GingerBeardMan: forum

Looking for offers. No lowballs. I know what I've got.

19:29:08 - 10/02/25 You opened a Melee Cache and gained a Kodachi (Bloodlust)

If no one offers the buyout price, highest offer by 00:00 TCT Saturday morning (Feb 15) takes it. Not trying to sit on it for weeks collecting "offers".

Buy Now: $25B

Current Offer: $17.5B

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• Do you have a drinking problem?
• Do you believe that alcohol and heavy machinery absolutely go together?
• Do you want to be like this guy?
• Do you have at least 100k in any work stat?
• Are you looking to get paid?

If so, I have the job for you!

Too Sober To Lay Pipe is an 9 star Oil Rig currently hiring new alcoholics employees. We were 10 stars very recently, but I got sick, ended up in the hospital, we dropped some stars, and a few employees decided to get sober. I'm feeling better, and ready to get this gravy train rolling again.

I am offering pay of up to 12.5x your highest work stat + 100k per EE merit, with a maximum of up to $5m per day!

Additionally, once we get back onto the 10 star rotation, you can earn up to an additional $5m+ per day by adding an extra $1B into your bank investment, and then using your job points to speed that sucker along.

If you feel that this job is right for you, Apply Today!

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~~The Gingerbeard House of Armaments~~
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40% Irrepressible Vanguard Body
$4B firm

Bump.

New version released, BSP + mobile compatibility fixed, color selection added. No API key required. Set colors and font size on your profile. 

Enjoy and let me know if you run in to any issues.

Lol

The rambling here about "you probably get fed sets and full houses" just tells me he straight up didn't even read the log I posted.

-$1B on a single postflop shove and I'm willing to say it's variance.

Homeboy loses $300m and "the game is rigged".

ChatGPT 5.6Sol Max.

Didn't want to spend hours taking the time to build it all myself just to provide evidence, so I had it build the tables and stuff and run the monte carlo to compare  against.

18:02:34 - 30/08/26 You lost on Poker with the cards 3d,3c,3s,7s,10h,4c,Ah vs 7d,7h,3s,7s,10h,4c,Ah

Dude, it literally happens to everyone  It's call variance.

Sometimes you have the hand. Sometimes someone has a better one.

That's called poker.

I always say "I'm 6ft above ground and that's a lot better than being 6ft under it."

Surgeon, I hope your day gets better! Mine was ok but exhausting. Seems like every day is like that anymore.

I've been  going through it this year, too. Keep your chin up, all storms pass eventually.

Nah.

Will didn't need Excel. This is me.

I'm not going to reply to most of this because the incoherent rambling, lack of grammar, punctuation, and general readability are a little hard to follow.

At this point I have to believe you're trolling, delusional, or some mixture of both.

But there are a couple of direct points I want to get to, not because I think you'll actually understand probability or statistics, but because then I can at least say I tried.

You talk to me about hand odds, but you don't seem to grasp that the board pairs too often on torn, also it goes suit suit suit suit, a bit too often

It's funny that you mention that, because I actually have data for that, too. 

Across 1,720 boards that saw a flop, only 95 (5.23%) came out monotone (single suited... or "suit, suit, suit" as you so eloquently put it).  The fair distribution? 5.194%

Expand that out to a full 5 card board and we're at 34.54%. The fair distribution fot that? 32.636%

As for the flop pairing? 17.209% observed vs 16.993% in a fair deal

One pair anywhere on the board? 42.634% vs 42.100%

So my conclusion to your "you don't grasp that the board pairs too often on Torn" is that you don't grasp that it really doesn't.

I'm not really interested in your data, when I have my own ''data'' in the form of observations and pattern recognition.. 

See, there are these things called Negativity Bias and Confirmation Bias. You should look them up. You remember the bad beats and the "set ups" because they sting, and they fit your internal narrative, so therefore they must be true.

But numbers and math don't lie. They don't "I remember this time that..." it either happened, or it didn't.

And the numbers all say that none of your claims are statistically viable against a fair card distribution.

But... let's pretend for one second that they are. Why?

That's the one question you haven't answered.

 Because after all, if the algorithm didn't hard cap me, I'd win wayyyy too much, and we can't have that now, can we?

That's bullshit and you know it. And I know it. And everyone in this thread knows it.

If "the algorithm" gave a single solitary fuck about how much any one person has won, Akenomics wouldn't have been "allowed" to have a net winnings over 2T. He's got 2.76x the winnings of the next net high winner. If "the algorithm" gave two shits about anyone "winning too much" it wouldn't be some 70 day old account not being able to climb past $300m.

some times the set ups are just so unreal, I have to check either way, and make sure they just utterly got fed a flush or straight, or board pairing/double pairing, just so they can beat my strong hand.... 

Maybe stop doing this? That would be a pretty good start.

All this being said, you're welcome at my table any time.

/thread

I'm gonna reply to this in bits and pieces, because there's a lot to unpack here.

I'd like you to pull the logs for my hands.. find out how many of each hand I've had in the last 1,000 hands (flush, straight, full house etc.), and compare it to a few other people.... 

I can't and wont do that. Not because I'm part of some conspiracy, but because that would require me asking for or you giving me access to your logs via API.  And trust me, you do not want anyone getting that much access to your account.

And as I've mentioned, every time I win some, it's always waiting hours/days for the next 2-3 ''hands of luck'', while trying to dodge endless set ups..

As my data shows, Torn Poker is completely consistent with a fair random card distribution. My analysis of MU, or as you call it "set ups",show that upsets happen no more often on Torn tables than is to be expected in a random deal. In more cases than not, you were already behind when you put money in the pot. The times that you weren't, can be explained by variance and hand selection. You play weak hands that make pairs, often those hands get beat.

and then got put into the gutter for like 4 days straight, got my 2-3 luck, then more set ups and sweet nothings only... 

Again, as my data shows, set ups aren't real. "Sweet nothings" can be attributed to running "card dead", which can happen a lot longer than you would think is statistically probable.

The MU analysis isn't based on just me. It's based on the observed actions of every person that played poker during the span that those records were taken. So across the entire Torn poker playing population, there is 0 chance you are the only one being "set up".

and now I can't even afford to play.. funny that

Two words. Bankroll Management.

Don't see any sign of any admin willing to let me play test with 500mill on a different account either, so I can quickly win 2-3 + bill in about 12 hours, and then ask people why that wasn't possible on this acc

Because as annoying as it might be, admin doesn't just give in to the whims of every player. If you have actual data that backs up your claims of unfair dealing, turn in the proof and get yourself a bug swatter. Otherwise take your lumps, get better bankroll management, read a book on Poker, reduce your VPIP, and be willing to take advice from the "donks" that are able to make $5B in a session... because maybe they just might know more than you.

Fair. But he's also not the only person who has ever said it, just one of the most dramatic 

So I said fuck it, let's do the math.

I pulled the logs for all 17,084 hands I have personally been dealt and compared them against a monte carlo simulation that ran over 2.9 million sets of 17,084 hands to determine whether certain cards are dealt more often than expected, certain suits are dealt more often than expected, or certain ranks were dealt more often than expected.

My findings are as follows:

• Raw-card level: broadly normal, with one real isolated tail. All four suits are ordinary, and the overall Broadway-card count is ordinary. Aces are the standout: 2,735 Aces versus fair P99 = 2,733, so that is a genuine P99-ish high-tail observation. Fives are mildly scarce at 2,526, P5-ish low. This does not look like a general high-card excess; it is specifically an Ace excess.

• Two-card grouping level: broadly normal. I had 1,004 pocket pairs, exactly the fair median. TT+ totaled 400, versus median 387, but remained below P95 = 419. TT+ were 39.84% of all pairs, also below the composition P95 of 40.98%. Every broad hand family, suited hands, connectors, one-gappers, broadways, etc., remained ordinary. The clearest exception is 55: only 53, below the fair P1 cutoff of 56. KK+ is P95-ish high at 176, but AA and KK individually are both ordinary.

• Temporal spacing/clustering: overall normal. This is one of the stronger reassuring results. All eight explicit clustering tests were ordinary, and every Any-Pair and TT+ block VMR from 50 through 1,000 hands was ordinary. Overall pair droughts, TT+ gaps, TT+ cold periods, and broad overdispersion do not show abnormal hot/cold cycling. There are isolated tails, particularly a 2,134-hand 55 drought, P99-ish, but that is closely related to the already-low lifetime 55 count rather than independent evidence.

• Overall: this archive shows a few expected tail observations, not affirmative evidence against fair dealing. The anomalies are real enough to record, especially Aces high, 55 low/long-drought, and the recent TT+ burst. But they do not line up into a coherent pattern: suits are normal, broadways overall are normal, total pairs are exactly normal, premium-pair composition is normal, all broad starting-hand families are normal, explicit clustering is normal, and overdispersion is normal. Nothing predeclared went beyond the observed fair-reference range. My overall read is that the multi-year one-seat dealer history looks substantially more like ordinary fair variance with a handful of tails than a persistent dealing distortion.

The raw report can be found here.

I also ran a test for what I nicknamed the "MU Hypothesis". MU meaning "manufactured upset."

This test occurred twice, once at 665 observed hands and once at 1,503 observed hands. All data was collected on the Cat's Chance table.

The question the MU Hypothesis is trying to answer is:

Do mathematically unlikely upsets occur more frequently in Torn Poker than the exact remaining-card probabilities predict?

I ran two different tests, MU-R and MU-E.

MU-R is the statistical likelihood of the post-flop leader being passed on the turn or on the river.

MU-E determines whether the pot equity favorite ended up with the correct share of the pot at the time of the next card. To do this, I took the total pot at the start of the turn before the turn card was revealed but after all flop betting had concluded. I calculated the equity favorite at that point, and by how much they were favored. I then determined if over the sample size, their share of the pot at the state equity was calculated was consistent with the equity they held. I then did the same for the turn to the river.

MU-Clean was used as my control.  The sample size is tiny, but involves All In automatic runnouts, in an effort to prevent decisions made after the observed point from contaminating the observation.

These tests are subject to selection bias due to them having reached a showdown. In order to know the amount of equity each player held at a given point, their hole cards had to be known, which can have an influence on the data.

Here are my findings:

At 665 observed hands:

MU verdict: weak deviation worth tracking. There is one interesting signal, but the clean sample is nowhere near large enough to call it a real inconsistency.

Strongest apparent MU signal: MU-R flop→turn reversals. There were 41 actual upsets vs 29.21 exact expected, about 1.40× expectation. The one-sided exact tail is about 1.2%.

What looks most normal: turn→river is almost absurdly ordinary at 32 upsets vs 32.48 expected. Turn equity favorites lost 32 times vs 32.17 expected. Flop equity favorites also ultimately lost at essentially normal rates across the strength bands.

The clean subset points weakly in the same direction, but it is tiny. Only 8 clean flop→turn states had a unique current leader: 3 upsets vs 1.13 expected. That is elevated, but still inside the exact 95% fair range and has a one-sided tail around 8.4%. Clean turn→river is 2 vs 2.18 expected.

MU-E currently looks normal. Event-weighted frozen-pot realization is close to exact equity both before the turn and before the river. The clean frozen-turn sample is especially ordinary. Dollar-weighted results move around more because a handful of large pots dominate them, but none is remotely a strong statistical inconsistency.

Nothing currently justifies a strong suspicion. The flop→turn result is interesting enough that I would absolutely keep tracking it. But the fact that turn→river, final equity-favorite losses, persistence, and frozen-pot realization are normal keeps this firmly in the “weak deviation worth tracking” category.

At 1,503 Observed Hands:

MU verdict: weak deviation worth tracking, but less suspicious than it looked at 665 hands. The main reason is replication.

The strongest apparent signal is still MU-R flop→turn upsets. At 665 hands it was 41 observed vs 29.21 expected, 1.40×. At 1,503 hands it is now 79 vs 60.74, 1.30×, with a one-sided exact tail around 0.68%. So the pooled result remains elevated, but the effect size decreased.

More importantly, the new 838 hands alone do not independently reproduce a strong anomaly: 38 flop→turn upsets vs 31.53 expected, 1.21×, with a tail probability of about 12.2%. Directionally high, yes; statistically remarkable, no.

The clean subset moved even more strongly toward normality:

Old 665 clean flop→turn: 3 vs 1.13 expected
New 838 alone: 1 vs 1.43 expected

Cumulative 1,503: 4 vs 2.56 expected, tail ≈ 24.5%

That is probably the single most important development. The bias-resistant sample did not replicate the apparent upset excess.

Everything downstream is also calm. Observed turn→river reversals are actually below expectation at 58 vs 68.29, while clean turn→river is 4 vs 4.69. Flop leaders ultimately losing showdown are 124 vs 113.06 expected, which is ordinary.

Frozen-pot MU-E is also very close to expectation, including the now-larger clean subsets: 28 clean pre-turn frozen-pot states and 45 clean pre-river states, with favorite realization slightly above expected rather than showing excess upset losses.

So compared with the 665-hand checkpoint, my interpretation shifts slightly toward ordinary variance/selection bias.

The MU-R flop→turn signal is still real enough numerically to keep watching, but it is being driven substantially by showdown-conditioned observations.

The new independent batch weakens the effect, and the clean sample actively regresses toward expectation.

Here are the tables with the observations.

TL;DR: The algorithm is fair and random. Your hand selection is not, and any losses or upsets are attributable to user error and variance, not the algorithm screwing you. That's the math.

I agree with the idea of a "welfare" report.

But I also don't think it's staff's job to provide addiction counseling/therapy.

Honestly, best all around solution should be- someone is begging for money after losing gambling, or begging for their money back, you should be able to trigger a welfare report on the chat itself.

If enough welfare reports get filed on a person, (say 10 in 60 days or slmething) there is a quick staff review of the chats to ensure that the reports are valid, and then a 90 day casino exclusion triggers.

This keeps people from reporting/triggering exclusions because they're salty, but also keeps staff from having to try and do something they naturally aren't trained to do. Staff never has to interact with the person- just check the chats, click the exclusion button.

Chain 1 lasted 17 hours. Dropped it.

Cooldown lasted 16.5 hours. 

During both of those, 14,038 hits were made.

After cooldown ended, we started the chain back up.

Did 100,000 hits.

14,038 + 100,000 = 114,038

We dropped the first chain, then because it was Halloween, people were obviously still making hits during cooldown.

We ended up doing a total of 114,038 hits between the start of the chain we dropped and the 100,000th hit of the one we finished.

14:44:10 - 12/11/25 You sent a Bottle of Stinky Swamp Punch to Mulle

TLDR;

Yes he did.

Was an asshat about it.

TSFTS blew up the faction with 100k chain bonus.

Madlad creates a new faction, puts "We open all caches." in the description.

🍺

Madlad just went and created a new one.

IMOTF has the chance to do the funniest thing.

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Faction page honestly looks a lot better like that  

Problem solved.

06:00:04 - 12/11/25 You used 25 energy anonymously attacking AdamWestMiles and defeated them (chain #100000) [view]

This project had kind of taken a back seat recently due to life just getting too hectic.  I have a lot more free time now, and am aiming to get back to working on this soon!

Stay tuned!