Roulette Machine Learning

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Probability and physics are helping make even roulette seem ultimately predictable.

In his new book,The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling, Adam Kucharski details how trying to understand dice games led one mathematician to develop probability theory, how one of the first wearable computers was designed to systematically yet covertly predict the fall of a roulette ball, and how poker-playing bots are advancing winning strategies more quickly than we think. As he shows, science, mathematics, and gambling have long been intertwined, and thanks to advances in big data and machine learning, our sense of what’s predictable is growing, crowding out the spaces formerly ruled by chance. At the same time, though, we’re letting more of our lives be influenced by algorithms, bits of code whose effects are beyond our full understanding. As in so many other areas, the creations are outpacing their creators. In the lightly edited interview below, Kucharski explains how we got here, what poker-playing bots can show us about being human, and what comes next .

Learning

In the book you call gamblers the godfathers of probability theory, noting that it’s a newer area of mathematics than we might expect. Can you talk a little bit about how probability theory came out of gambling?

One thing that I found remarkable about the history of math is that it’s only fairly recently that people started looking in to quantifying luck, so really for a long period of history, topics like geometry were the main study. There was a lot less interest in random events: it’s actually not until the 16th century that gamblers start to think of how likely things are and how that could be measured.

If the machine is purely random, there roulette be no way of using machine learning to win. So a physical game would be hard to trick, however virtual games are liable to have heuristics. Essentially if learning is any heuristic that is governing the winning of a player machine learning will be able to exploit it! Learn how to play roulette and how to maximize your chances of winning with this easy to understand tutorial.Find more info at http://www.casinator.com/casin. If the game is purely random, there would be no way of using machine learning to win. So a physical game would be hard to trick, however virtual games are liable to have heuristics. If the result of a roll was based on something. Some video roulette games have lower payouts than the table game counterparts. They pay only 30-34 for a single-number bet (vs. 35 on a table), and 15-16 on a double-number bet (vs. 17 for a table). This explodes the house edge. Don't play these machines.

There was a gambler called Gerolamo Cardano: although a physician by profession, he had a pretty keen gambling habit. He was one of the first people to outline what’s known as the sample space. This is all of the possible outcomes you could get, say, if you’re rolling two dice together, there’s 36 ways they can land. And then of these 36 ways you can home in on the ones you’re interested in. This provided a framework for measuring these kinds of chance events.

That was of the first foundations of probabilitytheory. From that point over the subsequent years, a number of other researchers built on those ideas, again often using bets and wagers to inspire the way they thought about these problems.

You recount several examples of scientists taking on certain gambling problems. Richard Feynman, for example, tells professional gambler Nick the Greek that it seems impossible for a gambler to have any advantage.

Feynman was obviously famous for his curiosity. On his trips to Vegas, he wasn’t a big gambler, but he was interested in working out the odds. I think he started with craps, figuring that although it was pretty poor odds, he wouldn’t lose that much, and it might be a fun game. On his first roll he lost a load of money, so he decided to give up.

Roulette Machine Learning Definition

He was talking to one of the showgirls, who mentioned Nick the Greek. He was this famous professional gambler, and Feynman just couldn’t work how you could have the concept of a professional gambler because all the games are stacked against you in Vegas. On talking to him he realized what was actually happening was Nick the Greek wasn’t betting on the tables. His gambling strategy was making side bets with people around the table. He was almost playing off human flaws and human superstitions, because Nick the Greek had a very good understanding of the true odds.

Roulette Machine Learning

If he made side bets at different odds he could kind of exploit that difference between the true outcome and what people perceive it to be. That’s a theme that continues throughout gambling: If you can get better information about what’s going to happen and you’re competing with people who don’t have much idea about how things are going to land or what the future might be, then that gives you a potentially quite lucrative edge.

There’s a sense that being at the whim of chance is somehow a very human position. Admitting things as totally unpredictable and leaving yourself up to fate is often part of the allure for the nonprofessional gambler. But then there’s a tension, because other people say maybe these are things that we can predict, and maybe this isn’t as much up to chance as we imagine it to be.

Roulette Machine Learning Tools

It’s really been this almost tug of war between believing something is skill and believing something is luck, whether it’s in gambling or just in other industries. I think we have a tendency if we succeed at something to think it’s skill and if we fail at something to almost blame luck. We just say, “Wow, that’s chance, there is nothing I can do about it.”

The work of a lot of people who study these games is trying to think of a framework within which we can measure where we are in terms of skill and in terms of chance. Mathematician Henri Poincaré was one of the early people, in the early 1900s, interested in predictability. He said that when we have uncertain events, essentially it’s a question of ignorance.

Learning

He said that there are three levels of ignorance. Depending on how much information you have about the situation and what you could measure, things will appear increasingly random. Not necessarily because it’s truly a lucky event, but really it’s our perception that makes it appear unexpected.

One of the games that I think we expect to be least predictable is roulette. But as you point out, Claude Shannon, considered the father of information science, and Edward Thorp, who would later write one of the most popular books on card counting, made a strong case for being able to systematically predict roulette spins.

For a long period of time, roulette has almost been like a case study for people interested in random events.Early statistics was honed by studying roulette tables because you had this process that was seen as very complicated to actually understand fully, but if you collected enough data then you could analyze it and try and look for patterns and see whether these tables are truly random.

Edward Thorp, who wrote while he was a Ph.D. physics student, realized that actually beating a roulette table, especially if it’s perfectly maintained, isn’t really a question of statistics. It’s a physics problem. He compared the ball circulating a roulette table to a planet in orbit. In theory, if you’ve got the equations—which you do because it’s a physics system—then by collecting enough data you should be able to essentially solve those equations of motion and work out where the ball is going to land.

The first wearable bit of tech was designed to be hidden under clothing so you could go into casinos and predict where the roulette ball will land.

Although in theory that could work, the difficulty is [that] in a casino, you actually need to take those measurements and perform those calculations to solve those equations of motion while you are there. So Thorpe then talks to Shannon, who was one of the pioneers of information theory and had all sorts of interesting contraptions and inventions in his basement. Thorpe and Shannon actually put together the world’s first wearable roulette system computer. The first wearable bit of tech was designed to be hidden under clothing so you could go into casinos and predict where the roulette ball will land.

Those early attempts were mainly let down by the technology. They had a method which potentially could be quite successful. But it was implementing it, for them at that time—that was the big challenge.

You mention, though, a much more successful attempt in 2004.

This is the Ritz casino in London, where you have these newspaper reports of people whose roulette system initially said to have used a laser scanner to try and track the motion of the roulette ball. In the end, they walked away with just over a million pounds.

That’s an incredibly lucrative take, even for high-stakes casinos like that. This [attempt] reignited a lot of the interest in these stories because, although Thorp and subsequently some students at the University of California had focused on roulette tables, they’d always left out a bit of their methods. They’d never published all the equations.

Sothere’s always this element of mystery and glamour around these processes. Actually, it’s only very recently that researchers in Hong Kong actually tested these roulette strategies properly andpublished a paper. It actually said, that if we have this kind of system for roulette it’s plausible that we can take into casinos and win. I think there’s been a number of other stories of gamblers trying out these techniques in casinos, but it’s always been very secretive. It’s interesting how long it’s actually taken for some researchers to test this problem.

Toward the end of the book you talk about poker. If probability and physics are helping make even roulette seem ultimately predictable, poker seems a tougher nut to crack.

Exactly. Poker, on the face of it, seems like a perfect game for a mathematician because it’s just the probability that you getthis card and someone else gets a card. Of course, anyone who’s played it realizes that it’s much more about reading your opponents and working out what they are going to do and what they think you’re going to do.

Early research into poker actually inspired a lot of the ideas of game theory: so, everything inA Beautiful Mind, about if players get together and try to optimize their strategy, they’ll come up with certain approaches. In more recent years, that link between science and gambling has continued, and actually a lot of the attempts at A.I. are focusing on these kinds of games.

Although historically we’ve seen games like chess beaten—and more recently, Go—these are what’s known as perfect information games. You have everything in front of you while you’re playing. So in theory at least there’s a set of moves that if you follow them exactly then you always get the optimal outcome.

If you’ve ever played tic-tac-toe: most people work it out pretty quickly. There’s just a set of fixed things that you can do, and that will always force a certain outcome. Whereas with poker, in poker you can’t do that because there’s an element of randomness. There’s hidden information in that you don’t know what your opponent’s cards are. You’ve got to adjust your strategy over time, and this is where things like bluffing and manipulating your opponents come into play.

I think for artificial intelligence that’s a really interesting challenge, because you’ve got this hidden information and risk-taking aspects. Arguably, that’s a lot closer to a lot of situations we actually face on a day-to-day basis. Whenever you go into a negotiation or you try to bargain for something, you’ve got information that you know, while they’ve got information that they know, and you have to adjust your strategy to account for the difference.

So much of our strategizing depends on accounting for having incomplete information. It reminds me ofBill Benter’s horse-racing models. He models hundreds of variables to predict how the horses will run, but he also cautions about mistaking correlation for causation, especially when the correlations seem wildly counterintuitive. He’s saying don’t try to explain the models, as long as they work. That’s what often happens with machine learning: we put a machine to work on some problem, feed it massive amounts of data, and it returns with these correlations that we never would have expected. They’re totally counterintuitive, but we kind of just have to take them because they’re revealingsomething.

I think the approach that these scientific bettors use has been really interesting. One of the questions that I found the most unexpected answer to was when I asked, “What criteria do you used to make your strategy?” And with Bill Benter and these horse-racing syndicates, they’re really just interested in which horses will win. They don’t want to explainwhyit will win.They just want a model where if you put in enough information you will get a reliable prediction.

I think that goes against a common notion that somebody who’s good at gambling is almost an expert and has a lot of knowledge of the narrative of the sport, whereas actually, a lot of these scientific teams treat it much more like an experiment. As long as it gives a good result, they don’t mind how they get there.

And similarly with these bots, because you’ve got so much complexity in terms of how they learn—they have billions and billions of games against each other. It’s very difficult to understand why bots might choose a certain strategy. This even goes back to the early days of machine learning, Alan Turing’s question of can machines surprise us? Can they come up with something unexpected? Machine learning is increasingly showing that they can, because they can just learn so far beyond what their creators are capable of.

In many cases, these poker bots are turning up with strategies that humans would never have thought to attempt, because they’ve simply been able to crunch through that many games, and they’ve refined their strategies. It’s a really interesting development we’re seeing in terms of what the minimal amount of information or strategy is that you need to be successful. We really try to identify questions that maybe people wouldn’t traditionally ask.

That brings us back to this notion that chance is both something that can’t be explained away any further, and yet there’s something deeply human about the desire to create a story to explain why things happen. Computers are now showing us strategies and explanations we never could have arrived at on our own; as you say, they’re outpacing their creators. What are some of the ramifications of that process?

One of the things that really surprised me in writing the book is how quickly these developments are happening. Even the Go victories this year: I think lots of people didn’t expect it to happen that suddenly. And likewise with poker: last year some researchers found the optimal solution for a two-player limit game. Now you got a lot of bots taking on these no-limits stakes games—where you can go all-in, which you often see in tournaments—and they’re faring incredibly well.

In many cases, these poker bots are turning up with strategies that humans would never have thought to attempt.

The developments are happening a lot faster than we expected and they’re going beyond what their creators are capable of. I think it is a really exciting but also potentially problematic line, because it’s much harder to unpack what’s going on when you’ve got a creation which is thinking much further beyond what you can do.

I think another aspect which is also quite interesting is some of the more simple algorithms that are being developed. Along with the poker bots which spend a huge amount of time learning, you have these very high-speed algorithms in gambling and finance, which are really stripped down to a few lines of code. In that sense, they’re not very intelligent at all. But if you puta lot of these things together at very short time scales—again, that’s something that humans can’t compete with. They’re acting so much faster than we can process information; you’ve got this hidden ecosystem being developed where things are just operating much faster than we can handle.

This goes beyond simply teaching bots to play poker or Watson winning atJeopardy!There are wider ramifications.

Yes. And I think the increasing availability of data and our ability to process it and create machines that could learn on their own, in many ways, it’s challenging some of those early notions about learning machines. Even some of the criticisms and limitations that Alan Turing put forward when they were first coming up with these ideas, they’re now being potentially surpassed by new approaches to how machines could learn.

You have these poker bots, instead of learning to play repeatedly, they’re developing incredibly human traits. Some of these bots, people just treat them like humans: they refer to them in human terms because they bluff and they deceive and they feign aggression. Historically, we think of these behaviors as innate to our species, but we’re seeing now that potentially these are traits you could have with artificial intelligence. To some extent it’s blurring the boundaries between what we think is human and what’s actually something that can be learned by machine.

You may think roulette computers are always sophisticated pieces of hardware. In actual fact, most are very simplistic, although people that sell them want to you believe it is space-age technology. Here I will explain the simplest possible roulette computer algorithm, and it is used by almost every roulette computer.

Understanding What Makes Roulette Beatable

First we'll need to identify various parts of the wheel so you know what I'm talking about:

Ball track: where the ball rolls

Rotor: the spinning part of the wheel where the numbers are

Pockets: where the ball comes to rest

Clocking: simply another word for 'take timings of'. ie if you 'clock' the rotor or ball, you are simply clicking buttons to take timings of revolutions.

What Happens During a Spin

When the ball is released, it gradually slows down, loses momentum and falls from the ball track. Sometimes the ball hits a metal deflector (diamond) and falls without much bounce. Sometimes it bounces everywhere. Sometimes there is still a fair bit of ball bounce. And while you can never predict exactly where the ball will fall, YOU DONT NEED TO. You need only to predict roughly where the ball will fall with enough accuracy to overcome the casino's slight edge against you (house edge). For some wheels, this is very easily done. For other wheels, it is much more difficult.

Here are some of the principles that are typically used to predict where the ball will land with professional roulette prediction techniques:

Dominant Diamonds

On most wheels, the ball will tend to hit a specific diamond more frequently than others. You can check this for yourself at your local casino by creating a chart like the one shown left. At the very least, you will find there are some diamonds that the ball almost never hits, or perhaps some areas where the ball almost never falls from the ball track. This is not random, and inevitably leads to more predictable spin results.

Now that we know WHERE the ball will fall at least an inordinate amount of times, what if we knew what number was under this area WHEN the ball fell? This is easy to determine, and I'll explain how later.

Consistent Ball Timings

You may think that when the ball is released, the timings of each revolution is random. The reality is especially the last few ball revolutions of the ball occur with much the same ball timings. The right chart shows the revolution timings for the last few revolutions of the ball on three different spins. You can see they are all very similar. The very bottom row shows the sum of all timings from these last seven ball revolutions. The greatest deviation in timings is no less than 300ms (0.3 seconds).

This means that if we knew when the ball timing (speed) was about 1350ms per revolution (about 1.3s per revolution), then we'd know the ball has about 12,500ms (12.5s) before it likely hits the dominant diamond and falls. Again of course this wont happen every time. It only needs to happen enough of the time.

Do you need to know the precise ball speed to know when there are 7 ball revolutions remaining? NO, you can virtually guess when there are roughly 7 revolutions remaining. Do you need to know exactly how many milliseconds are remaining? NO, because the ball revolution timings for the last few revolutions are much the same. This means finding which number will be under the diamond when the ball hits it is very easy to determine. This is a critical to understand.

Ball Scatter

Ball scatter is basically ball bounce. Sometimes the ball will miss all diamonds. Sometimes it hits a different diamond to usual. But a lot of the time, the ball will hit the dominant diamond, then bounce roughly 9 pockets along before coming to rest. There is a lot more to it in reality, but from a simplistic perspective, this is scatter.

If you check your local casino's wheels and compare where the ball first touches the rotor to its final restring place, you will see the ball bounce is usually still quite predictable over 15-30 or so spins. How we apply this knowledge is explained later.

Visual Ballistics

So far we know that on many wheels, the ball will mostly fall in the same region (dominant diamond), then mostly bounce 9 or so pockets. On many wheels we can actually skip the step where we consider how far the ball bounces after it hits the dominant diamond. This is because there is a more direct approach as explained below:

If you had a method to determine when the ball is about 1300ms (1.3s) per revolution, at that precise moment, you could look at the number under the reference diamond and write it down. Then wait for the ball to fall and come to rest. This will leave you with a first and second number like 'A,B'. For example say you got 0,21. This will tell you that the ball landed 5 pockets clockwise of your initial 'reference' number. See the left image for reference.

This tells us that starting from our REFERENCE NUMBER (A), the ball has about 12.5 seconds left before it hits the dominant diamond and bounces about 9 pockets, and ends up about +5 pockets from the reference number. Where the ball comes to rest is the WINNING NUMBER (B).

You may need to read this a few times, but the concept is very simple. Also see the video below which explains the concept too.


What I've explained above is a very simple method of beating roulette, or more like the science behind a method called 'visual ballistics'. The key component of any visual ballistics method is how you determine when the ball is at the targeted speed. Because when you have identified that target speed, you will know the ball has the same ball revolutions left before it falls and bounces however many pockets.

Can you virtually GUESS when the ball has 1 revolution remaining? How about 2 or 3 revolutions remaining? How about 5 or 6? It really is not at all difficult. If you can be accurate to within 1 ball revolution, then you can achieve exactly the same accuracy as most roulette computers without needing any device. Remember, you don't need to measure accuracy to within 5ms, 20ms or even 100ms because you are only determining how ball ball revolutions are remaining, and this automatically tells you the remaining ball travel time. You can be very sloppy and still be correct most of the time. And that's as accurate as you need to be to equal the accuracy as most roulette computers.

In a follow-up video I'll release soon, I'll teach you a method that can accurately tell you how many ball revolutions are remaining. And you will achieve the same accuracy as almost every roulette computer.

The Basic Roulette Computer Algorithm

This is what most roulette computer sellers don't want you to know. If you understand all of the above, you'd see how incredibly simple it all is. You'd also understand how you can afford to be very sloppy, and can just about guess how many revolutions are remaining and you'll still very accurately determine how many milliseconds are left before the ball falls. It is essential to note that ALL roulette computers use the above principles. You can look at the demonstration videos of basic roulette computers, and use basic visual ballistics to achieve almost exactly the same accuracy - without even using any electronic device. But because sellers want to make their products seem more competitive and exclusive, they'll tell you their devices are highly sophisticated with unparalleled accuracy.

Visual ballistics vs a Basic Roulette Computer

The main difference between typical visual ballistics and a basic roulette computer is that roulette computers are EASIER to use. There is no difference in accuracy between a skilled visual ballistic and computer player. Why? Because they both do exactly the same thing. They both just estimate when there are 7 or so ball revolutions remaining. They both 'tune' by looking at how far the actual winning number is from the reference number, then making a simple adjustment.

How Basic Roulette Computers Work

First the player finds a wheel where the ball mostly hits a particular diamond. Most wheels are like this. There are a few other basic procedures to evaluate a wheel, but this is just a simplified example. The player can create a small diagram l.ike the one shown left.

To use the computer, the player waits for the ball to be released then clicks a hidden button each time the ball passes a particular reference point (such as a diamond / metal deflector). This determines the timing of ball revolutions.

The player keeps clicking the hidden button until the time interval between clicks passes a certain threshold - this is when the ball is at a specific speed. When this threshold is passsed, the computer will vibrate at which time the player notes which number is under the reference diamond. Let's say it was number 32 (number A). This is an un-tuned prediction so we call it the RAW prediction. Then the player waits for the ball to fall and come to rest in a pocket. Let's say the winning number is 6 (number B). If we look at the distance between each number (A and B) in the chart left, we see this is +9 pockets (9 pockets clockwise) from the first to the second number.

It is important to understand that when the computer vibrates, this is telling the player that the ball has reached a target speed. And from this point, even on different spins, the ball will complete mostly the same number of revolutions before it likely hits the dominant diamond then falls.

The player repeat this process for 30-60 spins and add each jump value to a chart like the one shown left. After enough spins, we will find that certain areas of this chart have groupings of high bars (called 'peaks').

In the chart shown left, the peak is at about +10 pockets. This means for the player to win, they need to place bets around +10 pockets from the 'raw prediction' (Number 'A').

To Simplify

The player just keeps clicking a button until the interval between clicks is the say greater than 1,000ms (1 second). When this happens, the computer vibrates to inform the player the target ball speed is reached. From that point, the ball will mostly complete 5 or so revolutions before it hits the dominant diamond then bounces much the same distance.

To know where to bet each spin, the player notes the number under the reference diamond when the vibration is felt, then compares how far the ball actually lands from this original number. Then to know where to bet, the player just makes the adjustment on each spin.

Sounds simple enough? Almost every roulette computer you will find for sale will do only the very basics as explained above. It was all you needed 50 years ago, but beating modern wheels in modern casinos is far more complex.

Common Visual Ballistics Deception

Some sellers of visual ballistic methods will charge you thousands of dollars to learn visual ballistics methods you have learned here for free. Before you paid them, they would have told you that the method they teach is the best. But the truth is visual ballistic methods are all very similar. They all use exactly the same principles. Certainly some visual ballistic methods are overall better than others, but the differences are not often significant. One exception is if the method relies on a consistent rotor speed for accuracy to be achieved. For example, one individual claims his visual ballistics method is best because it enables you to obtain a visual ballistics prediction when the ball is at any speed. This may sound great, and he lures in uninformed people. But the reality is the method relies on the player having an unrealistic top-view of the wheel, god-like skill, and a rotor speed that is almost identical on all spins. The reality is such a methods cannot be applied in real casino conditions. Even slight variations in rotor speeds alone eliminate accuracy. On the other hand, one of his competitors who he unjustly attacks teaches a far better method that doesn't require consistent rotor speeds. So you need to be very careful about who you believe, or rather understand the principles for yourself, so you understand what is feasible.

NASA's roulette computer, or snake oil?

Roulette computers that you can buy typically range from $500 - $5000, yet most do exactly the same thing. How is the price difference justified? IT ISN'T. Don't just take my word for it. So you know this for yourself, try using visual ballistics on their demonstration videos, and you'll achieve the same accuracy without even using a roulette computer. Remember that no matter what a vendor tells you, you can easily expose nonsense with careful testing and research of your own. If you prefer to just take other people's word for it, don't expect to know the truth.

Roulette machine learning games

Roulette Machine Learning Game

Of course every merchant is expected to promote their product, and it is common for merchants to stretch the truth about their products. However, the gambling industry has far more deception and false advertising in it than any other area of business I've ever known. It seems every roulette computer seller wants you to believe their device is space-age technology that cannot be obtained anywhere else. But the reality is almost every roulette computer uses the same basic algorithm explained on this page, and the accuracy differences between them are virtually negligible. Don't let technical talk and fancy charts fool you. When you break it all down, you are left with a salesman trying to sell a basic computer that is no better than visual ballistics.

Roulette Machine Learning Games

The simplest roulette computer I offer is called the 'Basic roulette computer'. No fancy names. It is just a basic roulette computer using the basic design described above. It is FREE to my roulette system players because it realistically can beat only perhaps 5% of wheels, and still the accuracy is nowhere what could be achieved. Other device sellers sell comparable devices with exactly the same accuracy for between $500 - $5,000. Again, the price differences are not justified. I distribute this device for FREE. You can achieve exactly the same accuracy with basic visual ballistics methods. Alternatively you could buy a device for $2000 that does exactly the same thing, except the vendor blatantly lies and claims it does much more, and is the most accurate device available anywhere.

The various roulette computers I offer are compared to devices from other vendors at the roulette computer comparison page. There you can better understand the difference between a simplistic device that can only beat easily beaten wheels, and a device that squeezes every last bit of predictability from a roulette wheel while making application practical, covert and easy.