APBRmetrics

A lot here, but briefly, comparing players in year to year will never add up to season totals as rookies enter every year and guys fall out of the league every year. Players with fewer than 800 minutes were dropped both because their ratings are more noisy and because it's a floor for a subjective l...

Here are my final Richard Jefferson bombshell included projections. Made with a blend of PT-PM and RAPM: ATL 34.6 BKN 28.1 BOS 49.2 CHA 46.3 CHI 30.6 CLE 53.4 DAL 32.3 DEN 43.1 DET 34.7 GSW 69.0 HOU 55.4 IND 36.5 LAC 53.5 LAL 27.2 MEM 35.4 MIA 42.8 MIL 42.0 MIN 47.1 NOP 41.1 NYK 32.8 OKC 45.6 ORL 33...

Thanks to all on the congratulations. Should note, my projections used a blend of PT-PM and JE''s RAPM. And in terms of "best" ratings, of course I used two years of data and added aging adjustments, expected mean regression and I run the team ratings through the schedule and so forth. So ...

Moreover, if we all agreed on the minutes projections (which is how I think these projection contests should operate, because we're actually more interested in the player valuation models than the minutes projections), which method would win? And if I just use something "simple" like 2-ye...

colts18 wrote:I tried out weighted Minutes played and it had a .759 R value to SRS, same as NPI RAPM. Who knew that simple minutes played from the previous season was so predictive?

How does weighted minutes work in this, adding up NBA minutes played the prior year for each team?

This is my official entry, basically only adjustment from original Nylon post is dropping MKG's minutes: Team Wins CLE 58 ATL 50 BOS 49 TOR 48 CHI 48 WAS 43 CHA 38 IND 42 MIA 41 DET 38 MIL 36 ORL 27 BKN 25 NYK 25 PHI 24 GSW 66 SAS 59 LAC 53 OKC 54 HOU 52 MEM 50 NOP 45 DAL 40 UTA 37 SAC 35 PHX 34 POR...

Not sure if this link will take care of including my rankings in the piece, but has them all. I never ranked Tuttle since he doesn't appear in the DX top 100, which I use as a filter because the model is trained on drafted players. https://docs.google.com/spreadsheets/d/1HpiV31VgK_uuXSj8jkpP-CpTXaC7...

Follow Up submission: In a sense I would say that statistical analysis is the worst way to select draft picks, except all of the others. In reality though, best practices are to combine scouting information and analytic analysis. There are a number of things scouts and video reviewers are able to se...

Hey guys, thanks for reaching out to me, I'll go back through the thread to catch up with the discussion. Here's my PAWS model list as updated this morning. "PAWS-RSCI RANK" Player PAWS-RSCI 1 Jahlil Okafor 8.04 2 Karl Towns 7.75 3 Tyus Jones 7.29 4 D Angelo Russell6.99 5 Kevon Looney 6.58...

One issue looking at rookie performance and draft projections is that many models, including mine and Vashro's are not built to project rookie year. Mine is for year's 3 and 4 primarily. Though I do have a complete first four year version in the works. Still this is looking like my worst OOS year to...

Wanted to drop my predictions here just for the record. One using ASPM (pre-BPM) and my Player Tracking PM, which is in a Beta stage metric using SportVu and Boxscore data. Tm PT-PM Wins CLE 56 MIA 50 TOR 47 WAS 46 ATL 45 CHI 45 IND 43 BKN 38 CHA 37 DET 37 BOS 32 NYK 32 ORL 32 MIL 30 PHI 19 LAC 55 G...