u/AI_Predictions

9 games on the board tonight. Model V2 has a few spots where the win probability differs quite a bit from the current market.

Best positive EV spots right now:

• Nashville +133 | Model: 53.1% | EV: +23.6%
• LA Kings +106 | Model: 59.8% | EV: +23.2%
• Seattle +150 | Model: 47.0% | EV: +17.4%
• NY Islanders +130 | Model: 47.4% | EV: +9.1%
• Utah +115 | Model: 49.9% | EV: +7.2%
• Ottawa -104 | Model: 54.2% | EV: +6.3%

The two that stand out most to me are Nashville as a slight model favourite while getting +133, and LA at nearly 60% while still available at plus money.

Still very early in the season, so I'm interested to see how these probabilities shift as this year's team data starts replacing more of the preseason/previous-season information.

Full board with win probabilities, odds, EV and matchup details:
https://www.playerwon.ca/betting/daily-edge-board/

I’ve added shot data to the Data Hub, along with shot breakdowns and rink maps in individual game logs so you can explore where shots came from.

Shot-level data is the foundation of the expected goals models used by many analytics sites. Those models use details like shot location, angle and type to estimate how likely a shot is to become a goal. Exploring the underlying shots adds some context beyond the final shot totals.

Sharing here for anyone interested in digging into the data.

https://www.playerwon.ca/data/nhl/shots/

https://www.playerwon.ca/games/2026020039/vegas-golden-knights-vs-vancouver-canucks-2026-10-04/

Four games tonight. No teams above 60% in my V2 model, but a few prices stand out.

Positive EV based on the model and odds shown:

• Sharks +173: 49.1% win probability, +34.1% EV
• Flyers +191: 41.2%, +20.0% EV
• Jets +155: 40.6%, +3.6% EV
• Bruins +104: 50.7%, +3.4% EV

Model favourites:
Tampa Bay 58.8%
Boston 50.7%
Pittsburgh 59.4%
Dallas 50.9%

San Jose is the biggest gap between my model and the market today. Boston and Dallas are essentially coin flips.

Still early in the season, so I’m interested to see how these projections settle as more games are played.

Full board and matchup details:
https://www.playerwon.ca/betting/daily-edge-board/

Who are you taking tonight?

https://preview.redd.it/dnxnnxfenpth1.png?width=865&format=png&auto=webp&s=318165d754e8aaf23e5839f8d060183dd11af096

I run PlayerWON, a free NHL prediction site. Here’s what Model V2 has for today’s five games.

Carolina is the 60%+ favourite that also shows positive value: 62.9% to beat Florida, with fair odds around -170. At the -123 line I found, that’s a 7.7 percentage point edge and 14.0% calculated EV. The value will change if the line moves.

Model favourites for all five games:

• Carolina over Florida: 62.9%
• Montréal over Toronto: 59.0%
• Boston over the Rangers: 56.7%
• Edmonton over Vancouver: 60.3%
• Vegas over Chicago: 58.8%

Edmonton also clears 60%, but the available price doesn’t show positive EV by the model.

Here’s the Florida–Carolina matchup page as an example of what’s on the site. It includes win probabilities, fair odds, a market comparison, team form and head-to-head results:

https://www.playerwon.ca/matchups/nhl/florida-panthers-vs-carolina-hurricanes-2026-09-29/

What are you liking on the opening slate?

https://preview.redd.it/msbov1f7shsh1.png?width=1312&format=png&auto=webp&s=e2fc01c4688212564f84dc6c9474017659eb90d0

I’ve been working on matchup pages for PlayerWON with the goal of having most of what I want to check before a game in one place instead of jumping between a bunch of different sites.

Right now I have:
- Model win probability + fair odds
- Season stats/comparison
- Home vs. away performance
- Last 10 games
- Last 10 head-to-head games
- Goals, shots, goal differential, recent form, etc.

Example:
www.playerwon.ca/matchups/nhl/dallas-stars-vs-st-louis-blues-2026-09-19/?model\_version=v1

I’m looking at what to add next and thought this would be a good place to ask:

If you were checking a matchup before a game, what else would you want to see here?

A few things I’m considering are starting goalies, injuries, special teams, advanced stats, recent player performance, and possibly line combinations.

The goal is basically: open one matchup page and have everything useful there without needing five other tabs.

Would love some feedback on what’s actually useful vs. what would just add clutter.

I've been building an NHL player prediction model as a side project over the past year. It predicts next season's goals, assists, and points per game for every player, and I publish the projections on my site.

For evaluation, I'm currently using the standard regression metrics:
- MAE
- RMSE
- R²
- Error distributions

Those seem fine for measuring average prediction error, but I'm wondering if they're missing the bigger picture.

For example:
- Should I care more about ranking accuracy (Spearman/Kendall) than absolute error?

- Would calibration-style analysis make sense for player projections?

- Should I compare against simple baselines (previous season, 3-year average, Vegas projections, etc.)?

- Are there better ways to evaluate how useful the predictions actually are?

My game prediction model has a much more comprehensive evaluation framework (calibration curves, log loss, Brier score, ROC-AUC, etc.), but player-season prediction feels less straightforward since it's a continuous regression problem.

If you've worked on sports analytics or forecasting models, I'd love to hear how you'd evaluate something like this.
If anyone wants to see the model or the current projections, they're available here: playerWON.ca. I'm always looking for ways to improve it before the season starts.

I’ve finished generating this season’s player projections, and they’re now available on the site.

The model predicts:
Goals per game
Assists per game
Projected 82/84-game goals, assists, and points

Last season the model performed reasonably well:
620 players evaluated
Goals/Game MAE: 0.064
Assists/Game MAE: 0.097

I’m pretty happy with those results, but I also think this year’s projections are a bit conservative overall based on my own hockey knowledge.

Over the summer I’ll be continuing to refine the model, so expect updated projections before the season starts as I retrain and tune it.

I’d love to hear your thoughts:

Which players seem too high?
Which players seem too low?
Any breakout candidates you think the model is missing?

Check them out here:
https://playerwon.ca/predictions/players/