Baccarat Live – Technical opinion on a model with positive EV and very low drawdown
April 5th, 2026 at 10:47:13 AM
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Hello everyone,
My nickname is Source_Q.
First of all, I want to apologize for my English, because it is not my native language and I do not speak it very well. I hope the meaning of this post is still clear enough.
I am not here to sell anything, and I am definitely not claiming to have found some “magic Baccarat system”. I am simply looking for an honest and technical opinion from people who understand casino math, probability, statistics, variance and testing better than I do.
For the last several months I have been collecting and studying live Baccarat data and building a small quantitative model. My goal is not to predict the next hand, but rather to test whether a combination of:
entry filtering,
very strict risk control,
and variance compression
can produce something statistically interesting over a large enough sample.
Model setup
Data collected from more than 3,000 live shoes / sessions
Two separate logics running in parallel (A and C)
Progression strictly truncated to 2 steps (1–2)
Maximum loss per cycle = -3 units
For realism, I already apply an average payout factor of 0.975 to all wins
The main idea is not to “beat randomness with prediction”, but to see whether selective entries can improve the outcome relative to a random baseline.
Raw results
🔵 Strategy A
Net Profit: +285.92 units
Max Drawdown: -32.83 units
Wins: 1623
Full Stop Losses (-3): 404
Win Rate: 77.40%
🌸 Strategy C
Net Profit: +292.70 units
Max Drawdown: -32.50 units
Wins: 2096
Full Stop Losses (-3): 557
Win Rate: 76.66%
👑 Combined (A + C)
Total Net Profit: +578.63 units
Combined Max Drawdown: -35.57 units
Total Cycles Played: 4,831
Global Win Rate: 76.98%
Baseline / benchmark
The benchmark I am using is the following:
If I take a random 50/50 process with a 1–2 progression and a maximum full loss of -3, the rough break-even win rate is around 75.47%.
My observed model produces about 76.98%, which is only about +1.5 percentage points above the random baseline, so I am not talking about some absurd “too good to be true” number.
In raw terms, this means roughly +96 extra wins compared to the expected random baseline over 4,831 cycles.
Using a simplified binomial-style interpretation, I get a Z-score around 3.1 / 3.2.
What I would really like feedback on
I am not looking for emotional encouragement or “yes/no Baccarat is unbeatable” replies.
I would really appreciate technical opinions on these points:
1)Does my baseline assumption make sense,
or is it too simplified to be meaningful?
2)How much risk do you think there is here of:
overfitting
selection bias
data snooping
3)How much weight would you personally give to a drawdown this compressed
(about -35 units over almost 4.8k cycles)?
4)Would you consider this profile interesting enough to deserve a serious forward out-of-sample test,
or does it still look fully compatible with luck / favorable variance?
Final note
I am fully open to criticism.
If there is a flaw in my benchmark, in my logic, or in the way I am interpreting the data, I would honestly prefer to hear it from people who understand these things better than I do.
Thank you in advance to anyone willing to take a serious look at it.
I can also post the equity curve if useful, but I did not want to overload the post too much.
Best regards,
Source_Q
My nickname is Source_Q.
First of all, I want to apologize for my English, because it is not my native language and I do not speak it very well. I hope the meaning of this post is still clear enough.
I am not here to sell anything, and I am definitely not claiming to have found some “magic Baccarat system”. I am simply looking for an honest and technical opinion from people who understand casino math, probability, statistics, variance and testing better than I do.
For the last several months I have been collecting and studying live Baccarat data and building a small quantitative model. My goal is not to predict the next hand, but rather to test whether a combination of:
entry filtering,
very strict risk control,
and variance compression
can produce something statistically interesting over a large enough sample.
Model setup
Data collected from more than 3,000 live shoes / sessions
Two separate logics running in parallel (A and C)
Progression strictly truncated to 2 steps (1–2)
Maximum loss per cycle = -3 units
For realism, I already apply an average payout factor of 0.975 to all wins
The main idea is not to “beat randomness with prediction”, but to see whether selective entries can improve the outcome relative to a random baseline.
Raw results
🔵 Strategy A
Net Profit: +285.92 units
Max Drawdown: -32.83 units
Wins: 1623
Full Stop Losses (-3): 404
Win Rate: 77.40%
🌸 Strategy C
Net Profit: +292.70 units
Max Drawdown: -32.50 units
Wins: 2096
Full Stop Losses (-3): 557
Win Rate: 76.66%
👑 Combined (A + C)
Total Net Profit: +578.63 units
Combined Max Drawdown: -35.57 units
Total Cycles Played: 4,831
Global Win Rate: 76.98%
Baseline / benchmark
The benchmark I am using is the following:
If I take a random 50/50 process with a 1–2 progression and a maximum full loss of -3, the rough break-even win rate is around 75.47%.
My observed model produces about 76.98%, which is only about +1.5 percentage points above the random baseline, so I am not talking about some absurd “too good to be true” number.
In raw terms, this means roughly +96 extra wins compared to the expected random baseline over 4,831 cycles.
Using a simplified binomial-style interpretation, I get a Z-score around 3.1 / 3.2.
What I would really like feedback on
I am not looking for emotional encouragement or “yes/no Baccarat is unbeatable” replies.
I would really appreciate technical opinions on these points:
1)Does my baseline assumption make sense,
or is it too simplified to be meaningful?
2)How much risk do you think there is here of:
overfitting
selection bias
data snooping
3)How much weight would you personally give to a drawdown this compressed
(about -35 units over almost 4.8k cycles)?
4)Would you consider this profile interesting enough to deserve a serious forward out-of-sample test,
or does it still look fully compatible with luck / favorable variance?
Final note
I am fully open to criticism.
If there is a flaw in my benchmark, in my logic, or in the way I am interpreting the data, I would honestly prefer to hear it from people who understand these things better than I do.
Thank you in advance to anyone willing to take a serious look at it.
I can also post the equity curve if useful, but I did not want to overload the post too much.
Best regards,
Source_Q
