Machine Learning Times
Machine Learning Times
AI and ML in Health Care: A Brief Review
 Of the many disciplines that are active users of...
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...

2 months ago
The Science Behind NFL Next Gen Stats’ New Passing Metric

Originally published in Amazon Science, Aug 26, 2022. 

Spliced binned-Pareto distributions are flexible enough to handle symmetric, asymmetric, and multimodal distributions, offering a more consistent metric.

When football fans evaluate a player’s performance, they measure the player’s execution of specific plays against an innate sense of the player’s potential. Trying to encode such judgments into machine learning models, however, has proved non-trivial.

Fans and commentators have criticized existing quarterback (QB) passing stats, such as Madden QB, the NFL passer rating, ESPN’s total quarterback rating (QBR), and the Pro Football Focus (PFF) grade, for being calibrated to obsolete data, being unrelated to winning, or scoring players anomalously — as when Kyler Murray received the low Madden QB21 rating of 77 despite being the 2019 Offensive Rookie of the Year.

On January 13, 2022, just before Super Bowl LVI, the NFL announced its new QB passing score, which seeks to improve on its predecessors’ limitations and to isolate a QB’s contributions from those of the team in a completely data-driven way.

To continue reading this article, click here.

Leave a Reply