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.
Using Linear Regression to Predict Goals
In this Edition
* What is Linear Regression?
* What are the Different Types of Linear Regression Models?
* Which Models are Most
Are your Athletes Ready for Game Day?
In this Edition
* What is Sports Science?
* What is a Player Wellness Report?
* Walkthrough: Creating a Player Wellness Report
What
Can you use Winning Percentage to Predict a Team Winning?
In this Edition
* What is the Pythagorean Winning Percentage?
* How is it Different from the Regular Winning Percentage?
* What Data
What is a good predictor for goals?
In this Edition
* Breaking Down the Question
* Why are Goals Important?
* Considerations for Predictive Models
* Modeling Goals Prediction Walkthrough
Breaking