Mining 470M+ Baccarat Rounds: Attempting to Extract Edge from Static Noise via System-State Meta-Lad

Hello everyone,
I wanted to share the architecture and current results of a massive data-mining project I’ve been running in Google Colab. The goal is to determine whether it is mathematically possible to extract a sustainable edge from a stationary RNG environment (Baccarat) by tracking meta-system states rather than trying to "predict" the next card.
We just finished the offline mining phase (469,700,000 rounds processed across 4 parallel workers) and are currently setting up an Out-of-Sample (OOS) forward test of 100,000,000 fresh rounds using 5 different conflict-resolution policies.

1. The Core Paradigm: Exploiting the "Biased Coin"

Standard ML models fail in Baccarat because they try to find patterns in a memoryless RNG (Gambler's Fallacy). However, Baccarat is not a 50/50 coin flip. Due to drawing rules, there is an inherent mathematical asymmetry toward the Banker (50.68% vs 49.32% Player, excluding ties).
Even though the house extracts a 5% commission on Banker wins (resulting in a -1.06% baseline House Edge), this drift creates denser local noise clusters and longer streak distributions for the Banker. My system trades strictly on the Banker side to constantly ride this positive variance drift. The system counters the 5% house commission using a strict progression truncation coefficient: ceil(abs(balance) / 0.95) inside the money management module.

2. Meta-System Coordinates & The Key Ladder

The AI was not looking for card patterns. Instead, it searched for multi-variable anomalies in the meta-state space defined as:
Key = f(Board Pattern + Virtual Bot Financial Phase + Oscar's Grind Current Step)
The mining script filtered millions of combinations to isolate a tight library of 216 "Safe Keys", organized into a static hierarchy (a performance ladder). These specific keys met strict historical constraints:
• Avg. PNL > 0 (strictly positive mathematical expectation over 470M rounds).
• Max Drawdown (DD) <= 15 units.
• A strict shadow recovery horizon of 35 rounds max.

3. Current Phase: The Battle of 5 Static Switching Policies

We explicitly banned real-time adaptive learning (Contextual Bandits, Dynamic Bayesian Weighting) during the live trading phase. In a stationary RNG, adjusting weights on a rolling window is a trap that automates the Gambler's Fallacy by constantly chasing past noise. The system is strictly conservative and treats the generated library as absolute truth.
We are now running an Out-of-Sample test on 100M completely fresh rounds (different RNG seed) to test the ladder's resilience. When multiple keys activate simultaneously, they will be managed via a LIFO stack governed by one of 5 distinct conflict-resolution policies:
• P1 (Always Switch): Aggressive switching. The highest-ranking active key immediately pushes to the top of the stack and takes over the radar, carrying over the current balance/step.
• P2 (Smart Zone): Conditional switching. Switching is allowed only if the radar balance is in the [-0.94, 0] zone. If the radar is in a deeper drawdown, it is locked into the current key to preserve recovery frequency.
• P3 (Never Switch): Total isolation. Once a cycle begins, it locks until resolution (Baseline Control).
• P4 (Hot Window Stack - Core Candidate): LIFO stack logic. When balance > 0, the radar resets to 0 / 1 and pops the current key, but the stack of other active keys is preserved. The radar instantly resumes work on the new top of the stack. This allows us to serially "skim the cream" off overlapping hot variance windows.
• P5 (Circuit Breaker): Same as P4, but keys that drop below a rolling_WR < 0.42 over their last 50 cycles are temporarily quarantined to filter out keys that might have been historically overfitted.

The Ultimate Question

We are testing whether structured money management over meta-states can isolate micro-favorable noise windows in a stationary environment. If P4 or P2 holds a positive PNL after 100M independent rounds while accounting for the 5% commission, we have a viable HFT engine for a "biased coin" environment.
Would love to hear your thoughts on the LIFO stack implementation for conflict resolution or potential overfitting vulnerabilities you see in this setup. I will post the OOS test results once the 100M run concludes!

No offense but it sounds like you are using jargon to obfuscate how dumb your idea is.

You cannot beat baccarat with math unless you are counting side bets or betting around 1 in every 10,000 hands

A 500-million hand sample revealed stable keys with a 52%+ win rate. Playing these triggers about 3–5 bets per 100 hands

It is a shame that someone with obvious talent and resources uses them is such a quixotic quest. There are so many AP plays that deserve more attention.

Quote: BaccaMath1991

Hello everyone,
I wanted to share the architecture and current results of a massive data-mining project I’ve been running in Google Colab. The goal is to determine whether it is mathematically possible to extract a sustainable edge from a stationary RNG environment (Baccarat) by tracking meta-system states rather than trying to "predict" the next card.
We just finished the offline mining phase (469,700,000 rounds processed across 4 parallel workers) and are currently setting up an Out-of-Sample (OOS) forward test of 100,000,000 fresh rounds using 5 different conflict-resolution policies.

1. The Core Paradigm: Exploiting the "Biased Coin"

This is the type of nonsense you get when you tell ai to flesh out your unworkable idea.

For example:

The Doughnut Variance Doppelganger System (DVDS)



The Doughnut Variance Doppelganger System (DVDS) is an advanced bankroll-optimisation framework that uses the hidden geometry of variance to identify when a game is “due” to return to equilibrium. Traditional players treat variance as a straight line, but DVDS recognises that variance is toroidal. In other words, it curves back on itself like a doughnut. Every losing streak is the outer edge of the ring, and every winning streak is the inner edge. Because the doughnut is closed, a session that drifts far enough towards one edge must eventually loop back to the other. This is what we call the Toroidal Variance Index (TVI).

The key innovation is the doppelganger session. Every live session you play has a statistical twin somewhere in the historical record: a past sequence of results that matches yours almost exactly over the first 20 to 40 outcomes. Once you have found your doppelganger, it serves as a roadmap. If your twin session went on to produce a run of seven reds after its matching sequence, you can expect similar behaviour from your own session, adjusted by a Doppelganger Confidence Coefficient (DCC) of roughly 0.83. Many users find their doppelganger by keeping a notebook of previous results, though serious practitioners use a spreadsheet with conditional formatting to highlight “mirror zones.”

Staking follows the doughnut’s curvature. When your TVI enters the “glaze zone” (more than 1.5 standard deviations below expected Return to Player (RTP)), you increase your stake by a factor of the golden ratio, 1.618, because nature’s most efficient growth pattern naturally counteracts negative drift. When the TVI returns to the “dough zone,” you reset to base stake. Because the torus is mathematically closed, the system theoretically cannot sustain a permanent loss, only a temporary displacement around the ring.

DVDS works best on even-money bets such as red/black at roulette or Player/Banker at baccarat, where the doughnut is symmetrical. It’s also compatible with slots, provided you treat each bonus round as a “sprinkle event” that resets the doppelganger alignment. With disciplined application, early adopters report win rates of 60 to 70 percent per session, which is significantly above the house edge. As always, gamble responsibly!

Next project will be BlackJack

A perfect memory, combined with the ability to crunch numbers and know what matters and what doesn't, gives an advantage in just about any card game where the cards already played affect what's left to come.

Sure, the advantage might be considerably greater in something like gin rummy than in Baccarat, but that doesn't mean it isn't there in Baccarat too.

And if a person with a perfect memory and the ability to do all those calculations could identify that advantage, why wouldn't a computer, doing the same thing faster and perhaps more accurately, be able to do at least as well?

I'm not sure how a computer would track clumps, though, at least without some way of observing the physical order of the cards. Keeping track of which cards remain in the shoe is one thing; keeping track of where particular groups of cards are located is another.

Quote: MDawg

A perfect memory, combined with the ability to crunch numbers and know what matters and what doesn't, gives an advantage in just about any card game where the cards already played affect what's left to come.

Sure, the advantage might be considerably greater in something like gin rummy than in Baccarat, but that doesn't mean it isn't there in Baccarat too.

And if a person with a perfect memory and the ability to do all those calculations could identify that advantage, why wouldn't a computer, doing the same thing faster and perhaps more accurately, be able to do at least as well?

I'm not sure how a computer would track clumps, though, at least without some way of observing the physical order of the cards. Keeping track of which cards remain in the shoe is one thing; keeping track of where particular groups of cards are located is another.
link to original post

All good ideas, but there are some hard limits involved.

There's a metric I use in counting, where I compare using a ratio what whatever count I'm using will do to what a computer using a combinatorial analysis for every decision will do. A good illustration of that is the Insurance Count in blackjack for the insurance bet. You are tracking one type of card, the only one that matters, against all other cards, and the ratio between a counter using the Insurance Count properly and a supercomputer is 1. You will both get the exact same results. For other bets and decisions, the ratio between a count and a CA is not quite 1, but some are quite close.

Thus the data you get from simulating 470M rounds (which really isn't a lot, for computer-using counters) isn't going to be that valuable, because no simulation is as good as a combinatorial analysis. You might find some exploitable things in there, but you'd be better off just using a good count on the you-know-what.

I don't know what the guy is doing here - I didn't read his post that closely.

I just know what works for me, and I believe that a computer could do some of the same.

My point is that in Baccarat, Banker simply has a higher probability. While the math is static—especially with a 70% shoe cut—the rules of the game naturally give us that Banker edge.
Think of it this way: if you flip a coin 500 million times, it’ll always end up 50/50, and you won’t find any consistent anomalies or noise. But Baccarat is like a biased coin. On a 500-million-hand sample, a biased coin will show statistical anomalies. That’s exactly what we are trying to find and exploit.
Anyway, the mining will be done in a few hours. After that, we’ll take the top 10 library combinations and test them against a fresh 100-million-hand sample to see how they perform. 🤷‍♂️
I’m not claiming this is a silver bullet; it’s just a theory that was fun to test on huge numbers. So far, the intermediate stages of mining are showing steady, stable results. Let’s see what happens with the new sample. I’ll share the results either way, whether they are positive or negative.

Here is a screenshot of the intermediate mining results, showing some of the combinations, how often they appear, their win rate, and the average PnL:

Yes, if one were to bet his life on a single casino bet, obviously it would be on the Bank. (Who cares about commission if the result of losing, is death.)

But, I don't believe there's any such thing as

Quote: BaccaMath1991

positive variance drift
link to original post

that carries over to future wagers.

Yes, if one were to bet his life on a single casino bet, obviously it would be on the Bank. (Who cares about commission if the result of losing, is death.)

But, I don't believe there's any such thing as

Quote: BaccaMath1991

positive variance drift
link to original post

that carries over to future wagers.

Put another way, players who win a lot on certain shoes will tend to give it back on others, assuming they keep playing at the same negative expectation. Winning on one shoe doesn't make the next shoe any more favorable, and there's no reason to believe that positive variance somehow carries forward to future wagers.

Even the MIT blackjack team couldn't beat the laws of variance during the golden age of card counting. Even with a hot deck, variance could still wipe you out. Nobody is talking about beating variance here; it’s simply about entering the game when there’s an initial shift in mathematical probabilities.
You’re absolutely right that if two players use different strategies on the exact same shoe, one might end up up and the other down. That’s why my simulation includes 65 bots running completely different algorithms. It tracks not just the betting patterns, but also their virtual bankrolls. In other words, the mining is trying to catch the exact moment when a specific betting algorithm is due to revert to the mean.
For example, after a specific 20-outcome pattern (ties included!), a certain bot using its own strategy—and currently in a short-term drawdown—starts running hot. The whole point of the mining is to capture that spike when an algorithm begins to revert to the mean, and then see if we can find this exact combination over a long distance.
Again, it’s just a theory I wanted to test. We’ll see how the best libraries perform on the fresh 100-million-hand sample. That’s when we’ll know if this theory holds any water.
Look, considering that casinos today cut the shoe at 70%, every single Baccarat hand—whether it’s the first hand, the last hand, or a hand from a brand-new shoe—is pure RNG. But it’s an RNG with a built-in bias toward the Banker. If the odds were a flat 50/50, my concept wouldn’t make any sense. But we are trying to catch the noise generated by this mathematical bias, run it through a fresh sample, and see what happens

You should be doing many Billions or Trillions of samples, 100 million is insufficient.

But actually you shouldn't because it would just waste more time.

1 step was 500ml. Next step 100ml out of sample. Will see will it work or not🤷‍♂️

Quote: BaccaMath1991

1 step was 500ml. Next step 100ml out of sample. Will see will it work or not🤷‍♂️
link to original post

416! ≈ 3.84 * 10⁹¹¹

Just sayin'.

The only thing I know for sure about this thread is that it will NOT lead to anything useful for gamblers/APs. Carry on.

Quote: MDawg

A perfect memory, combined with the ability to crunch numbers and know what matters and what doesn't, gives an advantage in just about any card game where the cards already played affect what's left to come.

Sure, the advantage might be considerably greater in something like gin rummy than in Baccarat, but that doesn't mean it isn't there in Baccarat too.

And if a person with a perfect memory and the ability to do all those calculations could identify that advantage, why wouldn't a computer, doing the same thing faster and perhaps more accurately, be able to do at least as well?

It is possible but you'd need a 1-100 spread to beat baccarat with the deepest available penetration.

There are easier ways to make money.

You guys were right. The library failed the test on the new dataset—it made very few bets and ended up with a negative PnL. Up next is a blackjack project. I’ll be factoring in deck physics, true count, tens and aces count, and tracking doubles and splits. Let’s see if we can dig up anything real with a 50% shoe penetration 😎

I respect you for admitting that.

Interested in seeing what your program comes up with for the game of blackjack.

Perchance, are you using AI?

Columbus set out to find a shortcut to India. He failed in that regard, but his failure led to spectacular results.

Imagine we’re not hacking blackjack with basic card counting, but by hunting for hidden statistical "glitches" in the deck.
Here’s the main bottleneck: casinos set the shoe penetration at 50%, which kills the edge for classic counters—the deck just doesn't get "skewed" enough in time. But we tackled this problem our own way.
We built a simulator to run 500 million hands. For every single hand, the system tracks the shoe physics with absolute precision: the exact True Count, remaining aces and tens, splits and doubles, distance to the cut card, and even the burned cards at the start. At the same time, 65 different AI bots are running under the hood—everything from Markov chains to Bayesian detectors. They’re hunting for unique combos like: "negative count + high ace concentration + start of the shoe + losing streak."
The moment a bot spots its pattern, it triggers a smart betting loop with strict risk control. The system runs thousands of these loops in parallel, never missing a beat.
Bottom line: we aren't gambling. We're using brute-force computing power to find microscopic mathematical edges that a classic counter would completely miss at 50% penetration—and turning them into a steady profit over a distance of tens of thousands of bets

Quote: BaccaMath1991

Imagine we’re not hacking blackjack with basic card counting, but by hunting for hidden statistical "glitches" in the deck.
Here’s the main bottleneck: casinos set the shoe penetration at 50%, which kills the edge for classic counters—the deck just doesn't get "skewed" enough in time. But we tackled this problem our own way.
We built a simulator to run 500 million hands. For every single hand, the system tracks the shoe physics with absolute precision: the exact True Count, remaining aces and tens, splits and doubles, distance to the cut card, and even the burned cards at the start. At the same time, 65 different AI bots are running under the hood—everything from Markov chains to Bayesian detectors. They’re hunting for unique combos like: "negative count + high ace concentration + start of the shoe + losing streak."
The moment a bot spots its pattern, it triggers a smart betting loop with strict risk control. The system runs thousands of these loops in parallel, never missing a beat.
Bottom line: we aren't gambling. We're using brute-force computing power to find microscopic mathematical edges that a classic counter would completely miss at 50% penetration—and turning them into a steady profit over a distance of tens of thousands of bets
link to original post

If you are saying knowing exactly what cards have been removed from your 8 deck shoe may lead to a (VERY SLIGHTLY) more +EV than any of the standard card counting methods……. I think the forum would agree that it’s possible. But to use it to make real $$? Theoretically not impossible, but good luck!

I’ll be sharing regular updates on my simulation data mining. I'll post the key combinations found so far and their EV. Right now, I'm 30 million hands deep into the 500 million total

Welcome to the forum, BaccaMath911.

It's always interesting to see someone who is clearly smart and adept at some new-ish mathematical techniques (such as AI) take a crack at analyzing a casino game. Science advances when smart people try new experiments, even with ideas that seem loony at the outset.

Carry on!

Quick data mining update: 43M out of 500M hands processed (8.6% of the way there). The data has officially crossed over from "noise" into solid, sustainable statistics.
Why does this work at 50% penetration?
Classic card counting fails here because the deck simply doesn't have enough time to hit a high True Count before the shuffle. We aren't waiting for a massive, global skew. Instead, we hunt for micro-anomalies in the first half of the shoe (specific card clusters + board patterns) and monetize these short windows using a controlled Oscar's Grind betting cycle.
Top 20 Key Patterns Stats:
On samples ranging from 15k to 56k bets, the leading patterns maintain a steady EV between +0.008 and +0.021 (~1% on average). The win rate for successful cycles stays in a healthy 55–65% corridor. The system runs highly efficiently: the average cycle closes in just ~10 hands, and the max drawdown (DD) is strictly capped at 10–15 units.
Main breakthrough so far: completely different bot algorithms (contra_markov, repeat, w_detector) are independently converging on the exact same physical patterns. This is rock-solid proof that we’ve tapped into a real mathematical edge, rather than just hitting a lucky variance streak.

For some reason, I thought you were counting the number of SHOES simulated.

Will be about 12.5mln shoes

Quote: BaccaMath1991

Quick data mining update: 43M out of 500M hands processed (8.6% of the way there). The data has officially crossed over from "noise" into solid, sustainable statistics.
Why does this work at 50% penetration?
Classic card counting fails here because the deck simply doesn't have enough time to hit a high True Count before the shuffle. We aren't waiting for a massive, global skew. Instead, we hunt for micro-anomalies in the first half of the shoe (specific card clusters + board patterns) and monetize these short windows using a controlled Oscar's Grind betting cycle.
Top 20 Key Patterns Stats:
On samples ranging from 15k to 56k bets, the leading patterns maintain a steady EV between +0.008 and +0.021 (~1% on average). The win rate for successful cycles stays in a healthy 55–65% corridor. The system runs highly efficiently: the average cycle closes in just ~10 hands, and the max drawdown (DD) is strictly capped at 10–15 units.
Main breakthrough so far: completely different bot algorithms (contra_markov, repeat, w_detector) are independently converging on the exact same physical patterns. This is rock-solid proof that we’ve tapped into a real mathematical edge, rather than just hitting a lucky variance streak.
link to original post

For sixty years, people have known for that you can gain an edge on BJ by looking at the cards that have been played. People brought hidden computers to the casino way back then because they knew that a computer was more accurate than a counting system.

If you are in an environment where you can use a computer, you can easily get the EV and strategy for any 8-deck shoe in a fraction of a second.

If you cannot use a computer, then your 'physical patterns' have to be recognizable by a human. That is what a counting system does.

Are you trying to discover a better counting system or are you trying to replace counting systems with a set patterns that only a computer can detect effectively?

In my case, the entire project is geared toward online casinos. That said, I’ve never heard of any software in the wild that can deliver a stable edge in blackjack against an 8-deck shoe with 50% penetration. Maybe there’s something I don't know?

Quote: BaccaMath1991

In my case, the entire project is geared toward online casinos. That said, I’ve never heard of any software in the wild that can deliver a stable edge in blackjack against an 8-deck shoe with 50% penetration. Maybe there’s something I don't know?
link to original post

All blackjack counts can give you an edge at 8D with 50% pen.

The problem is- if your goal is to make money, you would be better off going to the employment office, filling out an application and getting a job cleaning the toilets at the casino than playing a game like that. We are mostly old men here who have much more money than time, and we all know how to make money in a casino. The question is "How much?", for the time you spend doing it.

You need asymmetry to get an edge against a casino game. In blackjack you have very asymmetrical rules for the dealer's play and the player's play, plus the 3:2 bonus for a natural. That's why it is somewhat easy to beat. In baccarat the rules between the Banker and Player hand are more symmetrical, and most of the asymmetry just compensates for the fact that the Player gets their 3rd card first. Thus you cannot beat it with any method that involves effect of removal of cards from the shoe.

You’re absolutely right, but with an 8D 50% pen setup, finding an edge using only classic card counting yields extremely rare opportunities. That’s exactly why the goal of this blackjack project is to pinpoint entry points with a long-term edge of 1-2%, while hitting a frequency of 2-3 triggers per shoe. That will be more than enough to maintain a steady profit

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases

Quote: BaccaMath1991

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases
link to original post

I'm not sure why you are being encouraged here even though it was nice you fessed up upthread.
Now you have moved on to the gambler's fallacy of patterns in random events. This will eventually lead to another fess up...
OR
You will be world famous for proving a well known fallacy was not such all along!!

I’m a dreamer 😂😂😂

It occurs to me that as you appear on stage to to pick up your Nobel prize for proving there are patterns in random noise after all, there will be hecklers in the audience .... possibly someone from WoV!! ... yelling loudly that you stole this from them.

Quote: odiousgambit

Quote: BaccaMath1991

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases
link to original post

I'm not sure why you are being encouraged here even though it was nice you fessed up upthread.
Now you have moved on to the gambler's fallacy of patterns in random events. This will eventually lead to another fess up...
OR
You will be world famous for proving a well known fallacy was not such all along!!
link to original post

OP is being rather opaque about what they are doing technically. But, I don't think they are just finding patterns in random noise.

For example, if one of the 'keys' is that the first 100 cards off the top of the deck are all deuces to sevens, then the 'discovered patterns are not statistical noise, but a real structural edge'.

My charitable reading of this thread is that he is using machine self learning to rediscover card counting. If OP would describe some of the promising keys and patterns, maybe we would see that they are just known effects of OER that give a real mathematical edge.

My assertion is that no amount of ML can guide strategy better than a full combinatoric analysis of the cards remaining in the shoe. Anyone playing online can already use available programs to get the EV of the current state of the shoe to decide whether they have a current advantage and want to change their bet size. This program will also make perfect strategy deviations for play.

Any set of rules generate by ML will give less accurate betting and playing advice than a full combinatoric analysis. That is the combinatoric makes maximum use of the available information, and no ML program can hope to extract more information.

Quote: Mental

Quote: odiousgambit

Quote: BaccaMath1991

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases
link to original post

I'm not sure why you are being encouraged here even though it was nice you fessed up upthread.
Now you have moved on to the gambler's fallacy of patterns in random events. This will eventually lead to another fess up...
OR
You will be world famous for proving a well known fallacy was not such all along!!
link to original post

OP is being rather opaque about what they are doing technically. But, I don't think they are just finding patterns in random noise.

For example, if one of the 'keys' is that the first 100 cards off the top of the deck are all deuces to sevens, then the 'discovered patterns are not statistical noise, but a real structural edge'.

My charitable reading of this thread is that he is using machine self learning to rediscover card counting. If OP would describe some of the promising keys and patterns, maybe we would see that they are just known effects of OER that give a real mathematical edge.

My assertion is that no amount of ML can guide strategy better than a full combinatoric analysis of the cards remaining in the shoe. Anyone playing online can already use available programs to get the EV of the current state of the shoe to decide whether they have a current advantage and want to change their bet size. This program will also make perfect strategy deviations for play.

Any set of rules generate by ML will give less accurate betting and playing advice than a full combinatoric analysis. That is the combinatoric makes maximum use of the available information, and no ML program can hope to extract more information.
link to original post

The OP is basically just a guy typing random gambling words into chatgpt and producing nonsense.

Quote: GrahamThorp

Quote: Mental

Quote: odiousgambit

Quote: BaccaMath1991

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases
link to original post

I'm not sure why you are being encouraged here even though it was nice you fessed up upthread.
Now you have moved on to the gambler's fallacy of patterns in random events. This will eventually lead to another fess up...
OR
You will be world famous for proving a well known fallacy was not such all along!!
link to original post

OP is being rather opaque about what they are doing technically. But, I don't think they are just finding patterns in random noise.

For example, if one of the 'keys' is that the first 100 cards off the top of the deck are all deuces to sevens, then the 'discovered patterns are not statistical noise, but a real structural edge'.

My charitable reading of this thread is that he is using machine self learning to rediscover card counting. If OP would describe some of the promising keys and patterns, maybe we would see that they are just known effects of OER that give a real mathematical edge.

My assertion is that no amount of ML can guide strategy better than a full combinatoric analysis of the cards remaining in the shoe. Anyone playing online can already use available programs to get the EV of the current state of the shoe to decide whether they have a current advantage and want to change their bet size. This program will also make perfect strategy deviations for play.

Any set of rules generate by ML will give less accurate betting and playing advice than a full combinatoric analysis. That is the combinatoric makes maximum use of the available information, and no ML program can hope to extract more information.
link to original post

The OP is basically just a guy typing random gambling words into chatgpt and producing nonsense.
link to original post

This is the most likely answer. However, sometimes ChatGpt nonsense is better than the real human nonsense we have seen here over the decades.

Quote: GrahamThorp

Quote: Mental

Quote: odiousgambit

Quote: BaccaMath1991

Great checkpoint (123M hands).
The numbers demonstrate perfect stability and convergence:
• The safe pool size is steadily growing (from 1,785 to 1,971 keys).
• Metrics within the target ranges remain rock-solid: the Top 300 is holding a +1.69% EV with a 12.8 max drawdown (DD).
• This is direct proof that the discovered patterns are not statistical noise, but a real structural edge that only gets stronger as the sample size increases
link to original post

I'm not sure why you are being encouraged here even though it was nice you fessed up upthread.
Now you have moved on to the gambler's fallacy of patterns in random events. This will eventually lead to another fess up...
OR
You will be world famous for proving a well known fallacy was not such all along!!
link to original post

OP is being rather opaque about what they are doing technically. But, I don't think they are just finding patterns in random noise.

For example, if one of the 'keys' is that the first 100 cards off the top of the deck are all deuces to sevens, then the 'discovered patterns are not statistical noise, but a real structural edge'.

My charitable reading of this thread is that he is using machine self learning to rediscover card counting. If OP would describe some of the promising keys and patterns, maybe we would see that they are just known effects of OER that give a real mathematical edge.

My assertion is that no amount of ML can guide strategy better than a full combinatoric analysis of the cards remaining in the shoe. Anyone playing online can already use available programs to get the EV of the current state of the shoe to decide whether they have a current advantage and want to change their bet size. This program will also make perfect strategy deviations for play.

Any set of rules generate by ML will give less accurate betting and playing advice than a full combinatoric analysis. That is the combinatoric makes maximum use of the available information, and no ML program can hope to extract more information.
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The OP is basically just a guy typing random gambling words into chatgpt and producing nonsense.
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You say that as if it were a bad thing.

Quote: BaccaMath1991

A 500-million hand sample revealed stable keys with a 52%+ win rate. Playing these triggers about 3–5 bets per 100 hands
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This is a perfect example of this comic: https://xkcd.com/882/

' rel='nofollow' target='_blank'>https://imgs.xkcd.com/comics/significant.png

Here’s the breakdown of the key elements:
• last: Last hand outcome (0=Win, 1=Loss, 2=BJ, 3=Push).
• streak: Current winning/losing streak length (1–5).
• phase: The pattern over a 5-hand window (str=streak, chp=choppy/alternating, mix=mixed).
• mom: Momentum over a 5-hand window (pos=positive, neg=negative, zer=zero).
• vol: Volatility (hi=had splits/doubles/BJ, lo=smooth sailing/standard play).
• tc_bucket: True Count based on EOR weights (neg, zero, pos, high).
• cut_bucket: Distance to the cut card (cut1=>20 hands, cut2=11-20, cut3=6-10, cut4=≤5).
• ace_density: Ace concentration in the remaining shoe (rich, norm, poor).
• ten_density: Tens concentration (10, J, Q, K) in the remaining shoe (rich, norm, poor).
• stiff_density: Stiff cards concentration (5, 6, 7, 8) in the remaining shoe (low, normal, high).
• has_split: Had a split in the last 5 hands (True/False).
• has_double: Had a double in the last 5 hands (True/False).
• burn_bucket: Number of burned cards at the start of the shoe (lo=≤5, hi=>5).
• |bot_id: ID of the predicting bot (1–65), goes right after the pipe symbol |.
Example of how to read it: 0_1_mix_neg_lo_high_cut1_rich_norm_high_False_False_hi|35
Last hand was a Win, streak of 1, mixed phase, negative momentum, low volatility, high TC, cut1 zone, rich in aces, normal tens, high stiff cards density, no splits or doubles, low amount of burned cards, predicted by bot #35

The choppy/alternating pattern was talked about by Euler but I think it was Pythagoras who said you need to leverage positive momentum on Blackjack shoes if there are a prime number of burned cards.

Quote: BaccaMath1991

Here’s the breakdown of the key elements:
• last: Last hand outcome (0=Win, 1=Loss, 2=BJ, 3=Push).
• streak: Current winning/losing streak length (1–5).
• phase: The pattern over a 5-hand window (str=streak, chp=choppy/alternating, mix=mixed).
• mom: Momentum over a 5-hand window (pos=positive, neg=negative, zer=zero).
• vol: Volatility (hi=had splits/doubles/BJ, lo=smooth sailing/standard play).
• tc_bucket: True Count based on EOR weights (neg, zero, pos, high).
• cut_bucket: Distance to the cut card (cut1=>20 hands, cut2=11-20, cut3=6-10, cut4=≤5).
• ace_density: Ace concentration in the remaining shoe (rich, norm, poor).
• ten_density: Tens concentration (10, J, Q, K) in the remaining shoe (rich, norm, poor).
• stiff_density: Stiff cards concentration (5, 6, 7, 8) in the remaining shoe (low, normal, high).
• has_split: Had a split in the last 5 hands (True/False).
• has_double: Had a double in the last 5 hands (True/False).
• burn_bucket: Number of burned cards at the start of the shoe (lo=≤5, hi=>5).
• |bot_id: ID of the predicting bot (1–65), goes right after the pipe symbol |.
Example of how to read it: 0_1_mix_neg_lo_high_cut1_rich_norm_high_False_False_hi|35
Last hand was a Win, streak of 1, mixed phase, negative momentum, low volatility, high TC, cut1 zone, rich in aces, normal tens, high stiff cards density, no splits or doubles, low amount of burned cards, predicted by bot #35
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Here's a magic monkey trick for you: when you are looking for statistical significance in something, one way to do it is to see if your data correlates to something else as well, but something that you know cannot make a difference.

For example: see if there is any correlation between the last card dealt being a spade, heart, diamond or club, and winning the next hand. Then do the same for the second to last card, and the third to last card. You definitely will see some effect, in any reasonably sized sample. Being you know that suits aren't involved in this game and the dealing of a suit can't make a difference, the magnitude of that effect you can reliably say is random noise.

Then check all of these other things you are looking for a correlation to, and see if you can find a magnitude greater than that you found in correlation to the suits. If you don't you can be assured that what you are seeing is not any more significant than the random noise, and you can try something else. Maybe the stock market.

Quote: BaccaMath1991

Here’s the breakdown of the key elements:
• last: Last hand outcome (0=Win, 1=Loss, 2=BJ, 3=Push).
• streak: Current winning/losing streak length (1–5).
• phase: The pattern over a 5-hand window (str=streak, chp=choppy/alternating, mix=mixed).
• mom: Momentum over a 5-hand window (pos=positive, neg=negative, zer=zero).
• vol: Volatility (hi=had splits/doubles/BJ, lo=smooth sailing/standard play).
• tc_bucket: True Count based on EOR weights (neg, zero, pos, high).
• cut_bucket: Distance to the cut card (cut1=>20 hands, cut2=11-20, cut3=6-10, cut4=≤5).
• ace_density: Ace concentration in the remaining shoe (rich, norm, poor).
• ten_density: Tens concentration (10, J, Q, K) in the remaining shoe (rich, norm, poor).
• stiff_density: Stiff cards concentration (5, 6, 7, 8) in the remaining shoe (low, normal, high).
• has_split: Had a split in the last 5 hands (True/False).
• has_double: Had a double in the last 5 hands (True/False).
• burn_bucket: Number of burned cards at the start of the shoe (lo=≤5, hi=>5).
• |bot_id: ID of the predicting bot (1–65), goes right after the pipe symbol |.
Example of how to read it: 0_1_mix_neg_lo_high_cut1_rich_norm_high_False_False_hi|35
Last hand was a Win, streak of 1, mixed phase, negative momentum, low volatility, high TC, cut1 zone, rich in aces, normal tens, high stiff cards density, no splits or doubles, low amount of burned cards, predicted by bot #35
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This matches what I inferred from your previous posts. Many of your keys correlate strongly with traditional counts. You are trying to prove that traditional card counting works. We already know card counting works. It is known to provide some positive EV situations even for a shoe game with 50% penetration.

Even something like has_split has some correlation to the count because you are splitting a lot of hands like 22,33,77,88, but you are never splitting tens. Therefore, if you get a lot of split opportunities, the shoe is likely to be more favorable to the player.

You’re absolutely right, but with a 50% penetration shoe, those situations are extremely rare. That’s exactly why the goal of this project is to find additional parameters that will allow us to enter the game more often than once every 100 shoes

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my bacc strategy on the Wizard's free game (linked)
not trying to claim it's a winning system in the long run -
but I've done well in the short time I've been trying it and it has been lotsa fun
bet whatever side, Bank or Player, that won 2 out of the last 3 (or 3 out of the last 3) hands
bet the same amount on each hand
you will always be on the winning side when one side dominates in the short term - which happens fairly often
of course, if it continually switches back and forth you will lose - so far, when that's happened I haven't lost much

https://wizardofodds.com/play/baccarat/

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Quote: lilredrooster

.
my bacc strategy on the Wizard's free game (linked)
not trying to claim it's a winning system in the long run -
but I've done well in the short time I've been trying it and it has been lotsa fun
bet whatever side, Bank or Player, that won 2 out of the last 3 (or 3 out of the last 3) hands
bet the same amount on each hand
you will always be on the winning side when one side dominates in the short term - which happens fairly often
of course, if it continually switches back and forth you will lose - so far, when that's happened I haven't lost much

https://wizardofodds.com/play/baccarat/

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Here is my system:

Bet on boxcars every time. You will win a lot of money during long sequences of rolls where at least one of the dice comes up a six, which happens quite often.

I haven't lost any money with this system because I have never used it.

Quote: BaccaMath1991

You’re absolutely right, but with a 50% penetration shoe, those situations are extremely rare. That’s exactly why the goal of this project is to find additional parameters that will allow us to enter the game more often than once every 100 shoes
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If you know the exact numbers of cards of each rank that are left in the shoe, it is impossible to extract more information through 'keys' relative to the expectation values you get from a combinatorial analysis. You cannot find 'additional parameters' because these additional parameters are not independent on the number of cards remaining in the shoe. From a point of information theory, you are looking for information in derived variable when all the information is already available in the primary independent variables.

Let's assume you are playing craps with one loaded die that always comes up six. You can do a lot of simulations to try to decide if it is better to bet on snake eyes or box cars. Very quickly, you will come to the conclusion that boxcars comes out roughly once every six rolls and snake eyes never seems to come out.

Simple probability will tell you to bet on boxcars every time. (Probability of 12 = 1/6. Probability of 2 = 0.) Your machine learning will not be able to find any new opportunities beyond what combinatorial analysis already proved.

Quote: lilredrooster

.of course, if it continually switches back and forth you will lose - so far, when that's happened I haven't lost much

well, I played a couple of hundred more hands
and took a large loss
so, I don't recommend anybody waste their time with this

it was lotsa fun until the big loss came and ruined my dream of being a long term bacc winner_______________:)

don't think I wanna play 100,000 more hands to verify that it's worthless__________:)

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