📉 📊 📈 Stats Nerdery

Here are some illustrative WAB-by-quadrant tables. (In green is how much WAB the team got from games in that quadrant.)

UVA:

VT:

SMU:

People like to say, “the way to game the system is to schedule bad teams and beat them by 40,” but that doesn’t work with WAB (it doesn’t look at MOV). Beat bad teams, you’ll gain next to nothing; slip up even once in those games (see VT), you wipe out all your gains. It’s a fantastic resume summary.

(And yes, there is value in predictive metrics too. For one they, they are more… predictive. But I love the simplicity of WAB for resume comparison.)

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This Is The Way GIF

The other thing I like about WAB is that you have a reasonable way to compare say…Miami (OH)’s resume to Kentucky’s in a way that you just can’t do by tallying Q1-Q2 records.

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Also, “just blowing out bad teams by 40” isn’t actually that easy. It may be in fact be the most reproducible sign of a good team.

Thanks for these WAB graphics.

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Missed this until Eamonn Brennan highlighted it, but yeah, we need to center WAB:

https://x.com/davidworlock/status/2019077448218890509?s=46&t=wAzMZR-0PRGT1LpN6U5x0g

One step closer to giving the committee a golf weekend and letting Steve (just gonna assume his name is Steve) push the button on the computer that runs WAB one more time on Selection Sunday.

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Quads are just a way to dumb things down into discrete chunks (losing signal in the process) enough so that humans are able to do an analysis that the continuous metric does a better job of already.

But we both know the quads aren’t going anywhere. The committee is going to have to miss a round of golf.

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Interesting that EvanMiya has Virginia as a pretty consistent team. Also, I guess he has the Hoos as a fringy title contender (or maybe I’m being overly optimistic).

https://twitter.com/evanmiya/status/2019852343156650492?s=46&t=vkjgQUekzGC7z44tIfnIRQ

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Their results might feel bouncy to us who watch them relentlessly, but are actually less bouncy than what other teams go through (who we watch a lot less relentlessly).

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Making some charts with Claude Code. First the distribution of freshmen’s PORPAG (Bart Torvik’s all-in-one impact metric) by star rating:

Second chart is plotting recruit rating against PORPAG and Win Shares/40:

This filtered for freshman with >100 minutes played. As one would expect, recruit rating is correlated with performance, but there’s a lot of player-to-player variability.

The outlier 4-star in the PORPAG graph is Ebuka Okorie, the outlier 3-stars are Keaton Wagler from Illinois and Isaiah Johnson from Colorado. The other 3-star in the list of top-15 freshmen by PORPAG is Leroy Blyden Jr, a hot-shooting 6’1” PG at Toledo.

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I wish Neckel didn’t have as much reach as he does. He makes UVA fans look innumerate.

https://x.com/DNeckel19/status/2025663526509965700

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I aM a StAtS gUy

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Mostly because what’s being measured isn’t aligned with the name of the stat!

But also because it doesn’t vary much, so talking about ranks is misleading. (E.g. #50 sounds way faster than #250, but the difference between those two is only like 4 possessions a game.)

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https://twitter.com/KySportsRadio/status/2041577182288396593

This is interesting. Good for Evan.

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This is about NBA, but tempo/pace isn’t getting it done in the Association.

https://twitter.com/zachkram/status/2042243961998160046?s=46&t=vkjgQUekzGC7z44tIfnIRQ

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Tony Bennett is already ruining the NBA

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This site is new to me, but has rankings, preseason projections, and portal rankings:

Cool that the author is writing about how he developed his player projection approach (warning: extremely stat nerdy):

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Cool site. Is there a name for the ‘clearly built a web app with Claude’ aesthetic yet?

Beyond “vibe coded”? Dunno, but it’s kinda nice that it’s raised the quality level of the hobbyist website (though there is the “increase in slop” tradeoff).

The author’s GitHub is public too, which is also very nice for transparency purposes:

Now that all of the major computer-based preseason ratings are out, maybe worth discussing EvanMiya’s recent analysis of prediction accuracy:

He has his own ratings and KenPom’s close to each other, with a gap between them and T-Rank.

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