Sports Analytics Blog

Weekly Roundups

Weekly Sports Analytics News Roundup – March 31st, 2020

Football: Josh Hermsmeyer: The NFL Was Weird Last Season. The SIS blog examines Which QBs received the most/least help from receivers on completions. Football Outsiders’ Aaron Schatz with QBASE 2020 (ESPN+ Version). Football Perspective writes The Shrinking Middle Class of QBs Applies To Age, Too. Football Outsiders’ QB Functional Mobility Model 2020. The SIS blog identifies the top red zone receivers among NFL prospects.

Baseball: FanGraphs Is Asking for Your Help. Ben Clemens Updates the Pinch Hit Penalty, with a Few Rules of Thumb. Tom Tango’s Statcast Lab: Is there a different run value needed based on the infield slice?. FanGraphs on High Fastballs and Hidden Strikeouts. Bill James continues his series of posts with Legally Stolen Bases, Saving Private Runs, Strikeout Runs SavedThe High Cost of the Free Pass. Baseball Prospectus Moonshot: Turning Video Into Data. Baseball Prospectus Baseball Therapy: Re-Re-Thinking the Two-Spot. Mark Simon finds The most dominant changeups and The most dominant curveballs. Daniel Marks The Best Players of the Last 50 Years – Part I – Kickoff & Catcher Review and The Best Players of the Last 50 Years – Part II – Shortstops. The SIS blog takes A closer look at the 2 best defensive seasons we’ve tracked.

Hockey: Meghan Hall’s Introduction to Tableau: A Tutorial on Basic Chart Types. Hockey Graphs Introduces Offensive Sequences and The Hockey Decision Tree. Hockey Graphs’ By the numbers: thinking about the World Championships a different way.

Soccer: StatsBomb’s beginner’s guide to analyzing teams using stats. American Soccer Analysis acknowledges Coaches Reward Goalscorers. But Should They?

Tennis: Stephanie Kovalchik examines What the World Wars Might Tell Us About Returning to Top Slam Form After a Long Hiatus From Play.

Olympics: Neil Paine tries to understand How Many Athletes Just Lost Their Shot At Olympic Glory?

Check out these Pythons: PC Python’s new football event tagger, create x/y data on any match event. Matthew Barlowe’s NBA parser package. Robert Seidl shares a repository he found that allows you to visualize tracking data in python using bokeh.

Your Moment of R: Video tutorial: Getting Started with R + StatsBomb | Analyzing Squad Rotation & Clustering Passes. Mark Wilkins’ Getting started in R with StatsBomb Data.

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