Welcome to the first day using my Machine Learning Algorithms for the NHL. I’m not sure how they will perform since I have never done NHL before, but I started testing them on last nights games and I will make edits and changes as needed throughout the remainder of the season. I started with yesterdays 2 games.
I do not bet NHL and I do not follow it extremely close either, so right now the plan is to keep it simple and post just the top performing model, I don’t have the time to go more in-depth like I do with NBA. If you have any suggestions please let me know!
Hope you all can get something useful from this and as always best of luck everyone!
Here is how the models have been performing after a whopping 2 games.
Top Performing Models
Based on Average Win % between ATS, Moneyline, and O/U.
Updated after todays games.
- kNN – TBD
- Random Forest – TBD
- Ada Boost – TBD
- Neural Net – TBD
- SVM – TBD
- Basic Model – TBD
- Linear Regression – TBD
- SGD – TBD
Summary of Projections
1. k-Nearest Neighbor (kNN)
The lazy learner. Nearest Neighbor has performed best with 60 Neighbors.
Record: TBD ATS, TBD ML, TBD O/U.
If you don’t understand what you are looking at, please read my post about betting tips. The percentages represent value for that particular number. Good value alone doesn’t mean you should bet it (To quantify, >35% is great value, 20-35% is really good, 10-20% is decent, <10 is ok value, blanks are negative value).