Hjälp mig minnas lite Malmö... Vad f*n hände helt plötsligt? Allt bara dog... från ingenstans.

Från centralen, till lilla torg, till Möllan, till Norra grängesbergsgatan och alla svartklubbarna där och däremellan.

Slagthuset, Etage, Skeppsbron Nere vid Malmö universitet vid den lilla minibron? Harrys/Heartbrake, (där någon hade blivit skjuten mid 2000's?), Tunneln, Club Buddha i det huset som bestod av 3 våningar (så jävla sjukt fett ändå, right?), Caramello, Prive (klassisk), Bodoni (alla brothers och fat white chick leftovers på söndagnatten?), Amiralen, och Klubb deep? KulturBolaget, Kärleksklubben med sina milslånga köer som ringlade ner till big mamas pizzeria, Inkonst med sin hipster crows, Chokladfabriken, hela Bergsgatan var fylld till bristningsgränsen med ställen, fan folk från Brasilien kom och frågade, vad är detta för stad och plats, det är fest i varenda jävla gathörn, Och Cuba Cafe med sina rytmiska vibes och latino music.. WOW! Ville man sedan vidare stack man till norra grängersberg med alla de hemliga jävla ställen omkring, rökiga smutsiga klubbar omringade barer med massa juggar från K-falangen och den andra...Amiralen och klubb deep, Moriskan med sina klubbar... Och för att inte tala om mascot, nyhavn, och metro och retro etc med sin sköna stämning, var med sin egna crowd och flavor

Aja... en gång i tiden kom folk från andra länder och undrade vad hela grejen var med malmö och varför det var ett sånt drag varje dag i veckan. Malmö was on top of the world för ett tag där alltså, det har gått 20 år gott folk...

Everything is in the links, might be a bit outdated but same rules apply.

This started with a fairly basic question:

>

At first, FG% seems like the obvious way to judge scoring efficiency. A player makes a certain percentage of his shots, so a higher percentage should mean more efficient scoring.

But FG% has an obvious problem: it treats every field goal as equal.

A player shooting 50% exclusively on two-pointers produces 1.00 point per field-goal attempt. A player shooting 40% exclusively from three produces 1.20 points per field-goal attempt, despite having the lower FG%.

That led me from FG% to effective field-goal percentage.

Step one: eFG% improves FG%, but still does not tell the whole story

Effective field-goal percentage gives additional value to three-pointers:

eFG% = (FGM + 0.5 × 3PM) / FGA

That solves the biggest problem with ordinary FG%. It recognizes that a three is worth 50% more than a two.

But eFG% ignores free throws.

A player who constantly attacks the basket, draws fouls and scores at the line can be a highly efficient scorer even if his field-goal percentages do not look exceptional.

That brought me to true shooting percentage.

Step two: TS% is better for overall scoring efficiency

True shooting percentage combines points, field-goal attempts and free-throw attempts:

TS% = PTS / [2 × (FGA + 0.44 × FTA)]

TS% is useful because it answers a very important question:

>

It includes:

• Two-point scoring
• Three-point scoring
• Free-throw scoring

That makes it much more complete than FG% or eFG%.

But after looking at different scorers, I noticed another problem.

TS% tells me how efficiently someone scored, but it does not tell me how he scored.

Two players can have similar true-shooting percentages while having almost opposite scoring games.

The Curry–DeRozan problem

Stephen Curry and DeMar DeRozan provide a useful real example from the 2021–22 regular season.

Curry recorded a .601 TS%, while DeRozan recorded a .590 TS%. Basketball-Reference’s league-adjusted figures place them at approximately 106 TS+ and 104 TS+, respectively. Both were clearly efficient scorers relative to the league, and the difference in overall efficiency was not enormous. (Basketball Reference)

But they did not score in remotely the same way.

Stephen Curry, 2021–22

25.5 PPG
7.4 2PA at 52.7%
11.7 3PA at 38.0%
4.7 FTA at 92.3%

DeMar DeRozan, 2021–22

27.9 PPG
18.3 2PA at 52.0%
1.9 3PA at 35.2%
7.8 FTA at 87.7%

Those are the official regular-season per-game figures. (Basketball Reference)

Their two-point percentages were nearly identical:

Curry:   52.7%
DeRozan: 52.0%

But Curry attempted 11.7 threes per game, while DeRozan attempted only 1.9.

DeRozan attempted 18.3 twos per game, while Curry attempted only 7.4. DeRozan also generated considerably more free-throw volume.

TS% correctly tells us that they were both efficient.

But TS% alone does not tell us that one was an extremely three-point-heavy scorer and the other was overwhelmingly dependent on two-point creation and free throws.

That was the problem I wanted to solve.

My first idea: show all the percentages

My first thought was to display:

TS% | 2P% | 3P% | FT%

That was better than TS% alone because it showed where the efficiency came from.

But it immediately created another problem:

>

A player shooting 42% from three on one attempt per game is not the same type of scorer as someone shooting 42% on twelve attempts.

Likewise, a player shooting 60% on two-pointers while attempting four per game is not providing the same scoring profile as a player shooting 60% while attempting fifteen.

The percentage describes accuracy.

The attempts describe how much of the player’s scoring game actually comes from that area.

You need both.

Why I chose attempts instead of attempt rates

I considered using statistics such as:

• 3PAr: the percentage of field-goal attempts that are threes
• FTr: free-throw attempts relative to field-goal attempts

Those are useful analytical statistics, particularly when comparing players who play different minutes or operate at different volumes.

But I wanted this format to be understandable immediately.

When I see:

11.7 3PA

I instantly understand what that means.

When I see:

.613 3PAr

I have to translate it mentally into “61.3% of his field-goal attempts were threes.”

Attempt rates may be better for certain analytical comparisons, but raw per-game attempts create a more recognizable picture of the actual player.

The purpose was not to build the most mathematically sophisticated model possible. It was to create a compact line where I could almost see the scorer.

Why I replaced TS% with TS+

Raw TS% creates another issue when comparing players from different eras.

League scoring efficiency changes over time. A .600 TS% may be far above average in one season but much closer to normal in another.

Basketball-Reference’s TS+ adjusts true-shooting efficiency relative to that season’s league average.

100 = league average
110 = approximately 10% above league average
90  = approximately 10% below league average

Sports Reference explains its adjusted-shooting scale using 100 as league average; for example, 125 represents performance 25% above average. (Sports Reference)

So instead of beginning the profile with raw TS%, I chose TS+.

That gives the profile an era-relative efficiency anchor.

Why PPG still belongs at the end

At one point I considered leaving points per game out.

In theory, the attempts and percentages already provide most of the scoring information. But without PPG, the profile feels unfinished.

PPG tells us the final result:

>

Two players could have similarly efficient shot distributions, but one might take substantially more shots, play more minutes or carry a larger scoring burden.

PPG gives the reader an immediate output number without requiring any calculation.

The finished format

That led to the Scorer Profile Matrix, or SPM:

TS+ | 2PA (2P%) | 3PA (3P%) | FTA (FT%) | PPG

It combines three different ideas:

1. Relative efficiency — TS+
2. Scoring distribution and accuracy — twos, threes and free throws
3. Final scoring output — PPG

SPM is not a formula that produces one final rating.

It is not supposed to tell us that one player has an “SPM score” of 95 and another has an SPM score of 92.

It is a profile.

The numbers are meant to be read together as a scoring fingerprint.

Curry and DeRozan as SPM profiles

Using the same 2021–22 seasons:

Stephen Curry

106 | 7.4 (52.7%) | 11.7 (38.0%) | 4.7 (92.3%) | 25.5

DeMar DeRozan

104 | 18.3 (52.0%) | 1.9 (35.2%) | 7.8 (87.7%) | 27.9

The first number tells us that both were above-average in overall scoring efficiency.

Everything after it shows that they achieved that efficiency through almost opposite methods.

Curry’s profile is dominated by enormous three-point volume.

DeRozan’s is dominated by two-point attempts and free-throw pressure.

That distinction is the entire reason I wanted SPM.

Another example: Curry and Jokić

The format also becomes interesting when comparing historically efficient seasons.

Stephen Curry, 2015–16

124 | 9.0 (56.6%) | 11.2 (45.4%) | 5.1 (90.8%) | 30.1

Curry attempted 11.2 threes per game and converted 45.4% of them while averaging 30.1 points. His TS+ was 124, meaning his true-shooting efficiency was approximately 24% above that season’s league level. (NBA.com)

Nikola Jokić, 2022–23

121 | 12.7 (67.5%) | 2.2 (38.3%) | 6.0 (82.2%) | 24.5

Jokić attempted far fewer threes but converted an extraordinary 67.5% of his two-point attempts. His TS+ was 121. (Basketball Reference)

This comparison also clarifies something important:

>

Curry has:

• Higher TS+
• Greater scoring output
• Historically extreme three-point volume and accuracy
• Better free-throw accuracy

Jokić has:

• Much greater two-point volume
• A massive advantage in two-point percentage
• Slightly more free-throw volume
• One of the most efficient interior profiles ever recorded

SPM lets you see why both profiles are extraordinary without forcing them into one composite score.

That is deliberate.

What SPM does better than TS% alone

TS% gives a strong answer to:

>

SPM tries to answer:

>

For example:

High 2PA + low 3PA + high FTA

suggests a scorer who operates primarily inside the arc and creates pressure at the line.

Low 2PA + very high 3PA + moderate FTA

suggests a perimeter-oriented scorer.

High 2PA + high 3PA + high FTA

suggests an unusually large and diverse scoring burden.

The percentages then show whether that volume was converted efficiently.

TS+ places the overall efficiency in league context, and PPG shows the eventual scoring output.

What SPM does not measure

SPM is intentionally compact, which means it leaves out a great deal.

It does not show:

• Rim attempts versus midrange attempts
• Short midrange versus long midrange
• Assisted versus unassisted baskets
• Pull-ups versus catch-and-shoot attempts
• Shot difficulty
• Defensive attention
• Shooting gravity
• On-ball creation burden
• Turnovers
• Passing and playmaking
• Overall offensive impact
• Pace or possessions
• Teammate and lineup context

The largest weakness is probably that all two-pointers are placed together.

A center taking shots near the basket and a wing taking difficult pull-up midrange shots may both show:

15 2PA

even though the actual shot diets are completely different.

A more advanced version could separate:

Rim | Short midrange | Long midrange | Three

But that would also make the profile much larger and less immediate.

SPM is meant to sit between two extremes:

One efficiency number

and

A complete shot chart with dozens of categories

It sacrifices detail in exchange for readability.

Per game or per 36 minutes?

The current version uses per-game attempts because they are easier to recognize.

Most basketball fans intuitively understand what “11 threes per game” means.

However, per-game figures are affected by:

• Minutes played
• Injuries
• Coaching roles
• Pace

A per-36 or per-100-possession version would be better for comparing players with very different playing time.

My current thought is:

Standard SPM: per game
Analytical SPM: per 36 or per 100 possessions

The underlying structure would remain the same.

What I am claiming — and what I am not claiming

I am not claiming that the individual statistics are new.

TS+, 2PA, 3PA, FTA, shooting percentages and PPG already exist.

The original part is the proposed presentation format: arranging them in one consistent sequence to create an immediately readable scoring profile.

I am also not claiming that SPM replaces:

• TS%
• Shot charts
• Offensive impact metrics
• Play-by-play analysis
• Film study

It is simply a compact way to display the relationship between:

Efficiency
Volume
Shot distribution
Scoring output

When I look at the full line, I feel like I can almost see the player.

That is what I wanted from it.

Final SPM format

TS+
|
2PA (2P%)
|
3PA (3P%)
|
FTA (FT%)
|
PPG

Or horizontally:

TS+ | 2PA (2P%) | 3PA (3P%) | FTA (FT%) | PPG

I would be interested to know whether an established format already presents these specific statistics together this way, and whether people think the profile would be useful for quick scorer comparisons.

The main question is not whether SPM explains everything. It clearly does not.

The question is:

>

Data sources

The player figures above are regular-season, per-game data from Basketball-Reference and NBA.com. TS+ comes from Basketball-Reference’s adjusted-shooting tables. Figures are rounded to one decimal place where appropriate.

A guy was about to get married, and all his friends were standing with him before the ceremony.

His best man leaned over and whispered,

"I'll pay you $100 for every second you wait before saying 'I do.'"

Last I heard, the bride is still waiting.

This is a side quest and nothing new or major in game.

Helping penttho beat that big bear, but my question is this in general, when it’s a specific characters mission to defeat something to gain the respect of others, i don’t get it when alloy has to interfere or deal with their shit, when it’s THEIR rite of passsafe and not alloys.

I basically beat that beat, not you, so you still a cleaner. You swung your sword once or twice, but nothing that really helped with he bear and then you go and reap the benefits? How can others respect you? If it was two equal clan members going of together, then yea, or one of them has a very unique skill that helps in a fight, but as you all/ we all now, it’s us/alloy doing all the heavy lifting here.

Make it make sense. Or, am I thinking wrong about this?

A man had spent the better part of his life—and most of his fortune—building the greatest invention mankind had ever seen:

Instant transportation.

Just before unveiling it, a giant corporation stole the design and dragged him through 25 years of legal hell.

Motions. Appeals. Delays. Endless billing.

By the end, he hated lawyers even more than the people who stole his life’s work.

One day, while clearing out his old basement to clear his mind, he found an old brass lamp.

As he cleaned it, muttering between exhausted breaths—

“I hate lawyers… God, I hate lawyers…”

Suddenly—

POOF.

A genie appears.

“Well hello there, my friend. You have summoned me, and for that, I shall grant you three wishes. Whatever you desire shall come true.”

The man doesn’t hesitate.

“Kill all the lawyers.”

The genie smiles.

“Nothing could be easier.”

SNAP.

The man blinks.

“…Done?”

“Done.”

The man grabs his phone.

Calls his lawyer.

No answer.

Calls opposing counsel.

No answer.

Slowly, a smile spreads across his face.

“My God… you actually did it.”

He looks at the genie.

“After what you’ve done for me… I’ll set you free.”

Pauses.

“Now… for my second wish—”

The genie climbs out of the lamp.

Turns.

And begins walking away.

The man freezes.

“Hey! You still owe me two wishes!”

The genie glances back over his shoulder, smirks, and says:

“So sue me.”