Sports Analytics Blog

Weekly Roundups

Weekly Sports Analytics News Roundup – August 6th, 2019

Football: Football Outsiders’ 2018 Slot vs Wide: Wide Receivers. Football Perspective’s Marginal Air Yards: 2019 Update by Adam Steele.

College Football: FiveThirtyEight says Want To Bet Now On The Heisman Trophy Winner? Maybe Don’t. Brian Fremeau’s 2007-2018 points per drive data organized by team.

Baseball: Baseball Prospectus’ Statcast Launch Angle: A Statistical Accuracy Assessment. FanGraphs finds the Second Wild Card Didn’t Ruin the Trade Deadline. Baseball Prospectus with Deep, But Playable: The Moral Hazard of Playing It Safe. FiveThirtyEight debates Who Got Better At The MLB Trade Deadline. Daniel Marks digs into Championship Win Probability Added. Mark Simon announces Hunter Renfroe & Chad Pinder as July’s Top Defenders. FanGraphs writes that The Astros May Have Salvaged Another Pitcher’s Career. The Hardball Times on The Physics of Throwing a Ball Out of the Yard. Tom Tango posts Trout has a .441 OBP and .441 wOBA: what does that mean? FanGraphs puts into perspective Just How Busy the 2019 Trade Deadline Was.

Basketball: Nylon Calculus: NBA trades and auction theory. Wizards’ new assistant coaches include analytics guru, a first-time coach.

College Basketball: Jordan Sperber’s Hoop Vision Weekly: MAILBAG Edition.

Hockey: Hockey Graphs finds Exit Types Don’t Affect Entry Quality (Much). Matt Donders put a very basic proof of concept on Github for generating shotmaps all contained within an AWS Lambda serverless function. The Athletic’s Ben Baldwin gives An introduction to hockey analytics and what hiring a numbers guru means for the Seattle NHL franchise.

Soccer: Peter McKeever blog post: Identifying progressive ball carriers. American Soccer Analysis piece on Using Expected Threat to Find the Best Shot-Creators in MLS.

Tennis: Heavy Topspin’s GOAT Races: Forecasting Future Slams With a Monkey.

General: A Journey Into the Heart of Sports: Data Viz with Daren Willman

Your moment of R: BaseballWithR post on Predicting Home Run Count using a Random Effects Model.

Check out these Pythons: FCrSTATS publishes a collection of Python code snippets and functions that I have used to work with Tracab data. Ryan Davis’ tutorial on writing a basic play by play parser for play by play data coming from the stats(dot)nba endpoint.

Conferences: The Saberseminar will be held August 10-11 in Boston.

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