Historical TS Add Analysis

[70sFan]

They fed him the ball and he didn’t always catch it.Happened a lot first couple of years in Chicago.

It didn't help that he played with very weak backcourts in Chicago - outside of Norm van Lier they had no capable passers. He didn't seem to have the same problems in San Antonio.

I think that Gilmore lacked awareness of the best centers ever and that was his problem - not bad hands. I mean, his shooting touch was fantastic and he had giant hands so he could palm the ball with ease.

The-us was actually a real good passer ; probably a more talented passer than Norm, but Reggie was all about Reggie, probably the anti-Norm. Unfortunately the NBA never saw a 100% Ronnie Lester.

Theus was a rookie in 1979 and as you said, he was a selfish player.

[kayess]

Excuse my ignorance here Doc but - what exactly is TS Add? Wasn't able to find anything on it :/

TS Add is about this;

In 1979-80, Kareem Abdul-Jabbar scored 2034 points on 1592.44 true shooting attempts (1383 fga and 476 fta).
If his efficiency was on the same level as the league average he would've scored 1692.172 points (2 * 1592.44 tsa * 0.531314 ts%).
2034 is more than 1692.172 by 341.828.

So, Kareem's efficiency made him get roughly 342 points more than what it should be the norm in that season.

That's how it works with TS Add.

Yeah this is what I thought it was when I used an example in my second post, but good to see some numbers; thanks!

[penbeast0]

I think that a lot of this is caused by how bad Bulls team became in 1978. It was a package of absolutely terrible defenders....

You say the 78 team was terrible defensively but what happened? They are almost exactly the same team as the 2nd in the league DRTG team from 77 except for the May injury and May was going from being a rookie to a 2nd year player which normally helps defensively. They shouldn't be aging out, the oldest guy in the rotation was Norm Van Lier who turned 30 (and played about 6 less minutes a game). They just quit playing defense.

1977 Bulls (2nd/22 defense) Coach Ed Badger
Norm Van Lier 82g/38mpg
Artis 82g/35mpg
Mickey Johnson 81g/35mpg
Scott May 72g/33mpg
Wilbur Holland 79g/31mpg

6th man John Mengelt 61g/19mpg
(everyone else played 1100 minutes or less)

1978 Bulls (20th/22 defense) Coach Ed Badger
Artis (age 28) 82 games/37mpg
Mickey Johnson (age 25) 81 games/35 mpg
Wilbur Holland (age 26) 82 games/35mpg
Norm Van Lier (age 30) 78games/32 mpg

Scott May was the 5th starter for 55 games/32mpg
John Mengelt was the 6th man 82games/22mpg

Everyone else played 1000 minutes or less

[DQuinn1575]

I think that a lot of this is caused by how bad Bulls team became in 1978. It was a package of absolutely terrible defenders....

You say the 78 team was terrible defensively but what happened? They are almost exactly the same team as the 2nd in the league DRTG team from 77 except for the May injury and May was going from being a rookie to a 2nd year player which normally helps defensively. They shouldn't be aging out, the oldest guy in the rotation was Norm Van Lier who turned 30 (and played about 6 less minutes a game). They just quit playing defense.

1977 Bulls (2nd/22 defense) Coach Ed Badger
Norm Van Lier 82g/38mpg
Artis 82g/35mpg
Mickey Johnson 81g/35mpg
Scott May 72g/33mpg
Wilbur Holland 79g/31mpg

6th man John Mengelt 61g/19mpg
(everyone else played 1100 minutes or less)

1978 Bulls (20th/22 defense) Coach Ed Badger
Artis (age 28) 82 games/37mpg
Mickey Johnson (age 25) 81 games/35 mpg
Wilbur Holland (age 26) 82 games/35mpg
Norm Van Lier (age 30) 78games/32 mpg

Scott May was the 5th starter for 55 games/32mpg
John Mengelt was the 6th man 82games/22mpg

Everyone else played 1000 minutes or less

The 77 team was weird in that they got hot 20-4 (I still remember the record) at the end of the season, and played well against the Blazers in the miniseries. The team caught lightning for that time period, especially Holland, and then fizzled away.

[70sFan]

I think that a lot of this is caused by how bad Bulls team became in 1978. It was a package of absolutely terrible defenders....

You say the 78 team was terrible defensively but what happened? They are almost exactly the same team as the 2nd in the league DRTG team from 77 except for the May injury and May was going from being a rookie to a 2nd year player which normally helps defensively. They shouldn't be aging out, the oldest guy in the rotation was Norm Van Lier who turned 30 (and played about 6 less minutes a game). They just quit playing defense.

1977 Bulls (2nd/22 defense) Coach Ed Badger
Norm Van Lier 82g/38mpg
Artis 82g/35mpg
Mickey Johnson 81g/35mpg
Scott May 72g/33mpg
Wilbur Holland 79g/31mpg

6th man John Mengelt 61g/19mpg
(everyone else played 1100 minutes or less)

1978 Bulls (20th/22 defense) Coach Ed Badger
Artis (age 28) 82 games/37mpg
Mickey Johnson (age 25) 81 games/35 mpg
Wilbur Holland (age 26) 82 games/35mpg
Norm Van Lier (age 30) 78games/32 mpg

Scott May was the 5th starter for 55 games/32mpg
John Mengelt was the 6th man 82games/22mpg

Everyone else played 1000 minutes or less

To be honest, I can't tell you why they regressed so much. I'd have to have much more footage from 1977 season but unfortunately I've seen only two games against the Blazers.

[70sFan]

I think that a lot of this is caused by how bad Bulls team became in 1978. It was a package of absolutely terrible defenders....

You say the 78 team was terrible defensively but what happened? They are almost exactly the same team as the 2nd in the league DRTG team from 77 except for the May injury and May was going from being a rookie to a 2nd year player which normally helps defensively. They shouldn't be aging out, the oldest guy in the rotation was Norm Van Lier who turned 30 (and played about 6 less minutes a game). They just quit playing defense.

1977 Bulls (2nd/22 defense) Coach Ed Badger
Norm Van Lier 82g/38mpg
Artis 82g/35mpg
Mickey Johnson 81g/35mpg
Scott May 72g/33mpg
Wilbur Holland 79g/31mpg

6th man John Mengelt 61g/19mpg
(everyone else played 1100 minutes or less)

1978 Bulls (20th/22 defense) Coach Ed Badger
Artis (age 28) 82 games/37mpg
Mickey Johnson (age 25) 81 games/35 mpg
Wilbur Holland (age 26) 82 games/35mpg
Norm Van Lier (age 30) 78games/32 mpg

Scott May was the 5th starter for 55 games/32mpg
John Mengelt was the 6th man 82games/22mpg

Everyone else played 1000 minutes or less

The 77 team was weird in that they got hot 20-4 (I still remember the record) at the end of the season, and played well against the Blazers in the miniseries. The team caught lightning for that time period, especially Holland, and then fizzled away.

Gilmore averaged 20.8 ppg, 14.0 rpg, 2.8 apg and 3.1 bpg on 58.8 TS% in this streak. It's fair to say that Gilmore needed some time to adjust to the new league, as he finished the season strong. He also played one of his finest NBA games in 1977 playoffs when he scored 27 points and blocked 5 shots against Portland and Walton.

[penbeast0]

Gilmore averaged 20.8 ppg, 14.0 rpg, 2.8 apg and 3.1 bpg on 58.8 TS% in this streak. It's fair to say that Gilmore needed some time to adjust to the new league, as he finished the season strong. He also played one of his finest NBA games in 1977 playoffs when he scored 27 points and blocked 5 shots against Portland and Walton.

That's not the question. Same personnel, pretty much all in their primes except maybe Van Lier and he was just turning 30. 20 missed games by Scott May and the went from the 2nd best defensive team in the league to the 3rd worse. And that's with a full season under Gilmore's belt to adjust to the NBA. So what happened in 78?

[70sFan]

Gilmore averaged 20.8 ppg, 14.0 rpg, 2.8 apg and 3.1 bpg on 58.8 TS% in this streak. It's fair to say that Gilmore needed some time to adjust to the new league, as he finished the season strong. He also played one of his finest NBA games in 1977 playoffs when he scored 27 points and blocked 5 shots against Portland and Walton.

That's not the question. Same personnel, pretty much all in their primes except maybe Van Lier and he was just turning 30. 20 missed games by Scott May and the went from the 2nd best defensive team in the league to the 3rd worse. And that's with a full season under Gilmore's belt to adjust to the NBA. So what happened in 78?

As I said - I can't tell you. If you are interested, we could start a new thread with 1977-79 Bulls games analysis. I can provide around 12 Bulls games from that period (I've collected them for a long time). We can go further with 1980-82 Bulls games as well. I'd love to discuss this topic with someone who has passion and knowledge to talk about one of my favorite players ever

[penbeast0]

Maybe we could find a few from the San Antonio years too and some Kentucky footage for comparison. I was so sure he would be the second best center in the NBA for the next decade after the merger and he just wasn't (Moses came over from the ABA to take that role). I'm not as hardworking as Trex but I'm willing to look at game film from that era if it exists and try to see why Artis wasn't as dominant as it seemed he should be.

[DQuinn1575]

I think that a lot of this is caused by how bad Bulls team became in 1978. It was a package of absolutely terrible defenders....

You say the 78 team was terrible defensively but what happened? They are almost exactly the same team as the 2nd in the league DRTG team from 77 except for the May injury and May was going from being a rookie to a 2nd year player which normally helps defensively. They shouldn't be aging out, the oldest guy in the rotation was Norm Van Lier who turned 30 (and played about 6 less minutes a game). They just quit playing defense.

1977 Bulls (2nd/22 defense) Coach Ed Badger
Norm Van Lier 82g/38mpg
Artis 82g/35mpg
Mickey Johnson 81g/35mpg
Scott May 72g/33mpg
Wilbur Holland 79g/31mpg

6th man John Mengelt 61g/19mpg
(everyone else played 1100 minutes or less)

1978 Bulls (20th/22 defense) Coach Ed Badger
Artis (age 28) 82 games/37mpg
Mickey Johnson (age 25) 81 games/35 mpg
Wilbur Holland (age 26) 82 games/35mpg
Norm Van Lier (age 30) 78games/32 mpg

Scott May was the 5th starter for 55 games/32mpg
John Mengelt was the 6th man 82games/22mpg

Everyone else played 1000 minutes or less

To be honest, I can't tell you why they regressed so much. I'd have to have much more footage from 1977 season but unfortunately I've seen only two games against the Blazers.

Found this for you - also note it references his "pixie hands", and note I just looked it up now. The Bulls ran a high post center with Boerwinkle, Cliff Ray, Jim Fox over the years, and started with Artis playing there.

https://www.newspapers.com/clip/63622696/

[Odinn21]

Is this somewhat normalized to a standard number of possessions?
If not it can be very affected by pace, when comparing different eras.

It's certainly going to be affected by pace, and a pace-adjusted version wouldn't be a bad thing.

I added a page for this; Gain/Loss%.

What it does is this;
Kareem Abdul-Jabbar scored 38387 points and his career TS Add is 4718.8.
[38387 / (38387 - 4718.8)] = +14.0%.

I think this is better than per poss numbers because per possession numbers would be so susceptible to balance between pace/minutes/3pt utilization over such a long period of time. And this approach would still give a proper idea about effect of efficiency.

Also added PPG numbers to showcase the importance of volume.
If it had only % values, I think it would make us overlook the importance of volume. Quick example;
Abdul-Jabbar +14.0 / Stockton +14.3% / Nash +13.9%
but!
Abdul-Jabbar 24.6 / Stockton 13.1 / Nash 14.3 ppg

For myself, I just added a small part next to Abdul-Jabbar's career totals because I know that Abdul-Jabbar's scoring numbers are hurt by his extreme longevity. I feel like those numbers would represent Abdul-Jabbar's scoring volume and efficiency far more accurately.

I know that similar cases can be made for multiple names. But other than Karl Malone, none of them had a scoring based impact like Abdul-Jabbar had. Duncan? Defense. Chamberlain? Defense. In fact transitioning to a lesser scoring role helped Chamberlain's Gain% (11.9% until end of '69 and 20.7% in his last 3 seasons, overall 12.9%).

[Doctor MJ]

Is this somewhat normalized to a standard number of possessions?
If not it can be very affected by pace, when comparing different eras.

It's certainly going to be affected by pace, and a pace-adjusted version wouldn't be a bad thing.

I added a page for this; Gain/Loss%.

What it does is this;
Kareem Abdul-Jabbar scored 38387 points and his career TS Add is 4718.8.
[38387 / (38387 - 4718.8)] = +14.0%.

I think this is better than per poss numbers because per possession numbers would be so susceptible to balance between pace/minutes/3pt utilization over such a long period of time. And this approach would still give a proper idea about effect of efficiency.

Also added PPG numbers to showcase the importance of volume.
If it had only % values, I think it would make us overlook the importance of volume. Quick example;
Abdul-Jabbar +14.0 / Stockton +14.3% / Nash +13.9%
but!
Abdul-Jabbar 24.6 / Stockton 13.1 / Nash 14.3 ppg

For myself, I just added a small part next to Abdul-Jabbar's career totals because I know that Abdul-Jabbar's scoring numbers are hurt by his extreme longevity. I feel like those numbers would represent Abdul-Jabbar's scoring volume and efficiency far more accurately.

I know that similar cases can be made for multiple names. But other than Karl Malone, none of them had a scoring based impact like Abdul-Jabbar had. Duncan? Defense. Chamberlain? Defense. In fact transitioning to a lesser scoring role helped Chamberlain's Gain% (11.9% until end of '69 and 20.7% in his last 3 seasons, overall 12.9%).

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I do think things like pace-adjusted and minutes-adjusted versions of the stat are worth while too, and may get around to doing it, but if Ryoga, Odinn, or someone else wanted to take a crack at it, they should let me know.

[Odinn21]

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I think this % based approach turned out to be better than rts because it is easier to understand that a player's efficiency produced how many more, or less, points.
rts numbers are very nice but we don't usually see how much it translated to extra scoring output. I think these % numbers really achieved that.

I'm yet to dive into observations but looking at the top of the chart, I can say that if we had this list before #11 or #12 on the top 100 project, I would've went for Robertson earlier and would've made far more stronger arguments for him.

We all knew how efficient Reggie Miller, Adrian Dantley, Kareem Abdul-Jabbar, Shaquille O'Neal, Charles Barkley, Stephen Curry, Kevin Durant are. They are known for their efficient scoring. And it turns out that Oscar Robertson was among them. Maybe still not an era definer like Curry or O'Neal. But his numbers are nothing of short.

[Ryoga Hibiki]

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I do think things like pace-adjusted and minutes-adjusted versions of the stat are worth while too, and may get around to doing it, but if Ryoga, Odinn, or someone else wanted to take a crack at it, they should let me know.

I would probably normalized per point scored in the league.
On average, last year a team was scoring 111.8 ppg, easy way would be to use 100 as the standard.
We can then multiply each player's result, last year, per 100/111.8,
In 1970, for instance, the average was 116.7, so you multiply each raw number per 100/116.7.

Not perfect (when teams are playing at different paces this value can be slightly distorted) but it's a quick fix, easy to implemet and all using very available data.

[Odinn21]

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I do think things like pace-adjusted and minutes-adjusted versions of the stat are worth while too, and may get around to doing it, but if Ryoga, Odinn, or someone else wanted to take a crack at it, they should let me know.

I would probably normalized per point scored in the league.
On average, last year a team was scoring 111.8 ppg, easy way would be to use 100 as the standard.
We can then multiply each player's result, last year, per 100/111.8,
In 1970, for instance, the average was 116.7, so you multiply each raw number per 100/116.7.

Not perfect (when teams are playing at different paces this value can be slightly distorted) but it's a quick fix, easy to implemet and all using very available data.

The issue with this is;
25 ppg on a 100 ppg team or league
30 ppg on a 120 ppg team or league
30 ppg is still harder to achieve. Direct linear adjustments usually lead us to non-accurate assumptions because they overlook distributions.

I can't be exact about the numbers I'm about to talk because BBRef made their service paid now but I remember an outlook like this;
In the '90s, ppg league average was 101 or 102.
In the '60s, it was 115.
But when we looked at 25+ ppg performances, the number of 25+ ppg seasonal performances in the '60s, the gap was like 7-8%, not 14-15%. And when we looked further with the names to see how many different players hit that mark, the gap wasn't there.

Another way to put it would be;
Jordan averaged 23.1 fga per game in 1998. That was 32.1 fga per 100. (90.3 pace season)
If we throw 1998 Jordan to the '60s (110+ pace quite possibly), Jordan wouldn't shoot 35 times on a season average if he was utilized in the same manner. That gap of 12 fga would be distributed among Jordan and his teammates.

This is just pace distribution.
Minute allocations changed so many times in history. Many players in '95-'05 time frame are at a direct disadvantage because they played insanely high minutes on the slowest pace. There are many instances with the gap between minute dynamics being more drastic than change in pace.
Utilization of three pointers is also very important.

I get that this need to regularize / normalize the data to see better. But I don't agree with using linear adjustments as easy escape / solution. I think we should leave per games numbers as they are and keep those in mind as context.

[Ryoga Hibiki]

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I do think things like pace-adjusted and minutes-adjusted versions of the stat are worth while too, and may get around to doing it, but if Ryoga, Odinn, or someone else wanted to take a crack at it, they should let me know.

I would probably normalized per point scored in the league.
On average, last year a team was scoring 111.8 ppg, easy way would be to use 100 as the standard.
We can then multiply each player's result, last year, per 100/111.8,
In 1970, for instance, the average was 116.7, so you multiply each raw number per 100/116.7.

Not perfect (when teams are playing at different paces this value can be slightly distorted) but it's a quick fix, easy to implemet and all using very available data.

The issue with this is;
25 ppg on a 100 ppg team or league
30 ppg on a 120 ppg team or league
30 ppg is still harder to achieve. Direct linear adjustments usually lead us to non-accurate assumptions because they overlook distributions.

I can't be exact about the numbers I'm about to talk because BBRef made their service paid now but I remember an outlook like this;
In the '90s, ppg league average was 101 or 102.
In the '60s, it was 115.
But when we looked at 25+ ppg performances, the number of 25+ ppg seasonal performances in the '60s, the gap was like 7-8%, not 14-15%. And when we looked further with the names to see how many different players hit that mark, the gap wasn't there.

Another way to put it would be;
Jordan averaged 23.1 fga per game in 1998. That was 32.1 fga per 100. (90.3 pace season)
If we throw 1998 Jordan to the '60s (110+ pace quite possibly), Jordan wouldn't shoot 35 times on a season average if he was utilized in the same manner. That gap of 12 fga would be distributed among Jordan and his teammates.

This is just pace distribution.
Minute allocations changed so many times in history. Many players in '95-'05 time frame are at a direct disadvantage because they played insanely high minutes on the slowest pace. There are many instances with the gap between minute dynamics being more drastic than change in pace.
Utilization of three pointers is also very important.

I get that this need to regularize / normalize the data to see better. But I don't agree with using linear adjustments as easy escape / solution. I think we should leave per games numbers as they are and keep those in mind as context.

By definition, the moment you reduce thousands of data points to one number you are losing depth and context.
No matter what, you would need way more information to get to the ultimate goal, that is estimating the VALUE each player is creating.

The way it's designed, this statistic measures something very tangible, how many points each player is adding vs the average. Pay attention, I didn't normalize to pace but to points. And it's quite easy to obtain. The real question is what is your ultimate goal, what do you want to measure? How would you use this stat?
By itself I think it's nice because it measures something clear, intuitive and not ambiguous.

[Odinn21]

By definition, the moment you reduce thousands of data points to one number you are losing depth and context.
No matter what, you would need way more information to get to the ultimate goal, that is estimating the VALUE each player is creating.

The way it's designed, this statistic measures something very tangible, how many points each player is adding vs the average. Pay attention, I didn't normalize to pace but to points. And it's quite easy to obtain. The real question is what is your ultimate goal, what do you want to measure? How would you use this stat?
By itself I think it's nice because it measures something clear, intuitive and not ambiguous.

I'll show you why it doesn't make sense.

Two different interpretations of what you're suggesting;

(Data for examples to come;
The teams Kareem Abdul-Jabbar played on had 112.8 ppg average.
The teams Ray Allen played on had 98.3 ppg average.)

-Adjusting TS Add first, then calculating a new Gain/Loss%
Kareem's TS Add total was 4718.8. By your method it's 4183.3. Kareem lost 535.5 TS Add. Kareem's gain/loss% goes from 14.0% to 12.2%.
Allen's TS Add total was 2130.1. Now it's 2166.9. Allen gained 36.8 TS Add. Allen's gain/loss% goes from 9.5% to 9.7%.

- Adjusting Gain/Loss% directly;
Kareem's gain/loss% goes from 14.0% to 12.4%.
Allen's gain/loss% goes from 9.5% to 9.7%.

But Abdul-Jabbar scored bigger portion of his team's point totals than Allen considering Allen missed way more games than Abdul-Jabbar. (Their games played ratios are similar. Abdul-Jabbar 95.1%, Allen 96.6%.)
24.6/112.8 = 21.8%
18.9/98.3 = 19.2%

So, why would we take roughly 1.5% from Kareem for playing a higher scoring era/team while he contributed more? Especially when the efficiency was already calculated relative to the era?
It doesn't make sense to penalize high volume / high efficiency players.

Also, using league average would reward players on high scoring teams and penalize players on low scoring teams within that season.
If I used league average (108.9 ppg) instead of directly team average, Abdul-Jabbar's +14.0 gain/loss ratio would be reduced to 12.7% on interpretation 1 and 12.9% on interpretation 2. I already corrected your approach in that aspect for this particular example and it's still highly flawed with or without that correction.

[Ryoga Hibiki]

By definition, the moment you reduce thousands of data points to one number you are losing depth and context.
No matter what, you would need way more information to get to the ultimate goal, that is estimating the VALUE each player is creating.

The way it's designed, this statistic measures something very tangible, how many points each player is adding vs the average. Pay attention, I didn't normalize to pace but to points. And it's quite easy to obtain. The real question is what is your ultimate goal, what do you want to measure? How would you use this stat?
By itself I think it's nice because it measures something clear, intuitive and not ambiguous.

I'll show you why it doesn't make sense.

Two different interpretations of what you're suggesting;

(Data for examples to come;
The teams Kareem Abdul-Jabbar played on had 112.8 ppg average.
The teams Ray Allen played on had 98.3 ppg average.)

-Adjusting TS Add first, then calculating a new Gain/Loss%
Kareem's TS Add total was 4718.8. By your method it's 4183.3. Kareem lost 535.5 TS Add. Kareem's gain/loss% goes from 14.0% to 12.2%.
Allen's TS Add total was 2130.1. Now it's 2166.9. Allen gained 36.8 TS Add. Allen's gain/loss% goes from 9.5% to 9.7%.

- Adjusting Gain/Loss% directly;
Kareem's gain/loss% goes from 14.0% to 12.4%.
Allen's gain/loss% goes from 9.5% to 9.7%.

But Abdul-Jabbar scored bigger portion of his team's point totals than Allen considering Allen missed way more games than Abdul-Jabbar. (Their games played ratios are similar. Abdul-Jabbar 95.1%, Allen 96.6%.)
24.6/112.8 = 21.8%
18.9/98.3 = 19.2%

So, why would we take roughly 1.5% from Kareem for playing a higher scoring era/team while he contributed more? Especially when the efficiency was already calculated relative to the era?
It doesn't make sense to penalize high volume / high efficiency players.

Also, using league average would reward players on high scoring teams and penalize players on low scoring teams within that season.
If I used league average (108.9 ppg) instead of directly team average, Abdul-Jabbar's +14.0 gain/loss ratio would be reduced to 12.7% on interpretation 1 and 12.9% on interpretation 2. I already corrected your approach in that aspect for this particular example and it's still highly flawed with or without that correction.

Not sure I follow, let me start from the beginning.
Original formula (x = player, l=league):
RawPointsAdded(x)=PointsScored(x)*(ts(x)/Avgts(l)-1)
AdjPointsAdded(x)=RayPointsAdded(x)/AvgTeamPoints(l) <-- avg for the league, not for each team

With this formula the % of added points can't change vs the original.
Moreover, as we are only looking at the total points scored we're totally including the volume. What you are losing is the total minutes and total games, hence how fast these point could be scored. Scoring 2000 point playing 30min*82 games, 48min*60 games or 20min*40games is the same thing, you just look as total points and efficiency.
Well aware of the limitation and the significance of it, I am fine with it, at the moment.

But maybe I couldn't catch your point.

[DQuinn1575]

I like your stat and how you present it. Seems to me it can be mapped on to Relative TS% for a player's career, but I think percentage gain and loss is perhaps a more intuitive way to present the information.

I do think things like pace-adjusted and minutes-adjusted versions of the stat are worth while too, and may get around to doing it, but if Ryoga, Odinn, or someone else wanted to take a crack at it, they should let me know.

I would probably normalized per point scored in the league.
On average, last year a team was scoring 111.8 ppg, easy way would be to use 100 as the standard.
We can then multiply each player's result, last year, per 100/111.8,
In 1970, for instance, the average was 116.7, so you multiply each raw number per 100/116.7.

Not perfect (when teams are playing at different paces this value can be slightly distorted) but it's a quick fix, easy to implemet and all using very available data.

The issue with this is;
25 ppg on a 100 ppg team or league
30 ppg on a 120 ppg team or league
30 ppg is still harder to achieve. Direct linear adjustments usually lead us to non-accurate assumptions because they overlook distributions.

I can't be exact about the numbers I'm about to talk because BBRef made their service paid now but I remember an outlook like this;
In the '90s, ppg league average was 101 or 102.
In the '60s, it was 115.
But when we looked at 25+ ppg performances, the number of 25+ ppg seasonal performances in the '60s, the gap was like 7-8%, not 14-15%. And when we looked further with the names to see how many different players hit that mark, the gap wasn't there.

Another way to put it would be;
Jordan averaged 23.1 fga per game in 1998. That was 32.1 fga per 100. (90.3 pace season)
If we throw 1998 Jordan to the '60s (110+ pace quite possibly), Jordan wouldn't shoot 35 times on a season average if he was utilized in the same manner. That gap of 12 fga would be distributed among Jordan and his teammates.

This is just pace distribution.
Minute allocations changed so many times in history. Many players in '95-'05 time frame are at a direct disadvantage because they played insanely high minutes on the slowest pace. There are many instances with the gap between minute dynamics being more drastic than change in pace.
Utilization of three pointers is also very important.

I get that this need to regularize / normalize the data to see better. But I don't agree with using linear adjustments as easy escape / solution. I think we should leave per games numbers as they are and keep those in mind as context.

I think your point/logic is correct in your pace distibution, but not sure how you got 35 - wouldn't it be 32.1per100/90.3 x 110 = 39.1 per 100, which is also 23.1/90.3 x 110 = 28.1 - fga per game? Now I "think" you're right in saying he wouldn't get to 28.1, but before looking at the numbers, I want to make sure I am not missing anything. Thanks

[Mazter]

This is the same for TS% and Individual Ortg.

Again, this is another tool in the tool box. We now have some 160 grit sandpaper to sand off some old paint of a desk we are re-finishing while before we only had 100 grit sandpaper.

Sorry--I am in the process of refinishing some older items in our home and this is the first analogy which came to mind.

Well, maybe it's not even the best tool possible. Since I already used Gobert as an example I will stick with him.
Gobert TS distribution was as followed:
63.5% from 0-3 ft at 75.5FG% against a league average of 66.7FG%
12.0% from 3-10 ft at 38.6FG% against a league average of 39.6FG%
0.4% from 10-16 ft at 0.0FG% against a league average of 41.6Fg%
24.1% from the line at 63.0FT% against a league average of 77.3FT%
He had 0% beyond 16 feet.
If Gobert would have made his shots at league average per range he would have scored 1005.5 points, while according to rTS he would score only 829.4 points. He would subsequently only have a +20.5 based on his shot selection rather than the +196.6 TSadd gave him based on a general shot selection.

But then again, since shot charts don't go back any further than the 96/97 season it would not cover full NBA hirstory. In that case, TSadd has it's advantage.