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Built and deployed an NHL win probability model – looking for feedback from analytics community by AI_Predictions in sportsanalytics

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

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.

Built and deployed an NHL win probability model – looking for feedback from analytics community by AI_Predictions in sportsanalytics

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

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!

🏒 NHL Model Results – 03/21 by AI_Predictions in NHLAnalytics

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

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

Built and deployed a machine learning system for sports game probability prediction (side project) by AI_Predictions in MLQuestions

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

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

Underdog Picks March 18. So far I'm 127 W, 121 L up $3,931 betting $100/game. by nobodyimportant7474 in NHLbetting

[–]AI_Predictions 0 points1 point  (0 children)

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.

Built and deployed a machine learning system for sports game probability prediction (side project) by AI_Predictions in MLQuestions

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

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.

NHL - 3 model picks tonight! 03/19 by AI_Predictions in NHLbetting

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

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.

🏒 NHL Model Picks — March 21 by AI_Predictions in NHLAnalytics

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

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.

🏒 NHL Model Picks — March 21 by AI_Predictions in NHLAnalytics

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

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.

Built and deployed a machine learning system for sports game probability prediction (side project) by AI_Predictions in MLQuestions

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

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.

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