Predicting Wins using k-Nearest Neighbors
In this week's newsletter, we feature another AI and hockey analytics topic on predicting game outcomes using k-nearest neighbors (KNN).
Optimizing your Predictive Models
In this week's newsletter, we continue our AI series with a seventh newsletter on AI and Hockey Analytics and introduce you to how you can optimize your predictive models to make them stronger and better-performing.
Driverless AI: Building the Perfect Predictive Model
This is the sixth newsletter in our AI and Hockey Analytics series. This week, we cover AutoML and how you can use it to create the perfect win prediction model using H2O.ai.
Predicting Wins using Support Vector Machine Models
This week's newsletter continues the AI and Hockey Analytics series and covers how you can use Support Vector Machine (SVM) models to predict game outcomes.
Predicting Game Wins using Logistic Regression
This week's newsletter introduces you to a common machine learning technique called Logistic Regression and shows you how you can use this technique to predict if NHL teams will win or lose their games.
Finding the Top Snipers using K-Means Clustering
In this week's newsletter, we'll introduce you to K-means clustering and walk through an example where we use clustering to find the top ten NHL snipers.
Overview of Goals Versus Threshold
Goals versus Threshold is an advanced statistic used by sports and data scientists to evaluate players and teams against a replacement statistic.
