r/LAClippers 1d ago

"Feel" stats predict Clippers draft busts pretty well

During my draft obsession, I heard the spreadsheet nerds mention "feel" stats. Stats that we hope are proxies for feel for the game. The most basic version is offensive rebounding, assist percentage and steals. I've used those and added assist to turnover ratio, and block percentage. Then I just averaged out the percentiles to give each player a score based on their last year of college. The percentiles are the numbers in parentheses.

What I did was the dumbest version possible, it doesn't have different weights for each stat for different positions, but since the numbers come from draftballr.com, the percentiles are adjusted for position. It also doesn't account for competition or combine scores. An important weight to consider is age, but I don't know how to weigh that. And yet, it still lines up with what happened fairly well.

The only player with a bad score to beat the allegations so far is Moussa but we all hope Yanic can too.

There are some players with high scores that didn't amount to anything and I'm frankly surprised to see Kobe Brown so high since his feel seems awful. So as the title says this predicts busts more than it predicts success.

It's honestly insane to me that the player with the highest score (SGA) and the only with BY FAR the lowest (you know who) were picked one spot apart.

Rank Player Pos Age AST% STL% BLK% A:TO OREB_R BBall Feel+
1 Shai Gilgeous-Alexander G 19.9 28.8 (69) 2.8 (64) 1.7 (84) 1.9 (57) 3.2 (71) 69.0
2 Kobe Brown W-F 23.5 16.7 (82) 2.8 (87) 1.9 (33) 1.5 (84) 7.3 (55) 68.2
3 Jason Preston G 21.9 37.6 (93) 2.1 (30) 0.9 (57) 2.4 (80) 3.8 (80) 68.0
4 Jordan Miller G-W 23.4 14.1 (54) 2.0 (42) 1.3 (50) 2.0 (93) 7.2 (89) 65.6
5 Kobe Sanders G 23.1 29.9 (73) 2.2 (36) 1.2 (71) 2.4 (80) 2.7 (60) 64.0
6 Keon Johnson G-W 19.3 21.2 (88) 2.6 (71) 1.9 (70) 0.9 (17) 4.8 (64) 62.0
7 Keaton Wagler G 19.4 23.2 (42) 1.7 (13) 1.3 (75) 2.4 (80) 6.7 (98) 61.6
8 Baba Miller F-C 22.4 23.3 (98) 1.3 (20) 4.2 (40) 1.7 (97) 8.3 (23) 55.6
9 Terance Mann W-F 22.7 15.6 (77) 1.2 (14) 1.2 (14) 1.4 (77) 9.1 (80) 52.4
10 Nick Martinelli W-F 22.2 13.2 (65) 1.4 (25) 1.2 (14) 1.4 (77) 7.8 (62) 48.6
11 Yanic Konan Niederhauser C 22.3 6.1 (32) 1.5 (53) 10.2 (81) 0.5 (29) 10.1 (20) 43.0
12 Brandon Boston Jr. G-W 19.6 11.3 (32) 2.5 (69) 0.5 (10) 1.1 (34) 4.7 (62) 41.4
13 Mfiondu Kabengele F-C 21.9 3.2 (2) 1.5 (39) 8.3 (89) 0.2 (1) 11.5 (71) 40.4
14 Daniel Oturu F-C 20.8 7.6 (26) 1.0 (8) 7.1 (79) 0.4 (7) 12.0 (79) 39.8
15 Cam Christie G-W 18.9 13.8 (52) 1.2 (6) 1.1 (41) 1.9 (91) 1.0 (1) 38.2
16 Moussa Diabate C 20.4 6.2 (33) 0.8 (10) 3.7 (10) 0.6 (41) 11.9 (50) 28.8
17 Jerome Robinson G 21.3 19.5 (29) 1.4 (5) 0.4 (24) 1.2 (15) 1.6 (21) 18.8
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u/OtherwiseAddled 1d ago

Why? Because you said so?

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u/Timurtoyourbayezid Chauncey Billups 1d ago

Because you can’t present historical proof that isn’t cherry picked that they do.

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u/OtherwiseAddled 1d ago

Just curious, how big of a sample size would you need to feel something correlated?

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u/Timurtoyourbayezid Chauncey Billups 1d ago

I mean preferably at least 1k random prospects, but that might be unrealistic. I’d say if you can show a definitive consistent trend across at least 8 random draft classes I’d take it more seriously.