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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Tape to Tape Tracker Visualization

tapetotapetracker.com has created an excellent way to track shots, shot assists, and zone entries. Using an 11 game sample of 5v5 data from here provided by Prashanth Iyer, I created a Tableau visualization to map the shot and shot assist data. This data includes all shot attempts classified by type, and when relevant the pass leading up to the shot. The "origin" is where the passer makes the pass, and "destination" is where the pass is received. Finally, each shot is denoted as "goal" "shot"- SOG or "missed shot", and the location is where the shot was taken. Some features include viewing by the shooter, passer, team, and game. You can also select specific events and results. Result filters an entire event by what its end result was. For example, if "goal" is selected, it will show all events (origin+destination+shot) which resulted in a goal. Similarly, event filters for individual events. This means that specific types

Tape to Tape Shot Visualization

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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

Three Point Games

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