Monte carlo model - what is it?
What is the Monte Carlo Method?
It is a common assumption that gambling comes down to luck and nothing more. You place your bet, cross your fingers, gather your four-leaf clovers and then leave the outcome to the finer points of fate. Sometimes it will go your way and sometimes it won’t. Lady Luck is a fickle mistress and she does not always smile down on you.
Such assumptions simply are not true. While luck certainly does play a part in the outcome of any bet (indeed, the outcome of any sporting event), betting is not just about good fortune. As we explored in our Best Betting Books article, there are certain hints and tips you can learn from fellow bettors - and even leading lights in the financial world - to improve your betting game. There are also certain strategies and methods that can be used by bettors to make smart decisions and increase their likelihood of winning.
One of the most well-known of these is called The Monte Carlo Method. But what is it, how does it work and how could it help you win more on your next bet?
What is the Monte Carlo Method?
Mathematics isn't all isosceles triangles and Pythagorean theory, you know; it can also play a part in understanding risk, randomness and ultimately betting. We won’t go into it in too much detail (because it is incredibly complex), but in a nutshell, The Monte Carlo Model is a mathematical concept that allows people who use it to bring together a large amount of data and forecast what might happen in the future. It does this by showing a vast range of possible outcomes and allowing the user to draw conclusions from there.
You’re probably wondering why it is called the Monte Carlo Method. It’s an unusual name for something that’s complex and mathematical, but there’s a good reason for it. Monte Carlo is famous for its casinos, and like the slot machines that are so popular in casinos, chance and randomness are key to its outcomes.
The Monte Carlo Method was invented by Stanislaw Ulam, a famous scientist who worked primarily in mathematics and nuclear physics. During the Second World War, he was a part of the famous Manhattan Project, which brought about the Atomic Bomb, and after the war went on to have an incredible and varied career.
The Monte Carlo Method was just one of the things he created, but he devised it in the most banal of circumstances. While recovering from brain surgery, Ulam found himself playing countless games of solitaire and started mapping the probability of winning. Working with John Von Neumann, he formed a partnership and eventually developed the Monte Carlo Method as we know it today.
The Monte Carlo Method is particularly useful in the financial industry because it can be used to understand the risk of a potential investment or decision. This means that it is certainly not for the faint of heart - it is, after all, maths, so if you hated the subject at school, it may be a stretch - but it can also be used to help anyone placing bets understand the possibilities of certain outcomes.
You could run a Monte Carlo Simulation to work out the probability of certain outcomes of a big football match and therefore more accurately forecast what will happen. Then you can see if the odds being offered by a bookmaker match those probabilities by using an odds calculator to see the implied probability in each price.
With a football match, the Monte Carlo method would employ an algorithm to run a simulation to calculate the probability of a home win, a draw or an away win. To inform the model you would add a number of parameters to system, such as the weather, the recent form of the teams, the managers’ head-to-head records, home form of the home team, away form of the visitors, the specific form and records of key players, injuries and so on.
The Monte Carlo Simulation would then allow for a cold, pure calculation of the potential outcome of the game.
What are the pros and cons?
Like all forecast systems, the Monte Carlo Method has positives and negatives. These should all be taken into account before using the model.
In terms of the pros, the Monte Carlo Method offers probabilistic results. This means that simulations don't just show what could happen, but the likelihood of these outcomes. This is a good thing because it helps the person using the method understand the potential ramifications of a decision in much more detail.
Another positive lies in the way the method produces its results. The Monte Carlo Method makes it much easier to visualise results as, for example, graphs. This makes them much easier to understand and derive learnings from, if you have an eye for graphs.
The major disadvantage of this method is very much the same as any other kind of modelling or forecasting: assumptions. It is impossible to get around it. Nobody can see the future, so all kinds of forecasting models require people to make assumptions and essentially take guesses. These can affect the results and make them far from perfect.
So anyone using the Monte Carlo Method should keep that in mind. It is a robust and intelligent approach, but it will not deliver perfect results every single time. So do not bet the farm on it.
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The Monte Carlo Method is complicated and certainly not for everyone. You need to have a strong understanding of some pretty complicated mathematics, formulas and forecasting principles to really get the most of it. If you do not, you could end up going down the wrong path and deriving incorrect learnings.
If you do have that knowledge (or are willing to put the hours in and learn it), the Monte Carlo Method can be incredibly effective and generate great results. Just remember to bring along you calculators and graph paper!