Introduction To Hockey Statistics – First Chapter
Table of contents Introduction Shot Statistics2.1 Shot Hierarchy2.2 Shot Formulas2.3 Shot Tracking2.4 Summary Predictability3.1 Descriptive vs. Predictive3.2 Test setup3.3 Results3.4 Conclusion3.5 Sustainability vs. Luck3.6 Summary Data categories4.1 Team-, Individual- and On-Ice Statistics4.2 Player models4.3 Summary Analyzing a team sport5.1 Productivity5.2 Output vs. talent5.3 Summary Data Interpretation6.1 Biases6.2 Sample size6.3 Context6.4 Summary Data Sources7.1 Statistics sources7.2Continue reading “Introduction To Hockey Statistics – First Chapter”
BDC 2022 – Transforming Event Data Into Possession Data
Abstract The event data by Stathletes for the Big Data Cup is very detailed. This should allow us to determine when (time), where (coordinates) and how (event) a player gains possession of the puck. Likewise, it should be possible to determine when, where and how a player losses possession of the puck. In other words,Continue reading “BDC 2022 – Transforming Event Data Into Possession Data”
Game projection model – Pre-season Model (Part IIIA)
This is the continued saga of “how to build a game projection”. I’d recommend reading the three previous articles of the series before you continue reading this post: The goal in this article is to lay the groundwork for the final game projection model. In the previous article I built the in-season model. Today IContinue reading “Game projection model – Pre-season Model (Part IIIA)”
Game projection model – In-season Model (Part II)
In this article I will start building my game projection model. I’d recommend reading the two first articles of the series before you continue reading this post: The model construction The idea is to build a game projection model that actually consists of two separate models: A pre-season model An in-season model The goal ofContinue reading “Game projection model – In-season Model (Part II)”
Hockey Deployment Management analysis using Machine Learning
This article is written by Chris from ImPCT Sport. Follow them on Twitter: @ImpctSport. Here’s the previous articles written by ImPCT Sport: Lineup deployment management is a subject of debate in the sport community. By lineup deployment, I mean the manner in which ice time and starts are divided (or spread) between players during aContinue reading “Hockey Deployment Management analysis using Machine Learning”
Game projection model – The Variables (Part IB)
After I published the last article, I had a great conversation @IanGraph. Based on this conversation, I’ve decided to make a follow-up article. The main question is: Why does my findings on predictability differ from other research on the subject? Tests typically show that corsi and expected goals are better predictors than actual goals. However,Continue reading “Game projection model – The Variables (Part IB)”
Game projection model – The Variables (Part I)
The goal with this series is to build a new game projection model. In this first article I will focus on the different variables/metrics. I want to test how well they predict future results. The old model But first a few comments on the old model. It was built on the basis of Evolving-Hockey’s GARContinue reading “Game projection model – The Variables (Part I)”
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)”
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Hockey-Statistics 