[UTILITY] SwongSim -- ROI/variance simulator for lottery-payout SNGs (2k post)
07-29-2017
, 08:06 PM
Select HU/Winner Take All from the presets.
Adjust Buyins to get the effective rate where you would like it (default is 1.95 = 2.5% rake).
Set your finish distribution (win rate).
Proceed as normal with #games, #sims, rake return, etc.
glgl
Adjust Buyins to get the effective rate where you would like it (default is 1.95 = 2.5% rake).
Set your finish distribution (win rate).
Proceed as normal with #games, #sims, rake return, etc.
glgl
08-20-2017
, 07:59 AM
A bit offtopic, but since you are variance calculator god you probably can (and hopefully will 
) answer my question.
I have a unique situation where someone gives me flips where he offers 10% more, i.e. we flip in a 50-50 situation where I have to put up $1000 and he puts up $1100. Does it mean I can use HU variance calculators to see how often a 100 buy-in downswing etc can happen and use 5% ROI (since he pays 10% more and i'm expected to win 50% of them) or it's quite a bit different because in HU hypers with 5% ROI you win more often than 50% of times and in my situation you win 50% but if you do, you win more than your fair share?
) answer my question.I have a unique situation where someone gives me flips where he offers 10% more, i.e. we flip in a 50-50 situation where I have to put up $1000 and he puts up $1100. Does it mean I can use HU variance calculators to see how often a 100 buy-in downswing etc can happen and use 5% ROI (since he pays 10% more and i'm expected to win 50% of them) or it's quite a bit different because in HU hypers with 5% ROI you win more often than 50% of times and in my situation you win 50% but if you do, you win more than your fair share?
08-20-2017
, 11:39 AM
Quote:
A bit offtopic, but since you are variance calculator god you probably can (and hopefully will ) answer my question.
I have a unique situation where someone gives me flips where he offers 10% more, i.e. we flip in a 50-50 situation where I have to put up $1000 and he puts up $1100. Does it mean I can use HU variance calculators to see how often a 100 buy-in downswing etc can happen and use 5% ROI (since he pays 10% more and i'm expected to win 50% of them) or it's quite a bit different because in HU hypers with 5% ROI you win more often than 50% of times and in my situation you win 50% but if you do, you win more than your fair share?
) answer my question.I have a unique situation where someone gives me flips where he offers 10% more, i.e. we flip in a 50-50 situation where I have to put up $1000 and he puts up $1100. Does it mean I can use HU variance calculators to see how often a 100 buy-in downswing etc can happen and use 5% ROI (since he pays 10% more and i'm expected to win 50% of them) or it's quite a bit different because in HU hypers with 5% ROI you win more often than 50% of times and in my situation you win 50% but if you do, you win more than your fair share?
You could also get the 5% ROI by adjusting the finish distribution to 52.5%/47.5% with a 2 buyin prize pool. As you suggest, the higher ITM causes the simulation to underestimate the variance/swings, but it's only slight in your particular situation.
Looks like a ballpark estimate is that there's about a 1% chance of a 100-buyin downswing over a large sample.
glgl!
Code:
--------------------------------------------------------------------------------------- Effective Specified Simulation Place Finish Distribution Finish Distribution 1 50% 49.999848% ITM 50% 49.999848% --------------------------------------------------------------------------------------- 50000 simulations of 50000 games Expected ROI (with rakeback/bonus/award): 5% (2500 Buyins) --------------------------------------------------------------------------------------- 99.9% had ROI below 6.46% (3231 Buyins) 99% had ROI below 6.08% (3042 Buyins) 97.5% had ROI below 5.91% (2956 Buyins) 95% had ROI below 5.77% (2884 Buyins) 90% had ROI below 5.60% (2800 Buyins) 80% had ROI below 5.39% (2697 Buyins) 70% had ROI below 5.25% (2624 Buyins) 60% had ROI below 5.12% (2559 Buyins) 50% had ROI below 5.00% (2502 Buyins) 40% had ROI below 4.88% (2441 Buyins) 30% had ROI below 4.76% (2378 Buyins) 20% had ROI below 4.61% (2303 Buyins) 10% had ROI below 4.40% (2200 Buyins) 5% had ROI below 4.23% (2116 Buyins) 2.5% had ROI below 4.07% (2036 Buyins) 1% had ROI below 3.90% (1948 Buyins) 0.1% had ROI below 3.54% (1769 Buyins) --------------------------------------------------------------------------------------- 99.9% had a downswing greater than 39 buyins 99% had a downswing greater than 43 buyins 97.5% had a downswing greater than 46 buyins 95% had a downswing greater than 48 buyins 90% had a downswing greater than 50 buyins 80% had a downswing greater than 54 buyins 70% had a downswing greater than 57 buyins 60% had a downswing greater than 60 buyins 50% had a downswing greater than 63 buyins 40% had a downswing greater than 66 buyins 30% had a downswing greater than 70 buyins 20% had a downswing greater than 75 buyins 10% had a downswing greater than 83 buyins 5% had a downswing greater than 91 buyins 2.5% had a downswing greater than 98 buyins 1% had a downswing greater than 109 buyins 0.1% had a downswing greater than 138 buyins --------------------------------------------------------------------------------------- 99.9% had a low point lower than 0 buyins 99% had a low point lower than 0 buyins 97.5% had a low point lower than 0 buyins 95% had a low point lower than 0 buyins 90% had a low point lower than -1 buyins 80% had a low point lower than -2 buyins 70% had a low point lower than -4 buyins 60% had a low point lower than -5 buyins 50% had a low point lower than -7 buyins 40% had a low point lower than -10 buyins 30% had a low point lower than -13 buyins 20% had a low point lower than -17 buyins 10% had a low point lower than -25 buyins 5% had a low point lower than -32 buyins 2.5% had a low point lower than -40 buyins 1% had a low point lower than -50 buyins 0.1% had a low point lower than -77 buyins --------------------------------------------------------------------------------------- 99.9% had a breakeven stretch longer than 872 games 99% had a breakeven stretch longer than 1027 games 97.5% had a breakeven stretch longer than 1122 games 95% had a breakeven stretch longer than 1214 games 90% had a breakeven stretch longer than 1330 games 80% had a breakeven stretch longer than 1506 games 70% had a breakeven stretch longer than 1653 games 60% had a breakeven stretch longer than 1794 games 50% had a breakeven stretch longer than 1943 games 40% had a breakeven stretch longer than 2115 games 30% had a breakeven stretch longer than 2329 games 20% had a breakeven stretch longer than 2607 games 10% had a breakeven stretch longer than 3056 games 5% had a breakeven stretch longer than 3510 games 2.5% had a breakeven stretch longer than 3970 games 1% had a breakeven stretch longer than 4610 games 0.1% had a breakeven stretch longer than 6385 games ---------------------------------------------------------------------------------------
10-20-2017
, 09:47 AM
Hi guys.
I am playing full-time twisters in ipoker and I am using SwongSim to estimate variance's effect on my game. I would like to thank you for this tool first of all.
The calculator's result is a projection of your overall probable outcomes based on your skill and the type of game you are playing. However, it does not distinguish between bad luck in card distribution, bad luck in hitting good multipliers and bad luck in all-in situations. (since I am constantly running below EV close to the low 5% of the distribution for 20k+ games, I need some more details to explain this bad luck to myself to stay focused and motivated
)
-By counting how many 100x and 1000x I have played I can somehow estimate the bad luck in lottery.
-By counting the all-in won chips and comparing it to the ev all-in chips I can be quite close in estimating the bad luck in all in situations.
My problem is that I have no idea how to estimate how big is the effect of bad card distribution and more specifically how much it does effect my cev. If for example my actual cev is 50, does it make sense for it to be 25 for lets say 2,000 games say because of bad card distribution?
I would be really happy if a more experienced player could give some actual figures of bad periods and how much they effected his cev temporally.
It is very important not to question your skill and stay motivated that you are playing fine and it is very tough when the only indicator that reflects your skill turns downwards..
I am playing full-time twisters in ipoker and I am using SwongSim to estimate variance's effect on my game. I would like to thank you for this tool first of all.
The calculator's result is a projection of your overall probable outcomes based on your skill and the type of game you are playing. However, it does not distinguish between bad luck in card distribution, bad luck in hitting good multipliers and bad luck in all-in situations. (since I am constantly running below EV close to the low 5% of the distribution for 20k+ games, I need some more details to explain this bad luck to myself to stay focused and motivated
)-By counting how many 100x and 1000x I have played I can somehow estimate the bad luck in lottery.
-By counting the all-in won chips and comparing it to the ev all-in chips I can be quite close in estimating the bad luck in all in situations.
My problem is that I have no idea how to estimate how big is the effect of bad card distribution and more specifically how much it does effect my cev. If for example my actual cev is 50, does it make sense for it to be 25 for lets say 2,000 games say because of bad card distribution?
I would be really happy if a more experienced player could give some actual figures of bad periods and how much they effected his cev temporally.
It is very important not to question your skill and stay motivated that you are playing fine and it is very tough when the only indicator that reflects your skill turns downwards..