Ranking 2021-22 regular season Scorers (Volume+Efficiency+Contribution)

[DarkStar48]

I have been trying to find of a good way to think about scoring effectiveness relative to league average that takes into account true shooting percentage (TS%), attempts (TSA) and playing opportunity. This is what I came up with.

TS Add per game, which is the mean number of extra points added, per game played, by estimated True Shot Attempts made above league average.

Formula: TS Add per gm. = ( ( PTS / TSA - Lg. Avg. PTS / TSA ) * TSA ) / GP

NB: TS is based on BBallRef estimated True Shooting — not “true” True Shooting, i.e., approximated (not actual) shot possessions.

Top League Average Scorers

Code: Select all

Player	        Team	G		TS Add▼		TS Add per gm.		Scorer Rank
Nikola Jokić	DEN	74		286.9		3.877027027		1
Rudy Gobert	UTA	66		233.3		3.534848485		2
Kevin Durant	BRK	55		177.1		3.220000000	        3
G. Antetokou.	MIL	67		211.1		3.150746269		4
Jarrett Allen	CLE	56		171		3.053571429		5
Karl-A. Towns	MIN	74		211.2		2.854054054		6
LeBron James	LAL	56		145.5		2.598214286		7
Joel Embiid	PHI	68		169		2.485294118		8
R. Williams	BOS	61		145.6		2.386885246		9
Deandr. Ayton	PHO	58		137.3		2.367241379		10
Camer. Oliver	ATL	2		4.5		2.250000000		11
Montr. Harrell	TOT	71		156.3		2.201408451		12
Dom. Sabonis	TOT	62		130.6		2.106451613		13
D.J. Wilson	TOR	4		8		2.000000000		14
Dwight Powell	DAL	82		157.5		1.920731707		15
Dani. Gafford	WAS	72		135.5		1.881944444		16
Richa. Holmes	SAC	45		83.6		1.857777778		17
Mit. Robinson	NYK	72		131.6		1.827777778		18
Trae Young	ATL	76		131.8		1.734210526		19
On. Okongwu	ATL	48		81.4		1.695833333		20
Zach LaVine	CHI	67		105.7		1.57761194		21
Jaxson Hayes	NOP	70		109.8		1.568571429		22
Damia. Jones	SAC	56		87.6		1.564285714		23
Stephe. Curry	GSW	64		96		1.500000000		24
S. Doumbouy.	LAL	2		3		1.500000000		24
Harris. Barnes	SAC	77		115.2		1.496103896		26
Brand. Clarke	MEM	64		95.2		1.487500000		27
Ivica Zubac	LAC	76		111.6		1.468421053		28
J. Valančiūna.	NOP	74		108.2		1.462162162		29
Rayjon Tucker	TOT	5		7.3		1.460000000		30
Nic Claxton	BRK	47		64.8		1.378723404		31
Mikal Bridges	PHO	82		112.6		1.373170732		32
Terry Taylor	IND	33		43.6		1.321212121		33
Ba. Adebayo	MIA	56		73.4		1.310714286		34
Mali. Newman	CLE	1		1.3		1.300000000		35
Is. Hartenstein	LAC	68		82.9		1.219117647		36
JaVal. McGee	PHO	74		88.8		1.200000000		37
Seth Curry	TOT	64		76.6		1.196875		38
Ha. Whiteside	UTA	65		77.3		1.189230769		39
Kyrie Irving	BRK	29		34.4		1.186206897		40
Ca. Johnson	PHO	66		78.1		1.183333333		41
Gary Payton II	GSW	71		83.7		1.178873239		42
John Collins	ATL	54		62.8		1.162962963		43
Norma. Powell	TOT	45		52.1		1.157777778		44
Fra. Kaminsky	PHO	9		10.3		1.144444444		45
Udo. Azubuike	UTA	17		19.1		1.123529412		46
Jared Harper	NOP	5		5.6		1.120000000		47
DeM. DeRozan	CHI	76		85		1.118421053		48
Olivier Sarr	OKC	22		24.2		1.100000000		49
Josh Hart	TOT	54		57.8		1.07037037		50
Joe Harris	BRK	14		14.6		1.042857143		51
Eric Gordon	HOU	57		59.3		1.040350877		52
Jakob Poeltl	SAS	68		69.9		1.027941176		53
Myles Turner	IND	42		42.8		1.019047619		54
Luke Kennard	LAC	70		71.3		1.018571429		55
Patri. Williams	CHI	17		17.1		1.005882353		56
Jeff Green	DEN	75		74.6		0.9946666667		57
Jordan Poole	GSW	76		74.9		0.9855263158		58
B. Bogdanović	UTA	69		68		0.9855072464		59
Jimmy Butler	MIA	57		54		0.9473684211		60
Miles Bridges	CHO	80		72.9		0.9112500000		61
Ahmad Caver	IND	1		0.9		0.9000000000		62
Joe Johnson	BOS	1		0.9		0.9000000000		62
Aaron Gordon	DEN	75		67.4		0.8986666667		64
P. Connaught.	MIL	65		56.6		0.8707692308		65
We. Carter Jr.	ORL	62		53.6		0.864516129		66
Drew Eubanks	TOT	71		61.3		0.8633802817		67
Christi. Wood	HOU	68		58.7		0.8632352941		68
Victo. Oladipo	MIA	8		6.9		0.8625000000		69
Dwig. Howard	LAL	60		51.4		0.8566666667		70
Gra. Williams	BOS	77		65.7		0.8532467532		71
Tyrese Maxey	PHI	75		62.1		0.8280000000		72
Jrue Holiday	MIL	67		55.4		0.8268656716		73
Max Strus	MIA	68		56.2		0.8264705882		74
Morit. Wagner	ORL	63		51.5		0.8174603175		75
Mike Muscala	OKC	43		34.9		0.811627907		76
Javont. Green	CHI	65		52.3		0.8046153846		77
LaM. Aldridge	BRK	47		37.7		0.8021276596		78
Juwa. Morgan	TOT	2		1.6		0.8000000000		79
Desmo. Bane	MEM	76		60.5		0.7960526316		80
Grayson Allen	MIL	66		52		0.7878787879		81
Kyle Lowry	MIA	63		47.6		0.7555555556		82
Ty. Haliburton	TOT	77		57.4		0.7454545455		83
Malik Monk	LAL	76		54.7		0.7197368421		84
Obi Toppin	NYK	72		50.8		0.7055555556		85
Clint Capela	ATL	74		52.1		0.7040540541		86
Anthony Gill	WAS	44		30.7		0.6977272727		87
D. Finney-Smi.	DAL	80		55.3		0.6912500000		88
Isaiah Roby	OKC	45		30.6		0.6800000000		89
Caleb Martin	MIA	60		40.6		0.6766666667		90
Zeke Nnaji	DEN	41		27.1		0.6609756098		91
Dew. Dedmon	MIA	67		43.9		0.6552238806		92
Jame. Harden	TOT	65		42.1		0.6476923077		93
Isaiah Livers	DET	19		12.1		0.6368421053		94
Kevin Love	CLE	74		46.9		0.6337837838		95
Hass. Gravett	ORL	8		5		0.6250000000		96
Mason Jones	LAL	4		2.5		0.6250000000		96
Derr. Jones Jr.	CHI	51		30.4		0.5960784314		98
Gar. Mathews	HOU	65		38.5		0.5923076923		99
Nicola. Batum	LAC	59		34		0.5762711864		100
Vlatk. Čančar	DEN	15		8.6		0.5733333333		101
Isaia. Jackson	IND	36		20		0.5555555556		102
Jayso. Tatum	BOS	76		42		0.5526315789		103
Ken. Martin Jr.	HOU	79		42.7		0.5405063291		104
Nick Richards	CHO	50		26.7		0.5340000000		105
Jusuf Nurkić	POR	56		29.3		0.5232142857		106
Jon. Kuminga	GSW	70		36.6		0.5228571429		107
Ja. Vanderbilt	MIN	74		38.1		0.5148648649		108
D. McDermott	SAS	51		26.2		0.5137254902		109
Royc. O'Neale	UTA	77		39.5		0.512987013		110
Cody Zeller	POR	27		13.7		0.5074074074		111
DeAn. Jordan	TOT	48		24.2		0.5041666667		112
Thom. Bryant	WAS	27		13.5		0.5000000000		113
Craig Sword	WAS	3		1.5		0.5000000000		113
Devon. Cacok	SAS	15		7.4		0.4933333333		115
Anfer. Simons	POR	57		28		0.4912280702		116
Amir Coffey	LAC	69		33.2		0.4811594203		117
Jalen Brunson	DAL	79		37.9		0.4797468354		118
Devin Booker	PHO	68		32.6		0.4794117647		119
Anthon. Davis	LAL	40		18.7		0.4675000000		120
Bru. Fernando	TOT	30		13.8		0.4600000000		121
Jabari Parker	BOS	12		5.5		0.4583333333		122
Jalen Smith	TOT	51		23		0.4509803922		123
Jad. Springer	PHI	2		0.9		0.4500000000		124
Tyrell Terry	MEM	2		0.9		0.4500000000		124
Ayo Dosunmu	CHI	77		34.5		0.4480519481		126
Georg. Niang	PHI	76		33.3		0.4381578947		127
Ja Morant	MEM	57		24.9		0.4368421053		128
Eric Paschall	UTA	58		25.1		0.4327586207		129
Charl. Bassey	PHI	23		9.8		0.4260869565		130
Austin Reaves	LAL	61		25.3		0.4147540984		131
L. Markkanen	CLE	61		25		0.4098360656		132
Mas. Plumlee	CHO	73		29.6		0.4054794521		133
Davon Reed	DEN	48		19.3		0.4020833333		134
Tyler Cook	CHI	20		8		0.4000000000		135
Daniel Theis	TOT	47		18.7		0.3978723404		136
P.J. Washingt.	CHO	65		25.4		0.3907692308		137
John Konchar	MEM	72		28		0.3888888889		138
D.J. Augustin	TOT	55		21.3		0.3872727273		139
Trey Lyles	TOT	75		28.8		0.3840000000		140
Kris. Porziņģis	TOT	51		19.3		0.3784313725		141
Monte Morris	DEN	75		28.1		0.3746666667		142
Chris Paul	PHO	65		24.2		0.3723076923		143
Treveli. Queen	HOU	10		3.7		0.3700000000		144
Dari. Garland	CLE	68		25.1		0.3691176471		145
Khr. Middleton	MIL	66		24.3		0.3681818182		146
Zach Collins	SAS	28		10.2		0.3642857143		147
Jericho Sims	NYK	41		14.4		0.3512195122		148
Mac McClung	TOT	2		0.7		0.3500000000		149
Jaylen Brown	BOS	66		23		0.3484848485		150
Isaac Okoro	CLE	67		22.9		0.3417910448		151
P.J. Tucker	MIA	71		24.1		0.3394366197		152
B. Marjanović	DAL	23		7.8		0.3391304348		153
Goga Bitadze	IND	50		16.9		0.3380000000		154
Q. Weathersp.	GSW	11		3.7		0.3363636364		155
Malcolm Hill	TOT	19		6.2		0.3263157895		156
Gorgui Dieng	ATL	44		14.3		0.3250000000		157
Sam Hauser	BOS	26		8.4		0.3230769231		158
Moses Brown	TOT	40		12.9		0.3225000000		159
McK. Wright IV	MIN	5		1.6		0.3200000000		160
S. Mamukela.	MIL	41		12.9		0.3146341463		161
Gabriel Deck	OKC	7		2.2		0.3142857143		162
Naz Reid	MIN	77		24		0.3116883117		163
Tren. Watford	POR	48		14.9		0.3104166667		164
Car. Anthony	LAL	69		21.4		0.3101449275		165
Way. Ellington	LAL	43		13.2		0.3069767442		166
Ja. McDaniels	CHO	55		16.6		0.3018181818		167
Chris Silva	TOT	10		3		0.3000000000		168
Jemerr. Jones	LAL	2		0.6		0.3000000000		168
Mo Bamba	ORL	71		21.2		0.2985915493		170
DeAn. Bembry	TOT	56		16.4		0.2928571429		171
Taj Gibson	NYK	52		15.2		0.2923076923		172
Bis. Biyombo	PHO	36		10.5		0.2916666667		173
Dono. Mitchell	UTA	67		19.5		0.2910447761		174
Taurea. Prince	MIN	69		19.7		0.2855072464		175
Larr. Nance Jr.	TOT	46		13		0.2826086957		176
Day'R. Sharpe	BRK	32		8.9		0.278125		177
Corey Kispert	WAS	77		21.3		0.2766233766		178
Mati. Thybulle	PHI	66		18		0.2727272727		179
Dani. Gallinari	ATL	66		17.5		0.2651515152		180
R. Hachimura	WAS	42		10.8		0.2571428571		181
Jock Landale	SAS	54		13.7		0.2537037037		182
W. Hernangó.	NOP	50		12.6		0.2520000000		183
R. Arcidiacon.	NYK	10		2.4		0.2400000000		184
Luka Dončić	DAL	65		15.3		0.2353846154		185
JaMyc. Green	DEN	67		15.5		0.2313432836		186
Natha. Knight	MIN	37		8.5		0.2297297297		187
Keld. Johnson	SAS	75		17		0.2266666667		188
Jaylen Nowell	MIN	62		13.7		0.2209677419		189
Otto Porter Jr.	GSW	63		13.7		0.2174603175		190
Lonzo Ball	CHI	35		7.5		0.2142857143		191
Draym. Green	GSW	46		9.8		0.2130434783		192
Kevon Looney	GSW	82		16.7		0.2036585366		193
Cory Joseph	DET	65		11.9		0.1830769231		194
Tony Bradley	CHI	55		9.3		0.1690909091		195
Cody Martin	CHO	71		11.9		0.1676056338		196
Dean Wade	CLE	51		8.4		0.1647058824		197
Nassir Little	POR	42		6.6		0.1571428571		198
Serge Ibaka	TOT	54		8		0.1481481481		199
Bruce Brown	BRK	72		10.5		0.1458333333		200
Al Horford	BOS	69		9.6		0.1391304348		201
Mike Conley	UTA	72		9.9		0.1375000000		202
Du. Robinson	MIA	79		10.8		0.1367088608		203
Bobby Portis	MIL	72		9.8		0.1361111111		204
Pay. Pritchard	BOS	71		9.3		0.1309859155		205
Bryn Forbes	TOT	75		9.5		0.1266666667		206
Enes Freedom	BOS	35		4.3		0.1228571429		207
J. Richardson	TOT	65		7.7		0.1184615385		208
K. Bates-Diop	SAS	59		6.9		0.1169491525		209
Ch. Hutchison	PHO	6		0.7		0.1166666667		210
Teranc. Mann	LAC	81		9.2		0.1135802469		211
Herbert Jones	NOP	78		8.8		0.1128205128		212
Alex Len	SAC	39		4.4		0.1128205128		212
A. Drummond	TOT	73		8.2		0.1123287671		214
Skylar Mays	ATL	28		3.1		0.1107142857		215
Gor. Hayward	CHO	49		5.3		0.1081632653		216
Ro. McGruder	DET	51		5.4		0.1058823529		217
Kai Jones	CHO	21		2.2		0.1047619048		218
Josh Green	DAL	67		6.8		0.1014925373		219
Brook Lopez	MIL	13		1.3		0.10000000000		220
Norvel Pelle	UTA	3		0.3		0.10000000000		220
Jale. Johnson	ATL	22		2.1		0.09545454545		222
Ed Davis	CLE	31		2.9		0.0935483871		223
Gary Harris	ORL	61		5.7		0.09344262295		224
Nerlens Noel	NYK	25		2.1		0.08400000000		225
Ro. Covington	TOT	71		5.9		0.08309859155		226
Delon Wright	ATL	77		6.1		0.07922077922		227
Kevin Huerter	ATL	74		5.8		0.07837837838		228
Nema. Bjelica	GSW	71		5.5		0.07746478873		229
T. Antetokoun.	MIL	48		3.2		0.06666666667		230
Chris Boucher	TOR	80		4.7		0.05875000000		231
Moses Moody	GSW	52		2.9		0.05576923077		232
Robin Lopez	ORL	36		1.8		0.05000000000		233
K. Caldwell-P.	WAS	77		2.9		0.03766233766		234
Jae'Sean Tate	HOU	78		2.1		0.02692307692		235
DeM. Cousins	TOT	48		1		0.02083333333		236
J. Champagn.	TOR	36		0.7		0.01944444444		237
Be. McLemore	POR	64		1		0.015625		238
Paul Reed	PHI	38		0.4		0.01052631579		239
David Nwaba	HOU	46		0.4		0.008695652174		240
Mark. Howard	DEN	31		0.2		0.006451612903		241

Most of this feels intuitively “correct.”

Jokic in 1st place aligns with all of the analysis from this season on his otherworldly scoring.

The most intriguing insight was probably on Gobert. His elite points generation highlights an underrated part of his skillset, and solidifies his position as debatably the best roll-cutter in the league.

[Crow]

David Locke (with Jazz) has done similar for a number of years.

Fine, as long as you don't totally ignore turnovers, offensive rebounding and plus / minus (on and on / off, overall and by factor).

Certainly helps identify difference makers and empty calorie scorers.

Mid to low usage bigs can have a notable scoring impact if they are very efficient. Don't " absolutely need" an 8, 15 or 23 ft shot if you can get 0-5 footers. 17 of 24 with a +1.5 impact or better are bigs and several more are really big "non-bigs".

Guards with a big scoring impact by this method are very rare, despite that being a main reason for their selection and payment.

Wouldn't get these impressions from conventional discussions about scoring.

How much is Jordan Poole going to get for that +0.986 pt scoring impact and from whom? $15-20 mil / yr seems possible from some but not all of the higher impact perimeter shooters. Some were talking even higher. GSW holding off to end / hasn't shown their valuation yet.

How many total players were positive on this vs. negative? About 220 positive and presumably about 385 negative?

[DarkStar48]

Of 605 players recorded, 241 had a positive TS Add per gm., 354 were negative, 9 did not score a single point, and 1 had a total TS Add in the season less than 0.05.

~$18-21M for a 6th Man scorer with defensive issues like Poole seems reasonable to me, especially if you also consider the playmaking skill as well.

Curry and Lillard would probably be higher on this list — even Top 10 — if not for the shooting slump and injury, respectively. Trae might get there one day as his accuracy continues to improve.

Bigs who are post scorers and roll-cutters may not be as “sexy” as shooters, but their potency as bucket-getters is undeniable.

Finding the right combination of volume, efficiency and player role is key to unlocking scoring output.

Yeah, like I said, the goal of this was to get a sense of scoring utility only — not overall offensive impact (like offensive rebounding, passing, creation, etc.).

[Mike G]

Shai G-A isn't among the net-positives because his .557 TS% is below the league avg of .566
However it's well above the OKC norm of .530 (league low) and even more better than his team's .525 when you exclude all his shots and points.
So he raises his team shooting by a not-insignificant .005, yet his shooting is considered to make a negative contribution?

Similarly, Marcus Smart shot .540 , well below norm for the league. But in Celtics games, the rest of the league shot just .531 (league low); so he actually out-shot the actual competition he played against.

Should these team and opponent TS% standards be applied instead of (or in addition to) league averages?
In individual Playoff series, regular-season league avg has zero meaning, for shooting, rebounding, whatever. You have one opponent and your own team to gauge your effectiveness.

And of course, Rudy Gobert needs others to break down the defense, sometimes getting charged with a turnover (which he mostly avoids), to shoot near 70%
About 72% of his points were assisted, with 74% happening right at the basket.

A lot of this is difficult or impossible to quantify from mere box score statistics. Yet we intuitively "know" it's easier to get good shots on a good-shooting team, and harder when you're the go-to guy.

[Crow]

Comparison on scoring efficiency can be made to league average, team average, replacement level or other (median player, median or mean salary player, playoff team average, even to title winner). It depends what you want to do. There is not just one right answer. Comparison to league average is going to be a common first choice.

[Mike G]

Last year's Blazers, Anfernee Simons, Jusuf Nurkic, Norman Powell, Ben McLemore, Nassir Little, Trendon Watford, Larry Nance, all shot better than the league avg .566 TS%. And they were all out-shot by their competition, who went for .595.
This was almost exactly the same competition -- 29 NBA teams -- that the Celtics and Thunders etc played against.
All these Por players were typically out-shot by their opponents; yet they rank above "average" as if that is the same for all teams.

Ranking Stats are never just "right"; and they can always be improved. Improvement is a wonderful thing.

[Crow]

Shooting quality by itself is one thing, net shooting difference another. Lots of things that can done. Do what you want.

1 version of shooting quality or 30. 1 version of net shooting difference or 30. Or 30 others if you use counterpart opponent estimates instead of team opponent averages.

[DarkStar48]

I agree that some sort of “team/opponent-adjustment” would be ideal, but this is simply the inherent problem of any individual player statistic without using more sophisticated calculation methods, like iteration.

The closest thing I could think of to address some of the concerns (but that doesn’t take into account all scoring) would be an “adjusted FG Add” per game:

aFG Add per gm. = ( ( 2 * ( RA-OeFG% - Lg. RA-OeFG% ) ) * FGA ) / GP

where, RA-OeFG% is regularized adjusted offensive effective field goal percentage.

However, as explained, the above does not count free throws made — RAPM modifications could possibly be made to this effect.

Nonetheless, as a “raw” (as in, unadjusted) stat, I still think there is insight in the basic idea of TS Add per gm.

[DSMok1]

Box Plus/Minus uses a somewhat similar approach to valuing scoring.

It works on the formula of:

A * TSA * (TS% - Tm Avg TS% + B)

Where B is a constant that varies based on offensive role. This means that low usage players have to have greater efficiency to be considered a net positive than high usage players.

Note that this normalization is vs. the rest of the players on the team. BPM still also adjusts so that the team's player's offensive ratings sum up to the team's offensive efficiency, but for the scoring piece alone the comparison is vs. the overall team shooting efficiency.

[Mike G]

... this normalization is vs. the rest of the players on the team. ..

After suggesting that as an incremental improvement, it seemed to me that just normalizing to opponent TS% would serve the same purpose and then some.
After all, your 29 opponents are pretty much the NBA in your team's games.

And then you can rely less on magic numbers like " B is a constant that varies based on offensive role",
Is a variable constant aka a parameter?
And what is A ?

[DSMok1]

... this normalization is vs. the rest of the players on the team. ..

After suggesting that as an incremental improvement, it seemed to me that just normalizing to opponent TS% would serve the same purpose and then some.
After all, your 29 opponents are pretty much the NBA in your team's games.

And then you can rely less on magic numbers like " B is a constant that varies based on offensive role",
Is a variable constant aka a parameter?
And what is A ?

I disagree on normalizing to the opponent. I want to figure out where the break even point on usage vs. efficiency is within the team's context. If the rest of my team can't shoot, a big volume at mediocre efficiency is a benefit to the team. If the rest of the team is scoring at a high efficiency, a big volume at mediocre efficiency is much less of a benefit to the team's offense.

B is accounting for the fact that if a player is carrying a big load, their breakeven efficiency for positive offensive impact is lower. That is found empirically.

A is just the translation coefficient to convert the whole term into points of impact per 100 possessions.