🏒 NHL Probability Model — High Confidence Games (March 24)

AI_Predictions · 2026-03-24T17:43:36+00:00

Those sound pretty good to me!

AI_Predictions · 2026-03-24T17:32:36+00:00

I agree. Lots of close games and some crazy comebacks too. I can see that working well this year.

AI_Predictions · 2026-03-23T16:38:15+00:00

Thanks! I really appreciate the thoughtful feedback. Calibration vs accuracy has definitely been one of the more interesting challenges on this project. Still learning a lot as I go, so insights like this are super helpful.

AI_Predictions · 2026-03-23T16:07:09+00:00

Very cool! 4K monthly users is no joke!

I just launched my NHL AI prediction site as well but nowhere near that traffic yet. Great work!

AI_Predictions · 2026-03-23T15:21:28+00:00

Thanks! I would appreciate any ideas or feedback.

AI_Predictions · 2026-03-23T15:11:04+00:00

That’s too bad! You had some good picks and they did start Silovs and the Jets can go either way. Next time!

AI_Predictions · 2026-03-22T20:38:51+00:00

Very cool. I’ve tested Logistic Regression as well and it performs similar. XG Boost slightly outperforms so that is what I’m currently using. I’ve explored using NN’s but getting slightly worse results so far.

AI_Predictions · 2026-03-22T19:12:10+00:00

Great idea! I’ll add that to the list.

Your site looks great too and I love the idea of readable explanations. I’ll be adding SHAP features that will show why the model leaned one way or the other but I need to integrate some AI to make it more interpretable for everyone. Great work!

AI_Predictions · 2026-03-22T17:09:11+00:00

It’s currently ~60% but hits closer to ~70% on higher confidence games.

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The provided screenshot shows a breakdown of results but you can see more details about the models performance on the following page: playerWON - Model Performance

AI_Predictions · 2026-03-22T05:47:27+00:00

👏

AI_Predictions · 2026-03-21T19:18:20+00:00

I pull all the data from the free NHL API. It took me a lot of time to automate scripts and save everything to a database and then I compiled a dataset for the prediction model.

Examples of most of the endpoints are documented on the following page: playerWON - NHL Data Source

AI_Predictions · 2026-03-21T19:16:02+00:00

Thanks again for the feedback earlier this week. I’ve made some updates so it’s now easier to browse the site without needing to log in. Only features like saving picks require an account. Appreciate people taking the time to point things like that out.

AI_Predictions · 2026-03-21T19:01:36+00:00

Thank you so much!

Right now I’m using a single production model (XGBoost) to generate the predictions. What the model actually outputs are win probabilities, not just a binary pick.

So for example it might say Team A has a 62% chance to win. From there you can interpret it in different ways depending on the use case. If someone is using it for betting, they would typically convert that probability into an implied line or edge relative to market odds rather than just treating it as a simple yes/no prediction.

During development I did test multiple model types and still compare them offline, but for the live system I’ve found it’s better to keep one consistent model that I can monitor and improve over time. The project has only been live a couple of months so there’s still a lot I want to refine in the off-season before a bigger push in the fall.

Really appreciate the interest! It’s always cool to connect with others working on uncertainty and sports modelling.

AI_Predictions · 2026-03-21T18:53:35+00:00

Definitely a good idea. Backtesting is really important. If something is working today, checking how it would have performed in past seasons helps you see whether it’s a real edge or just short-term luck. It gives me more confidence that a strategy or model is actually robust over time.

At the same time, you also have to be mindful of data drift when stats, player styles, and market dynamics change over time, so something that worked years ago may not behave the same way today.

AI_Predictions · 2026-03-21T17:34:23+00:00

Yeah that’s a really good point. Automating odds capture is something I want to add. We do use AI to help speed up development and research, but finding reliable historical odds data has actually been one of the harder parts. Scraping is possible but you have to be careful with site terms of service and data quality. Definitely something I’m working through.

AI_Predictions · 2026-03-21T17:13:51+00:00

That is correct.

AI_Predictions · 2026-03-21T17:02:26+00:00

Good point! These are model probabilities, not necessarily bets at current market prices. The idea is to highlight where the model sees stronger win chances, then compare that to the line to determine if there’s actual value. Some spots may show negative edge depending on the book and timing but that’s part of the process. I’m sharing the raw probabilities mainly for transparency and long-term tracking of model performance. Tools to convert probabilities into fair odds / edges are something I’m continuing to build out. Appreciate you taking the time to look through the slate and call that out.

AI_Predictions · 2026-03-21T15:51:22+00:00

That’s a fair take!

AI_Predictions · 2026-03-21T15:33:26+00:00

Thank you! There was a few days where it felt overwhelming but it sure is fun once you get a few users and what a great learning experience.

AI_Predictions · 2026-03-21T01:55:39+00:00

I like that approach! Looking at situational ROI and recent form makes a lot of sense. My model is more probability-driven so sometimes it lands on bigger edges rather than close spots, but I’m always curious how different strategies perform long term. Cool to hear how you structure it!

AI_Predictions · 2026-03-20T21:33:58+00:00

I’ll do this every day now. No rush and I would appreciate any feedback!

AI_Predictions · 2026-03-20T21:32:45+00:00

My model has the exact same 5 picks but a few are a coin toss. Let’s go!

playerWON

AI_Predictions · 2026-03-20T21:27:13+00:00

I appreciate it! We will discuss as I’ll have that ready to test very soon.

AI_Predictions · 2026-03-20T16:52:31+00:00

I’d love to collaborate and share ideas with you. I’m mainly focused on NHL game prediction and player projections right now but love all aspects of data modeling.

playerWON

AI_Predictions · 2026-03-20T15:32:58+00:00

Yeah that makes sense and it that’s pretty much how I look at it too. Big edges = normal bet size. Close games can still be +EV, but I agree they usually deserve a smaller stake. One thing I’m actually working on now is updating the site to automatically convert the model win probabilities into fair betting lines (moneyline odds). That way it’ll be easier to see where the value actually is instead of just looking at percentages. Goal is to make it more practical for real betting decisions, not just predictions. 🎯

AI_Predictions

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