APBRmetrics

This is a very cool idea. Do you have any retrodictions available so we can go "ooh, ahh" about the sleeper picks this system nailed? Haha yes for sure. I’ll put them all up on a website soon and post the link here. I just found out I’ll be presenting a poster on it at NESSIS this year to...

Here’s the model’s top few picks. Enjoy the draft tonight!

Player
Zion Williamson
De'Andre Hunter
Ja Morant
Jarrett Culver
Brandon Clarke
Jaxson Hayes
Coby White
R.J. Barrett
Bol Bol
Grant Williams
Cameron Reddish
Rui Hachimura
Cameron Johnson
Darius Garland

Who does the model like this year? The important questions Likely to be better than consensus mock drafts: Brandon Clarke and Grant Williams. Likely to be a little worse: R.J. Barrett and Darius Garland. But ultimately the main takeaways for me are the importance of having an analytics process, and...

Which do you think is the bigger issue... GMs not having the right analytics, not getting strong enough guidance from analysts or just not using whatever they have well enough because of own thoughts or scouting guidance? I think it's a combination of a lack of rigor, illusion of control, and avail...

Hmm, that was kind of what I thought and I'm not sure I agree, as it penalizes players who play but play poorly more harshly than players that don't play at all (if I'm reading it correctly). That's exactly the right reading. And you are also right about your qualms. It's part of a bigger issue aro...

If you isolated combine measurement information from everything else, is the correlation with performance stronger or weaker in the playoffs compared to the regular season? That's a really interesting question. I didn't look at playoffs at all, only regular season NBA performance, to keep it identi...

Interesting model/approach. Ok I do not really follow (or claim to understand) NBA drafts, but I was wondering what are those three variables? Can you explain what they mean. Especially Estimated Wins Added. Crow's answer is exactly right about EWA, based off of PER. All three of these methods try ...

Looks interesting for sure, I'm sure I'll think of more questions, but one first one - how do you handle players with negatives in a stat like WS? It's just negative. ¯\_(ツ)_/¯ Just means a player contributed net losses to his team through his performance (eg turnovers, fouls, missed shots, etc.).

I just finished training nearly one thousand LOOCV individual random forests machine learning models, one for each collegiate NBA draft prospect who was in the ESPN Top 100 prospects list prior to the draft, using their college efficiency and production stats, combine measurement information, high s...

Hate to be mysterious, but lack of success was definitely not the problem or the reason the public service was taken down. FYI, the paper is currently under review at a technical journal. (And by the way, the "p value" calculation/hypothesis above is not correct.)

I thought you'd been setup with access quite a while ago? Send me an email if you're having difficulties, let's get you sorted.

Thank you so much for the kind words!!

just fyi here's another piece about the approach, this one more descriptive than quantitative:
http://www1.vantagesports.com/Articles/ ... 8AACwqCgYv

Thought you might be interested -- my new paper suggesting a deep-learning market based approach to answering this question generally, and applied to some NBA datasets in particular.

paper: http://ssrn.com/abstract=2703636

or video with slides here: https://youtu.be/vsaAOAgja7g

Thank you all for your thoughts and comments.

FYI, some more optical facts and responses in this follow-up article:

http://www.vantagesports.com/story/Vg7l ... o-the-best

Adam Yudelman just wrote a piece for us at Vantage Sports comparing the optical "contested" numbers with our own definition.

Would love your reactions and thoughts.

http://www.vantagesports.com/story/Vg1Z ... hot-metric