigning opportunity in the form of icetime, draft selections, one-way vs. two-way contracts, cap hit and term.
From an outsider’s point of view, the team has most of the power because it has most of the information.
Do you know what your frequency and success rates are when you make a play?
Do you know what areas of the ice you create the most (and least value) for your team?
Do you know how you can refine and evolve your game to move up to the next level (if you’re under 27) or avoid being demoted (if you’re over 27)?
If you answer no to any of these questions, you are at an informational disadvantage to the team that is signing your paycheques. Because my colleagues and I know.
Perpetuating this information gap means not being able to act on upon it.
An inability to act upon critical information means leaving much more money on the table than you realise.
Lesson for players: Invest between 2% and 5% of your annual income on specific information.
This mean that if you make $1 million per year, you can and should reinvest up to $50,000 to hire private video & statistical analyst(s) to identify trends before they become destiny.
The best NHLers do it already. You don’t want to fall farther behind.
Think the Game Differently
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3) Private Input Data
Biometrics are body measurements and calculations related to human characteristics.
Biometric authentication is used in computer science as a form of identification and access control. It is also used to identify individuals in groups that are under surveillance.
During every Leafs and Marlies practice, a big-screen TV next to the bench relays information captured by the players’ heart-rate monitors. Additional sensors (Catapult) measure information related to energy expenditure and physical load.
There is a very good reason why the NHL CBA does not currently allow biometrics to be captured during games.
The use of wearable technology in sports, however, is a new, disruptive development that has wide-ranging impacts beyond injury prevention and performance optimization.
Does (a player such as the NBA’s Joel) Embiid have exclusive ownership of the biometric data that teams and leagues derive from the wristband he wears?
Can these entities license his biometric data to third-party organizations such as television broadcasting companies, video game companies, fantasy football companies, and casino operators that are looking for new ways to engage consumers?
(Player)’s rights regarding his own biometric data seem unclear.
Skyler R. Berman, Brooklyn Law Review
The team-level collection and analysis of biometric information perpetuates the data gap that is unfavorable to players.
In the best case players are able to access another revenue stream, if unions and agents bargin well enough to gain a slice of the teams’ data-related revenue pie.
But in the worst case certain players can be traded, bought out or non-drafted because of correct or even incorrect conlusions drawn via biometric analysis.
Owning a person’s body is illegal and immoral. But owning the second-by-second biometric information generated by a person’s body is not subject to law.
Think about that for a second.
Lesson for players: Tread carefully. Work with your agent and union to restrict the collection of biometrics, then push for greater transparency and equity for the information already being collected, analysed and commercialized.