New Work on Other Websites

I recently wrote a fan post on Broad Street Hockey breaking down how the Flyers have drafted since the lockout, as well as some general leaguewide draft analysis. Here's a link. Additionally, several of these graphs or a close equivalent were featured in this article on the Islanders draft history by Arthur Staple from The Athletic.

I also wrote a tutorial on using gganimate and R to create shot location game gifs. This can be found at http://barloweanalytics.com/gganimate.html

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Measuring Consistency

A while back I decided to look into player consistency, but after doing initial calculations, I never went any further. After Namita Nandakumar's VANHAC presentation on consistency, I decided to go back, refine my old work, and release the results. Namita's methodology is likely much more statistically relevant and meaningful, but nonetheless, I use a different approach that I think is worth sharing. The methodology I adopted was taken from this article on Nylon Calculus on NBA player consistency written by Hal Brown. This consistency metric gets the normalized variance of a player's performance for a given metric. In this post, I will be calculating the consistency of a player's game score (GS) in individual seasons, from 2007-08 to 2015-16, using the data provided at the bottom of the linked game score article. I also have a folder with my code + better resolution graphs + data at the bottom of the article if you'd like to check it out. Calculation T...

Who Plays Where? Determining Skater Positions Using Clustering

While browsing through various different websites keeping NHL player stats, I realized that the league does a terrible job of keeping updated player positions. I’m not exactly sure how or where they get their data from, but it is quite inaccurate. All sites do distinguish between forwards and defenseman, which is enough for most analysis, but I still think more specific player positions hold value, especially when looking at team depth and related areas. In an attempt to solve this problem, I decided to use k-means clustering on location information within play-by-play data (thanks to Emmanuel Perry and Corsica Hockey for making this cleaned data available to the public). Clustering has been used pretty frequently in hockey analysis, most recently (I believe) to identify different styles of goal scorers by Alex Novet. It has also been used by Ryan Stimson to identify team and player styles with data collected from his passing project and similarly on DTM About Heart’s old blog...

New Project Announcements

I recently completed two projects that were primarily shared through twitter but I'll briefly explain them on here. My first project is a shiny app that allows users to select two players from the entire history of the NHL, NBA, or MLB and view the shortest path between their careers by who they've played with. For example, here is how Jack Laviolette (born 1879) and Alex Formenton (born 1999) can be connected. You can use the tool here jflancer.shinyapps.io/shiny_app/ . My second project was scraping NWHL play by play data and cleaning it into an easy to use format with R. This project entailed a lot of cleaning and formatting, and I additionally wrote a formula to calculate strength state from just the penalty and goal data available. If you'd like to check out the code or use the data it can be found here https://github.com/jflancer/nwhl-scraper .

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