There is something of a boom in the sports data economy at the moment. It has come about for various reasons, ranging from fueling the AI economy to the rush to better understand sports injuries.
Leagues and teams have quickly understood that data is highly valuable, and the boom has allowed many to cash in on their proprietary data.
And yet, it’s important to note that access to that data is unequal. Moreover, there is a broad consensus that ice hockey data lags behind the other Big 4 sports (football, basketball & baseball).
Yet, it is important that fans get access to it, especially those who engage in hockey betting or NHL DFS games. The good news is that the availability of hockey data is improving.
Why good hockey data is important
For fans, it’s a no-brainer that access to data can help them understand the game better.
There is a massive market for hockey betting and fantasy sports, sure, but there is also a demand for good statistical data for reporters, podcasters, and fans with a general interest in what makes their favorite sport tick.
The best resources for hockey data
As mentioned, the amount of available data has been improving. There are some obvious sources that we will list below, as well as some underrated platforms.
We will also split them into free and paid-for/premium categories:
Free Sources
- NHL.com – Good for basic box scores and player stats.
- MoneyPuck.com – Excellent for xG, shot quality, goalie stats.
- Natural Stat Trick – Great for advanced stats and in-game situational splits.
- Hockey-Reference.com – Historical stats and comparisons.
- Daily Faceoff / Left Wing Lock – Line combinations, goalie confirmations.
Paid or Subscription Services
- EliteProspects Premium – In-depth scouting and analytics.
- The Athletic (hockey coverage) – Some data-driven content, though not raw data.
- Stathletes – Pro-level puck tracking and micro-stats (more for teams, but shows where things are heading).
- FantasyLabs NHL / RotoGrinders NHL – For DFS-specific tools and projections.
Using AI for hockey stats
Artificial intelligence is the elephant in the room when it comes to sports data. It occupies a dual position, being both the future of data access and being caught in a contentious present. AI is not omnipotent.
While it can access some of the data from the sources above, it requires specialist permissions, including data access deals.
Some AI companies have been taken to court for accessing data without permission. In short,
AI can be useful for accessing different data sources, but it is not always reliable. It should, however, get better as AI models improve and the data economy for AI training becomes normalized.
Challenges for hockey data providers
We have consistently mentioned that hockey lags behind other sports. There are a few reasons for this, not least the NHL’s reluctance to roll out data-tracking metrics with the same gusto as other sports.
There’s also a lag in the infrastructure, like APIs, that give developers access to raw data sets.
In addition, some hockey teams have been guarding their in-house data, possibly not wishing to make it public lest other teams use it to their advantage.
The ice is melting
Nonetheless, the catch-up is occurring at a steady clip. There has been a recognition that the data is more valuable than first thought, so there has been a marked improvement in data collection as well as the infrastructure to make a hockey data economy by selling it on to sportsbooks,
DFS platforms, and so on. For NHL fans, it seems like the gap is closing between hockey and other major sports.
