greenbets saque mínimo-rapant-mcelroy.com

Previous chapters:

The roulette bias winning method of García Pelayo

Betting system for

biased wheels

As we can observe, if we have a 💷 thousand spins taken from a truly random

table, without bias, we would hardly find the most spun number having something 💷 beyond

15 positives. Likewise, we have a soft limit for the best two numbers, the two which

have been spun 💷 the most, of +26. If we continue searching for different groups of best

numbers, we can center in the sum 💷 of the best nine, which have a soft limit of +67. Why

the soft limit only? Because the hard limit 💷 is too erratic and luck might make a number

to fire-up without actually having any bias. It is more trustworthy 💷 to work with the

soft limit, which occurs 95% of the time, making decisions based on it. These tables

are 💷 more reliable the larger the numerical group is. Application to a single number

being more doubtful than the sum of 💷 the best six, where it is harder for luck to

interfere in a decisive manner. I make the study only 💷 up to the best nine, because if

there are ten or more best numbers outside the limit, it tells the 💷 table is entirely

good, and this is already studied on the first part.

How do these tables complement the

previous analysis? 💷 It might be the case that a roulette as a whole doesn’t goes beyond

the soft limit, as we studied 💷 at the beginning, but the best four numbers do go beyond.

They can be bet without much risk, awaiting to 💷 collect more data which defines with a

higher accuracy the quality of the current roulette table. When a roulette is 💷 truly

good, we will likewise reinforce on its quality by proving it does go outside of the

limits set on 💷 these tables.

Always using simulation tests on the computer, this is, in

a experimental non-theoretical way, I studied other secondary limits 💷 which assist to

complete the analysis of any statistics taken from a roulette. For instance, “how many

consecutive numbers, as 💷 they are ordered on the wheel, can be throwing positives?”, or

“How many positives can two consecutive numbers have as 💷 a maximum?”. I do not show

these tables because they are not essential and only confirm BIAS which should have

💷 been detected by the tables previously shown. Any way, we will see some practical

examples below.

So far the system was 💷 based on evidence that -although simulated- was

being empirical; these were made with the help of the computer in order 💷 to verify the

behavior of a random roulette.

I found the limits up to where luck alone could take it,

then 💷 I was able to effectuate a comparison with real-life statistics from machines

which were clearly showing result outside the limits 💷 of pure chance, this is, they

pointed to trends that would remain throughout its life if their materials would not

💷 suffer alterations. These physical abnormalities could be due to pockets of unequal

size, however small this inequality, lateral curvatures leaving 💷 sunken areas with the

counterweight of other raised areas. Or even a different screwing of the walls from the

pockets 💷 collecting the ball so that a harder wall means more bounce. With the

consequent loss of results that are increased 💷 in the neighboring pockets which collect

these bounced balls with a higher frequency than normal.

On theoretical grounds I

studied areas 💷 of mathematics unknown to me, in the probability branch, and worked a lot

with the concept of variance and standard 💷 deviations. They helped me, but I could not

apply them correctly given the complexity of roulette, that is more like 💷 a coin with 18

sides and 19 crosses bearing different combinatorial situations, which invalidate the

study with binomials and similar.

The 💷 major theoretical discovery was forwarded to me

by a nephew, who was finishing his career in physics. He referred me 💷 some problems on

the randomness of a six-sided die. To do this they were using a tool called the « 💷 chi

square », whose formula unraveled -with varying degrees of accuracy- the perfection or

defects from each drawn series. How 💷 come nobody had applied that to roulette?

I handled

myself with absolute certainty in the study of the machines, to which 💷 the fleet had

already pulled out great performance up to that date, thanks to our experimental

analysis, but theoretical confirmation 💷 of these analyzes would give me a comforting

sense of harmony (In such situations I’m always humming «I giorni

dell’arcoballeno»*.

We 💷 carefully adapt this formula to this 37-face die and it goes as

follows:

The chi square of a random roulette should 💷 shed a number close to 35.33. Only

5% of the time (soft limit) a number of 50.96 can be reached 💷 -by pure luck- and only

0.01% of the time it will be able to slightly exceed the hard limit of 💷 67.91.

We had to

compare these numbers with those from the long calculations to be made on the

statistics from the 💷 real wheel we were studying. How are these calculations made?

The

times the first number has showed minus all tested spins 💷 divided by 37, all squared,

and divided by the total of analyzed spins divided by 37.

Do not panic. Let’s suppose

💷 the first number we analyze is the 0, to follow in a clockwise direction with all other

roulette numbers. Let’s 💷 suppose on a thousand spins sample number 0 has come out 30

times:

(30-1000/37) squared and the result divided by (1000/37) 💷 = 0.327

The same should

be done with the following number, in this case in wheel order, proceeding with 32 and

💷 following with all roulette numbers. The total sum of results is the chi square of the

table. When compared with 💷 the three figures as set out above we will find if this

machine has a tendency, more or less marked, 💷 or it is a random table instead.

The

calculation, done by hand, frightens by its length but using a computer it 💷 takes less

than a flash.

Statistical analysis of numbers and wheel bias identification

strategy

While in my experimental tests I only watched 💷 leader numbers , this chi-square

test also has in mind those numbers that come out very little and also unbalance 💷 the

expected result.

There was a moment of magic when I found that the results of the

previous tables were perfectly 💷 in accordance with the results that the chi-square test

threw.

With all these weapons for proper analysis I did a program 💷 from which, finally,

we’ll see some illustrations:

TOTAL POSITIVE + 127 HIGHER + 24 L1 + 41 L2 + 70 L3 💷 + 94

L4 + 113

LB + 174 A + 353 B + 243 C + 195 NA 4 AG 60 💷 AD 46 N.° P 12 SPINS 10.000

CHI

37,18 50,96 67,91 35,33 DV-7,51 ROULETTE/DAY: RANDOM

*LB = Límite blando = Soft

limit.

In 💷 this chart I created throwing 10,000 spins to simulate a random table, we can

find all patterns of randomness; this 💷 will serve to compare with other real tables

we’ll see later.

In the bottom of the table, to the left at 💷 two columns, there are all

European roulette numbers placed on its actual disposition starting at 0 and continuing

in clockwise 💷 direction (0, 32 15, 19, 4, 21, 2, 25, etc.). We highlighted those which

have appeared more, not only based 💷 on their probability, which is one time out of 37,

but also based on the need to profit, i.e. more 💷 than once every 36.

If the average to

not lose with any number would be 1.000/36 = 27.77, our 0 has 💷 come out forty times;

therefore it is on 40, to which we subtract 27.77 = 12.22. Which are its positives, 💷 or

extra shows; therefore we would have gain. When 20 is – 4 4, 7 8 it is the number 💷 of

chips lost on the 10,000 spins thrown.

In the first row we find the total positive sum

of all the 💷 lucky numbers is +127 (the mean of a random table in our first table is

+126), away from the soft 💷 limit* (*Soft limit = Límite blando = LB), which is at the

beginning of the second row, and which for 💷 that amount of spins is +174. Next to it is

the reference of known biased tables, (All taken from the 💷 first table) which shows that

even the weakest (table C) with +195 is far from the poor performance which begins 💷 to

demonstrate that we are in front of a random table where drawn numbers have come out by

accident, so 💷 it will possibly be others tomorrow.

Returning to the first row we see

that our best number has +24 (it is 💷 2) but that the limit for a single number ( L l )

is +41, so it is quite normal 💷 that 2 has obtained that amount, which is not

significant. If we want to take more into account we are 💷 indicated with L2, L3 and L4

the limits of the two, three and four best numbers, as we saw in 💷 the second tables (our

two best would be 2 and 4 for a total of +42 when their limit should 💷 be +70). Nothing

at all for this part.

In the middle of the second row NA 4 it means that it 💷 is

difficult to have over four continuous single numbers bearing positives (we only have

two). AG 60 tells us that 💷 the sum of positives from continuous numbers is not likely to

pass sixty (in our case 0 and 32 make 💷 up only +21). AD 46 is a particular case of the

sum of the top two adjacent numbers (likewise 0 💷 and 32 do not reach half that limit).

After pointing out the amount of numbers with positives (there are 12) 💷 and the 10,000

spins studied we move to the next row which opens with the chi square of the table.

💷 this case 37,18 serves for comparison with the three fixed figures as follow: 50.96

(soft limit of chi), 67.91 (hard 💷 limit) and 35.33 which is a normal random table. It is

clear again that’s what we have.

Follows DV-751 which is 💷 the usual disadvantage with

these spins each number must accumulate (what the casino wins). Those circa this amount

(the case 💷 of 3) have come out as the probability of one in 37 dictates, but not the one

in 36 required 💷 to break even. We conclude with the name given to the table.

From this

roulette’s expected mediocrity now we move to 💷 analyze the best table that we will see

in these examples. As all of the following are real tables we 💷 played (in this case our

friends “the submarines” *) in the same casino and on the same dates. The best, 💷 table

Four:

(* Note: “Submarines” is the euphemism used by Pelayo to name the hidden players

from his team).

TOTAL POSITIVES + 💷 363 HIGHER + 73 L1 + 46 L2 + 78 L3 + 105 L4 + 126

+ 185 A + 💷 447 B + 299 C + 231 NA 4 AG 66 AD 52 N.° P 13 SPINS 13.093

CHI 129,46 50,96

💷 67,91 35,33 DV-9,83 ROULETTE/DAY: 4-11-7

What a difference! Here almost everything is

out of the limits: the positive (+363) away from 💷 the soft limit of 185. The table does

not reach A but goes well beyond the category of B. The 💷 formidable 129.46 chi, very far

from the fixed hard limit of 67.91 gives us absolute mathematical certainty of the very

💷 strong trends this machine experience. The magnificent 11 with +73 reaches a much

higher limit of a number (L1 46), 💷 11 and 17 break the L2, if we add 3 they break the

L3, along with 35 they break the 💷 L4 with a whopping +221 to fulminate the L4 (126). It

doesn’t beat the mark for contiguous numbers with positives 💷 (NA 4), because we only

have two, but AG 66 is pulverized by the best group: 35 and 3,along with 💷 that formed by

17 and 37, as well as the one by 36 and 11. The contiguous numbers that are 💷 marked as

AD 52 are again beaten by no less than the exact three same groups, showing themselves

as very 💷 safe. Finally it must be noted that the large negative groups ranging from 30

to 16 and 31 to 7 💷 appear to be the mounds that reject the ball, especially after seeing

them in the graph on the same arrangement 💷 as found in the wheel.

Playing all positive

numbers (perhaps without the 27) we get about 25 positive gain in one 💷 thousand played

spin (the table is between B and A, with 20 and 30 positives of expectation in each

case). 💷 It is practically impossible not winning playing these for a thousand spins,

which would take a week.

Another question is chip 💷 value, depending on the bank we have.

My advice: value each chip to a thousandth of the bank. If you 💷 have 30,000 euros, 30

euros for each unit. These based on the famous calculations of “Ruin theory” precisely

to avoid 💷 ruining during a rough patch.

Another interesting table for us, the

Seven:

TOTAL POSITIVES + 294 HIGHER + 83 L1 + 56 💷 L2 + 94 L3 + 126 L4 + 151

LB + 198 A +

713 B + 452 C + 325 💷 NA 4 AG 77 AD 62 N.° P 13 SPINS 21.602

CHI 77,48 50,96 67,91 35,33

DV-16,22 ROUILETTE/DAY: 7-9-3

This table seven, 💷 with many spins, is out of bounds in

positives and chi, but the quality is less than C. It has, 💷 however, a large area

ranging from 20 to 18 having almost +200 by itself, that breaks all NA, AG and 💷 AD,

which while being secondary measures have value here. No doubt there’s something,

especially when compared with the lousy zone 💷 it is faced with from 4 to 34 (I wouldn’t

save the 21). Here should be a “downhill area” which 💷 is detected in this almost

radiography. The slope seems to end at the magnificent 31. Also add the 26. Finally, 💷 a

typical roulette worth less than average but more than B and C which is out of bounds

with three 💷 well defined areas that give a great tranquility since even as it doesn’t

has excessive quality, with many balls it 💷 becomes very safe.

Table Eight:

TOTAL

POSITIVES + 466 HIGHER + 107 L1 + 59 L2 + 99 L3 + 134 L4 💷 + 161

LB + 200 A + 839 B + 526

C + 372 NA 4 AG 83 AD 73 N.° 💷 P 14 SPINS 25.645

CHI 155,71 50,96 67,91 35,33 DV-19,26

ROULETTE/DAY: 8-12-7

It is the first time that we publish these authentic 💷 soul

radiographies of roulette. My furthest desire is not to encourage anyone who,

misunderstanding this annex, plays happily the hot 💷 numbers on a roulette as seen out

while dining. That’s not significant and I certainly do not look forward to 💷 increase

the profits of the casinos with players who believe they are practicing a foolproof

system. It takes many spins 💷 to be sure of the advantage of some numbers. Do no play

before.

Be vigilant when you find a gem to 💷 detect they do not touch or modify it in

part or its entirety. If this happens (which is illegal but 💷 no one prevents it), your

have to re-study it as if it were a new one.

Regardless of how much advantage 💷 you have

(and these roulette tables are around 6% advantage, ie, more than double the 2.7%

theoretical advantage of the 💷 casino) it does not hurt that luck helps. I wish so to

you.

vocational rehabilitation success stories.