Talent distribution – Contract value (Part V)

This is the fifth part of the talent distribution series. You can find the parts here: Percentiles, Predictability, Forwards vs. Defenders and goaltending. In this post I will focus mainly on contracts and contract value. The data set All contract data for this article is from capfriendly.com, and the data goes back to the 2013-2014Continue reading “Talent distribution – Contract value (Part V)”

Deep Dive into Offensive and Defensive Quality in the NHL

This article is written by ImPCT Sport. Make sure to give them a follow on Twitter @ImpctSport. In my previous publication, I covered the ImPCT Engine. The ImPCT engine is a machine learning based algorithm that was developed to process large amounts of sport related data and provide meaningful intelligence on the performance of players.Continue reading “Deep Dive into Offensive and Defensive Quality in the NHL”

Talent distribution – Goaltending (Part IV)

This is the 4th article in the talent distribution series. You can find the other articles here: Part 1, Part 2 and Part 3. This piece will focus on goaltending. What is rink bias? You can’t really discuss goaltender statistics without also mentioning rink bias. Very basically, you can say that rink bias is shotContinue reading “Talent distribution – Goaltending (Part IV)”

Deep Learning Modeling of Hockey Game Contribution

The article below is written by Chris Tremblay from ImPCT Sport. Make sure you’re following them on Twitter @ImpctSport. They’ve created a player evaluation model based on machine learning, and here’s a short introduction to the model. More to come from ImPCT Sport in the future. Player profiles using the ImPCT engine Assessing player’s entireContinue reading “Deep Learning Modeling of Hockey Game Contribution”

Talent distribution – Forwards vs. Defenders (Part III)

In this piece of the talent distribution series, I will compare forwards and defenders. I recommend reading Part 1 and perhaps Part 2 first. How the sGAA model is designed Before we get started it’s important to understand how my sGAA model is calibrated. It’s designed to describe goal differential at the team level. So,Continue reading “Talent distribution – Forwards vs. Defenders (Part III)”

Talent Distribution – Predictability (Part II)

I didn’t really plan to discuss predictability in this series about talent distribution, since the two are not directly connected. However, I was asked about it, and it does open up for a good discussion about descriptive vs. predictive modelling. I recommend reading part 1 before you read this piece. Descriptive vs. Predictive models TheContinue reading “Talent Distribution – Predictability (Part II)”

Talent distribution – Percentiles (part I)

This piece will be the first in a series about talent distribution. I’ve wanted to write about this for a while, but I haven’t really had the time until now. In this article I will focus on skaters and how their “talent” is distributed across percentiles. What is talent? The first thing we have toContinue reading “Talent distribution – Percentiles (part I)”

Goaltenders have no apparent influence on shot misses!

Introduction In this article I basically seek answers to these two questions: Can a goaltender impact shot misses? How does this affect current goaltender metrics? But before we get to that point, we need to answer a wide variety of questions. In this piece I’m solely looking at 5v5 data, and the shot information isContinue reading “Goaltenders have no apparent influence on shot misses!”