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Distance Between Sevens
By Mike In Hawaii
There are two primary ways to do Craps math. One is the way God would do the
Math (Capital M). To infinity and beyond! as a certain toy super hero likes to
yell.
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The other way is to "Monte Carlo" it. By that we mean make a "machine" on which
you can turn the "crank". In this case a Craps shooting machine. It simulates
throwing the dice a LOT of times! __and__ allows you to record the results.
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I wrote one in a language called C and can run it on a very powerful computer.
Boy does it shoot Craps! In about 1 second it can shoot the dice about 2/3rds
of a million times and record all sorts of interesting statistics. (It is not
really the computer that makes the difference, it is the simplicity of the code.
No animations, no graphics, no dice layout, nothing but bare minimum gears and
wheels.)
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As for the data, Seven appears (111288 / 667722) times or 16.6668% of the time.
Very reassuring since God Math would say exactly 1/6th of the time or 16.6667%
of the time you should find a Seven. Keep in mind that throwing Seven is the
most common thing that can happen in Craps.
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Originally I had it using just 10,000 rolls of the dice. Sounds like a lot, but
it is impressive how skewed the data on some of the less common outcomes such as
hardway 4's turned out. In fact even the number of Sevens could be off by quite
a bit in only 10,000 rolls. So I upped the ante until I found a number that
would give me some statistics I could believe in. That started to happen when I
got to about 500,000 rolls. (More on this later.)
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Where did I get my random numbers? Well random.org of course. Assembled in
batches at random times over a number of sessions over the period of a week.
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So how many hands in 667,722 rolls? 197,905. That would be the average length of
a Dice hand, if you define "hand" as the series of events that starts with a
come out roll, ends with immediate craps 2,3,12, or an immediate win with 7 or
11, or sets a number and continues rolling until you either make that number or
Seven Out.
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In other words the series of events from when you first pick up the dice until
there is something that results in a decision. The program says that number is
about 3.37 rolls of the dice on average.
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Some might object to my use of "hand" for this. Saying a "hand" is from when you
first get the dice until you have to pass the dice. I have not taught my little
engine to capture that exact data yet.
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So what does the little engine have to say about the distance between Sevens? __
Before you peek__ answer this question:
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**What is the most common, most likely, distance between Sevens in Craps?**
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ZERO! Yes indeed. The most common distance between Sevens in Craps is no
distance at all! The most common distance is consecutive Sevens.
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That one had me chasing my tail for a bit. I could not get my "Sevens" to add
up. Finally the light came on. I was recording all distances between Sevens from
1 to 127 and a whole bunch were missing! Duh.... When I fixed the program to
carefully record consecutive (gap of zero) Sevens, shazam, like Bo Peep, I found
all my missing sheep.
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In fact, there were 18,539 consecutive Sevens out of a total of 111288. That
is 16.66% of the time. WOW! Bingo! What is the chance of rolling a Seven on any
given throw? One in Six. or 16.667%. Choirs of Angels Sing. (As they say when
Math works). Hard core proof that given enough throws, dice in fact seem to have
no memory.
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So what about the other Monte Carlo distances:
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Gap # Percent Cumulative
0 18539 16.66% 16.66%
1 15632 14.05% 30.71%
2 12684 11.40% 42.10%
3 10706 9.62% 51.72%
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So four rolls of the dice cover just over half of the possible cases. Starting
from a Seven and then hitting Seven again on the very next, second, third or
fourth roll.
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What is scary for certain strategies is the rest of this chart. It fades away,
but because there is fully a 5/6th chance the next number will NOT be a Seven,
it does not fade away that fast.
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Here is the Rest of the data out to a gap of 25 between Sevens. This really
starts to enter the Twilight Zone:
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Gap # Percent Cumulative
4 9023 8.11% 59.83%
5 7380 6.63% 66.46%
6 6209 5.58% 72.04%
7 5286 4.75% 76.79%
8 4320 3.88% 80.67%
9 3574 3.21% 83.88%
10 2985 2.68% 86.57%
11 2426 2.18% 88.75%
12 1973 1.77% 90.52%
13 1765 1.59% 92.11%
14 1517 1.36% 93.47%
15 1274 1.14% 94.61%
16 1001 0.90% 95.51%
17 788 0.71% 96.22%
18 729 0.66% 96.88%
19 614 0.55% 97.43%
20 471 0.42% 97.85%
21 413 0.37% 98.22%
22 336 0.30% 98.52%
23 293 0.26% 98.79%
24 223 0.20% 98.99%
25 195 0.18% 99.16%
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There is one case of a gap of 72 rolls! The first zero appears at a gap of 54
rolls. Two cases of 55 rolls, one case of 57 rolls, two cases of 59 rolls, and
one case of 65 rolls between Sevens. Every gap of 50 or less has at least
several occurrences. From 29 to 40 every gap has 10 to 92 occurrences. From 28
down there are at least 115 occurrences.
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By Math (Capital M)
We have stated that we are going to start with a Seven so the chances of the
first number being a Seven are 100%.
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The chances the next number will be a Seven are the same as the chances you will
get a Seven any time you roll the dice (random roller of course). 1/6th or
16.67%
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So what about the other numbers? Well the chances of NOT getting a Seven are
5/6th. So the chances of skipping one number are:
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1 * 5/6 * 1/6 or .1389 or 13.89% = the chances of a distance of one gap
between two Sevens.
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For larger gaps you just multiply by more 5/6th's
1 * 1/6 16.67% 0 gap
1 * 5/6 * 1/6 13.89% 1 gap
1 * 5/6 * 5/6 * 1/6 11.57% 2 gaps
1 * 5/6 * 5/6 * 5/6 * 1/6 9.65% 3 gaps
Total so far: 51.77%
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Notice several things here. First these numbers do not exactly match those from
the Monte Carlo example above even though we used nearly 668,000 rolls. They are
close, life is good, they do agree, the Monte Carlo at about 2/3rds of a million
rolls is working OK. But it is not perfect! Expressed as percentages we are
still getting errors in the first decimal place even with these "very common"
occurrences.
Gap Percent Cumulative
0 16.67% 16.67%
1 13.89% 30.56%
2 11.57% 42.13%
3 9.65% 51.77%
4 8.04% 59.81%
5 6.70% 66.51%
6 5.58% 72.09%
7 4.65% 76.74%
8 3.88% 80.62%
9 3.23% 83.85%
10 2.69% 86.54%
11 2.24% 88.78%
12 1.87% 90.65%
13 1.56% 92.21%
14 1.30% 93.51%
15 1.08% 94.59%
16 0.90% 95.49%
17 0.75% 96.24%
18 0.63% 96.87%
19 0.52% 97.39%
20 0.43% 97.83%
21 0.36% 98.19%
22 0.30% 98.49%
23 0.25% 98.74%
24 0.21% 98.95%
25 0.17% 99.13%
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What is the house advantage when you bet the pass line and take double odds?
Expressed as a percentage it is down in the first decimal place. And the Monte
Carlo data represents about how many dice rolls a dedicated shooter throwing
once a minute, 24 hours a day, seven days a week for over 15 months might make.
Now that is a session. And still there is "noise" in the Monte Carlo data even
with this most common of events. And we are only looking at absolute errors. A
better idea of the "noise" would be to look at the relative error size, which in
this case is six times larger.
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**How Noisy is 10,000 Rolls?**
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Noise in this case is error. As pointed out before there are two ways to try to
analyze a betting system or the probability of something happening. You can try
to calculate it using Math. Or you can build a model of it that carries out the
steps of the game over and over again like a little mechanical version of the
game, turn the crank and keep track of what happens over time.
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With Craps there are 36 possible outcomes just on the roll of the dice. That is
a LOT.
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As questions get more complicated and you look at events that are relatively
rare, the number of turns of the crank required on such a simulation or Monte
Carlo starts to either become extremely large, or the data wobbles around so
much it becomes almost nonsense due to the overly small sample.
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More bad news! In order to double your confidence in your result, you do not
have to double the number of rolls, you have to increase them by 4 times. The
noise goes down with the square root of the number of observations. So if you
want to increase your confidence by a factor of 10, one decimal place, you need
to increase the number of rolls by 100. Yes, you have to go get 100 times more
random numbers.
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One might think that 10,000 rolls is a reasonable number. So let's have a look
at what happens when we Monte Carlo Craps with a bit over 10,000 random rolls,
66 times, using a different set of 10,000 random numbers each time.
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In terms of hands, that is defining a "hand" as pick up the dice, throw them,
either win immediately with Seven or 11, or crap immediately, or set a number
and then continue until you Seven Out or make your point. Start throwing dice,
reach, one of the above conclusions. On average the slightly over 10,000 random
rolls generate approximately 3,000 such hands. So that again sounds like a LOT.
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Let's just look at two things that can happen frequently, and make up fully one
third of all come out roll results on average. You either (A) win with 7 or 11,
or you (B) lose with craps 2, 3, or 12. The odds on the first result is a nice
clean 2/9ths. The odds on the second result is a nice clean 1/9th. As
percentages those are: 22.22% and 11.11% for a total of 33.334% of what happens
to all come out rolls. (The other 2/3rds of the time you set a point and have to
try to make it).
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Let's begin with a __very carefully selected__ example from the 66 trials. It
is enough to make Angels weep. There are 2,971 "hands" in this run which
generate 659 wins with either 7 or 11 on the come out roll. And generate 332
loses with 2, 3 or 12. That is an absolute error of only -0.04% on the
immediate win column (22.18% vs. 22.22%) and only 0.06% on the loss column
(11.17% vs. 11.11%). Even viewed as relative error it is just -0.19% on the win
column and 0.57% on the loss column.
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Now what is the big deal? What is wrong with that!! Well very little. But this
was a most carefully selected example from the 66. The majority of these runs
have rather nasty errors. The absolute errors on the win side go from -1.79%
(too low) to 1.57% (too high). Something that is supposed to be happening 22
percent of the time off by over a full percent much of the time. In relative
terms it is a shocking -8.06% to 7.09% off base.
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The loss side which is supposed to be happening 11% of the time has similar
absolute errors, ranging from -1.56% (too low) to 1.29% (too high). In relative
terms that is a horrifying -14.05% to 11.59%. Yes that is over 11% relative
error on one side and 14% relative error on the other side. With 10,000 rolls!
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So what does that mean for a real Craps session of (be generous) 250 rolls?
Well chances you will see percentages like the Math (Capital M) predict for even
the most common occurrences are not good. Any real Craps session is most likely
to be significantly off on its probability distributions. (With just 250 rolls,
confidence, or chance for drifting off the Math derived probabilities, would
actually be about six times worse).
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Think for a minute about a session in which people are losing to craps on the
come out roll 11% more often than they should be, or winning with 7 or 11 fully
8% less often than they should be, or conversely losing to craps 14 % less often
than they should or hitting 7 or 11 fully 7% more often than they should.
Nothing to do with dice control, or how they throw the dice, just the
variability in the math (lower case real world math) itself with a sample size
of just 10,000 rolls.
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Even at 10,000 rolls of the dice, the numbers are not settling. Even at 2/3rds
of a million rolls the less common events are still drifting off the absolute
values calculated by Math, such things as the number of 12's, the number of
hardways, and even the number of times someone will set a four and then make
their point.
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Whenever you extract a real life subset of dice rolls at a Craps table, you will
very likely get something significantly different than the razor edged, 10,000th
of an inch, ultra balanced, spun and serial numbered precision of infinite Math
probabilities.
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Next time someone says "I tested my system once on WinCraps with 1000 rolls and
it works!" Please raise at least ONE eyebrow...
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Copyright © 2006 Mike in Hawaii
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