WNBA lineup stints and player impact ratings, 2003-2026. 267,293 stints reconstructed from play-by-play, with ARC ratings and channel decompositions.

This is my first public GitHub release of an ongoing project, an ongoing open-source initiative of releasing normalized and reconstructed data sets for open use. We'll be adding more and more to this, including a Python helper instead of just raw JSON over the coming days. Just wanted to do this before I forget.

Shai in the best two seasons has just been

An extraordinary combination of volume, efficiency, and possession economics.

This season he averaged:

46.8 points per 100 possessions on
66.6% True Shooting (+8.4 relative TS)
Just 13.8 empty possessions per 100

Since 1999, among guards with 35+ possessions used per 100, that ranks:

PPP — #5
True Shooting — #3
Relative TS — #3
oShot Make — #4, behind only Curry's 3 monster seasons
(team conversion with him on the floor)

oTOV — #2, only behind his own 2023–24 season
Empty & Lost Rate — #1
(missed FG + 0.44 × missed FT + TOV per possession)

Past Postseason without his co-star may cause the masses to take a bit longer to realize

that we just witnessed what would be one of the best two-year guard peaks, one arguably only rivaled by Curry

now to be fair and objective, there is something to be said about his playoff. His value does not translate to the playoffs as cleanly. He still has a ways to go to figure out or add another dimension to his game to get past the extremely aggressive defensive sets he sees.

But I will say I think his playoff drop is nowhere near the extent some would have you believe.

Among guards since 1999 with a minimum of 30 possessions used per 100 and at 10 games played his 2026 playoff run ranks

• Points per 100 — #12
• True Shooting % — #11
• Relative TS — #19
• PPP — #12
• UW-rTS — #18
• Empty & Lost — #7
• oTOV — #28
• Playoff ARC — #11

And just among other 2026 PLayoff guards he ranks

• True Shooting % — #1
• PPP — #1
• Usage Weighted RTS — #1
• Empty & Lost Rate — #1
• Points per 100 — #2
• Playoff ARC — #2
• oTOV — #10

just now really getting deep into his prime. If you were to retire tomorrow, he would still retire with one of the better two-year peaks we've seen from a guard. Curious to see if he can ease into more off-ball looks as OKC attempts to give him more creators around him and get his three-point rate up.

Good morning, y'all.

Recently wrote a paper introducing a new metric called xPTS, which essentially quantifies shot quality by measuring the expected value of each field goal using:

Shot location
Shot family
Possession context (I.E: Transition, Second Chance, Halfcourt, etc etc)
Actual defender distance

A dunk at the rim with a defender 2-4 feet away is worth 1.88 points, a cut to the rim from 2-4 feet 1.47, a driving layup with 4-6 feet of space 1.43, a contested layup at 2-4 feet 1.27, and a smothered shot at the rim with a defender inside 2 feet just 0.55, with the league average PPP being 1.09.

With expected points, you can do a lot of cool things, such as:

• Player leaderboards
• Team leaderboards and Expected Wins
• a player’s own baseline for over and underperformance. (Curry's PPP from a smothered above-the-break 3 is different from Kuzma)
• Presence and geometry RAPM

Even cooler, you can measure how a player’s presence bends his team’s offensive and defensive geometry, and its chance creation on both ends, a different flavor of regularized adjusted plus-minus.

xpts rapm: a player's regularized on/off impact on their team's shot diet and shot quality as well as suppress the opposing teams' shot die-in shot quality

conversion rapm: a player's regularize on/off impact on their team's ability to make and suppress shots above or below expected

Kevin Durant

His net xPTS-RAPM is a very interesting story, especially when you pair it with his conversion.

He ranks in the FIRST PERCENTILE, as in his presence contributes to one of, if not the very worst, shot diet and chance creation in the entire league.

His shot-quality impact is among the single worst in the entire league. He provides no rim pressure, there is no transition game, the offense becomes a half-court jousting match every possession.

His influence turns his team into a midrange engine, the quantified worst shot in basketball, at roughly 7% more than league average.

He still rates well in all RAPMs and off metrics, because he is, even at his age, among the most elite converters in the sport.

He ranks in the 99.8th PERCENTILE in conversion RAPM. When he is on the floor, his team's offense converts far more than the chance created would predict. (Even with his average defensive conversion impact)

A whopping 270 points above expected this past season, a bigger gap between him and 2nd Place (Jokic) than between 2nd and 5th.

He makes the poorly graded 8-to-10-foot pull-ups at about 1.21 expected points, versus a league average of 0.94.

Functionally, making what is graded as the worst shot in basketball worth more than a three-pointer taken from the average NBA player.

Extreme Dimorphism

His total on-court impact is shown thorugh :

Net RAPM, the additive sum of xPTS-RAPM and conversion RAPM, which is to say, a pure RAPM with no prior, just basic regularized adjusted plus-minus, where he ranks in the 83rd percentile

ARC (Adjusted Regularized Contribution), a possession-weighted, box-blended impact, has him in the 92nd percentile

Houston is 3 points better on offense with him on the floor, minus 2 in total due to defense.

My question is, even with his positive impact on Houston's offense, which isn't the most spaced or geometrically balanced, is this the level of shot-making required to have a positive impact when your shot diet is this bad?

It reminds me of a lot of the conversations around DeMar DeRozan and how much of a winning player he is. He has a similar level of poor shot selection with his usage, which bent the whole team's geometry into less efficient and effective shots. He did not have anywhere near the shot-making lift.

Even with this shot-making lift, is it not a self-fulfilling prophecy that there is a limit to how big of an offensive impact a "tough" shot maker can have?

Good morning, y'all.

Recently wrote a paper introducing a new metric called xPTS, which essentially quantifies shot quality by measuring the expected value of each field goal using:

Shot location
Shot family
Possession context (I.E: Transition, Second Chance, Halfcourt, etc etc)
Actual defender distance

A dunk at the rim with a defender 2-4 feet away is worth 1.88 points, a cut to the rim from 2-4 feet 1.47, a driving layup with 4-6 feet of space 1.43, a contested layup at 2-4 feet 1.27, and a smothered shot at the rim with a defender inside 2 feet just 0.55, with the league average PPP being 1.09.

With expected points, you can do a lot of cool things, such as:

• Player leaderboards
• Team leaderboards and Expected Wins
• a player’s own baseline for over and underperformance. (Curry's PPP from a smothered above-the-break 3 is different from Kuzma)
• Presence and geometry RAPM

Even cooler, you can measure how a player’s presence bends his team’s offensive and defensive geometry, and its chance creation on both ends, a different flavor of regularized adjusted plus-minus.

xpts rapm: a player's regularized on/off impact on their team's shot diet and shot quality as well as suppress the opposing teams' shot die-in shot quality

conversion rapm: a player's regularize on/off impact on their team's ability to make and suppress shots above or below expected

Kevin Durant

His net xPTS-RAPM is a very interesting story, especially when you pair it with his conversion.

He ranks in the FIRST PERCENTILE, as in his presence contributes to one of, if not the very worst, shot diet and chance creation in the entire league.

His shot-quality impact is among the single worst in the entire league. He provides no rim pressure, there is no transition game, the offense becomes a half-court jousting match every possession.

His influence turns his team into a midrange engine, the quantified worst shot in basketball, at roughly 7% more than league average.

He still rates well in all RAPMs and off metrics, because he is, even at his age, among the most elite converters in the sport.

He ranks in the 99.8th PERCENTILE in conversion RAPM. When he is on the floor, his team's offense converts far more than the chance created would predict. (Even with his average defensive conversion impact)

A whopping 270 points above expected this past season, a bigger gap between him and 2nd Place (Jokic) than between 2nd and 5th.

He makes the poorly graded 8-to-10-foot pull-ups at about 1.21 expected points, versus a league average of 0.94.

Functionally, making what is graded as the worst shot in basketball worth more than a three-pointer taken from the average NBA player.

Extreme Dimorphism

His total on-court impact is shown thorugh :

Net RAPM, the additive sum of xPTS-RAPM and conversion RAPM, which is to say, a pure RAPM with no prior, just basic regularized adjusted plus-minus, where he ranks in the 83rd percentile

ARC (Adjusted Regularized Contribution), a possession-weighted, box-blended impact, has him in the 92nd percentile

Houston is 3 points better on offense with him on the floor, minus 2 in total due to defense.

My question is, even with his positive impact on Houston's offense, which isn't the most spaced or geometrically balanced, is this the level of shot-making required to have a positive impact when your shot diet is this bad?

It reminds me of a lot of the conversations around DeMar DeRozan and how much of a winning player he is. He has a similar level of poor shot selection with his usage, which bent the whole team's geometry into less efficient and effective shots. He did not have anywhere near the shot-making lift.

Even with this shot-making lift, is it not a self-fulfilling prophecy that there is a limit to how big of an offensive impact a "tough" shot maker can have?

ARC Paper
College ARC Paper

Happy Summer League! Made a post here about a paper I wrote introducing a new impact metric called ARC that blends box-weighted production with a wrap-em to give the most robust and comprehensive view of a player's value in a given time frame.

huge update that shows data on players to be viewable all the way back to 1997

The goal of ARC was always to cleanly and transparently quantify NBA player impact. A blend between box production and on/off impact

ARC is now able and now cleanly decomposes into the following parts.

O-ARC (O-Box + O-Impact)
D-ARC( D-Box + D-Impact)
O-Box
D-Box
O-Impact
D-Impact

O-Impact (offense):

* **oTS** — True Shooting

* **oTOV** — Ball Security (turnovers)

* **oREB** — Offensive Rebounding

* **oPACE** — Tempo

D-Impact (defense):

* **dTS** — Shot Defense

* **dTOV** — Turnovers Forced

* **dREB** — Defensive Rebounding

* **dPACE** — Transition Defense

And TS itself decomposes further into the shot channels:

* **oTS** → Rim finishing · Mid making · 3PT making · FT making + shot selection (Rim pressure · Mid rate · 3PT rate · FT drawing)

* **dTS** → Rim contest · Mid suppress · 3PT suppress · FT-line D + shot deterrence (Rim deter · Forcing mid · 3PT diet · Foul discipline)

TL;DR: I built C-ARC, College Adjusted Regularized Contribution,
a college basketball player impact metric that aims to quantify the best players in any given college basketball season while handling the historical weirdness and constraints of NCAAB: small sample size, rigid lineups, schedule disparity, and uneven talent density.

Its more stable and reconstructs and predicts winning much better than PORPAG and Win Shares while sitting in a very comparable tier nuemriacally to BPM.

hey y'all, happy post-draft.

I've been really deep into analytics and data science recently. I made a new player impact metric for the NBA that blends possession-by-possession weighted box metrics with more abstract on-off metrics (RAPM). (Post here)

I worked to take that same framework and apply it to college basketball, which, as we know, is extremely difficult to model due to issues with the sourcing of the data in the first place.

Why College is hard to model

Sample size
Talent density
Schedule disparity

Especially sample size and talent density.

Even with 82 games and over 5,000 possessions per player in the NBA, metrics like RPMs are often served with multi-year weighting to help offset collinearity, players sharing so many minutes together that individual effects are hard to separate, and small, unstable samples.

In contrast, the NCAA basketball season is only 30 games. High-end starters who play 30 minutes a night will only max out at about 900 minutes and roughly 1,500 possessions, 70% lower.

In addition, college lineups are far tighter and more rigid, with starters routinely playing 80% or more of the entire game. A Division I team uses only about 78 unique five-man lineups a season versus roughly 510 in the NBA, and even after you adjust for fewer games and slower pace, the NBA still generates nearly twice the lineup variety per possession.

And with the best players often being one-and-done freshmen, or even sophomores, it is very hard for RAPM to describe the best players in any given season.

With talent density, impact metrics such as RAPM, EPM, and DARKO, while accounting for differences in the opponent quality relative to the league, do not account for a difference in the competitive environment itself. They assume every player is operating inside the same band of competition, that every team is more or less playing the same level of basketball. Thus, they derive an incredible amount of value in the NBA, where the players occupying those roughly 500 roster spots compete against the same 99th-percentile talent every night, and work poorly with NCAAB.

As an extreme example: a top prospect might face a mid-major opponent with zero NBA talent on the floor one night, then turn around and play in the SEC the following week against players much closer to NBA athleticism and NBA size. Metrics are often unfairly punishing players who play tougher bands of competition.

Net rating and current college metrics

My main gripe with a lot of the comtemporary metrics that try to sidestep using RAPM, such as win shares and PORPAG, is that they do a horrible job at reconstructing team net rating.

(The best metrics by tether themselves to something real and objective, and asking how faithfully they can reconstruct it.

Net rating is the best choice as it is quite literally, the accumulation of how much every individual on the floor moves the team's margin; offensive and defensive impact are nothing more than a player's contribution to it. Decomposing a team's net rating into each player's share of it is therefore the natural move. Anything else is needless abstraction. It is also the cleanest target available: objective, a direct record of points for minus points allowed per 100 possessions)

BPM is probably the best, most stable and effective one-on-one metric that does exist for college right now as It regresses box-score production onto adjusted plus-minus to approximate how much a player moves team net rating per 100 possessions.

And as we will get to later: C-ARC is numerically comeptivitive with it.

What C-ARC is

The core idea of C-ARC is the most robust, and comprehensive way of describing team player value. It blends both box and RAPM to give stability and a tangible foundation about what a player is producing possession by possession, while also capturing more of the latent value a play produces.

For the box side, I ran through thousands of collegiate possessions and estimated the tangible value created per possession for shooting efficiency, turnovers, assists, rebounds, steals, blocks, and etc.

Points are credited directly, turnovers cost about -1.30, steals are worth about +1.54, offensive rebounds +1.18, blocks +0.45, assists +0.40, defensive rebounds +0.10, summer to per 100 possesions with an efficiancy charge.

FOr the Impact Side i used a normal Reguarlised Adjusted Plus Minus (RAPM)

To deal with the schedule disparity issue, I added a slight schedule adjustment.

Box side

• using CBBD adjusted team ratings.
• Offensive production is adjusted by the strength of the defenses faced.
• Defensive production is adjusted by the strength of the offenses faced.
• Formula:

oBox+ = oBox per 100 + 0.50 × (League Avg Defense - Avg Opponent Defense)

dBox+ = dBox per 100 + 0.10 × (Avg Opponent Offense - League Avg Offense)

C-Box+ = oBox+ + dBox+

Impact side

• team strength as a prior/stabilizer instead of box or no prior
• Formula-ish:

Impact Prior_i = 0.50 × (Team Adjusted Net Rating_i / 10) × min(1, Minutes_i / 1000)

• Then the RAPM fit estimates the player’s impact around that prior.
• summary
  • players on stronger teams start with a slightly stronger prior
  • players on weaker teams start with a lower prior
  • the prior gets stronger as the player’s minutes sample grows
  • the model can still move the player up or down based on actual on/off stint results

used CBBD adjusted ratings for both team adjustments

• Similar idea to KenPom-style adjusted ratings they are
  • opponent-adjusted
  • conference-aware
  • puts teams from different schedule environments onto one comparable scale

I then blend the two by Z-scoring them so they're on the same scale (60/40), with a heavier emphasis on the box plus. This can account for the schedule disparity and sample size issues better for the added abstraction of the impact to fill in the gaps.

Leaderboard:

C-ARC Top 25 — 600 minute gate

(0 is average. So Boozer creates ~13.54 more points per 100 than average) Ranking should be read as more tier and band based than specific ratings

Full Leaderboard here

Rk	Player	Team	Min	C-ARC	Box+	Impact	Opp Net
1	Cameron Boozer	Duke	1274	+13.54	+54.45	+5.75	+15.98
2	Yaxel Lendeborg	Michigan	1210	+12.04	+44.58	+6.96	+20.33
3	Tarris Reed Jr.	UConn	957	+11.28	+51.07	+4.40	+16.90
4	Morez Johnson Jr.	Michigan	1005	+10.06	+45.79	+4.65	+20.43
5	Keaton Wagler	Illinois	1257	+10.06	+44.11	+5.11	+17.28
6	Oscar Cluff	Purdue	964	+10.04	+46.93	+4.31	+18.27
7	JT Toppin	Texas Tech	871	+9.74	+50.68	+2.98	+17.73
8	Brayden Burries	Arizona	1160	+9.65	+39.12	+6.07	+16.14
9	Motiejus Krivas	Arizona	984	+9.49	+40.56	+5.51	+17.31
10	Trey Kaufman-Renn	Purdue	1043	+9.46	+45.70	+4.07	+19.57
11	Tyler Tanner	Vanderbilt	1205	+9.46	+45.94	+4.00	+15.78
12	Joshua Jefferson	Iowa State	1083	+9.24	+41.37	+5.03	+12.96
13	Flory Bidunga	Kansas	1102	+9.21	+42.80	+4.63	+19.03
14	Aday Mara	Michigan	928	+9.20	+44.01	+4.29	+19.93
15	Zuby Ejiofor	St. John's	1113	+9.14	+46.82	+3.46	+14.99
16	Izaiyah Nelson	South Florida	929	+9.11	+45.85	+3.70	+5.09
17	Duke Miles	Vanderbilt	828	+9.04	+44.34	+4.04	+15.57
18	AJ Dybantsa	BYU	1208	+9.02	+49.09	+2.70	+15.82
19	Caleb Wilson	North Carolina	755	+9.01	+48.58	+2.83	+10.29
20	Henri Veesaar	North Carolina	973	+8.99	+41.76	+4.68	+12.80
21	Isaiah Evans	Duke	1075	+8.85	+38.18	+5.52	+15.79
22	Ja'Kobi Gillespie	Tennessee	1286	+8.83	+39.77	+5.07	+17.47
23	Patrick Ngongba II	Duke	702	+8.72	+42.18	+4.29	+15.14
24	Malique Ewin	Arkansas	776	+8.70	+42.91	+4.07	+17.76
25	Thijs De Ridder	Virginia	997	+8.69	+41.73	+4.42	+12.38

Validation

The fun part of validating C-ARC was seeing how it numerically compared extremely well with other widely used collegiate stats

1. Reconstruction

Question: If we aggregate player value back up to the team level, does it recover team net rating?

Metric	R² vs Team Net	MAE	RMSE
BPM	0.984	1.44	1.82
C-ARC	0.964	2.13	2.71
Win Shares	0.656	6.90	8.32
PORPAG	0.521	7.91	9.82

2. Retrodiction

Question: If we use last year’s player ratings with this year’s minutes, can the metric predict this year’s team strength?

Metric	Avg Retrodiction R²	2024→25	2025→26
BPR	0.685	0.694	0.676
C-ARC	0.661	0.665	0.656
Win Shares	0.589	0.617	0.561
BPM	0.585	0.617	0.554
PORPAG	0.505	0.551	0.459

3. Reliability

Question: Does the metric stabilize year over year for returning players?

Metric	YoY R²
C-ARC	0.63
BPM	0.60
Win Shares / 40	0.28
PORPAG	0.23

4. Independence / Blend Value

Question: Are the box and impact sides actually adding different information, or is the blend arbitrary?

Metric Layer	Avg Retrodiction R²
C-ARC blend	0.661
C-Impact only	0.627
Pure APM	0.623
C-Box+ only	0.484
BPM	0.585

TL;DR: I built C-ARC, College Adjusted Regularized Contribution,
a college basketball player impact metric that aims to quantify the best players in any given college basketball season while handling the historical weirdness and constraints of NCAAB: small sample size, rigid lineups, schedule disparity, and uneven talent density.

Its more stable and reconstructs and predicts winning much better than PORPAG and Win Shares while sitting in a very comparable tier nuemriacally to BPM.

hey y'all, happy post-draft.

I've been really deep into analytics and data science recently. I made a new player impact metric for the NBA that blends possession-by-possession weighted box metrics with more abstract on-off metrics (RAPM). (Post here)

I worked to take that same framework and apply it to college basketball, which, as we know, is extremely difficult to model due to issues with the sourcing of the data in the first place.

Why College is hard to model

Sample size
Talent density
Schedule disparity

Especially sample size and talent density.

Even with 82 games and over 5,000 possessions per player in the NBA, metrics like RPMs are often served with multi-year weighting to help offset collinearity, players sharing so many minutes together that individual effects are hard to separate, and small, unstable samples.

In contrast, the NCAA basketball season is only 30 games. High-end starters who play 30 minutes a night will only max out at about 900 minutes and roughly 1,500 possessions, 70% lower.

In addition, college lineups are far tighter and more rigid, with starters routinely playing 80% or more of the entire game. A Division I team uses only about 78 unique five-man lineups a season versus roughly 510 in the NBA, and even after you adjust for fewer games and slower pace, the NBA still generates nearly twice the lineup variety per possession.

And with the best players often being one-and-done freshmen, or even sophomores, it is very hard for RAPM to describe the best players in any given season.

With talent density, impact metrics such as RAPM, EPM, and DARKO, while accounting for differences in the opponent quality relative to the league, do not account for a difference in the competitive environment itself. They assume every player is operating inside the same band of competition, that every team is more or less playing the same level of basketball. Thus, they derive an incredible amount of value in the NBA, where the players occupying those roughly 500 roster spots compete against the same 99th-percentile talent every night, and work poorly with NCAAB.

As an extreme example: a top prospect might face a mid-major opponent with zero NBA talent on the floor one night, then turn around and play in the SEC the following week against players much closer to NBA athleticism and NBA size. Metrics are often unfairly punishing players who play tougher bands of competition.

Net rating and current college metrics

My main gripe with a lot of the comtemporary metrics that try to sidestep using RAPM, such as win shares and PORPAG, is that they do a horrible job at reconstructing team net rating.

(The best metrics by tether themselves to something real and objective, and asking how faithfully they can reconstruct it.

Net rating is the best choice as it is quite literally, the accumulation of how much every individual on the floor moves the team's margin; offensive and defensive impact are nothing more than a player's contribution to it. Decomposing a team's net rating into each player's share of it is therefore the natural move. Anything else is needless abstraction. It is also the cleanest target available: objective, a direct record of points for minus points allowed per 100 possessions)

BPM is probably the best, most stable and effective one-on-one metric that does exist for college right now as It regresses box-score production onto adjusted plus-minus to approximate how much a player moves team net rating per 100 possessions.

And as we will get to later: C-ARC is numerically comeptivitive with it.

What C-ARC is

The core idea of C-ARC is the most robust, and comprehensive way of describing team player value. It blends both box and RAPM to give stability and a tangible foundation about what a player is producing possession by possession, while also capturing more of the latent value a play produces.

For the box side, I ran through thousands of collegiate possessions and estimated the tangible value created per possession for shooting efficiency, turnovers, assists, rebounds, steals, blocks, and etc.

Points are credited directly, turnovers cost about -1.30, steals are worth about +1.54, offensive rebounds +1.18, blocks +0.45, assists +0.40, defensive rebounds +0.10, summer to per 100 possesions with an efficiancy charge.

FOr the Impact Side i used a normal Reguarlised Adjusted Plus Minus (RAPM)

To deal with the schedule disparity issue, I added a slight schedule adjustment.

Box side

• using CBBD adjusted team ratings.
• Offensive production is adjusted by the strength of the defenses faced.
• Defensive production is adjusted by the strength of the offenses faced.
• Formula:

oBox+ = oBox per 100 + 0.50 × (League Avg Defense - Avg Opponent Defense)

dBox+ = dBox per 100 + 0.10 × (Avg Opponent Offense - League Avg Offense)

C-Box+ = oBox+ + dBox+

Impact side

• team strength as a prior/stabilizer instead of box or no prior
• Formula-ish:

Impact Prior_i = 0.50 × (Team Adjusted Net Rating_i / 10) × min(1, Minutes_i / 1000)

• Then the RAPM fit estimates the player’s impact around that prior.
• summary
  • players on stronger teams start with a slightly stronger prior
  • players on weaker teams start with a lower prior
  • the prior gets stronger as the player’s minutes sample grows
  • the model can still move the player up or down based on actual on/off stint results

used CBBD adjusted ratings for both team adjustments

• Similar idea to KenPom-style adjusted ratings they are
  • opponent-adjusted
  • conference-aware
  • puts teams from different schedule environments onto one comparable scale

I then blend the two by Z-scoring them so they're on the same scale (60/40), with a heavier emphasis on the box plus. This can account for the schedule disparity and sample size issues better for the added abstraction of the impact to fill in the gaps.

Leaderboard:

C-ARC Top 25 — 600 minute gate

(0 is average. So Boozer creates ~13.54 more points per 100 than average) Ranking should be read as more tier and band based than specific ratings

Full Leaderboard here

Rk	Player	Team	Min	C-ARC	Box+	Impact	Opp Net
1	Cameron Boozer	Duke	1274	+13.54	+54.45	+5.75	+15.98
2	Yaxel Lendeborg	Michigan	1210	+12.04	+44.58	+6.96	+20.33
3	Tarris Reed Jr.	UConn	957	+11.28	+51.07	+4.40	+16.90
4	Morez Johnson Jr.	Michigan	1005	+10.06	+45.79	+4.65	+20.43
5	Keaton Wagler	Illinois	1257	+10.06	+44.11	+5.11	+17.28
6	Oscar Cluff	Purdue	964	+10.04	+46.93	+4.31	+18.27
7	JT Toppin	Texas Tech	871	+9.74	+50.68	+2.98	+17.73
8	Brayden Burries	Arizona	1160	+9.65	+39.12	+6.07	+16.14
9	Motiejus Krivas	Arizona	984	+9.49	+40.56	+5.51	+17.31
10	Trey Kaufman-Renn	Purdue	1043	+9.46	+45.70	+4.07	+19.57
11	Tyler Tanner	Vanderbilt	1205	+9.46	+45.94	+4.00	+15.78
12	Joshua Jefferson	Iowa State	1083	+9.24	+41.37	+5.03	+12.96
13	Flory Bidunga	Kansas	1102	+9.21	+42.80	+4.63	+19.03
14	Aday Mara	Michigan	928	+9.20	+44.01	+4.29	+19.93
15	Zuby Ejiofor	St. John's	1113	+9.14	+46.82	+3.46	+14.99
16	Izaiyah Nelson	South Florida	929	+9.11	+45.85	+3.70	+5.09
17	Duke Miles	Vanderbilt	828	+9.04	+44.34	+4.04	+15.57
18	AJ Dybantsa	BYU	1208	+9.02	+49.09	+2.70	+15.82
19	Caleb Wilson	North Carolina	755	+9.01	+48.58	+2.83	+10.29
20	Henri Veesaar	North Carolina	973	+8.99	+41.76	+4.68	+12.80
21	Isaiah Evans	Duke	1075	+8.85	+38.18	+5.52	+15.79
22	Ja'Kobi Gillespie	Tennessee	1286	+8.83	+39.77	+5.07	+17.47
23	Patrick Ngongba II	Duke	702	+8.72	+42.18	+4.29	+15.14
24	Malique Ewin	Arkansas	776	+8.70	+42.91	+4.07	+17.76
25	Thijs De Ridder	Virginia	997	+8.69	+41.73	+4.42	+12.38

Validation

The fun part of validating C-ARC was seeing how it numerically compared extremely well with other widely used collegiate stats

1. Reconstruction

Question: If we aggregate player value back up to the team level, does it recover team net rating?

Metric	R² vs Team Net	MAE	RMSE
BPM	0.984	1.44	1.82
C-ARC	0.964	2.13	2.71
Win Shares	0.656	6.90	8.32
PORPAG	0.521	7.91	9.82

2. Retrodiction

Question: If we use last year’s player ratings with this year’s minutes, can the metric predict this year’s team strength?

Metric	Avg Retrodiction R²	2024→25	2025→26
BPR	0.685	0.694	0.676
C-ARC	0.661	0.665	0.656
Win Shares	0.589	0.617	0.561
BPM	0.585	0.617	0.554
PORPAG	0.505	0.551	0.459

3. Reliability

Question: Does the metric stabilize year over year for returning players?

Metric	YoY R²
C-ARC	0.63
BPM	0.60
Win Shares / 40	0.28
PORPAG	0.23

4. Independence / Blend Value

Question: Are the box and impact sides actually adding different information, or is the blend arbitrary?

Metric Layer	Avg Retrodiction R²
C-ARC blend	0.661
C-Impact only	0.627
Pure APM	0.623
C-Box+ only	0.484
BPM	0.585

Paper: https://court-share.com/prism/papers/introducing-prism
Leaderboard: https://court-share.com/prism/leaderboard

TL;DR: built PRISM, an NBA impact model that blends RAPM with possession-level weighted box production. With The average NBA possession in 2026 worth about 1.18 points, actions like steals came out to around 1.54 points and blocks around 0.70. To better illustrate the best individual players in the league, I believe we should combine the more intangible latent value captured by RAPMs with the tangible objective floor of the actual points created on a possession-by-possession basis.

Hey y’all, I’ve been diving really deep into the analytics of the NBA recently and just concluded a research project where I had, when I was curious to see if I could create a better all-in-one metric that better illustrates the best individual players in the league

The current best way to do that, from what I’ve seen, is using RAPM, (regularized adjusted plus-minus), which essentially measures your team's point differential with you on vs off the court.

Extremely very good framework, especially as it accounts for a lot of the latent, intangible value created, such as:

• communication
• rotations
• connective passing
• on-ball defense
• even rim protection that doesn't end in a block

Captures a lot of those intangible things that the box score could never.

Though as with any all-one metric there are a couple of blind spots.

• attribution between teammates and against opponents
• opponent strength
• undercounting the tangible value created per possession

What do I mean by tangible value created per possession?

The goal of basketball is to put up points. If you break it down to an atomic level, the game of basketball is about scoring more points than the other team or creating more value, more numeric value with actions than the opposing team.

The box score, for all its faults, can be used to provide a tangible floor for player value on a possession-by-possession basis.

In a single possession you can score anywhere from zero to four points, with the average NBA possession being worth about 1.18 points.

With 1.18 as the basis, you can look at the actions on the court that you can tangibly see and count as contributing to scoring above or below 1.18 points per possession. For example, a two is worth two, and a three is worth three, but how much is a steal worth? How much is a rebound worth?

After watching and computing thousands of NBA plays, a steal was found to be worth about 1.54 points per action for example

My idea was to blend both lineup impact and box score tangible production, not in terms of counting stats, but in terms of possession value created/lost per possesion.

Allowing the tangible value created per possession to serve as a strong foundation for more abstract calculations of a player’s value. genuinely think this is the better way to identify the best players in the league.

The closest thing I’ve seen is the box score prior to APMs, but all of those metrics like EPM and DARKO try to use the box score to predict impact metrics such as RPM, instead of describing the tangible value created in any given season.

So I built PRISM — the Production-Regularized Impact Statistical Model.

PRISM blends regularized adjusted plus-minus with a possession-level valuation of box production, expressed as expected points added per 100 possessions.

The following is the 3-year weighted leaderboard for 2026.

Rank	Player	PRISM	Impact	Box+
1	Shai Gilgeous-Alexander	13.12	10.01	21.94
2	Nikola Jokić	12.76	10.04	20.16
3	Giannis Antetokounmpo	11.25	7.73	22.83
4	Victor Wembanyama	10.23	8.22	16.14
5	Kawhi Leonard	9.30	7.15	16.29
6	Luka Dončić	7.18	4.55	17.38
7	Donovan Mitchell	7.00	5.36	13.22
8	Stephen Curry	6.34	4.90	12.08
9	Jimmy Butler III	6.31	5.02	11.45
10	Chet Holmgren	5.65	5.42	6.81
11	Franz Wagner	5.55	4.81	8.86
12	Lauri Markkanen	5.46	4.35	10.35
13	Derrick White	5.42	6.21	2.50
14	Karl-Anthony Towns	5.39	4.10	11.07
15	Jarrett Allen	5.19	4.43	8.80

TL;DR: built PRISM, an NBA impact model that blends RAPM with possession-level weighted box production. With The average NBA possession in 2026 worth about 1.18 points, actions like steals came out to around 1.54 points and blocks around 0.70. To better illustrate the best individual players in the league, I believe we should combine the more intangible latent value captured by RAPMs with the tangible objective floor of the actual points created on a possession-by-possession basis.

Hey y’all, I’ve been diving really deep into the analytics of the NBA recently and just concluded a research project where I had, when I was curious to see if I could create a better all-in-one metric that better illustrates the best individual players in the league

The current best way to do that, from what I’ve seen, is using RAPM, (regularized adjusted plus-minus), which essentially measures your team's point differential with you on vs off the court.

Extremely very good framework, especially as it accounts for a lot of the latent, intangible value created, such as:

• communication
• rotations
• connective passing
• on-ball defense
• even rim protection that doesn't end in a block

Captures a lot of those intangible things that the box score could never.

Though as with any all-one metric there are a couple of blind spots.

• attribution between teammates and against opponents
• opponent strength
• undercounting the tangible value created per possession

What do I mean by tangible value created per possession?

The goal of basketball is to put up points. If you break it down to an atomic level, the game of basketball is about scoring more points than the other team or creating more value, more numeric value with actions than the opposing team.

The box score, for all its faults, can be used to provide a tangible floor for player value on a possession-by-possession basis.

In a single possession you can score anywhere from zero to four points, with the average NBA possession being worth about 1.18 points.

With 1.18 as the basis, you can look at the actions on the court that you can tangibly see and count as contributing to scoring above or below 1.18 points per possession. For example, a two is worth two, and a three is worth three, but how much is a steal worth? How much is a rebound worth?

After watching and computing thousands of NBA plays, a steal was found to be worth about 1.54 points per action for example

My idea was to blend both lineup impact and box score tangible production, not in terms of counting stats, but in terms of possession value created/lost per possesion.

Allowing the tangible value created per possession to serve as a strong foundation for more abstract calculations of a player’s value. genuinely think this is the better way to identify the best players in the league.

The closest thing I’ve seen is the box score prior to APMs, but all of those metrics like EPM and DARKO try to use the box score to predict impact metrics such as RPM, instead of describing the tangible value created in any given season.

So I built PRISM — the Production-Regularized Impact Statistical Model.

PRISM blends regularized adjusted plus-minus with a possession-level valuation of box production, expressed as expected points added per 100 possessions.

The following is the 3-year weighted leaderboard for 2026.

Rank	Player	PRISM	Impact	Box+
1	Shai Gilgeous-Alexander	13.12	10.01	21.94
2	Nikola Jokić	12.76	10.04	20.16
3	Giannis Antetokounmpo	11.25	7.73	22.83
4	Victor Wembanyama	10.23	8.22	16.14
5	Kawhi Leonard	9.30	7.15	16.29
6	Luka Dončić	7.18	4.55	17.38
7	Donovan Mitchell	7.00	5.36	13.22
8	Stephen Curry	6.34	4.90	12.08
9	Jimmy Butler III	6.31	5.02	11.45
10	Chet Holmgren	5.65	5.42	6.81
11	Franz Wagner	5.55	4.81	8.86
12	Lauri Markkanen	5.46	4.35	10.35
13	Derrick White	5.42	6.21	2.50
14	Karl-Anthony Towns	5.39	4.10	11.07
15	Jarrett Allen	5.19	4.43	8.80

TL;DR: built PRISM, an NBA impact model that blends RAPM with possession-level weighted box production. With The average NBA possession in 2026 worth about 1.18 points, actions like steals came out to around 1.54 points and blocks around 0.70. To better illustrate the best individual players in the league, I believe we should combine the more intangible latent value captured by RAPMs with the tangible objective floor of the actual points created on a possession-by-possession basis.

Hey y’all, I’ve been diving really deep into the analytics of the NBA recently and just concluded a research project where I had, when I was curious to see if I could create a better all-in-one metric that better illustrates the best individual players in the league

The current best way to do that, from what I’ve seen, is using RAPM, (regularized adjusted plus-minus), which essentially measures your team's point differential with you on vs off the court.

Extremely very good framework, especially as it accounts for a lot of the latent, intangible value created, such as:

• communication
• rotations
• connective passing
• on-ball defense
• even rim protection that doesn't end in a block

Captures a lot of those intangible things that the box score could never.

Though as with any all-one metric there are a couple of blind spots.

• attribution between teammates and against opponents
• opponent strength
• undercounting the tangible value created per possession

What do I mean by tangible value created per possession?

The goal of basketball is to put up points. If you break it down to an atomic level, the game of basketball is about scoring more points than the other team or creating more value, more numeric value with actions than the opposing team.

The box score, for all its faults, can be used to provide a tangible floor for player value on a possession-by-possession basis.

In a single possession you can score anywhere from zero to four points, with the average NBA possession being worth about 1.18 points.

With 1.18 as the basis, you can look at the actions on the court that you can tangibly see and count as contributing to scoring above or below 1.18 points per possession. For example, a two is worth two, and a three is worth three, but how much is a steal worth? How much is a rebound worth?

After watching and computing thousands of NBA plays, a steal was found to be worth about 1.54 points per action for example

My idea was to blend both lineup impact and box score tangible production, not in terms of counting stats, but in terms of possession value created/lost per possesion.

Allowing the tangible value created per possession to serve as a strong foundation for more abstract calculations of a player’s value. genuinely think this is the better way to identify the best players in the league.

The closest thing I’ve seen is the box score prior to APMs, but all of those metrics like EPM and DARKO try to use the box score to predict impact metrics such as RPM, instead of describing the tangible value created in any given season.

So I built PRISM — the Production-Regularized Impact Statistical Model.

PRISM blends regularized adjusted plus-minus with a possession-level valuation of box production, expressed as expected points added per 100 possessions.

The following is the 3-year weighted leaderboard for 2026.

Rank	Player	PRISM	Impact	Box+
1	Shai Gilgeous-Alexander	13.12	10.01	21.94
2	Nikola Jokić	12.76	10.04	20.16
3	Giannis Antetokounmpo	11.25	7.73	22.83
4	Victor Wembanyama	10.23	8.22	16.14
5	Kawhi Leonard	9.30	7.15	16.29
6	Luka Dončić	7.18	4.55	17.38
7	Donovan Mitchell	7.00	5.36	13.22
8	Stephen Curry	6.34	4.90	12.08
9	Jimmy Butler III	6.31	5.02	11.45
10	Chet Holmgren	5.65	5.42	6.81
11	Franz Wagner	5.55	4.81	8.86
12	Lauri Markkanen	5.46	4.35	10.35
13	Derrick White	5.42	6.21	2.50
14	Karl-Anthony Towns	5.39	4.10	11.07
15	Jarrett Allen	5.19	4.43	8.80

TL;DR: built PRISM, an NBA impact model that blends RAPM with possession-level weighted box production. With The average NBA possession in 2026 worth about 1.18 points, actions like steals came out to around 1.54 points and blocks around 0.70. To better illustrate the best individual players in the league, I believe we should combine the more intangible latent value captured by RAPMs with the tangible objective floor of the actual points created on a possession-by-possession basis.

Hey y’all, I’ve been diving really deep into the analytics of the NBA recently and just concluded a research project where I had, when I was curious to see if I could create a better all-in-one metric that better illustrates the best individual players in the league

The current best way to do that, from what I’ve seen, is using RAPM, (regularized adjusted plus-minus), which essentially measures your team's point differential with you on vs off the court.

Extremely very good framework, especially as it accounts for a lot of the latent, intangible value created, such as:

• communication
• rotations
• connective passing
• on-ball defense
• even rim protection that doesn't end in a block

Captures a lot of those intangible things that the box score could never.

Though as with any all-one metric there are a couple of blind spots.

• attribution between teammates and against opponents
• opponent strength
• undercounting the tangible value created per possession

What do I mean by tangible value created per possession?

The goal of basketball is to put up points. If you break it down to an atomic level, the game of basketball is about scoring more points than the other team or creating more value, more numeric value with actions than the opposing team.

The box score, for all its faults, can be used to provide a tangible floor for player value on a possession-by-possession basis.

In a single possession you can score anywhere from zero to four points, with the average NBA possession being worth about 1.18 points.

With 1.18 as the basis, you can look at the actions on the court that you can tangibly see and count as contributing to scoring above or below 1.18 points per possession. For example, a two is worth two, and a three is worth three, but how much is a steal worth? How much is a rebound worth?

After watching and computing thousands of NBA plays, a steal was found to be worth about 1.54 points per action for example

My idea was to blend both lineup impact and box score tangible production, not in terms of counting stats, but in terms of possession value created/lost per possesion.

Allowing the tangible value created per possession to serve as a strong foundation for more abstract calculations of a player’s value. genuinely think this is the better way to identify the best players in the league.

The closest thing I’ve seen is the box score prior to APMs, but all of those metrics like EPM and DARKO try to use the box score to predict impact metrics such as RPM, instead of describing the tangible value created in any given season.

So I built PRISM — the Production-Regularized Impact Statistical Model.

PRISM blends regularized adjusted plus-minus with a possession-level valuation of box production, expressed as expected points added per 100 possessions.

The following is the 3-year weighted leaderboard for 2026.

Rank	Player	PRISM	Impact	Box+
1	Shai Gilgeous-Alexander	13.12	10.01	21.94
2	Nikola Jokić	12.76	10.04	20.16
3	Giannis Antetokounmpo	11.25	7.73	22.83
4	Victor Wembanyama	10.23	8.22	16.14
5	Kawhi Leonard	9.30	7.15	16.29
6	Luka Dončić	7.18	4.55	17.38
7	Donovan Mitchell	7.00	5.36	13.22
8	Stephen Curry	6.34	4.90	12.08
9	Jimmy Butler III	6.31	5.02	11.45
10	Chet Holmgren	5.65	5.42	6.81
11	Franz Wagner	5.55	4.81	8.86
12	Lauri Markkanen	5.46	4.35	10.35
13	Derrick White	5.42	6.21	2.50
14	Karl-Anthony Towns	5.39	4.10	11.07
15	Jarrett Allen	5.19	4.43	8.80

Good morning! This is kind of long so

TLDR:
started a personal project to create a platform that preserves our opinions as a personal journal for NBA discourse where everything is time-stamped and saved to your profile like an append-only ledger, and I want to expand it into a place where projects, articles, and ideas can have a home, a GitHub for basketball.

looking for people interested in making projects like this and helping shape it

avid basketball fan and my experience enjoying the game has only been enriched because of communities like ours. we go deeper than surface-level narratives.We prioritize appreciating the game as an art form, as a concept.

One of my biggest gripes is that a lot of this discourse has been poisoned and fractured by the platforms that serve it. Everywhere we currently talk is run by algorithms that prioritize engagement and divisiveness for profit over actual discussion.

Detailed write-ups or projetcs disappears from the timeline within a couple of minutes or hours. are scoffed at by the algorithm and favored for punchy one to seven word quips.

About three or four months ago I started a personal project and wrote a couple of papers about the idea of trying to crack the code and creating a platform that preserves our opinions and is a place where true NBA discourse can thrive.

It’s been going pretty good. It’s a small growing community (a little over 100), right now it’s used as a personal journal for NBA draft prospects. You can say your opinion about a player and it’s saved forever on your profile. You can never delete it; you can only append, like an append-only ledger. Everything is time-stamped and saved to your profile.

I had a new vision to expand it a little bit, to make it a place where all of these amazing projects I’m seeing in this Reddit community, what I see on Twitter, and the articles I see people writing can have a home.

a website dedicated to basketball, the celebration of basketball analysis.

For every project you make, whether it’s, “This is why power forwards in 2026 are going out of style” and you go into a deep dive about the statistical reasons, it’s served to users on a plate where they can engage and collaborate.

If you make a new formula that’s somehow better than RAPM and you want to share it, or if you try to quantify defensive gravity in your own way instead of just putting it on GitHub where it gets lost, or tweeting about it where it disappears in two hours, you have a place.

You have a website that’s dedicated entirely to that type of creation and discourse.

I wrote out spme features called Projects and Articles. A lot of the current user base is more interested in writing player evals and creating draft boards. We even have a contribution heatmap similar to GitHub.

I am posting here to see if anyone is interested in making this a reality and being one of the first people to use it like that.

I would add you to a group chat and work closely with you to design it, including:

• the feature
• how it looks
• how it works (since there is no social component)

There is no liking, no following, and no algorithm. It is literally just a personal journal rn. I would want to move very softly, never trying to be social media per se, but create some sort of discovery mechanic for these projects. I know this was long. If you are interested, please let me know.