The thirteenth issue of Unexpected Points Added! Please do me a favor and share it with at least one person this week.
A little more balance this week with a new NFL Python Package, looking into how the Premier League balances attacks (or lack thereof), clustering to understand Tennis styles, an inflated MLB team defensive runs metric, and the historical exploration of players going on hitless streaks.
This Week's Lineup
New NFL Python Package
nfl-data-py is a new Python library for interacting with NFL data.
Premier League Attacking Balance
By looking at Progressive Value, a score given to each pass, and how it contributes to a goal, we can see how teams spread the playing are receiving passes around their players to identify teams attacking balance.
Classifying Tennis Players Based on Style
Using K-Means Clustering to identify four main playing styles and assign them to the ATP Top 100. Looks at a regression analysis to understand which play styles benefit the most over others.
DRS team fielding seems overinflated
The standard deviation of team Defensive Runs Saved is almost 3x as high as UZR and OAA. Be wary of using this statistic without accounting for this inflation.
Historical Exploration of Ofers
Tries to answer what's an unusual length for an "ofer" streak by utilizing R and Tretosheet play-by-play files. Touches on patterns over the past 20 years too.
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Unexpected Points Added is curated and maintained by Patrick Hayes. If you have questions or suggestions for the newsletter, just reply to this email. I answer every single one.