Reinventing Basketball Media.

Joined October 2020
Grading the Los Angeles Lakers' Off Season๐Ÿ€ (A Thread) In collaboration with @dlee4three
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Predicting the Los Angeles Lakers' Floor Record๐Ÿ”ฎ
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Predicting the Los Angeles Lakers' Ceiling Record๐Ÿ”ฎ
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Replying to @tipoffball
Appreciate the collab @TipOff and the beautiful graphics want to add a couple more words on LOKI: LOKI is a predictive metric built on the philosophy of possession-by-possession fundamentalism. Tested out of sample, it matches or beats EPM, DARKO, and LEBRON in predicting games and team win percentages using preseason ratings (2020โ€“21 to 2025โ€“26). Quantifying the answer to the following question: "Given the limited number of possessions, in an environment where every action within a possession can be quantified, whether tangibly (EPV) or abstractly (RAPM), what is the incremental value a player generates for his team?" One of the key tenets of LOKI was to stay decomposable rather than being an opaque number. Building on the great RAPM decomposition work of @databallr and 4 factor framework of @DeanO_Lytics, can we take the opaque total number and contextualize it into the channels through which a player actually affects the game? If you're curious to learn more, my DMs are completely open. Here's some reading, and here's a mini paper I wrote on the development of ARC v3, the descriptive metric it was built on: xitter.cf/Starro____/status/2108โ€ฆ
Contemporary all-in-one impact metrics often regress box score actions onto RAPM to find their weights. A fitted model can give a steal about 3 times the value of a point scored leaving us with an opaque number that can't be decomposed into how or the degree they affect the game why not just count em up? Introducing ARC v3 An Expected Box Value Prior RAPM (EBV RAPM) That aims to quantify impact using possession by possession accounting ๐Ÿงต
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Can We Predict the NBA's Most Impactful Players This Season?๐Ÿงจโ‰๏ธ (A Thread) In collaboration with @Starro____
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