Decision Making and Chance

SADIK: We're very pleased to have Dr. Michael Orkin here to
give us a talk today.
Dr. Michael Orkin has a very distinguished career.
He's currently Managing Scientist at the Exponent, a
publicly traded center scientific consulting company
here in Menlo Park.
He's a well-known personality.
He's been nationally quoted, CNN, NBC's Dateline and ABC
World News.

He's written several books, including What are the Odds?:
A Chance In Everyday Life.
He's consulted for clients in both government and private
sector, including the FBI, and Las Vegas odds makers.
His software has also been featured in the January 2002
issue of Wired magazine.
So here he is today.
Let's give a Google welcome to Mike.
Could I please ask that because this talk is being
video recorded, any confidential Google questions
should be left till after the cameras have
been switched off.
Thank you.
[APPLAUSE]
DR. MICHAEL ORKIN: Thanks, Sadik.
So today-- well, the title of my talk-- and I'm happy to be
here-- is Chance, Data Mining, and Sports Betting, and we'll
see what those things have in common.
I am currently located at Exponent.
If you haven't heard of it, it's sometimes called Exponent
Failure Analysis.
It's located right on 101 North in Menlo Park.
It's a publicly traded company that has about 800 employees,
in 18 offices and three international offices.
Consultants at Exponent, a majority of whom are
engineers, have expertise in science, math, and
engineering.
And Exponent has been consulted on various problems
and disasters, including some of the major ones you read
about in the paper, such as the grounding of the Exxon
Valdez, the walkway collapse of the Kansas City Hyatt, the
bombing of the Alfred P. Murrah Federal building in
Oklahoma City.
By the way, in this talk, just stop me at any time if you
have any questions, and we'll have more of a
conversational tone.
So I'm in what's called the data and risk analysis group.
I'm trained as a statistician, and do stuff in probability
theory, decision theory, game theory.
And our group develops strategies for decision-making
under uncertainty, and in a variety of contexts.
One of the things our group specializes in is determining
whether a particular activity or product poses an
unreasonable risk, and if so, what to do about it.
Many of our projects involve litigation involving such
things as consumer product issues and recalls.
Automotive lawsuits, and doing risk analyses and failure
analyses to try to develop strategies for companies who
have products.

I can't really to go into too much detail about things that
are in litigation right now.
But that's not what I want to talk about anyway.
I want to talk about one of my specialties, which is chance
and gambling games.
I'll actually be talking about actually a case that is an
Exponent case, along with some software and things that are
not Exponent things, just things I've done.
So first, this a little primer on gambling.
So roulette--
how many of you have been to a casino and have seen a
roulette wheel.
Most of you.
So roulette's a pretty simple game to understand.
There are 38 sections labeled from 1 to 36 along with a zero
and a double zero.
And half of the sections from 1 to 36 are red, half are
black, zero and double zero are green.
You can bet on a color, you can bet on a combination of
numbers, you can make varieties of bets at the
roulette table.
One of the bets is number 17.
In other words, you put some chips on number 17.
The wheel is spun, the ball is dropped in, 17--
the ball lands in the slot marked 17.
You win.
Otherwise you lose.
And since there are 38 sections on the wheel, your
chance of winning is 1 in 38.
If you bet on red, since there are 18 red sections out of 38,
your chance of winning is 18 out of 38.
So you're much more likely to win a red bet than you are a
bet on number 17.
So to get people to make this bet, what does the
casino have to do?
They have to offer higher payoff odds.
So the payoff odds for this bet, a bet on a particular
number, are 35:1.
That means if you bet $1 and number 17 comes up, you'll
make a $35 profit.
You'll get your dollar chip back plus another 35.
Well, there's a mathematical theorem
called the law of averages.
It's just called the law of large numbers by
mathematicians.
It says that if you repeat an experiment independently over
and over again, the fraction of times that something comes
up will be the same as the probability of it
happening in one trial.
So the probability of it happening in one trial on this
bet is 1 in 38, so you don't know what's going to happen in
one bet or two bets or a few bets, but in repeated bets
you'll win an average of 1 in 38, and lose an average of 37
for a net loss of $2.
So that's $2 and $38 bet, which becomes 5.3% profit for
the house, the house being the casino.
So that's called the house edge in roulette.
And it's very straightforward, cut and dry math.
This is what happens if you repeatedly
make a bet in roulette.
Like I said, you can get lucky, you can get unlucky and
lose sooner than at this rate, or you can win and quit.
But in repeated play, you will eventually lose and go broke
at roughly the rate of 5.3 cents per dollar bet.
Sooner if you have a losing streak.
So that's sort of the facts on betting on number 17 in
roulette, and in fact, in making just about any roulette
bet, or a combination of roulette bets, or other
betting strategy applied to roulette.

So you can show mathematically that this is
what happens, period.
For instance, people use some sort of sophisticated
strategies, like double your money if you lose, and keep
going until you eventually win.
You've heard that one, the doubld-up strategy.
So you start out by betting $1, and if you lose you bet
$2, and then if you lose again you bet $4, and eventually
you'll win, thereby covering your losses.
So that's an interesting strategy that people use in
various types of investment situations, namely, you start
losing, bet more.

You can show that that's not a good thing to do.
In fact, it's really not a good thing to
do in a losing game.
One of the reasons it's not a good thing to do is the house
has a betting limit, and if you have an unlucky sequence
of losses, you can't double your bet because they won't
let you bet that much.
Another problem is if you have a betting limit, if you have
an unlucky sequence of losses, you might run out of money
before you can double your bet and come out ahead.
Anyway, so that's sort of the quick tour roulette.

Now, if you talk about roulette betters, you could
say a roulette better who makes a profit after 10 bets
would be lucky.
But a long-run profit would be very weird.
The math says that's really unlikely to happen.
So a possible explanation where somebody makes a
long-run profit in roulette, they're cheating.
So they have some scheme worked out, and they're not
really playing according to the regular roulette odds.
On the other hand, you can also detect if a roulette
better is losing more than he or she should.
In fact, if a roulette better is losing after 10 bets, so
what, they're unlucky.
But if they lose in the long-run and repeated bets
faster than they should, faster than chance predicts,
5.3 cents per bet, that would also be weird.
That would be equally weird, as weird as the winner.
And then possible explanation for that, the
better is being cheated.
So there are two different kinds of weird.
Weird good and weird bad.
But anyway, where do you draw the line between luck and
something else is going on?
So statisticians, that's sort of the basic question of
statistical inference.
Traditionally, 5% is a cut-off point, which is called
statistically significant.
If what you observe has less than a 5% chance of happening
under ordinary circumstances, you conclude that something
else is going on.
So in roulette, it's simple, you can make probability
calculations and answer this statistical question.
Is what you've seen weird, or is it just within
the bounds of chance?
What about betting on football games?
What's the chance that the 49ers will win next week?
Well, 49ers started out looking pretty good in their
first couple games, but what's going to happen next week?
Well, football is not like roulette.
The same experiment is not repeated under identical
independent conditions, and so the law of averages and
various probability models don't necessarily apply.
So you can't make these cut and dried mathematical
statements about the persistent football better
goes broke, which you can say about roulette, or craps, or
Keno, or any of the casino games of pure chance.

Well, football betting is an interesting industry.
It's about a $10 billion industry right now, almost
entirely illegal.

AUDIENCE: How are illegal industries
such as this measured?
How do you-- where does the $10 billion come from?
DR. MICHAEL ORKIN: Surveys.
There are trade organizations that do surveys and find out--
in fact, I'll give you a link in a minute.
It's a good question.
But they're illegal--
Well, here's one answer.
Football betting is illegal in this
country, except in Nevada.

Football betting is legal just about
everywhere else in the world.
So internet gambling is very popular.
And internet gambling is legal, except here.
So that's sort of an interesting legal issue, as
you probably know, because if you bet on a football game
here, you're violating some Federal law, but if you're
playing in an online casino, that's perfectly legal, that's
located in Costa Rica, or Jamaica, or Canada.
So legal is sort of this strange term in this world.

But yeah, how do you come up with a number like?
So there are trade organizations that do surveys
and find out things like that.
So a typical football bet, not all, but a typical football
bet is done with a point spread.
So a point spread is a handicap given to
the inferior team.
And who decides that it's an inferior team?
Well, in some sense the bettors decide.
So here's how it works.
Let me go to the third bullet point.
First week of the season, the 49ers played Arizona, and the
Niners were nine point underdogs.
So that means if you bet on the Niners in that first week,
you get nine free points tacked on to their score.
So it turns out San Francisco lost that first game, 37 to
27, but if you add nine points, the Niners win.
So people who bet on the 49ers in that first
game won their bets.
And the people who bet on Arizona, even though Arizona
won the game, lost.
So that's fine.
Now, the casino has to make a profit, so
they pay 10:11 odds.
So that means if you make an $11 bet with a point spread,
and you win, you make a $10 profit.
So now how does that ensure that the casino is going to
make some money?
Well, what the odds makers want to do is get the same
amount of money to be bet on each team in a game.
And if that happens, because of the 10 to the 11 odds, the
losers pay off the winners and there's a little bit money
left over for the casino.
So if there are two bettors on a game and each bet's $11,
that's $22 bet, and one wins, the other loses.
So the $10 pay is paid off out the $11, that's a $1 left for
the casino.
1 out of 22 is 4.5%.
So if the casino gets an even split of the money on a game,
then they make a 4.5% profit on that game.
It doesn't matter which team wins.
So that's what they want to do.
Now, if one team has an 80% chance of winning and the
other team has a 20% chance of winning, whatever that means,
but everybody likes the 20% team, then the point spread is
going to be shifted.
So to get more money bet on the team, that really should
be the one that everybody's betting on.
So the betting public is foolish, the point spread may
give a statistical advantage to the smart bettor who knows
that the money's coming in on the wrong team.
So that's different from roulette.
On the other hand, if the betting public is--
it's different from roulette in the sense that it's sort of
like the stock market.
The odds are determined by the public, and if the public
doesn't know what they're doing, the bettor who does
know what he or she's doing can take
advantage of that situation.
On the other hand, if the betting public is smart,
equalizing the betting action will also equalize each team's
chance of winning, and the game will be
equivalent to a coin toss.

So you don't want to get 10:11 odds for
betting on a coin toss.
So in order to win in the long run, you have to find
situations in which the betting public isn't smart.
In other words, in which your chance of winning is
greater than 50%.
So it turns out that if you just look at some basic
situations, like how often does the home team win, well
if you look at the years 1993 to the present, the home team
actually wins the game almost 60% of the time.
But when you factor in the point spread, it's almost a
50/50 split.
So if you just think I'm just going to bet on the home team
because I know they win 60% of the time, you're going to lose
because the point spread completely equalizes that.
So what do people do?
Well, in one form or another, a lot of sports bettors do
data mining.
Of course, some of them don't actually use software, they
just pour through data by hand.
But nowadays there are a lot of sophisticated sports
bettors who actually use software.
So in general, data mining software is used to find
predictive patterns in large data sets.
So people use data mining for all kinds of things--
investment strategies, web surfing patterns just to try
to find patterns.
I wrote some software to search football data that sort
of points out some of the issues of data mining.
I was actually motivated by badgering from some attorney
friends who were avid sports bettors and didn't like doing
all the stuff by hand.
They thought it would be great to automate the process of
trying to find good betting situations.

So I did that a couple years ago.
It basically uses sequel to query a football database.
It has a user-friendly interface
necessary for lawyers.
There's a procedure in the software that automatically
generates query, so it does a random queries.
And it finds good results and saves them in a separate file,
and just forgets about bad results where good means
better than coin tossing.

So it uncovers trends or possibly--
it uncovers trends that the user wouldn't think of.
There's no best strategy when you look for
patterns like this.
But instead you try to collect a bunch of good strategies,
such as it turns out that by data mining from 2000 to 2006,
the Baltimore Ravens were 17 and 3 versus the point spread.
That's an 85% success record when they lost their previous
game and their opponents play their
previous game on the road.
So the question, of course, is for any thoughtful data miner,
is this really meaningful or is it just a random
coincidence that you found by data mining?
So first of all, you have to define what a
good strategy is.
Win percentage doesn't work because 1 and zero is 100%,
and that's, of course, meaningless.
17 and 3 is only 85%.
So as I mentioned a minute ago, one way to define good,
or weird, or out of the ordinary is to compare the
strategy to an appropriate probability distribution.
In this case, coin tossing.
So you see if the strategy does a lot
better than coin tossing.
It turns out that that 7 and 3 record of the Ravens, if
that's really coin tossing.
In other words, if it's just a random fluke.
The chance of getting something like that
is about 1 in 1,000.
So just use the binomial distribution.
For those of you who know basic probability
distributions, the chance of getting 17 and 3 are better,
out of 20 is about 1 in 1,000.
So a statistician would reject the null hypothesis, which
means you would say that's not just chance.
So in other words, that would be considered, in some
context, a good strategy.
So the question is should you bet on
Baltimore in this situation?

Well, interestingly enough, technology changes the meaning
of unlikely and context in situations like this.
There's something called the law of very large numbers,
which says given enough opportunity, weird things
happen just due to chance.
So when you're mining through data doing lots of queries,
saving the good ones--
by good, I mean good compared to coin tossing--
discarding the bad ones, the meaning of 1 in 1,000 sort of
changes a little bit.
So in fact, using a modest PC, this particular data mining
software does about 1,000 queries a minute when it's
doing the automated query thing.
It's generating queries at random, analyzing to see if
they're better than coin tossing, saving them in a file
if they are.
Anyway, that's lots of opportunity for weird things
to happen just due to chance.
So even if you generate this data by tossing coins, you'll
find 17 in 3 result about once a minute.
And you can turn this on and let it run all night, so you
can find all kinds of great stuff.

So statisticians consider something statistically
significant if the chance of occurring is less than 1 in 20
under a suitable hypothesis.
In the data mining environment, even 1 in 1,000
doesn't necessarily mean rule out chance.

So more generally, these kinds of techniques in which you're
doing repeated queries and analyzing them sort of comes
under the general heading of multiple comparisons.
You're testing lots of statistical
hypotheses at once.

What happens is you might find some useful things.
You might find some strategies in which you'll win money, but
a lot of the stuff you're going to find
is just random stuff.
So that's one of the issues with--
it's one of the big issues when you're doing things like
data mining of this type that you have to deal with.
And of course, there are some standard procedures for
dealing with that.
The main remedy is you apply your strategy that you've
developed by looking at some data to a fresh set of data
and see how it works there.
So what statisticians would call it, or decision
theorists, you'd have a training sample and then a new
data set that you'd apply your thing to.
So if it's just due to chance from mining the training set,
it won't work on the new data.
Or you can do a simulation, do a lot of random queries and
compare your results to a probability model.
So for example, if you're doing a lot of random queries
on this football data, and you draw a graph of the results,
you should see a record of 17 and 3 occurring with a
frequency of about 1 in 1,000 if it's just due to chance.
If you get some weird pattern in your histogram or whatever
graph you're looking at, then maybe there's something other
than coin tossing going on.
Any questions so far?
AUDIENCE: Well, it seems like you could also just bet on all
of these clues.
And the ones which are still-- which are useless are going to
be 50/50 for you.
It would be a small amount, as we've seen today.
And the ones which are actually useful
will win the money.
DR. MICHAEL ORKIN: That's exactly what lots of people
do, except with the part about winning money.

It turns out that the 10:11 are a little bit deceptive,
and you have to win more than 52.4% of the time in order to
make a profit because of that.

But that is a standard strategy.
People they, either by hand or using software, find lots of
these types of situations that look like they give you an
edge, and they'll just bet on them.
And they'll even write them, like this one's really good.
So I'll bet more on the one that's really good than the
one that's just sort of good, and I think some people are
modestly successful doing things like that.
Any other questions?

So let's see, an earlier version of the software was
featured in Wired magazine in 2002 in the
street credit section.
But let's actually look at the software for a sec, if I can
get it up Here So this is called the optimizer.
Let's say you want to see how the home team does from--
so it has this sort of user-friendly interface--
2001 to 2006 in the month of September.
Can you see that?
I guess you can sort of see it.
You get the statistics, and you can see that the home team
from 2001 to the present, not including last week's games.
So SU means straight up, they're 144 and 110.
They won close to 57% of their games.
But versus the point spread they're almost 50/50 at home--
this is just the home team.
And this Z-value is sort of that's the measure of how far
away from coin tossing it is.
A Z-value, a large Z-value means very
unlikely under coin tossing.
There's another kind of bet in football called the over/under
where you bet on whether the total score will be over or
under a particular number.
So this software keeps track of that, too.
So if I wanted to look at a particular team,
let's look at Atlanta.
How did they do in September?
So Atlanta's 5 and 3 on the road, 4 and 4 at home versus a
spread in September and so on.
Suppose I wanted to see which team had the best September
record, looking at all records of this type.
I just click optimize and I see that Jacksonville is
actually 12 and 4 versus the spread in September, which
gives them a--
so that's another one of these good records.

So you can also look at what happened in the last
game, won or loss.
So let's look at games where the home team--
just playing around here--
won their last game and played at home in their last game,
how did they do?
So pretty much 50/50 against the spread.
So this is basically just a statement of
the query up here.

Then you can also look at a list of the
games for that query.
So this is just a query output.
Then this part here accesses the file that stores good
situations.
So now the automated part.

So I'm going to clear this.
Let's say you want to look at just--
I'm going to restrict the year range--
2000 to 2006.
Let's see if we can find any good
strategies for the Niners.
So I'm just going to click random search.
So it's looking for good strategies for the 49ers, and
that search count tells how many queries it's looked at
and analyzed.
It's only saving the ones with Z-values greater than 2.5, and
so far it hasn't found anything.
Oops, there it found one.
I'll let it run for another couple seconds.

Two.
So it's found two strategies.
Let's just look at the best one.
This is a strategy.

The 49ers from 2000 to the present, when their opponent's
last game was on the road, and the opponent's last game was
under-- that means not a lot of points were scored--
then the 49ers on the road are 2 and 13 versus the spread
with two ties.
That's a Z-value of 2.84.
What that suggests is when this situation comes up, you
bet against the Niners because they're 2 and 13.
Then here's a list of all those 49er games.
Then, as I said, you can look at the save
situations in there.

Another thing you can do with the software is you can get
updates from the web.
So if you click on that, it'll actually automatically
download the scores and point spreads for this week.
You can manage the situations--
that means the good strategies you've already found.
You can share it with other people, you can import last
year's, and so on.
You can also print out any of this stuff.
So I don't have many saved situations, but if I wanted to
look at all the games in September, I can go
through each one.
I don't think anything's on here, but let's see.
I'll just look real quickly.

So if you have a lot of-- some people use this software, have
thousands of situations.
It'll find the ones that apply to a particular game.
So anyway, the part about this software that I think is sort
of interesting is the random search thing.
And that is just an automated query generator.

So any questions?
Yeah.
AUDIENCE: Is it interesting to do things like to know that
you got rid of those instead of just randomly generating--
DR. MICHAEL ORKIN: Yeah, so that's an interesting
question, and we were messing with that for a while.
So a genetic algorithm would be taking a good strategy,
looking at parameters of a good
strategy, like won or loss.
Let's say that that Baltimore strategy they won, when they
lost the last week and their opponent was whatever.
So you have a bunch of variables in some vector.
A genetic algorithm would do something like perturb the
different values in the components of the vector and
see if you can get something better.
So would sort of try to genetically mutate the vector,
which defines your strategy, and get a
better one out of it.
So yeah, we sort of messed around with that.
It's not part of the software right now.
Yeah.
AUDIENCE: Can you potentially use the software to identify
features which are aspects of the--
bookmakers themselves are using to come up with the
point spread?
And more importantly, [INAUDIBLE]?
DR. MICHAEL ORKIN: Well actually, that's a good
question. the answer is the way the bookmakers come up
with the point spread is they look at the amount of money
that's being bet on each team.
And if it's not equally balanced, they'll make the
point spread more favorable for the team that's
not being bet on.
They're not really looking at much of anything, except the
betting money, and that's all automated.
Now, they have to start with something.
So at the beginning of the week, like right now, in fact
on my way in, in the lobby, I stole the sports section of
today's Mercury News, and even though football betting is
illegal, sitting right here in the lobby is the Mercury News
latest line, Pro Football.
So I can see that this coming week Philadelphia's a six
point favorite over the 49ers.
Buffalo is a 5 and a half point favorite over the
Jets, and so on.
Then they also have the lines for college football.
If it's bad to bet on pro football, it's supposed to be
a real sin to bet on college football because you're sort
of corrupting the integrity of the game.
But here it is in the San Jose Mercury News.
I don't know.
AUDIENCE: That's the Nevada issue.
DR. MICHAEL ORKIN: What?
AUDIENCE: That's the Nevada issue.

DR. MICHAEL ORKIN: That's the Nevada issue, right.
This is the online issue.
AUDIENCE: So doesn't work if the point changes--
DR. MICHAEL ORKIN: Correct.
So that's a good question.
So he had asked, so your question was how do the odd
makers decide what are the important factors?
Well, they start out with something, because here it is
Monday and they've already printed a line, which is
roughly resembling the various lines in Las Vegas.
So there hasn't been that much betting action yet.
So they had to start with something.
So what they start with is they look at software, and
they look at the records of teams, and they have formulas
that give, what we called, power ratings, which are just
ratings of how good teams are.
Then they just sort of sit around and actually just sort
of talk about how they think the public is going to bet.
Then they'll come out with what's called an opening line,
and they'll modify the opening line throughout the week.
And it doesn't change by that much, actually.
They're pretty good at coming out with the opening line.
So the things that they are-- and they know a lot about
football, and whatever sport they're handicapping.
In fact, the companies in Las Vegas, handicapped sports has
experts in each sport who really know a
lot about the sport.
They also have software.
But the software is software that just gives trends and
patterns, not exactly data mining software.
But they use that too.
But they rely heavily on how they think the
public is going to bet.
So right now the 49ers--
sorry, I didn't answer your question.
Right now, if you bet on Philadelphia, you have to give
six points since Philadelphia's playing 49ers
next week, and they're a six point favorite.
So let's say that during the week lots of people bet on
Philadelphia until they move the point spread up to seven.
When you made your bet it was six, so you get six.
That was your question, right?
Yeah.
So you get the point spread that was available when you
made your bet.
Yeah.
AUDIENCE: [INAUDIBLE]?

DR. MICHAEL ORKIN: Well, if you're doing this data mining
method or something similar to it, that's correct.
AUDIENCE: [INAUDIBLE].
DR. MICHAEL ORKIN: That assumption is only moderately
reasonable for a few important reasons.
One is that teams change, coaches
change, stadiums change.
So it's not like the roulette environment.
We get these new situations.
Like this year, the Raiders have a new coach, a new
offensive coordinator--
offensive is a good word for him--
and they have a new quarterback.
So it's like--
AUDIENCE: [INAUDIBLE].

DR. MICHAEL ORKIN: So what people who bet on sports do is
they get these patterns, they get these things like
Baltimore Ravens are 17 and 3, let's bet on this week.
And then they look at other things that might be more
connected with the real world, namely--
and they do actually what a statistician would do.
They would say OK, I have this weird situation now.
What's a plausible explanation?
And if there's no plausible explanation, a smart person
would say this is just random nonsense.
AUDIENCE: So why not weight things differently.
So the further they are [INAUDIBLE]?

DR. MICHAEL ORKIN: People have used other techniques.
What people tend to do, use software like this or is to
restrict the year range.
I mean I know some people who say if you go back more than
two years you're an idiot, because everything that's
relevant has happened within the last two years.
That's almost like mark-off change or something.
But then I know this guy who's a sports bettor who says you
have to go back at least 15 years, because only then will
you get enough data to establish a pattern.
And that there are certain underlying physical
characteristics and dynamics of football that you can
uncover by looking at lots of years.
So it's a mixed opinion on that.
Yeah.
AUDIENCE: [INAUDIBLE PHRASE]
what position you play.
At least some combinations of scores might be because of the
way that the [INAUDIBLE PHRASE].

DR. MICHAEL ORKIN: That's actually an important issue
for a couple of scores things.
Like a point spread, like 17% of games land on a three-point
difference.
So if you're a bookmaker and you change the point spread
from 3 to 3.5, you're doing a huge thing.
Whereas if you change the point spread from 1 to 1.5,
that doesn't mean very much.
So in order to move the point spread off of 3, then there
has to be a huge imbalance in the betting.
So there are certain score differences, like 7 is the
other number.
But 3 is the big number.

And then, in fact, you could even buy a half a point at
some casinos.
So instead of getting 10:11 odds, you'll get--
or 11:10, however you want to say it, you'll get 6:5 odds,
which is worse, and they'll move the point spread a half a
point just for your bet.
So yes, the attention is mainly from the book makers
who are much more hesitant to move off of a popular actual
difference in score, a frequent one like 3, and then
also bettors.
For instance, if the point spread's 3.5, then a lot of
the so-called wise guys who are the professional gamblers
will take the underdog, because they know that a lot
of games will end on 3, so they'll take the 3.5 points
knowing that they'll win a three-point game, a
three-point loss.
So that brings up another little dynamic of sports
betting, and that is there are basically two camps of bettors
who the bookmakers call the squares and the wise guys.
It sounds kind of funny, and it is.
The wise guys are the professional bettors and the
squares are like, you go to Las Vegas for the weekend and
you bet on the home team.
But they actually use that.
They will not move the point-- if some wealthy square comes
in and makes a huge bet on a team, they're not going to
move the point spread in the same manner that they would if
a well-known gambler comes in and makes the same bet.
So there are some well-known gamblers--
this is a little football betting lore, or sports
betting lore--
well-known gamblers who actually hire people to go
make bets for them, because they don't want to have an
effect on the odds.
And the gamblers who go bet on them are called beards who bet
that for them.
AUDIENCE: It sounds like a whole [INAUDIBLE PHRASE].

DR. MICHAEL ORKIN: So your question I think is do to the
wise guys do better than the squares?
AUDIENCE: Not only that, but are there people who are so
good that they make a lot of money from it, as much as--
DR. MICHAEL ORKIN: Well let's put it this way.
There are people who are so good that they make a lot of
money from the sports betting industry.
Now, I know a couple of these people, and I know that they
make a lot of money by selling information.
Whether they actually make any money by betting on games is
an open question.
But they will have tout services, in other words,
they'll sell.
There's this publication called The GoldSheet that
comes out of Los Angeles, which you
can buy at any newsstand.
It costs I think $7 now or something.
It comes out every week.
They did it in football and basketball.
So the guy who originally found it was
an avid sports bettor.
He passed away now, but I used to know the guy, and I don't
know if he ever made anything actually betting.
But he had 35,000 subscribers to this weekly little sheet,
plus lots of people who buy off of newsstands.
So he made a lot of money in the sports betting industry.

So typical gamblers--
but there are some people who seem to make money at it.

I don't know how much.
Anything else?
AUDIENCE: [INAUDIBLE].

DR. MICHAEL ORKIN: Some of them have performed well.
I see there are a few.
I don't really market it anymore because I'm too busy
doing other things, but it's available on our website.
There are some people those who--
a group of users who swear that the strategies work, and
that they made lots of money.
I personally haven't really been doing much betting.
I'm more of the developer, not the gambler.
AUDIENCE: I was wondering--
I don't know too much about sports betting in the US, but
where I'm from, the [INAUDIBLE]
have quite reasonably different odds spreads against
what they're offering.
Is that the case here or would that be
something else entirely?
DR. MICHAEL ORKIN: Typically in football, it'll stay pretty
much the same, because as soon as one changes a little bit,
people will go and bet on what they think is the right team
and it'll force them to comply.
So it's just the betting public is so versatile,
especially because of computer technology and stuff like
that, they won't let odds be much different from one casino
to another.
Because you can do things like play hedge, you could hedge if
there are different odds.
And so you can make bets on each team in different places
if you have different points spreads.
So hedging is not easy in pro football betting because
everything, especially with online gambling, there's so
much of it going on that everything is within a half a
point usually.
So you can say it's the point spread plus or minus half a
point just about everywhere by the end of the week.
During the week, if an injury occurs or something happens,
then, of course, the point spread might move a lot.
Any other--
I'm going to go to something else now.
It still has to do with sports betting.
So let's see.

So this is the website.
If you're interested you can download a free 10-day copy.
If you send me an email I'll send you an
authorization key to use.
You can use it for 10 days anyway.
It's snoopdata.com of the software.

I'm going to talk about how about another little--
we have a few minutes.
I'm going to talk a few minutes about an actual case
that I got involved in as an expert.
So I'm going to talk a little about internet gambling.
And I'm going to answer your question in a minute.
Internet gambling is about a $12 billion industry.
Actually, that's more like a $20 billion industry now.
Illegal in the US except for horse racing.
So I'm not just talking about sports betting.
Internet gambling is illegal in this country, technically,
except for horse racing.
Now, what does that mean?
So I mean I'm sure a lot of you have seen websites like
PartyPoker where you can play poker online.
If you play on PartyPoker in the State of Washington, you
are guilty of a felony right now.
You're not going to get arrested, but there's a new
law in the State of Washington that makes internet poker
playing a felony.

So it's a very weird situation.
Now, notice I say except for horse racing.
So that's interesting.
And in fact, there's a law that's sort of moving its way
through Congress right now, in which the people supporting
the bill and making these impassioned speeches on the
floor of Congress about how internet gambling is
destroying our moral culture, and ruining the lives of
compulsive gamblers, and it has to be outlawed, except
horse racing.

Horse racing, of course, betting on horses is legal in
California and various states.
So they sort of have a lot of lobbying pressure.
So horse racing for some reason is above the fray here
and isn't immoral.
You can bet on horse racing on the internet and it's
perfectly OK.
So right now, except for horse racing, there are over 2,000
internet casinos, and they're all offshore, except for the
horse racing websites.
There are about 23 million internet gamblers in 2005,
eight million from the US.
In April of 2005 the World Trade Organization ruled that
the US is violating certain rules because they don't treat
foreign and domestic internet gambling businesses equally in
the sense that everything's illegal except horse racing.
So far nothing has happened.
However, recently, the Federal government has arrested and
thrown in jail two people who are CEOs of internet gambling
websites offshore.
One guy from England who is the CEO of the company that--
a well-established website completely legal in England.
And in fact, it is traded on--
it's a publicly traded company on the London Stock Exchange.
He was going to Jamaica and he had a stopover in Dallas, and
the Feds busted him in Dallas and threw him in jail where he
still sits a couple months later.

It's not quite clear--
he's charged with violating the US wire laws.

But still, no one's gone after individual
bettors at this point.
So there are some articles that talk about these things.
So here's an article from the Louisville, Kentucky Courier
Journal, which talks about how horse racing is good.
Here's an article about that poker law from the Seattle
Post Intelligencer, which talks about how poker is bad.
Then here's one of these trade organizations, the American
Gaming Association, so if you Google the American Gaming
Association, you might find this article.
I can email a copy of this if you want, if you're interested
in where these stats come from that I'm putting on some of
these slides.
They're one of the organizations that gathers
data on that industry.

So here's a story.
There was a businessman a few years ago,
about five years ago.
And he raised $50 million in investments for a company that
was supposed to be on the verge of obtaining a patent
for a lucrative new tooth whitening product.

Raised $40 million.
There was such a product, but this guy had no
connection to it.
He was a swindler.
In fact, he had seen this product advertised on the
shopping network.
So he started raising money for it, claiming he had a
patent and that he was going to sell it
to Proctor & Gamble.

Well, he spent $10 million of this $40 million to buy homes,
condos, nice cars.
And he gambled away all the rest of it, $30 million
betting on baseball at an offshore casino.

So he went completely broke.
The investors started asking for their money.
He promised them 100:1 return on their investment, and he
didn't have any.
So he got busted, he declared bankruptcy,
he was sent to prison.
So the victims of the swindle retained a law firm that
specializes in recovering funds from bankruptcy cases.
So they got the homes and the car seized and got the money
back from that--
they got a few million bucks back from that.
Enough to pay the attorney fees, basically.

Then they filed a lawsuit against the casino.

They said a bunch of things.
One, they said the casino wasn't
doing its due diligence.
They should have known this guy was a swindler.
This was all done online and by telephone.
They said with this kind of money and stuff, they should
have checked up.
Well, in fact, the guy was betting directly by wires from
the Bank of Montreal that were sent into
the offshore account.
So the Bank of Montreal was equally culpable.
So they sort of gave up on the due diligence argument.
So then they alleged in this lawsuit that the online casino
has to return the money because that the casino was
either cheating the swindler, or the casino was involved
with the swindler in a money laundering scheme.
In other words, the swindler wasn't really losing this
money, he was just feeding it to this offshore account, and
he was going to later split it with the
guy who ran the casino.

Well, the guy who ran the casino had pretty good records
and he could show that he was keeping that money for himself
and wasn't going to share it with anybody, especially some
guy who just got out of jail and was
working at a gas station.
So they decided to focus on this.
If the casino had given the swindler proper betting odds,
he couldn't possibly have lost $30 million
over a two year period.
In other words, the result couldn't
have been due to chance.
Sort of what I was talking about before
with the good strategy.
In other words, the swindler's losses were just too extreme
to be explained by chance.

Well the swindler was betting on baseball games.
He was betting on 10 games a day, 15K a game.
And when the baseball season wasn't on, he
was betting on hockey.

So if you translate that, $15,000 a bet, 10 bets a day
over two years, you get about $110 million in betting
action, which is called churn.
So that's how much betting action we have from this guy
before he finally got busted.
He didn't go broke, he just got busted.
I guess he was pretty much broke at the time.
So the question is how much on average will a gambler lose
when he bets $110 million in relatively small
increments over time?
And of course, the answer is it depends.
It depends on the bets.
In particular, it depends on the house edge, which is
sometimes called the hold percentage in sports betting.
So with roulette, the house edge is 5.3%.

So if the gambler makes roulette bets with a house
edge of 5.3% at that rate, 10 bets a day, $15,000 a bet,
you'd lose an average of about $6 million.

In fact, you can also compute a
probability here for roulette.
The chance of losing $30 million or more making those
kinds of bets is less than 1 in 1,000.
So it's really unlikely that if this guy was playing
roulette he would lose that kind of money.

As we've seen, sports betting is different from roulette,
but still you can talk about the hold percentage.
Well, in baseball betting, just real quickly, there's
something different than a point spread, it's called the
money line, and the money line is just another way of stating
payoff odds.
So the favorite has a negative money line, which means you
have to bet more to win a certain amount.
The underdog has a positive money line.
So it's all in units of 100.
So anyway, you can take these money lines, and what they do
is they shade one of the money lines so that there's a slight
imbalance so it isn't a fair bet.
That if you bet on both teams you break even.
So when they do that, what they'll do is they'll take 20
points off the underdog line, starting with some number that
they think is good for dividing the
betting action again.
The bookmaker makes a certain profit.
And in fact, for a typical baseball bet, the hold
percentage will be in the neighborhood of 4%.
Similar to point spread bets.
So baseball, just betting on a team in baseball, yields about
the same profit margin for casinos per dollar bet.

and it depends very much on how much money is bet on each
team in a game.
But anyway, so typically that's what bookmakers make
though on baseball betting, somewhere around 4%.

Well, if you do that, if the swindler had been making
random bets with a 4%, 3.8% house edge for that whole run
of $110 million, he would have lost about $4 million.
If he actually was using any skill in baseball betting, he
might have even done better.
He might have lost less than $4 million.
But you can have skill and still lose money, of course.
You can have skill and just do worse than randomness, and
because of the odds, you can still go broke.
So you can be skillfully bankrupt.
But in any event, this guy lost $30 million.
So the question is was he being cheated?
Well it turns out there's another kind of baseball bet
called a parlay bet.
Some of you may know what a parlay bet is.
Parlay bet just means you bet on a bunch of different events
and they all have to happen for you to win.
So for example, here's a 14--
and you can do that in any sport--
here's a 14 parlay.
You bet that San Francisco beats Florida, Pittsburgh
beats Milwaukee, Oakland beats Kansas City, and the Yankees
beat Boston.
If you make that bet, all those teams have to win in
order for you to win your bet, otherwise you lose.
So the payoff odds are somewhat high.
They're huge payoff odds.
In fact, you get the payoff odds by multiplying the
probabilities together for winning for each of those
teams, roughly speaking.
Taking a little bit off for the bookmaker profit.
So it turns out that these types of bets are highly
profitable if you win, but on the other hand, there's a huge
house edge or hold percentage because the casinos don't want
to expose themselves to the risk of taking huge parlay
bets because of the big payoff odds.
So they'll make the odds such that they're not so good for
the bettor.
So a typical 14 parlay bet, the house edge is
between 25% and 30%.
The same is true if you make--
if you look at games with high payoff odds, the house edge is
always worse, like the State Lottery, for instance.
The house edge equivalent is 50%.
The State keeps 50% of all the money bet.
Keno is another game.
If you've seen the game of Keno in a casino, that's a
type of lottery.
Keno bets typically have a 20% to 30% house edge because you
can win a whole lot of money for a $1 bet.
So on games like that have very high payoffs with a very
low chance of winning, the house edge is typically high.
So these 14 parlay bets, this guy was betting 4, 5 and 16
parlay bets generally.

So he was exposing the casino to-- he was betting $15,000 a
bet, these 14, 15, 16 parlay bets.
So any win, he would get hundreds of
thousands of dollars.
So the online casino was hesitant to take the money on
such large bets.
They don't usually, so they spread it out around other
casinos, so the money trail gets sort of dispersed.
And anyway, let's just look at the bottom line.
Let's say the house edge is about 27.5%.
And that's what this guy was doing was making
these parlay bets.
And the $110 million bet, you multiply by
27.5%, you get $30 million.
And that's exactly what the guy lost.
So I'm going to stop with that because we're running out of
time, but I will answer questions.

Anybody want any other information, just come on up
and get it.
Thank you very much.