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Reason #3 why the Wizard likes Bodog:
Excellent Odds
In my opinion many online casinos are too stingy when setting the odds on their games. They think they will make more money that way but I believe they are misguided, because when players lose too quickly it's not fun, and those players might not come back.
Bodog is one of the few casinos that understands this. They offer generous odds to let you play longer and get you a better chance of winning. Among their generous offerings are Full-Pay Jacks or Better returning 99.54%, six other video poker games paying over 99%, single-zero roulette, two blackjack variants with a house edge under 0.2%, and my favorite, Pick 'em Poker, returning 99.95%!
Kudos to Bodog for not being afraid to give their players a good gamble. (Visit Bodog)
Try blackjack at Bodog. One click and you're in:
 No popups, no download, no registration, no B.S., just the game.
See important note about Bodog payouts & deposits.
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Ask the Wizard: |
Probability and gambling math |
Select two random numbers between 0 and 1 (evenly distributed).
Now select the smaller of the two. What is the average of the selection? What about the general case of n numbers?
— Hagay
For two numbers, the answer is 1/3, and for n numbers it is 1/(n+1). I posted the solutions to my page of math problems, questions 194 and 195. March 4, 2008
In a race, if the competitors’ race numbers are allocated randomly, and
have no effect on race performance, what are the chances that at least one person will finish the race in a position that matches his race number? For example, the winner has number one on his chest or the person who finishes three hundred and fifth happens to sport number 305. – Stewart from Glasgow
Assuming that no numbers are skipped, the probability depends very little on the number of participants, as long as that number is fairly large. The greater the number of participants, the more the probability of at least one match will approach 1-(1/e) = 63.21%. November 4, 2007
If I make 1,000,000 spins on the event that has 1 in 1,000,000 chance of winning, what are my chances of winning at least once? – Ares75 from Petrovce
If the probability of winning is 1/n, and you play n times, as n approaches infinity, the probability of winning at least once approaches 1-(1/e), where e = 2.7182818..., or about 63.21%. The exact answer can be expressed as 1-(999,999/1,000,000) 1,000,000 = 0.63212074. My estimate is 1-(1/e) = 0.63212056, which agrees to six decimal places. September 1, 2007
On an airplane with 180 seats, what are the odds of me sitting next
to the good looking girl I see who will be on the same flight? – Ted H. from Salt Lake City
It depends on the number of seats in a cluster. Most domestic flights have three seats on either side of the aisle. That would make 60 3-seat clusters. After the first one of you is seated, there will be two seats in the same cluster out of the remaining 179, so the chances of being in the same cluster are 2/179 = 1.12%. Then you can’t have somebody else in the middle seat. The chances of the third person being in the middle seat are 1/3. So the answer is (2/179)*(2/3) = 0.74%, or 1 in 134.25. July 25, 2007
Five persons are in a room. What is the probability that at least 2 of them were born in the same birth month? – Amy
To keep things simple let’s assume that each person has a 1/12 probability of being born in each month. The probability that all five people are born in different months is (11/12)*(10/12)*(9/12)*(8/12) = 0.381944. So the probability of a common month is 1 - 0.381944 = 0.618056.
November 23, 2006
If there are three people, then what is the probability that at least two persons have the birthday on the same date? – Manish from New Delhi
Ignoring leap day, the probability of all three different birthdays is (364/365)*(363/365) = 0.99179583. So the probability of at least one common birthday is 1 - 0.99179583 = 0.00820417.
October 4, 2006
What is the chance of getting a sum over 100, when rolling 20 dice? Kind regards, Terje from Stockholm
I started to use the Normal approximation to solve this, but the probability of over 100 points is too low for that method to be accurate. So I did a random simulation of 8.25 million trials and the number of trails that were 101 points or more was 127. So the probability is about 1 in 65,000.
August 9, 2006
I have a free bet coupon from the Mohegan Sun, which can be used on baccarat, sic bo, or big six. If I win I get to keep the winnings but win or lose I must give up the coupon. What is the best bet to use it on in each of these games? – Mike H. from New Jersey
Generally speaking, you want to put it on a long shot. This is because you don’t get to keep the coupon on a win, which lowers the value by the probability of winning. The less the probability of winning the less the value is reduced. Following are three tables for the three games listed. You'll see the best bet is a tie between the 12, 30, 60, triple, and any triple in sic bo.
Baccarat
| Free Bet Coupon Value in Baccarat |
| Bet |
Pays |
Probability |
Return |
| Banker wins | 0.95 | 0.458597 | 0.481484 |
| Player wins | 1 | 0.446247 | 0.493175 |
| Tie | 8 | 0.095156 | 0.761248 |
Big Six
| Free Bet Coupon Value in Big Six |
| Bet |
Pays |
Probability |
Return |
| 1 | 1 | 0.444444 | 0.444444 |
| 2 | 2 | 0.277778 | 0.555556 |
| 5 | 5 | 0.12963 | 0.648148 |
| 10 | 10 | 0.074074 | 0.740741 |
| 20 | 20 | 0.037037 | 0.740741 |
| Joker | 40 | 0.018519 | 0.740741 |
| Logo | 40 | 0.018519 | 0.740741 |
Sic Bo
| Free Bet Coupon Value in Sic Bo |
| Bet |
Pays |
Probability |
Return |
| Small, Big | 1 | 0.486111 | 0.486111 |
| 4, 17 | 60 | 0.013889 | 0.833333 |
| 5, 16 | 30 | 0.027778 | 0.833333 |
| 6, 15 | 17 | 0.046296 | 0.787037 |
| 7, 14 | 12 | 0.069444 | 0.833333 |
| 8, 13 | 8 | 0.097222 | 0.777778 |
| 9, 12 | 6 | 0.115741 | 0.694444 |
| 10, 11 | 6 | 0.125 | 0.75 |
| Triple | 180 | 0.00463 | 0.833333 |
| Any triple | 30 | 0.027778 | 0.833333 |
| Double | 10 | 0.074074 | 0.740741 |
July 22, 2006
Just thought you might find this interesting. At bodog they offer the following bet, “Will Britney Spears and Kevin Federline's second baby be a Boy or Girl?” The odds on a boy are +105, and for a girl –145. Last I checked this has been 1:1 since the beginning of mankind. I’d like to know who is taking the -145 side of this one.
Love the site, visit often, and click your sponsors in appreciation.
Thanks for the kind words. To be honest nobody cares much about click-through rates any longer. So don't feel obligated to click through the banners if it is just for show. To answer your question, in the United States the probabilities are very close to 50.5% boy and 49.5% girl. Assuming no other information is known by the betting community the player advantage on the boy bet would be .505*1.05 - .495 = 3.53%. It could be that somebody with inside knowledge is betting on a girl. Another theory is that some people incorrectly believe you can tell the gender by the shape of the mother’s belly, and these people are betting on a girl. Personally I’m going to leave this one alone.
June 23, 2006
There is a drawing for a $27,000 car, with tickets being sold at six for $500.00, or one for $100.00. 68 tickets have been sold, and tomorrow is the deadline for purchase. I know that for a 50% probability of winning, I must spend $5666.44, and for a 66.66% of winning, I must spend $11,332.88 (Right?). How much should I spend (or how many tickets must I buy) to virtually ensure that I "win" the car? (90%? 95%?) Is this raffle worth playing, or must I spend the cost of the car? – Annette from Boise
You are right regarding the 1/2 and 2/3 probabilities. If you buy t tickets your probability of winning is t/(68+t). So for a 90% probability, solve for t as follows.
0.9 = t/(68+t)
0.9*(68+t) = t
61.2 = 0.1t
t = 612, or $51,000
For 95%…
0.95= t/(68+t)
0.95(68+t) = t
64.6 = 0.05t
t = 1292, or $107,666.67
Assuming the car is worth $27,000 to you then you should quit buying tickets as soon as the next ticket sold does not increase your probability of winning enough to warrant the price.
For a ticket to be worth the price it should increase your probability of winning by p, where...
27000*p=(500/6)
p=0.003086
Let’s say t is the number of tickets your purchased where you are indifferent to buying one more ticket.
[(t+1)/(t+68+1)] – [t/(t+68)] = 0.003086
[(t+1)/(t+69)] – [t/(t+68)] = 0.003086
[((t+1)*(t+68))/((t+69)*(t+68))] – [(t*(t+69))/((t+68)*(t+69))] = 0.003086
[((t2 +69t+68)/((t+69)*(t+68))] – [(t2+69t)/((t+68)*(t+69))] = 0.003086
68/((t+68)*(t+69)) = 0.003086
((t+68)*(t+69)) = 220.32
t2+137t+4692 = 22032
t2 +137t - 17340=0
t=(-137+/-(1372-4*1*-17340)2)/2
t = 79.9326
Let’s test this by plugging in some values for tickets purchased, assuming the player can always buy tickets at $500/6 = $83.33 each.
At 79 tickets your cost is 79*(500/6) = $6,583.33, your probability of winning is 79/(79+68) = 53.74%, your expected return is $27,000*0.5374 = $14,510.20, and your expected profit is $14,510.20 - $6,583.33 = $7,926.87.
At 80 tickets your cost is 80*(500/6) = $6,666.67, your probability of winning is 80/(80+68) = 54.04%, your expected return is $27,000*0.5405 = $14,594.59, and your expected profit is $14,594.59 - $6,666.67 = $7,927.92
At 81 tickets your cost is 81*(500/6) = $6,750.00, your probability of winning is 81/(81+68) = 54.36%, your expected return is $27,000*0.5436 = $14,677.85, and your expected profit is $14,594.59 - $6,750.00 = $7,927.85.
So we can see that the maximum expected win peaks at 80 tickets.
June 23, 2006
There are 75 multiple choice questions in an exam. Each question contains 4 possible answers only 1 is correct. The exam pass mark is 50%. What are the chances of passing the exam by guessing each answer? -- Wendy from London
Hi, fantastic site, keep up the good work :) I'm trying to work out risk of ruin statistics for any given combination of number of betting units and number of hands to play. Currently if the pair of values don't appear in your RoR [Risk of Ruin] table in the blackjack appendix then I have no accurate percentage chance of ruin. Would you publish the formula for us please? Many thanks. - Hector from Cardiff, UK
Thanks. I explain how I calculate the risk of ruin in video poker in my video poker appendix 1. However for games where the bet amount is not always the same the calculations get very messy and computer simulations are necessary.
Jan. 3, 2006
My understanding of "wait time" for an event is the reciprocal of the probability of that event. I'm interested in calculating the wait time to roll consecutive 2's using one die. In a Monte Carlo simulation I get 42 rolls on average. How do I make the connection with the probability of rolling consecutive 2's? - Lee from Andover
It is true that for single events if the probability is p then the average wait time is 1/p. However it gets more complicated with consecutive events. Let x be the state that the last roll was not a two. This is also the state at the beginning. Let y be the state that the last roll was a two. After the first roll there is a 5/6 chance we will still be in state x, and 1/6 chance we will be in state y. Let Ex(x) be the expected number of rolls from state x, and Ex(y) the expected number from state y. Then...
Ex(x) = 1 + (5/6)*ex(x) + (1/6)*ex(y), and
Ex(y) = 1 + (5/6)*ex(x)
Solving for these two equations...
Ex(x) = 1 + (5/6)*ex(x) + (1/6)*( 1 + (5/6)*Ex(x))
Ex(x) = 7/6 + (35/36)*Ex(x)
(1/36)*Ex(x) = 7/6
Ex(x) = 36*(7/6) = 42
So the average wait time for two consecutive twos is 42 rolls.
I have the same type of problem, only the expected flips to get two heads, in my site of math problems, see problem 128.
Dec. 13, 2005
Suppose a hotel has 10,000,000 rooms and electronic 10,000,000 keys. Due to a computer mistake each key is programmed with a random code, having a 1 in 10,000,000 chance of being correct. The hotel is sold out. What is the probability at least one customer has a working key? - Danny from London, U.K.
The exact answer 1-(9,999,999/10,000,000) 10,000,000 = 0.632121. This is also the same as (e-1)/e to seven decimal places.
Dec. 13, 2005
What are the odds a deck could be shuffled back into starting order, with either a totally random shuffling method or with a perfect rifiling shuffle, and how many times would it take? - Andrew from Pewaukee,WI
The probability of a random shuffle resulting in starting order is 1 in 52!, or 1 in 8.06582*1067. If you did a perfect shuffle, in which last card was the first to come down, thus remaining last, it would only take 8 shuffles to be back to the starting order. If the 26th card was the first two come down then it would take 72 shuffles to back to the starting order.
Nov. 22, 2005
Two people are playing rock paper scissors. It is presumed the game doesn't involve strategy. If you are playing 'best of 3', and player A wins the first round, what are the odds that player B will win the game? - Andrew from Pewaukee,WI
Player B would need to win the next two (not counting ties) so the probability is (1/2)*(1/2) = 1/4.
Nov. 22, 2005
The weekly salaries of teachers in one state are normally distributed with
a mean of $490 and a standard deviation of $45. What is the probability
that a randomly selected teacher earns more than $525 a week? I can't remember how to calculate a probability from just the mean and SD without the population. - Sue from Queen Creek
That would be $35 above average, or 7/9 standard deviations. The probability of being more than 7/9 standard deviations above expectations would be 1-Z(7/9) = 1- 0.78165 = 0.21835.
Nov. 2, 2005
I remember that if 22 people are in a room the odds are even that 2 will celebrate the same birthday [month and day, not year]. I have forgotton how to do the math to prove this. Could you please provide it. Thanks,
Dean from Bainbridge Island, WA
I think I have answered this before but the 50/50 point is closer to 23. To make things simple let's ignore leap years. The long answer is to order the 23 people somehow. The probability that person #2 has a different birthday from person #1 is 364/365. The probability person #3 has a different birthday from persons #1 and #2, assuming they are different from each other, is 363/365. Keep repeating until person 23. The probability is thus (364/365)*(363/365)*...*(343/365) = 49.2703%. So the probability of no match is 49.27% and of at least one match is 50.73%. Another solution is the number of permutations of 23 different birthdays divided by the total number of ways to pick 23 random numbers from 1 to 365, which is permut(365,23)/36523 =
42,200,819,302,092,400,000,000,000,000,000,000,000,000,000,000,000,000,000,000 / 85,651,679,353,150,300,000,000,000,000,000,000,000,000,000,000,000,000,000,000 = 49.27%.
Nov. 2, 2005
Which do I have better odds of winning:
A. one shot at 1 in 4
B. five shots at 1 in 20
- Mike from Lansing
The probability of A is obviously 25%. The probability of getting zero shots out of five is 0.955=77.378%. So the probability of getting at least one out of five is 100%-77.378%=22.622%. So A has the higher probability.
Oct. 18, 2005
Would you tell me the probability of a 19% chance
coming in exactly 18 out of 34 trials?
That would be combin(34,18)*.19^18*(1-.19)^(34-18)
= 0.000007880052468.
Sept. 4, 2005
Just for simplicity, let's say there are 322 cups on a
table and one has a ball under it. What are the chances that
I will pick the ball if i pick a cup 75 times (and the cups
don't go away after I pick it up, it's always a random pick
with 322 cups). At first I just thought to say 75/322, but I
realized that was incorrect, as 322 picks does not result in
a 100% chance of getting the ball because I could pick a
million times and not get the ball. - John from
Miami
Your answer would be correct if you removed cups
after an incorrect pick. Since you leave the cups on the
table each pick as a 1/322 chance of being right, or
311/322 of being wrong. The probability of 75 picks being
wrong is (311/322)75 = 78.6022%. So the
probability of getting at least one correct in 75 picks
is 100% - 78.6022% = 21.3978%.
Aug. 21, 2005
Imagine a island that is inhabited by 10 people, and
the politics is such that each day an islander is chosen at
random to be chief for exactly one day; after the day has
elapsed another islander is chosen at random (so the same
islander who was just chief has a 1/10 chance of being chief
again). The question to be solved: on average, how many days
would have to elapse before each islander would have been
chief at least once?
It will only take 1 day so that 1 person has
served as chief. For the second day the probability of a
new chief is 0.9. The expected number of days it will
take to get a new chief, if the probability each day is
0.9 is 1/0.9 = 1.11. This is true for any probability:
the expected number of trials until a success is 1/p. So
after 2 people have served the probability of a new chief
on the next day is 0.8. So the waiting period for a 3rd
chief is 1/0.8 = 1.25 days. The answer is the sum of the
waiting periods, which is 1/1 + 1/.9 + 1/.8 + ... + 1/.1
= 29.28968 days. March 10,
2005
How many eggs do you start with if each day you sell
1/2 the eggs plus 1/2 an egg; after 3 days you have zero
eggs? At the end of each day, the number of eggs is a whole
number.
Let's let d (for day) be the number of eggs at
the beginning of the day and n (for night) be the number
at the end. The problem tells us that d/2 - ½ = n. So,
let's solve d in terms of n.
d/2 = n + ½
d= 2n + 1
So on the third day n=0, so d=1.
On the second day n=1, so d=3.
On the third day n=3, so d=7.
So there you have it, you started with 7 eggs.
Jan. 16, 2005
If you have 30 people, all born in the same 365-day
calendar year, what is the probability that any two of them
will have the same birthday? Please explain the formula in
your response. Thank you very much, Scott in Madison,
Indiana
Think of the 30 people as lined up. The
probability the second person doesn't match the first
person is 364/365. Then, assuming they didn't match, the
probability the next person does not match either of the
first two is 363/365. Then keep going one person at a
time. The overall probability no two people match is
(364/365)*(363/365)*...*(346/365) = 29.3684%. It is often
asked what is the fewest people you need for the
probability of a match to be at least 50%. The answer is
that with 23 people the probability of at least one match
is 50.7297%. Jan. 16,
2005
I saw in the paper last week that the latest
earthquake that ravaged Indonesia hit on December 26th. It
also showed that of the eight deadliest earthquakes to hit
over the last 100 years, three of them have been on December
26th. I was wondering what the odds are of having three
massive quakes hit on the same day knowing these facts:
Earthquakes of this magnitude (8.0 or larger) happen only
once per year. The last big quake was exactly one year ago,
12/26/03 in Iran (back to back probabilities?) I look
forward to hearing from you. Steve A., Fort Collins,
Colorado
After discovering the claim that the Florida
hurricanes only hit Bush voting counties was a hoax (see
the October 17, 2004
column) I am going to be more skeptical about such
alleged coincidences. According to the
National Earthquake Information Center of the top 11
earthquakes since 1990 only the recent one of 2004 hit on
a December 26. The Iranian
earthquake you mention was only 6.7 in magnitude,
which is far from making the top eight.
Jan. 9, 2005
With five different toppings to choose from, how many
different pizzas can you make, with any number of
toppings?
There is 1 way with 0 toppings, 5 ways with 1
topping, 10 ways with 2 toppings, 10 ways with 3
toppings, 5 ways with 4 toppings, and 1 way with 5
toppings. So the answer is 1+5+10+10+5+1 = 32. Another
way to solve is either topping can be used or not. So the
total is 25 = 32.
Dec. 27, 2004
If a university's football team has a 10% chance of
winning game 1 and a 30% chance of winning game 2, and a 65%
chance of losing both games, what are their chances of
winning exactly once?
If we assumed the games were independent then
the probability of losing both would be 90%*70%=63%. But
since you say the probability of losing both is actually
65% (which is more than the 63%), that means the two
events are correlated. If the probability of losing both
is 65% and just losing game 2 is 70%, then the
probability of winning game 1 and losing game 2 must be
5%. Using the same logic the probability of losing game 1
and winning game 2 must be 25%. That only leaves 5% for
winning both games. So the probability of winning exactly
once is 25%+5% = 30%. Sept. 23,
2004
I am about to take a professional licensure
examination. The regulations provide that:
- The examination shall consist of 7
subjects.
- For each subject, 60 multiple choice questions
shall be asked.
- Each multiple-choice question shall have four
possible answers, but only one correct answer.
- In order to pass, an examinee must obtain a
general average of at least 75% and must not have a grade
lower than 65% in any subject.
My question is, if an examinee merely guesses all his
answers, what is his chance of passing the exam? Stated
differently, what is the probability of passing the exam by
sheer luck?
To satisfy the 75% requirement the student must
get at least 315 out of the 420 questions right. The
expected number of correct answers from guessing is
420*0.25=105. The standard deviation is
(420*0.25*0.75)^0.5 = 8.87412. So the candidate must
exceed expectations by 210 questions, or
210/8.87412=23.66432 standard deviations. The probability
of doing this is way off the charts. If every living
thing on earth took this test, answering randomly, I
doubt anyone or anything would pass. I won't even get
into the other requirement. Aug.
23, 2004
In a St.
Louis Post-Dispatch article, the reporter says,
"A 500-year flood is one that has a 1-in-500 chance of
happening in any given year. Stated another way, that would
be a 1-in-10 chance of happening over 50 years, or a 1-in-5
chance of happening over a century." After reading through
all your gambling pages, I believe this is not a correct way
to put it, right? Extrapolating their assertion, it would
mean that there is a 1-in-1 chance that a flood will occur
every 500 years, and that can't possibly be right.
You are right, that article is incorrect. The
probability of a 500-year flood in a period of x years is
1-e-x/500. So the probability of at least one
500-year flood in 50 years is 9.52% and in 100 years is
18.13%. Aug. 7, 2003
Could you please help me understand why it's a
"fallacy" that a win becomes more probable after a series of
losses? The way I see it, since the expected outcome is an
approximately 99.5% return, then if after 1000 hands, you're
at 78%, then, by definition, it would necessitate that the
next hand being a win must be more likely to occur. People
say that cards "don't have a memory", but isn't the natural
curve, in essence, its memory? Please help me understand
this point! Thanks a lot. - Steve From Canada
The bell curve is a forward looking estimate of
the sum of many random variables. You can not mix
together past and future events. Once an event has
happened it is no longer a random variable but a cold
hard outcome. If you played 1000 hands of blackjack with
a return of 78% then you fell on the tail end of the bell
curve during that play. Starting from hand 1001 your
results could fall anywhere on a new bell curve. I hope
this helps, but it really takes a course in statistics to
truly understand. Mar. 6,
2002
My Grandmother was born on October 28, 1912, she
recently passed away on October 28, 2001 (her 89th
birthday). My cousin asked me what the statistical odds were
of this occurring. I know there is about a 1 in 365 chance
of dying on any day throughout the year. But what are the
odds of that day being a person's birthday? - Loren Thompson
from Petersburg, Alaska
You should have asked me this while I was still
an actuary at the Social Security Administration. I could
have easily have done a nationwide query on death
records. I would say the answer is close to 1 in 365. It
probably is a little less because infant mortality rates
are disproportionately high after birth. For births in
the year 2000 the probability of death within the first
year is 0.71% of male infants and 0.59% for female
infants. In other words those infant deaths are unlikely
to occur on the birthday because once the first birthday
arrives the child is outside of the danger period. Also,
and I don't know this to be a fact, but on the show 'Six
Feet Under' they said that the business for funeral
directors picks up in January, evidently because people
try to hold on for just one more Christmas holiday and
then let go. This same logic might apply to reaching a
birthday. Consider George Burns, he died 48 days after
his 100th birthday. Nov. 11,
2001
If multiple choice questions have possible answers of
a,b,c,d,and e: what is the probability that among 100
guesses, there will be at least 25 correct responses? -
Daniel Gould from Portales, USA
The probability of getting exactly x right in
your example is
combin(100,x)*(1/5)x*(4/5)(100-x).
To get the exact answer you have to calculate this for
all values of x from 0 to 24, add them up, and take the
difference from 1. The answer is 13.14%.
April 15, 2001
I'd really like to know how to read odds like 12 to 1,
or 3 to 2. Which one shows the best chances of winning? 12
to 1 or 3 to 2? - Louis-Philippe from Montreal, Canada
I don't like using probabilities in that form
but they are generally used in this kind of syntax, "The
odds against drawing a royal flush are 649739 to 1." That
means there are 649739 ways you can't draw a royal flush
and 1 way you can. In your examples 12 to 1 is a
probability of 1/13, or 7.69%, and 3 to 2 is 2/5, or
40.00%, so the 3 to 2 is the better chance of winning.
April 15, 2001
I have been mulling over some money management
techniques, and wanted to thank you for some sound advice
(more to the point, probability theory) and it seems that
win probabilities are based roughly on your starting
bankroll. For example, estimating the probability of winning
$100 on a $200 buy-in without going broke first. This is
very helpful, but without doing the math (shame on me, I
know) I get the feeling win limits should be based more on
your betting unit, i.e. $1, $5, $10, etc. Basically the idea
you will have smaller fluctuations over time using a smaller
bet than a larger one. I guess my actual question is this:
if I have a given bankroll (say $100) and a stated win limit
(say $50), what (if any) betting unit would give the
greatest possibility of success? I am thinking too small of
a bet would give lower my chances of getting much above the
mean and too large would run the risk of bankruptcy. Any
advice or comments? - Scott Miller from Saline, Michigan
In your example, assuming a negative expectation
game, the best bet size to maximize the probability of
reaching your win goal is $50. In a positive expectation
game the best bet size is as small as possible. The
reason is that the more you play the more the house edge
will grind you down, or the more you will grind the
casino down if you have the edge.
March 11, 2001
This is a question regarding fixed odds betting. If
you say that the odds are 4 to 1 on something happening is
that equivalent to saying the probability is 1 in 4 i.e.
0.25? If you consistently bet on 4 to 1 shots would you
simply break even over time? Therefore could you not beat
fixed odds betting by doubling up after every loss since you
would expect a winner every fourth bet? - Calvin Broaddus
from Long Beach, USA
If the odds against something are 4 to 1 then
there are 4 chances it wonít happen and one chance
that it will. So in this example the probability would be
1/5. It doesnít matter what the probability is, if
the events are independent then the past does not matter.
Jan. 20, 2001
Can you please explain what the term "Law of
mathematical averages" mean? Thank you, and keep up the good
work. ñ Dennis from Canada
I think what you are referring to is actually
called the "Law of Large Numbers." This states that for a
random sample of n random variables with mean x, that the
sample mean xn converges to x as the size of
the sample approaches infinity. We can think of the
outcome of a bet as a random variable. This law tells us
that as the number of bets becomes very large the average
result will get closer to the house edge.
Dec. 2, 2000
Great site Mike! Often times I hear the word,
"binomial distribution" being used in gambling. Can you
explain to me what it means? Thanks in advance. -- Dennis
from Toronto, Ontario
Thanks for the compliment. Any introductory
probability and statistics book should give good
treatment to the binomial distribution. Briefly the
binomial distribution is the probability that any given
number of events will happen given a specific probability
for each event and a specific number of trials.
Specifically if the probability of each success is p, the
number of success is s, and the number of trials is n
then the probability of s successes is ps *
(1-p)n-s * combin(n,s). The combin function is
explained in my section on probabilities
in poker. For example suppose you want to know the
probability that in 100 spins of a roulette wheel the
number of reds will be exactly 60. According to the
binomial distribution the probability is
(18/38)60 * (20/38)40 *
combin(100,60) = 0.003291. Nov.
11, 2000
Can it actually be true that what I experience has a
statistical base? It seems to me that it takes a lot longer
to win X number of chips that to lose the same amount (I
only play blackjack). For example, if I start with 300
chips, it might take hours to double my money (my goal), yet
I can lost that number in what seems like almost no time at
all. Can this really be true? Also, do you have a rule of
thumb about when to leave the table when you are winning?
ñ Chris in Gaithersburg, Maryland
What you have experienced is likely the result
of some very bad losing streaks. It may also be the
result of progressive betting or mistakes in strategy.
The basic strategy flat bettor should have a roughly
symetrical expectation in terms of steep ups and downs,
slightly favoring steep downs due to the house edge and
48% chance of a losing hand compared to 43% chance of
winning. Personally I don't set win limits on myself but
do set loss limits. Nov. 4,
2000
I disagree with a statement you made regarding random
number generation in computers. While it is true that a
sequence will appear and repeat after time, it is not true
that this is unavoidable. The trick is setting a correct
seed. If you are using a UNIX-based architecture, one method
is to set the seed to the seconds passed since 1/1/70, which
is a constantly updated variable inside the system. Since
you are using Visual C++ and J++, they should reset
themselves to some random seed each time they are run, but
it would be wise to set the seed yourself during the
program. I think you would be wise to set the random seed
each time a new deck is 'dealt' to the current time on the
machine or something similar. In this way, yes, you will be
using the same loop of numbers, but at least you will be
picking moderately 'random' points along the way, so as to
not make it a total loop. ñ Joe B. from Pittsburgh,
Pennsylvania
When using Visual C++ the seed is evidently
always the same. If I give the program the same input the
output will always be the same after a random simulation.
It is my understanding that this is what Microsoft
intended, so that experiments could be replicated
exactly. Visual J++ is evidently different based on my
games, otherwise the same hands would occur in the same
order every time. I would be interested to know how to
assign the seconds since 1/1/70 as the seed. Is this a
function and if so what is the name of it?
Sept. 26, 2000
Postscript: Since this writing I have a
slower but much better way of calling random numbers.
How can I convert your probabilities into the x to y
format? - Ralph Harpster from Turlock, USA
Saying the odds of something happening are x to
y means that the event in question will happen x times
for every y times it doesn't happen. To make the
conversion let p be the probability of some event. The
odds could also be expressed as (1/p)-1 to 1. Lets look
at an example. The probability of drawing a full house in
five-card stud is 0.00144058. This could also be
represented as 693.165 to 1.
Sept. 10, 2000
Q: I have read a couple of articles about 'Parrando's
Paradox'. Is there a way that you could explain what is
going on as it is extremely counter-intuitive that two
losing games played in a certain sequence could add up to a
winner - I thought I understood the mathematics of gambling
/ probability! I can see that there is a subtle link between
games A & B as game B is dependent on capital that is
affected by game A; I am unable to carry the logic any
further. Does Parrando's Paradox have any implications for
negative expectation casino gamblers? I doubt it but would
love to hear it from someone with a greater understanding. -
Gavin Parnell from Burt St Edmunds, England
A: Parrando's Paradox states that two suboptimal
games of chance can show a long term gain if played
alternately. However the games can not be independent of
each other, which eliminates any comparison to casino
games. To learn more there is a New York Times article
here:
http://oll.temple.edu/economics/Econ_92/Parrando/012500sci-statistics-paradox.html.
July 30, 2000
Q: How can I determine the odds of flat betting ( no
counting, no progressions , etc ) of being ahead in a
negative game such as blackjack w/o counting with a .5%
disadvantage after 45000 or so hands? Is it even possible? -
Kevin
A: This is a typical question one might
encounter in an introductory statistics class. Because
the sum of a large number of random variables always will
approach a bell curve we can use the central limit
theorem to get at the answer. From my section on the
house edge we find
the standard deviation in blackjack to be 1.17. You won't
understand this if you haven't studied statistics but the
probability of being at a loss in your example will be
the Z statistic of 45000*0.005/(450001/2*1.17)
=~ 0.91. Any basic statistics book should have a standard
normal table which will give the Z statistic of 0.8186.
So the probability of being ahead in your example is
about 18%. June 18,
2000
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