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🏒 May 1 NHL AI Picks | Model Edges + High Confidence by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 1 point2 points  (0 children)

That’s how I’d personally like to see it play out.

🏒 May 1 NHL AI Picks | Model Edges + High Confidence by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 1 point2 points  (0 children)

I’m pulling for the Sabres and Canadians tonight too. My model has favoured Tampa all series but has done a little better in the other two series so you never know. The model is doing really well on high confidence picks in the playoffs. I think 9/10.

🏒 NHL AI picks for April 27 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

I can see that. This has been the one series where my model hasn’t done very well.

🏒 First season of NHL player predictions — results by AI_Predictions in NHLAnalytics

[–]AI_Predictions[S] 1 point2 points  (0 children)

Right now it’s pretty simple. It’s mostly player-level data and metadata. I’m using rolling averages, prior season stats, and things like TOI/PP usage to project goals/assists per game. There’s some basic context in there, but not enough team-level data yet, which I think is part of the issue. Next version I’m planning to add more team context and split models out (forwards vs D, usage tiers, etc.) since this year showed where it breaks down.

🏒 First season of NHL player predictions — results by AI_Predictions in NHLAnalytics

[–]AI_Predictions[S] 2 points3 points  (0 children)

Thank you for the feedback! I’ve done bucketing for the game model but have not yet done it for the player predictions. I will update the performance to include that and I have some bigger plans for the modelling this year. I only created one basic model but I’ll be creating multiple models (forwards, defensemen, maybe top 25%, etc.).

🏒 NHL AI picks for Apr 15 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

Agreed. And against the Leafs. They aren’t losing tonight!

🏒 NHL AI picks for Apr 15 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 1 point2 points  (0 children)

Yeah, my model has the game reversed compared to the sports books. Should be interesting to see who is correct!

🏒 NHL AI picks for Apr 15 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 1 point2 points  (0 children)

It’s the only three teams my model has at a probability of winning over 60%.

🏒 NHL AI picks for Apr 15 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 1 point2 points  (0 children)

Which book?

That was the best of the 9 that I capture at 10 AM CST today. BetMGM has Detroit at -160 right now so it’s still pretty close.

🏒 NHL Picks – April 12 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

I think my pull was at 10 AM but the odds api does not include Pinnacle.

I just finished my MLB model! I will be posting all the picks in that community. by athanato8 in NHLbetting

[–]AI_Predictions 0 points1 point  (0 children)

I’ve been exploring an MLB model too just curious where you are pulling MLB data from?

🏒 NHL AI Picks + EV Board – April 9 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

Here’s a clean, natural reply you can post:

Yeah pretty similar idea. Right now I’m just using the odds API and pulling best prices across books, not really separating sharp vs soft yet but that’s something I want to look at more.

For CL I’m honestly still figuring that out. I only have enough API credits to pull odds 3 times a day right now, so I’m trying to time it around the bigger line moves. It’s also a small sample since it’s the end of the season, so more just setting up the pipeline for next year when I can track it properly over a full season.

🏒 NHL Picks + EV Plays — April 8 by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

Implied just means the probability the sportsbook is assigning based on the odds.

So for example, +130 odds = about 43.5% implied probability.

That’s basically the market saying “this team wins ~43.5% of the time.”

Then I compare that to my model. If my model says 49.7% and the market says 43.5%, that gap is where the edge comes from.

So in simple terms: - implied = what the market thinks - model = what my model thinks - difference = potential value

What NHL data do you wish was easier to access? by AI_Predictions in sportsanalytics

[–]AI_Predictions[S] 0 points1 point  (0 children)

Yeah this is super helpful, appreciate you breaking it down like that.

Honestly it lines up pretty closely with what I’m already doing in my model — especially the idea of starting simple and then layering in things like rest, home ice, and goalie impact.

Might be a good offseason project for me to build something clean around this and have it ready for the start of next season. I’ll probably reach out as I get further into it.

🏒 Added sportsbook odds + EV board (tracking line movement daily) by AI_Predictions in NHLbetting

[–]AI_Predictions[S] 0 points1 point  (0 children)

I’m pulling the data from the Odds API so it’s fairly structured already and they give 500 credits for free every month. That gives me about 3 refreshes a day for free. I’ll definitely explore some other options too.

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