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2018 NBA Draft - June 21

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A cop out that calls into question K's reputation as a maker of men.

Can't argue with you there. Coach K is just coasting by on reputation and his past as a Team USA coach to pull in recruits, but nowadays he isn't a very good schematic or in-game coach.
 
A shame Michigan State wasn't so aggressive in attacking Syracuse this afternoon, they might still be playing :chuckle:

Boeheim is a great human being, loyal to his city and team for his whole life. Great presence in the community.

The key to the Syracuse zone when it works is rapid motion, defensive awareness and effort, and CONTESTING ON THE PERIMETER. Syracuse allows a very low three-point percentage on average. Those are all skills that translate very well to the NBA.

I've watched all those 'Cuse players over the years and most didn't make it in the NBA because they weren't that good. It's just very rare for Syracuse to get the top no-doubter type players. If you think Jonny Flynn was actually good I don't know what to tell you.
 
I think JP Macura is going to have a long successful NBA career. He’s likely a late first to mid second rd pick, but his skill level and shooting ability is special. He’s sneaky athletic too.

I’d start him at 2G for the Cavs tomorrow if I could.
 
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They had SO MANY CHANCES, but just ran out of gas.

Lyles should have attacked much more... have no idea why he was passing so much on the perimeter up until the last few minutes.

His defense was pretty nice. Good group of guys...they missed every open shot. I actually thought they were the better team. Disappointing.
 
The main lower-end guy that I expect to rise up draft boards due to the tourney is Keenan Evans from Texas Tech.

Senior guard without much point guard skill, but undersized as a 2-guard. There's so damn many of these sorts of guys that they get sort of swept under the rug because they usually don't pan out. I think Texas Tech will get to the Elite 8 and if he has another good game, he'll get more attention.

He's pretty much solid across the board offensively. Well above average PPP in isolation and as the P&R ball handler. Shoots 59.2% at the rim. In the 92nd percentile at shooting off the dribble.

While he doesn't have a ton of ability to playmake for others on the ball, his main issue is playing off the ball. He shoots just 27.1% on catch and shoot jumpers. Not sure if he'll be able to maintain his efficiency shooting off the dribble... that's the key to his game.

Haven't watched a ton of him closely on defense to be able to have any opinion there, and don't really trust the Synergy numbers. He has a solid BPM (sorta shitty stat I know) for a PG, and Texas Tech is one of the better defensive teams in the country for whatever that's worth.

ESPN doesn't have them in their top 100, or even in their top 25 seniors right now (though have no idea when the last time it was that they updated that list).

These sorts of older score-first guards that will probably have efficiency questions in the NBA are a dime-a-dozen coming out of the NCAA season (Jarius Lyles another one that just came out of nowhere), but I think that if Texas Tech has another quality game against Purdue and has at least a good showing vs Villanova that you'll hear a lot more about Keenan Evans as a possible 2nd round pick.
 
Updated my statistical rankings through yesterday's games:

zKZiY.png


Smith and Gilgeous-Alexander by far the biggest risers. With JJ's season over, Jontay has most likely clinched the top spot in my rankings. Bamba squeaks ahead of Ayton as they both end their seasons.

As always, I'm happy to calculate ratings for any other interesting prospects from this season or from previous seasons. I'm also happy to explain my methodology in any level of detail.
 
Updated my statistical rankings through yesterday's games:

zKZiY.png


Smith and Gilgeous-Alexander by far the biggest risers. With JJ's season over, Jontay has most likely clinched the top spot in my rankings. Bamba squeaks ahead of Ayton as they both end their seasons.

As always, I'm happy to calculate ratings for any other interesting prospects from this season or from previous seasons. I'm also happy to explain my methodology in any level of detail.


Explain your methodology you bitch!

Shai should be at the top... he is a bae. His strides, smoothness, ability to get in the the lane and his floor game is top level. He also oozes with confidence and iq. Watch his interviews.

He is ugly And skinny, but that's something you have to live with.
 
Explain your methodology you bitch!

Shai should be at the top... he is a bae. His strides, smoothness, ability to get in the the lane and his floor game is top level. He also oozes with confidence and iq. Watch his interviews.

He is ugly And skinny, but that's something you have to live with.

Main things hurting Shai's rating are his low 3-point volume and his low offensive rebound rate (which could be considered a measure of physicality/athleticism). Another thing that hurts him is that I'm just looking at season averages, and he was pretty meh for the first 2/3 of the season.

I'll link to my earlier explanation in case you didn't see it when I posted a couple weeks ago; happy to answer questions about any of it of course:

My model attempts to predict NBA adjusted plus/minus. The major benefit of predicting APM, instead of an NBA box score metric, is that my model doesn't inherit any biases at this stage (e.g. a model that tries to predict PER will necessarily end up with all the same biases/flaws of PER). The major drawback is that adjusted plus/minus is a very noisy stat, and the price I pay for trying to predict a noisy stat is relatively high uncertainties in my model coefficients (e.g. the marginal value of an NCAA assist, or rebound). Ultimately, it's a good tradeoff because in most cases the extra uncertainty in my predictions due to uncertainty in model coefficients is small relative to other sources of uncertainty.



My model assumes that there are no interaction terms between parameters. That means that the value of an NCAA player's assist according to my model does not depend at all on how many rebounds he gets, or how many points he scores.

It turns out that this assumption is absolutely crucial. Without it, the space of possible models is too large relative to the available sample size. In general, if you have an N-dimensional problem (and all the dimensions are important), you need at least 2^N data points for general smooth manifold fitting methods (like machine learning type stuff) to work. Here, N>15 (at a minimum), so we would need a sample size of around 50,000.

One way to visualize this problem is as follows. Picture a thousand points in one dimension, i.e. on a line. Only two points are "on the frontier"; the maximum and the minimum. All other points have neighbors on either side. Now picture them in two dimensions, on a plane. Now dozens of points around the perimeter of this blob are on the frontier. By the time you get to 15 dimensions, virtually all of the points are on the frontier, and very few have neighbors nearby.

The assumption that there are no interaction terms avoids this problem nicely by effectively breaking up the 15-dimensional problem into 15 one-dimensional problems, where a sample size on the order of a thousand is more than enough for a smooth curve fit (which is what I do).

In reality, it's a big enough sample size to get away with *some* interaction terms, but this is tricky business and often ends badly. The classic example is the assists*rebounds interaction term in basketball-reference's box plus/minus. At the time it seemed clever; the top 6 seasons of all time belonged to LeBron and MJ. Then, last year Russ had his triple double season and shattered the all-time BPM record, and the reputation of BPM as a stat. I chose to keep it simple and safe, and avoid any dabbling in interaction terms.



The last important thing my model does is estimate uncertainties in its predictions, something notably lacking from most such models people have published. This illuminates some of the strengths and weaknesses of my model. For example, the largest contributor to uncertainty is made two-pointers, that is, my model is generally less accurate in predicting players who make a lot of two pointers. This makes some sense; a player's two pointers made per game (even taken together with two point percentage, or equivalently two pointers missed) falls far short of describing how good a scorer the player really is inside the arc. There's just not enough information in this part of the box score to properly evaluate a player.



Some more minor things:

-All stats are per possession. I also include height, and minutes per game.

-I assume a quadratic aging curve. I found that on offense, better prospects actually follow a steeper aging curve than worse prospects, and I accounted for this as well.

-My sample only goes through the 2012 draft. This hurts my sample size, and also hurts because my model is really tuned to predict how players entering the NBA a decade ago would be expected to perform. Obviously the NBA has changed since then and I have no way of adjusting for that.

-My sample only includes prospects that went on to play significant NBA minutes, so it suffers from "survivor bias" and therefore tends to be slightly too optimistic in its projections. How to correct for this is an interesting question in its own right that I won't get into for now (but could talk more about if you're interested).

-My model has some interesting artifacts because of the relatively large uncertainties in the coefficients I mentioned earlier. For instance, made two pointers have a slight (not statistically significant) negative value. In reality, they probably have (at least) a slight positive value. I could manually correct things like this to make my model slightly better, but that's obviously a slippery slope toward tweaking and tuning my model in retrospect to make it look like I think it "should." So I decided to just let it be, even in cases where the helpful tweak is obvious.

EDIT 2: Forgot one other note

-My model doesn't account for strength of schedule or team strength.
 
Main things hurting Shai's rating are his low 3-point volume and his low offensive rebound rate (which could be considered a measure of physicality/athleticism). Another thing that hurts him is that I'm just looking at season averages, and he was pretty meh for the first 2/3 of the season.

I'll link to my earlier explanation in case you didn't see it when I posted a couple weeks ago; happy to answer questions about any of it of course:

Cal was busy giving Diallo and Quade Green run lol.
 
Cal was busy giving Diallo and Quade Green run lol.

Almost like coaches are too close to their players to properly see things that are obvious from a distance. Funny how that happens.

EDIT: Not saying coaches are dumb or anything, to be clear. Just a case of "can't see the forest for the trees." Happens to all kinds of professionals in all kinds of situations.
 
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From SDSU so I fully expect him to be as good or better than Kawhi because that's how this works, right?
 
Almost like coaches are too close to their players to properly see things that are obvious from a distance. Funny how that happens.

EDIT: Not saying coaches are dumb or anything, to be clear. Just a case of "can't see the forest for the trees." Happens to all kinds of professionals in all kinds of situations.

I'm pretty convinced that a good portion of NCAA coaches are nothing more than cheerleaders in dress clothes for the majority of the time lol
 

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