u/Glokk39Spaz

r/rockets r/GoNets r/denvernuggets r/Thunder r/nbadiscussion r/nba r/NBAstatsmuse r/ripcity r/NYKnicks r/NBA_Draft r/NBAanalytics

▲ 23 r/nbadiscussion

Every Player Graded Through a Skill Wheel [OC]

Ive spent the past couple weeks working on this personal project of mine. I built a skill wheel that grades 54 different aspects of basketball. I tried breaking down each big skill (such playmaking) into smaller skills and use that to sort of evaluate a player as a whole. Now, of course the data available to the public is limited so a lot of niche skills are pretty hard to quantify but I think I got a sort of good estimate of the skills. If you guys have any ideas for skill grading/skills to add or anything, just let me know I am very open to feedback.

Here is a picture of on of the diagrams, it is much better to see image form than to read:

https://imgur.com/a/wml7Jqc

Here are the skills I decided on:

Offense: 12 groups, 33 skills

Finishing: Rim Finishing, Contact Finishing
Above the Rim: Dunking, Lob Threat
Touch Shots: Floaters, Hooks, Short Jumpers
Midrange: Pull-up Midrange, Catch-and-Shoot Midrange, Turnarounds & Fadeaways
Three-Point: Catch-and-Shoot 3s, Quick 3s, Off-Dribble 3s, Deep 3s
Free Throws: Free Throw Shooting, Foul Drawing
Self-Creation: Isolation, Pick-and-Roll Ball Handler, Post Scoring
Driving: Rim Pressure, Drive & Kick
Off-Ball Scoring: Cutting, Movement Scoring, Transition Scoring
Playmaking: Creating for Others, Creation Quality, Rim Feeding, Shooter Feeding, Connective Passing, Transition Passing
Ball Security: Passing Security, Handle Security, Avoiding Offensive Fouls
Screening & Glass: Screen Setting, Rolling, Offensive Rebounding, Putback Scoring

Defense: 6 groups, 21 skills (There is less because defense is harder to quantify)

Perimeter: Isolation Defense, Closeouts, On-Ball Stripping, Perimeter Blocking
Interior: Shot Blocking, Rim Contesting, Rim Deterrence
Rebounding: Securing Boards, Boxing Out
Disruption: Passing Lanes, Charge Taking, Loose Ball Recovery
Assignments: Post Defense, Positional Versatility, Guarding Stars
Discipline: Fouling Discipline, Transition Defense

I can't really explain each grade because it will be too long, but if you have any questions, you can ask me and I'll respond but I will explain certain ones.

Three Pointers

For Three Point grading I spilt them up into Catch and Shoot 3s (0 dribbles), Quick 3s (1-2 dribbles) and Off Dribble 3s (3+ dribbles), Deep 3s (27ft+). For the whole project I used NBA play by play data a LOT so that data comes in lines like this MISS Herro 27' 3PT Step Back Jump Shot,Missed Shot,Step Back Jump shot,1,3,260,0022501055,2026-03-25,2025-26

Parsing the distance for Deep 3s was easy. For the dribble 3s, the NBA API carries data for shots off certain number of dribbles, but you can also mix this with defender distance so for example there is data on each players 3-6 Dribble 3FGM/FGA with a defender 2-4 feet. So I parsed through all that and each player's attempts and FGs are separated by dribbles and defender distance. Each group also has a league-average rate, so a player's expected 3P% for lets say Catch and Shoot is just the league average 3p% on their defender distance (so for Curry if he takes way tighter catch and shoots his expected 3p% on that is skewed for the tight defender catch and shoot league average). So then a player is compared by their volume and efficiency. Each player gets some league average since if you make lets say like 15/30 Catch and Shoot 3s that is a pretty low sample over like 150/350. The volume is a per 75 basis so not a minutes thing, and then score is bascially a volume * effieciency metric that is Z scored across the league so each skill has the same scale.

Here is Curry 2025-26 for example

Catch-and-Shoot 3s 97th of 388 Att 247 3P% 37.7% x3P% 36.2% +0.3

Quick 3s 1st of 388 Att 87 FG% 48.3% xFG% 33.4% +3.1

Off-Dribble 3s 14th of 388 Att 236 3P% 40.7% x3P% 33.1% +1.1

Deep 3s 1st of 388Att 261 FG% 37.2% xFG% 31.9% +4.8

Finishing

Layups only, from the play-by-play: driving layups, finger rolls, reverse layups. Dunks, cuts, transition, alley-oops and putbacks are left out, because they're graded elsewhere. The efficiency is graded against the same position in this case.

And then again adding some of the league-average shots, volume x efficiency with volume being per 75 and then Z scored the league again.

Rim Deterrence

Rim deterrence is pretty much like a RAPM style regression, a matrix in which the opposing teams Rim Rate is taken into account. Transition shots are removed because then the best Rim Deterers would just be the best point guards that prevent turnovers not the actual best defenders. It is a RAPM regression so it adjusts for teammates but this one requires more possesions so some low usage role players might not have the grade.

A lot of these defense ones are self explanatory

Positional Versatility

This one uses the matchup API to see who he guarded 1 on 1 and then the spilt and compare the percentage

Group Listed positions

G (guard) W (wing G-F, F-G) F (forward) B(big F-C, C-F) C (center)

u/Glokk39Spaz — 7 days ago

▲ 20 r/nbadiscussion

The Value of the Free Throw

It is no secret that the free throw is the most valuable shot in basketball. Just looking at it from a mathematical stand point, the PPS for a free throw is the highest.

Shot Type	Average NBA Shooting %	Formula / Calculation	Expected Points Per Shot (PPS)
Free Throw (2-shot foul)	78%	0.78 × 2 points	1.56 PPS
Rim / Layup / Dunk	64%	0.64 × 2 points	1.28 PPS
Corner Three	39%	0.39 × 3 points	1.17 PPS
Above-the-Break Three	36%	0.36 × 3 points	1.08 PPS
Mid-Range Jumper	41%	0.41 × 2 points	0.82 PPS

Taking this further,

I graphed a couple different things and tried to find the correlation.

Pretty much on the X axis there is either Share of PTS From FT, Share of PTS from 3P, Share of PTS from Midranges, Share of PTS from RIM and then on the Y its ORAPM (3 YEARS)

For all of them, min. 1750 Minutes in the 2025-26 season, I can't upload pictures so here is the correlations.

Free Throws had the highest correlation (r = .34) (the people with most points coming from ft were Deni, Banchero, Harden, Book, AR, Keyonte George, Demar, Zion)

Share of Points from 3 v oRAPM 3 years had a r = -.07 (being completely one dimensional isn't good on offense)

Share of Points from Midrange v oRAPM had r = .24 (This one made sense to me just because the players taking a lot of midranges are stars that are allowed by their coaching, like Shai, KD, Book, etc

Share of Points from Rim v oRAPM had r = -.12 (This one is pretty surprising; I guess if your value only comes from cutting/easy layups and stuff its harder to be productive. Obviously, there are outliers with high share on the rim and high value)

Overall, I guess free throws are a good way to predict offensive impact. Getting fouled is very good for team success. Let me know what you guys think

reddit.com

u/Glokk39Spaz — 24 days ago

▲ 32 r/nba

The Value of the Free Throw

It is no secret that the free throw is the most valuable shot in basketball. Just looking at it from a mathematical stand point, the PPS for a free throw is the highest.

Shot Type	Average NBA Shooting %	Formula / Calculation	Expected Points Per Shot (PPS)
Free Throw (2-shot foul)	78%	0.78 × 2 points	1.56 PPS
Rim / Layup / Dunk	64%	0.64 × 2 points	1.28 PPS
Corner Three	39%	0.39 × 3 points	1.17 PPS
Above-the-Break Three	36%	0.36 × 3 points	1.08 PPS
Mid-Range Jumper	41%	0.41 × 2 points	0.82 PPS

Taking this further,

I graphed a couple different things and tried to find the correlation.

Pretty much on the X axis there is either Share of PTS From FT, Share of PTS from 3P, Share of PTS from Midranges, Share of PTS from RIM and then on the Y its ORAPM (3 YEARS)

For all of them Min 1750 Minutes in the 2025-26 season

https://preview.redd.it/jg84gcyrixqh1.png?width=2000&format=png&auto=webp&s=436b64144a246b5167dccc1bb56e35e088157d6f

Free Throws had the highest correlation (r = .34)

Share of Points from 3 v oRAPM 3 years had a r = -.07 (Being completely one dimensional isn't good on offense)

Share of Points from Midrange v oRAPM had r = .24 (This one made sense to me just because the players taking a lot of midranges are stars that are allowed by their coaching, like Shai, KD, Book, etc

Share of Points from Rim v oRAPM had r = -.12 (This one is pretty surprising, I guess if your value only comes from cutting/easy layups and stuff its harder to be productive. Obviously, there are outliers with high share on the rim and high value)

Overall, I guess free throws are a good way to predict offensive impact. Getting fouled is very good for team sucess. Let me know what you guys think

reddit.com

u/Glokk39Spaz — 24 days ago

▲ 108 r/Thunder

Shai on PNR offense/defense compared to guards (2025-26 2k + mins)

Shai creates the most points per possession as a guard ball handler in the pick-and-roll within the entire league but also prevents the most points by quite a wide margin. The size of the bubbles are frequency defending the PNR, SGA lands at 29% of possessions, and the spread within this graph is Devin Booker at 25% and Stephon Castle at 38%. For comparison, some forwards like Kawhi had 26% and defended at .74ppp and Jaylen Brown had 31% at .85ppp. SGA is at .62 which is ridiculous, crazy levels of defensive and offensive talent

u/Glokk39Spaz — 1 month ago

▲ 32 r/NBA_Draft

Most League Ready Archetypes?

These are some aging curves based on 1 Year RAPM of every player from 2015-16 onwards, grouped by some roles. The roles were decided using cosine similarity from some play type tracking/shot selection data, which is why its only from 2015-16 onwards, since the data started being tracked in 2014.

Some interesting things: first being that Postmen in the league have the highest peaks, but I think it is partly inflated by the archetype of All-Star level big men like Embiid, Jokic, Sengun, etc, etc. Also, the fact that Rim protection is more valuable than perimeter defense means that the roles that are highly correlated with being tall, have a lot of value.

The role that starts out with the highest OVERALL value is Rollmen, and again I think this is because just being tall and defending the rim provides value in the NBA. Looking at just the offensive side of the ball. The roles that start with the highest initial values are 2 Level (Layup and 3P) with -.24 and Movement Shooters at -.29, and then the highest peak is 3 Level Iso Scorers, which makes sense.

I think it makes sense that the primarily shooting roles have the easiest time starting out in the NBA in terms of impact; their job doesn't require changing much from college. 3 Level creators have to adjust to new defenses and learn to create in a harder environment, while shooters can be schemed open. This leads to a ceiling on non-self creators, though later on, as we see in the peaks.

Some caveats: The role is decided from that year of play in the NBA, so rookies aren't being evaluated from a college role. I assume that most players are in the same role they had in college, shot selection / playtype wise. The usage is the biggest change, but I think someone who was coming off screens and shooting a ton of long-range shots would be taking threes primarily in rookie year. Also, the only people evaluated here have to actually play, so that could skew the results for rookies who never made it onto the court.

u/Glokk39Spaz — 1 month ago

▲ 2 r/NBAanalytics

Analyzing Playoff Risers/Droppers through Playoff RAPM

RAPM is one of the more useful stats when trying to analyze a player's impact since it doesn't focus on the box score but rather on performance on/off. KAT's playoff run was obviously crazy, as he was playing out of his mind, but the box score doesn't really FULLY capture that impact.

RAPM pretty much works by splitting up games into stints where a stint is the same 5 players on offense and 5 players on defense, and seeing the result in points. Over a whole season, and thousands of possessions, and the Ridge regression trying to bring back the player to 0, you get your list of the impact leaders. Playoff RAPM can be the same thing but for possessions for only playoff games, but the issue that arises is the possession count is WAY lower, especially if you get bounced early. In order to fix this issue, I used the prior as the players previous 3-year RAPM.

Logically, players aren't going to magically become 10x better in the playoffs, so regression attributes the impact initially through the regular-season impact, but the playoff-level possession data changes it.

Here are the results I got:

1. KAT: +3.42 → +6.37 (Δ +2.95)

2. Wembanyama: +7.31 → +9.87 (Δ +2.56)

3. Brunson: +2.89 → +5.33 (Δ +2.44)

4. Bridges: +0.55 → +2.75 (Δ +2.20)

5. Hart: +0.68 → +2.70 (Δ +2.02)

Just for fun the lowest:

White: +5.92 → +4.47 (Δ -1.45)

Mobley: +2.97 → +1.65 (Δ -1.32)

Brown: -0.17 → -1.48 (Δ -1.32)

Dort: -0.05 → -1.28 (Δ -1.24)

D. Mitchell: +4.95 → +3.73 (Δ -1.21)

Gilgeous-Alexander: +9.63 → +8.42 (Δ -1.21)

My thoughts:

KAT as number one makes sense; his playmaking value and defensive value were on full display and overall the Knicks (which took over the leaderboard) had a crazy shooting streak for the whole run. The rebounding and defense was pretty elite watching too.

Its much harder for stars to keep up a high level of play in the playoffs since they face a lot more defensive attention, so Wemby and Brunson being high is really impressive. While other stars like Shai clearly did drop somewhat, he's still the second most impactful on this list, only behind Wemby. (Who he went against)

RAPM isn't a perfect stat and other all-in-one metrics are better when it comes to ranking players so I think it kinda underrated OG's playoff rise which was very impressive. Anunoby: +5.28 → +6.50 (Δ +1.22). Also Jaylen Brown is prior already a negative player, which I think is far too harsh, even though I am not high on him.

Alex Caruso and Ajay Mitchell had noticable offensive impact jumps which makes sense since Alex Caruso was shooting really well compared to the season and had an outlier game.

I have results for 20 years back on my website SimulNBA under playoff risers page if anyone is interested.

reddit.com

u/Glokk39Spaz — 1 month ago

▲ 13 r/NYKnicks

KAT Playoff Performance through Playoff RAPM (best playoff riser itl)

Here are the results I got:

1. KAT: +3.42 → +6.37 (Δ +2.95)

2. Wembanyama: +7.31 → +9.87 (Δ +2.56)

3. Brunson: +2.89 → +5.33 (Δ +2.44)

4. Bridges: +0.55 → +2.75 (Δ +2.20)

5. Hart: +0.68 → +2.70 (Δ +2.02)

Just for fun the lowest:

White: +5.92 → +4.47 (Δ -1.45)

Mobley: +2.97 → +1.65 (Δ -1.32)

Brown: -0.17 → -1.48 (Δ -1.32)

Dort: -0.05 → -1.28 (Δ -1.24)

D. Mitchell: +4.95 → +3.73 (Δ -1.22)

KAT had the biggest jump out of anyone in the playoffs, most of which came from his defense (+1.79) and I think he shut down a lot of narratives with this run. KAT can clearly produce championship-level impact aside from scoring, KAT's defensive and playmaking value showed out like crazy. The team as a whole just shot ridiculously, explaining all the Knicks' jumps.

Some notes:

This is throughout the whole playoffs

Its much harder for stars to keep up a high level of play in the playoffs since they face a lot more defensive attention, so Wemby and Brunson being high is really impressive.

RAPM isn't a perfect stat and there are many other versions of RAPM type stats that are better and I think this kinda undervalues OG's playoff run since he shot insane. 13th best rise: Anunoby: +5.28 → +6.50 (Δ +1.22)

I have results for 20 years back on my website SimulNBA under playoff risers page.

reddit.com

u/Glokk39Spaz — 1 month ago

▲ 41 r/denvernuggets

Jokic Efficiency Pre Injury

Jokic Stats First Half v Second Half (SimulNBA)

Jokic was having one of the most insane stretches of basketball I've ever seen in terms of effciency.

If he finished with a 73.6 TS% that would put him at a 15.5% rTS, even when comparing to center average TS (61.2% which I don't think is far since his shot diet is different) he has rTS of 12.4%. Using roles to see what his expected TS% would be results in 59.9% and an rTS of 13.7%.

(Roles just uses his shot diet and compares to other similar shot diets to see expected TS, SimulNBA)

For comparasion, Curry in 2016 had an rTS of 12.9%, position rTS of 13.9 and role rTS of 13.6%. Curry still had the edge in scoring, but from Jokic we were seeing goat level efficiency, goat level playmaking, and one of the greatest peaks of all time. Falling from the goat level of play to mvp level play hurt his MVP case a lot more than it should have, the baseline he set at the start of the season was crazy

u/Glokk39Spaz — 3 months ago

▲ 9 r/NBAanalytics

Analyzing Rim Finishing thru Roles Standpoint

In order to quantify best RIm Finishers in the league, i wanted to look closer in Rim FG% "Rim" attempts/percentage pull straight from ZONE_FGA_RIM/ZONE_FGM_RIM. That's the NBA's official Restricted Area shot zone

Looking at Rim Fg% gives us this list of top 5 (min Att of 100):

Anthony Gill - 83.6%

Jericho Sims - 83.4%

Karlo Matkovic - 81.7%

Luka Doncic - 81.6%

Deandre Ayton - 80.1%

The issue that I had when looking at this, is FG% doesn't take into account any sort of context surrounding the player. Simply looking at this list we can see Luka Doncic is the only guard surrounded by 4 low usage centers. Using the simplest role, the position, I averaged out each positions Rim Fg% and subtracted it to get a rFG%

Positions rFG% leaders (min 100 att)

Luka Doncic - +17.8%

Anthony GIll - +15.3%

Jericho Sims - +13.5%

Karlo Matkovic - +13.4%

Kevin Huerter - +11.4%

The list changes a fair amount, as expected Luka shoots up and then Kevin Huerter makes an appearance, as a SG he shot 76.2% at the rim which is up quite a fair amount from the average. But this list can do better, by applying roles to each player we can find an average role FG% and then subtract it.

I used 9 offensive role archetypes (Rollman, Postman, 3-Level Scorer, 3-Level Iso, 2-Level [3s], 2-Level [Mid], Movement Shooter, Spot Up, Cutters) using cosine similarity to find scores for each player for each role.

Role rFG leaders (min 100 att)

Luka Dončić (LAL) — 81.6% on 163 att, +15.3 vs role
Anthony Gill (WAS) — 83.6% on 110 att, +13.6 vs role
Jericho Sims (MIL) — 83.4% on 163 att, +13.4 vs role
Karlo Matković (NOP) — 81.7% on 115 att, +12.6 vs role
Giannis Antetokounmpo (MIL) — 77.4% on 402 att, +10.5 vs role

Bottom 5 (worst vs. role expectation)

Nolan Traore (BKN) — 45.2% on 135 att, -19.5 vs role
Patrick Williams (CHI) — 48.6% on 107 att, -17.9 vs role
Andre Drummond (PHI) — 54.1% on 196 att, -15.9 vs role
Rob Dillingham (CHI) — 52.3% on 128 att, -14.0 vs role
Steven Adams (HOU) — 56.1% on 107 att, -13.9 vs role

Finally, there is one more addition we can do to this stat. Luka being elite is clear, but with Giannis entering the top 5 we see an issue. Giannis has 402 attempts to Luka's 163. Another stat that we can look at is Points Added (compared to Role) This is calculated by doing 2 * attempts * diff in FG% role. Its basically the role rFG weighed by volume. We get these results:

Top 5 — Rim Points Added vs. Role Expectation (2025-26, min. 100 rim attempts)

Giannis Antetokounmpo (MIL) — 77.4% on 402 att, +84.4 pts
Victor Wembanyama (SAS) — 75.1% on 386 att, +63.3 pts
Deandre Ayton (LAL) — 80.1% on 312 att, +62.9 pts
LeBron James (LAL) — 75.6% on 340 att, +59.9 pts
Luka Dončić (LAL) — 81.6% on 163 att, +49.8 pts

Bottom 5 — Rim Points Added vs. Role Expectation

Karl-Anthony Towns (NYK) — 61.7% on 493 att, -82.4 pts
Jeremiah Fears (NOP) — 57.4% on 399 att, -72.0 pts
Andre Drummond (PHI) — 54.1% on 196 att, -62.4 pts
Nolan Traore (BKN) — 45.2% on 135 att, -52.6 pts
Derik Queen (NOP) — 60.4% on 374 att, -48.5 pts

So What?

- Helps us analyze one part of the game doesn't decide if a player is good or bad

- Roles aren't perfect; I think the roles that are being created through playtype frequency can be expanded and be more accurate

- Doesn't account for shot difficulty / shot type, ISO shots are much much harder.

I think overall, there are some limitations, but this could still be valuable, at least directionally. I have all this data and more on my website SimulNBA, its all under the role stats section of the page. Any feedback welcome!

u/Glokk39Spaz — 3 months ago

▲ 1 r/NBAstatsmuse

Grading Contracts League Wide

Grading Contracts - Contract Score

I created this stat as part of my website side project. My main goal was trying to grade every contract in the league and then put it all together for overall team scores.

BEST/WORST CONTRACTS ITL (at least 30 games played):

Scored 0-10 (5 being average)

Top 5 Best Contracts (2025-26): (1 2 and 3 are tied)

Neemias Queta (BOS) — $2.3M salary, $42.9M underpaid, Score: 10.0
Chet Holmgren (OKC) — $13.7M salary, $38.0M underpaid, Score: 10.0
Victor Wembanyama (SAS) — $13.4M salary, $46.8M underpaid, Score: 10.0
Donovan Clingan (POR) — $7.2M salary, $35.3M underpaid, Score: 9.8
Cason Wallace (OKC) — $5.8M salary, $33.3M underpaid, Score: 9.5

Top 5 Worst Contracts (2025-26):

Zach LaVine (SAC) — $47.5M salary, $41.2M overpaid, Score: 1.2
Jordan Poole (NOP) — $31.8M salary, $26.7M overpaid, Score: 2.4
Paul George (PHI) — $51.7M salary, $28.6M overpaid, Score: 2.9
**De'Andre Hunter (**SAC) — $23.3M salary, $19.3M overpaid, Score: 3.3
Jonathan Kuminga (ATL) — $23.8M salary, $15.8M overpaid, Score: 3.4

Interesting Contracts

Jaylen Brown - 53.1M, predicted 40.8, Score: 5.0 (While he is overpaid, he was their star player in 2025-26 and was very available, giving the 5.0 score)
Going off skill, the first player to be overpaid is Devin Booker being paid 3m more.

Methodology

In order to determine how much money a player should get, we need to evaluate their skill. For the skill in this regression, I used a blend of my own RAPM, bbindex LEBRON, and then BPM. It was a ratio of 9:9:2 approximately.

After having an estimate of each players skill, I had to choose a regression. I found the best fit was a Sigmoid curve, with a floor and ceiling set at the NBA floors and ceilings. Fitting the regression I just removed rookie deals because those aren't market value deals. If you draft a 2nd rounder, they make 2nd round money, but can turn out to be worth a lot more.

https://preview.redd.it/2fapytoz6hdh1.png?width=2764&format=png&auto=webp&s=4ecd6b826c7b8cfc2bbdada14bb7cf385adce3fc

This is the regression, the basic idea is the amount over is the overpay and the amount under is the under pay, but there were some factors I added to make the scores make more sense.

Forgiveness

There is a forgiveness factor on skill and GP. Pretty much, if you overpay a high-skill player by 5m that doesn't really knock the score down because they are valuable assets for the team, and the curve isn't perfect. For example, Curry has a contract that is worth more than his skill shows (highest paid ITL but not the best ITl anymore), but his contract score isn't horrible since he the team's franchise, highest skill player. If you overpay a player by 5m and they play all 82 games, that overpay is also mostly forgiven because they are a valuable team asset.

Team Contract Scores:

Sum of a team's top 10 individual scores (not full roster, so benches/throw-ins don't skew it), min-max normalized 0-100 across the league.

#1 OKC

#2 POR

#3 DET

#4 BOS

#5 CHA

Limitations:

- Sigmoid isn't perfect and can reflect some market biases

- Rookie deals are always the best deals

- Doesn't reflect how rare certain deals are, for example, Jokic is underpaid by 5M (prediction) but his score isn't that high. It is very rare, though to see an underpaid superstar, should boost the score a bit more.

Full team breakdowns, all contracts, etc are on my website SimulNBA. Any feedback is appreciated.

reddit.com

u/Glokk39Spaz — 3 months ago

▲ 5 r/rockets

Brice?

Brice Sensabaugh, John Konchar, Cody Williams, Svi Mykhailiuk, Okogie, all competing for a similar role in Utah. There is now a ton of talent on the team and a lot of mouths to feed and I'm not sure how Brice fits within those lineups, especially as a scorer guard/wing.

During the first half of last season his minutes varied a ton due to coaching decisions and I guess they were figuring out his role with the team, also with Ace Bailey being new.

He played great last season during the second half and but clearly won't get any of the same minutes/opportunities next season. Do you guys think he can fit in the Nets lineups somehow? It is his last year on a rookie deal in Utah and he might want out after ton of roster/role changes.

Brice Sensabaugh stats from first half to second half szn changes

simulnba

No secret that the Rockets had bad bench scoring last season, this can be a bright spot and somewhere that Sensabaugh can be used to his full potential, biggest flaw is defense but I think he improves in the system and can maybe get away with it with certain lineups? Another question would be the spacing, his 3pt shooting did go down a bit from 2025 but it's kind of hard to evaluate on a tanking team I would expect it to go back up

I think he has a lot of potential and he doesn't really align with the Jazz timeline. What do you guys think?

reddit.com

u/Glokk39Spaz — 3 months ago

▲ 3 r/GoNets

Brice Sensabaugh?

During the first half of last season his minutes varied a ton due to coaching decisions and I guess they were figuring out his role with the team, also with Ace Bailey being new.

Brice Sensabaugh stats from first half to second half szn changes

I think he has a lot of potential and he doesn't really align with the Jazz timeline. What do you guys think?

reddit.com

u/Glokk39Spaz — 3 months ago

▲ 15 r/ripcity

Excited for Ja

The Case for Ja?

I am late to this trade since I am not a Blazers fan, but from an outsider's perspective, it is very exciting to see what they are going for. Ja Morant was shipped at a low price and I think it is a great bet to take and see how this will turn out.

His box score stats last season were pretty bad, and that is no secret, but when looking at impact metrics, they aren't as bad as I was expecting. LEBRON has him at #66th ITL and my RAPM has him at #46, following his bad box score stats, BPM has him at around #200.

The main issue I think that can be fixed is his efficiency.

simulNBA

His situation in Memphis last year was pretty horrible; he had 14th highest USG and the 3rd highest AST rate (very surprising). Looking at his shot chart and effciency its all red, which is scaring but there is a good case for his efficiency improving. The Blazers have a much better team in place, and with more star power around him, there will be more spacing for him to actually get to the rim where he excels and also get open looks from distance.

Concerns:

My main concerns with the trade is how Ja Morant can play off the ball, since no matter what, he will see it less with a lot of mouths to feed. Shooting hasn't exactly been his strong suit but maybe he can get value from cutting; not sure.

Another concern I have is the backcourt defense, but there are some good defensive players on the team I'm not sure how they fit into lineups, though. Not the biggest concern, honestly

Health, as a Blazers fan, I would be praying, but hopefully it will turn it around. Maybe playing for Memphis extended that injury.

Overall

I am pretty excited to see this play out in the regular season, and I am not even a Blazers fan. Getting a past all star is always great and I hope he can return to his past self. It's making a lot more sense the logic behind the trade, I am not 100% convinced but I will defintely watch some games and see how this turns out.

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u/Glokk39Spaz — 3 months ago

▲ 1 r/NBAstatsmuse

Best Momentum Bench Players

Who are the Best Momentum Bench Players?

https://preview.redd.it/ftlu9q9365dh1.png?width=1238&format=png&auto=webp&s=372f4a227d17062063f35a615c486b1684c25254

For my website SimulNba, I worked on a stat called Firestarters. Basically, it tracks scoring runs, and then notes how much a player contributed to the run and gives them a score. The score is then tallied across the season and its by 1000 minutes.

Methodology:

I used NBA API possession data to get every game into possession by possession to start parsing the runs. A run is defined to be by a team scoring 70% of total points scored within certain possession windows (8, 10, 15, 20) and outscoring the opponent by 6 or more points.

Each run is then scored on quality so pretty much margin (15-0 > 7-0 run), the ratio of points scored (10-0 > 14-4), leverage moments (just weighs garbage time runs a lot less) and then a slight multiplier for team DRTG so runs against tanking teams aren't weighted AS heavily.

And to attribute to each player, the scoring actions in each run are handed out as percentages, so a 12-2 run with run score of 6.4, if a player took part in 4 of the 6 scoring possessions, they get like 67% so a 4.3 tagged onto their season total. Everyone on the court even if you didn't score gets 10% just by helping with spacing etc other intangibles.

Quick Limitations:

- Purely scoring runs; just wanted to see who gets on and creates their points

- Data parsing probably isn't 100% perfect

- The runs are weighted kinda arbitrarily; I guess it's up to you if you value a 15-3 or a 14-2 run more

Best Momentum Bench Players: (< 30% GS) (percentiles for people with <%30

Fire Score Leaders

Brice Sensabaugh — Fire Score 76.34 (100th) | Runs Involved 87 | Total Run Score 368.4 | Minutes 1763 | Start% 29%
Oso Ighodaro — Fire Score 75.89 (99th) | Runs Involved 108 | Total Run Score 522.4 | Minutes 1812 | Start% 29%
Daniss Jenkins — Fire Score 72.25 (99th) | Runs Involved 87 | Total Run Score 411.7 | Minutes 1454 | Start% 26%
Quentin Grimes — Fire Score 69.41 (98th) | Runs Involved 69 | Total Run Score 320.2 | Minutes 2205 | Start% 25%
Ayo Dosunmu — Fire Score 67.64 (98th) | Runs Involved 71 | Total Run Score 350.0 | Minutes 1884 | Start% 28%

So What?

This stat doesn't really mean much when evaluating a player, just something fun to notice. The top 5 here makes sense; a lot of these players are some talented bench scorers, Ayo had a bigger role after the trade, though. I am excited to see Brice Sensabaugh's role on the upcoming Jazz team with more talent.

Full tables for the season & past seasons on the website SimulNba, some key findings if you don't want to check

Shai is first for the season (who would have thought), score of 544

Best run score for the past 10 seasons is 15-16 Curry with a score of 731

OKC, BOS, SAS went on the most runs as a team

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u/Glokk39Spaz — 3 months ago

▲ 26 r/NBAstatsmuse

Best Midrange Shooters? Through a Role Adjusted Lens

Who are the Best Midrange Shooters?

I have been working on an NBA analytics site, and one of the questions I set out to answer was who the best midrange shooters ITL are. The NBA API shows us each players shot taken from 4 different areas of the court: LEFT_SHORT + RIGHT_SHORT + LEFT_LONG + RIGHT_LONG + CENTER_MID. Taking these 4 areas of the court, summing total shots, total attempts gives us the most basic stat to quantify this: Midrange FG%.

This gives us the following results: (min 100 atts)

Midrange Fg%:

Jokic 55.6%

Rui 55.6%

Shai 54.9%

T.J McConnell 54.1%

Jalen Brunson 50.2%

The basic FG% stat already gives us a pretty good idea on who the best midrange shooters in the league are, but doesn't account for roles. The midrange is already a shot that is at a premium, mostly for the stars on the team and guards are hitting it at around 43% on average, while bigs are on average at around 40%. The most basic role is the position, and by position midrange rFG we get this top 5 (same 100 min attempts)

Midrange rFG%:

Jokic +15.6%

Rui Hachimura +14.0%

Shai + 11.9%

TJ McConnell + 11.0%

Tobias Harris +8.4%

Not much change in the list; Tobias Harris rising up, as a forward hitting 50% from midrange is out of the ordinary. Trying to continue with the role idea, I took each player's shot diet, (frequency from 0-5, 5-10 feet, etc etc) and took each area as a percentile from 0-1. For example, Mitchell Robinson shoots 98.5% of his shots at the rim, which puts him at 100 percentile for rim shooting, and his role is clear from this number.

Using these roles, and each player's TOP SCORE, I found a baseline midrange FG% for each role. And then finally, each player took their 3 top roles and to find their own baseline midrange % (what is expected of them) a weighted average of their top roles was computed. So someone who's mostly a "2-Level Mid" guy but also plays some "Postman" and "Spot Up" gets a baseline blended across those three, not forced into one bucket. midRRole = (player's actual mid-range FG% − their blended-role baseline) × 100

Role Midrange rFG:

Jokic +16.5%

Rui +14.5%

Shai + 12.5%

TJ +11.7%

Tobias Harris +7.8%

Still pretty much, the same with some rearrangements. But still we can go one step further. Each midrange is worth 2, and using this rFg% we can see approximately how many points a player added throughout the season purely from their midrange success (just weighing against shots)

Top 5 — Points Added role rFG:

Shai Gilgeous-Alexander (OKC, 3 Level Iso Scorer) — 54.9% actual vs 42.4% expected = +89.0 pts (355 attempts)
Rui Hachimura (LAL, 3 Level Scorer) — 55.6% vs 41.1% expected = +41.6 pts (144 attempts)
DeMar DeRozan (SAC, 3 Level Iso Scorer) — 47.6% vs 43.6% expected = +38.8 pts (483 attempts)
Nikola Jokić (DEN, Postman) — 55.6% vs 39.0% expected = +38.7 pts (117 attempts)
T.J. McConnell (IND, 3 Level Iso Scorer) — 54.1% vs 42.3% expected = +37.3 pts (159 attempts)

Bottom 5 — Points Added:

Shaedon Sharpe (POR, 3 Level Iso Scorer) — 33.1% actual vs 43.5% expected = −32.1 pts (154 attempts)
Pascal Siakam (IND, Postman) — 34.5% vs 41.5% expected = −29.9 pts (194 attempts)
Amen Thompson (HOU, 2-Level Scorer lays/middies) — 28.2% vs 41.6% expected = −28.8 pts (103 attempts)
Ace Bailey (UTA, Movement Shooter) — 35.9% vs 43.2% expected = −28.6 pts (195 attempts)
Jaylen Brown (BOS, 3 Level Iso Scorer) — 41.4% vs 43.6% expected = −22.6 pts (365 attempts)

So What?

To no ones suprise, Shai is a special blend of efficiency and volume and adds the most point when looking at this metric. There are limitations to this metric though,

- Roles aren't perfect; I think the roles that are being created through playtype frequency can be expanded and be more accurate

- Doesn't account for shot difficulty / shot type, ISO shots are much much harder.

I think overall, there are some limitations but this could still be valuable, at least directionally.

I have this same methodology for Rim shots and 3PT shots on my website, the rFG, the PTS ADDED metric:

Luka Doncic +15.398% rimFg (min 100 att)

Luke Kennard +10.9 3ptFg (min 100 att)

https://www.simulnba.com/role-stats

There are player cards as well that go more in-depth into each shot zone breakdown.

A lot more features as well!

- More indepth about the methodology

- Contract Value Analysis

- More Role-Based Stats

- Team Cards

- Playoff Performance

- RAPM

- Etc. Any feedback on anything is greatly appreciated!

u/Glokk39Spaz — 3 months ago