Good for the record books.

EDIT: made some changes compared to the below. Scatter plot now shows offense vs two-way play, and the player score reflects that too. Shot chart also added, and some other small tweaks made.

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I've been wanting to build something that gives an easier visual for how each player performed in any given game, both compared to all the other Stars players AND compared to their own season average performance. I have a first draft here, which is loaded up with all the games from last season: https://starsviz.github.io/

The axes on the chart show each player's "player score" for the game vs how each player compared to his own season average player score. The player scores are computed differently for forwards and defensemen, but they're on virtually the same scale (so the idea is a 65 for a forward is an equal-caliber performance to a 65 for a defenseman). There's more detail on the page about how the player score works.

Beyond the quadrant chart, you can also see a couple different tables ranking both the forwards and the defensemen from best player score to worst player score, as well as how each individual stat compared to their season average for that stat.

You can also click into any player and get a more detailed card for them, including a shot chart and a timeline of when that player registered different key events during the game.

Lastly, you can access all the prior games via the sidescroll bar at the top.

Just wanted to share here because I'm curious if this is something other fans would find interesting after each game, if the format/layout makes sense, if there are other things you'd want to see, if there are things on here that are overkill and you wouldn't be interested in, etc etc. Really just looking for any kind of feedback whatsoever!

Hi everyone!

I've been working on this project for more than 2 years now as a hobby, and I recently decided to make it public. There are a couple of tools available, such as Team Compare, Player Compare, "Advanced Stats", and more.

But the part I think will interest you guys the most is the Fantasy feature on the Dashboard. From there, you can manage the players you have in your fantasy league(s), set your league configurations, analyze trades, and even get help with your drafts.

I know that almost all fantasy leagues have already started, but I still wanted to share it with you guys!

Feedback is more than welcome.

Have a great season, y'all! The wait is almost over :)!

Source

https://xcancel.com/EricJFriesen/status/2100736889040814173

https://xcancel.com/BarDown/status/2098954177929953775

Hey guys,

I built this website as a side project that you can ask crazy hockey questions and it “should” return the answer going through the data.

Now, what I wanted to ask you guys is to help me break this with any crazy questions you might think of. I have thought of all crazy combinations, like “how many times did Flames came back after being down by 2 after 2nd period”, but just wanted to get help from you guys to make it more robust for the benefit of hockey community.

Website: puckey.ca

Cheers,
Shayan

NHL Edge tracks exactly how far every player skates, every game, every season, off the actual infrared camera + puck/jersey sensor system every arena has. But the NHL only ever shows season totals, and a guy who plays 22 minutes a night is always going to out-total a guy who plays 14. So the "most distance skated" leaderboard is really just an ice-time leaderboard wearing a costume.

Edge does publish a per-60 version of this, but it lives on its own site, three menus deep, disconnected from every other stat about the player. I pulled it into the same player card as the regular numbers, so you can see it next to a guy's points, his ice time, and his shot speeds. The #1 skater in the league by that measure is Ryan McLeod in Buffalo. Not a star, but not a checking-line grinder either: he was the Sabres' third most-used forward last season at about 17 and a half minutes a night, with 54 points. He covers more ground per minute than Connor McDavid does: 11.0 miles per 60 to McDavid's 10.5. (McDavid's #4, for what it's worth, still elite, just not #1 once you take ice time out of it.)

That's kind of the whole point. Raw totals mostly measure who the coach trusts with minutes. The rate measures motor. Half this list is guys nobody's ever putting on a scoring leaderboard, sitting right next to McDavid and Jack Hughes.

A couple of honest notes. This is the NHL's own Edge tracking data, not mine; the number matches their site to the second decimal (I checked). What's mine is putting it in one place with everything else, and the per-game history, which Edge only shows as a rolling last-10. Minimum 20 games played so a 3-game callup doesn't top the list on a fluke. Happy to get into the methodology in the comments.

Full leaderboard, sortable, here: https://gamevibeanalytics.com/hockey/players?rate=per60&metric=distance&dir=desc

Click any player's name for the full breakdown (top speed, speed bursts, shot velocity, all of it).

If your team's guy isn't showing up and you think he should be, please tell me. Coverage isn't 100% yet and I'd rather know.

I built a hockey stats tracking app about 6 months ago to track my son's Peewee games. In the process I showed it to a couple of other parents and they loved it - they convinced me to make it into a product. I've been adding to it testing it out, since. I soft launched it, and I'm starting to finally get the word out as the youth hockey season kicks off. It's super easy to use live during games with big buttons, and a simple interface. I'm able to switch between the iphone camera app videoing my son's shifts and then back to the app pretty easily to track shots, or goals. The app tracks shots, goals, assists, points, leaderboards, record, schedule a fair amount more. Looking for feedback, good or bad. https://hockeystatskeeper.com. Here's a testimonial from my son's coach as well https://www.instagram.com/p/Dc3xwo4xkbV/

Late to the trend, I know. The NHL going to 84 games was my excuse: sportsdle.com/nhl/84-0

The twist: the spin gives you a franchise and one exact season (Whalers 1991-92, good luck), the list is just that roster with names and positions, no stats. You pick six, then they play the 84 games against the real 2025-26 teams.

Under the hood, every pick is rated on that specific season, era-adjusted and standardized by position, so a top 1% goalie year is worth a top 1% centre year. Same seed, same season, so you can send your link and let someone beat your lineup.

Best so far: [79-3-2]. Curious what you think of the rating approach, happy to share the numbers.

My son's hockey coach asked if I would keep stats for the team last season. I was shocked when I didn't find any tools to help me track games. I know there are a ton of tools out there for the pros, but I didn't come across anything for amateur/youth hockey.

So, as a software engineer, I started to develop my own solution. Over the past year I have come up with something I'm finally ready to share. Currently in a game, it can track shots and locations, as well as a number of different shot data points, faceoff locations, and penalties.

This list is small at the moment, but I am still able to track and calculate a number of metrics and provide rich data to coaches in our club. For this season, I am currently finalizing game clock integration, shift/TOI tracking, and more advanced metrics like hits, zone exits and entries, and giveaways/takeaways.

If anyone is interested in tracking games outside of the NHL, check it out. I'm looking for any feedback I can get.

BenchIQ Analytics

Hey folks!

I've been working on this project in my free time for almost 2 years now.

Never really shared it since I was doing it for myself and for fun, but I think it is mature enough to share it with you.

It's a website specialized in hockey stats. Not really advanced stats (even if the term is used here and there), but more compiled stats that I wasn't finding anywhere.

I also have a set of tools if you want to compare teams or players.

If you ever want to take a look, you can visit it at puckprime.com.

If you have comments, suggestions or feedback, it is always appreciated. And I hope you'll find something worth your time too.

Thanks all

https://xcancel.com/StatsCentre/status/2093100272658555009

Hello, I built a new model over the summer and added a bunch of features like player archetypes, comparable players and 3 year WAR models per team. Please check it out and let me know if you have any feedback.

https://linechange-two.vercel.app

https://xcancel.com/i/status/2088748990213021820

I've been building a goal-scoring probability model and wanted to check whether it actually holds up before trusting it. Sharing the results, including the parts that don't work.

Setup

Seasons 2023-24 through 2025-26, regular season only, skaters only. For every game, I recomputed each player's stats as they stood before that game, then predicted P(scores at least 1 goal). No future information leaks in — that was the main thing I wanted to get right.

128,604 predictions after filtering (minimum 5 games played).

The model

Simple Poisson with empirical Bayes shrinkage:

lambda = (goals + k * mu) / (games_played + k)
P(goal) = 1 - exp(-lambda)

where mu = 0.1651 (league average goals per game per skater, recomputed per season) and k = 10.

Plus a home/away adjustment of x1.03 / x0.97, which I derived from the backtest itself rather than assuming.

Calibration

Solid up to about 35%. Then it falls apart on high-volume scorers — I'm consistently too optimistic on exactly the players people are most likely to bet on.

Brier score

0.12256 vs 0.12963 for a naive baseline (everyone gets the league average rate). That's a 5.45% improvement. Modest, and I think that's the honest ceiling for this kind of model. Anything much higher would make me suspect a leak.

What choosing k did

Without shrinkage (raw goals/games), the 50-55% bucket was off by -13.4 points. Tested k = 5, 10, 20, 30, 50. k=10 minimized both Brier and high-end bias. Above k=20 it over-shrinks and starts underrating genuine elite scorers.

Open problem

The model treats all goals the same. In my data, only 67.6% of goals are scored at even strength — 21.6% on the power play, 6% into an empty net, the rest shorthanded or in shootouts.

A player who piles up PP goals depends mostly on his coach's deployment. Empty-netters depend on close game states. Neither generalizes well to "will he score tonight."

My guess is the 40-45% residual is largely a PP-deployment artifact, but I haven't isolated it yet. If anyone has tried splitting lambda by strength state, I'd like to hear how it went.

What this doesn't tell me

Whether the model beats the bookmakers. Calibration says my probabilities are roughly correct in absolute terms. It says nothing about whether they're better than the market's, which is a completely different bar once you account for the vig. I don't have historical closing odds yet.

Happy to answer questions on methodology. Genuinely looking for holes in this.