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What Is the Monte Carlo Method?

The Monte Carlo method is a computational tool that helps you determine the odds of various outcomes through many simulations. It is helpful in different fields, including engineering, oil and gas, and project management.

With the broad ways it can be used, it is no surprise that this technique is also prevalent in the gambling scene. This strategy is very effective in gambling as it can help you determine the potential outcomes of a hand before you place a wager. With the Monte Carlo method, you can determine the probability of winning your wager, losing, spreads, and many more.

You are on the right page if you are new to this system. This guide will break down the Monte Carlo Method, how it works, the common Monte Carlo techniques, and more. You’ll also learn how to leverage this method in sports gambling, even as a newbie.

What Is the Monte Carlo Simulation?

The Monte Carlo Simulation is a strategy for solving various math problems. With this model, you can simulate possible results of an event that are influenced by randomness. Gambling is a good example of this kind of event, as the outcome of a bet is usually random. For instance, you can’t determine the team you back in a sports match will win. Better still, you’ll be unable to confidently determine that your next spin on a slot game will land you a winning combination.

Regardless of how the odds seem, the outcome is meant to be based on luck. However, the introduction of the Monte Carlo analysis has pointed out ways to improve your winning chances. Using the Monte Carlo Simulation, bettors can forecast the result of a bet and make more calculated decisions.

Brief History of the Monte Carlo Simulation

The first person credited with the study of Monte Carlo Simulations was Stanislaw Ulam. During a solitaire game, he considered his winning probability in the game. This led him to try to determine the odds, but he wasn’t successful due to the huge number of combinations in the game.

To deal with this, he decided to simulate the game using a computer instead of playing the hands manually. With the help of John Von Neumann, a well-known mathematician, and his ENIAC machine, they ran the first solitaire game simulation. This led to the creation of the Monte Carlo system we know and love.

The Simulation was named after a Monaco casino because it shared the same randomness with a roulette game. In 1946, Ulam released the first document extensively explaining the Monte Carlo Simulation.

How Does the Monte Carlo Simulation Work?

In a gambling scenario, the Monte Carlo Simulation uses a model of chance and an algorithm to carry out simulations and determine the likelihood of a wager. This system works best with the rule that the house always wins.

The Monte Carlo Method gets you closer to your outcome by randomly selecting from various probability distributions. It uses random numbers to simulate the outcome of a model numerous times. With every Simulation, the MCS calculates the impact of uncertainty and risk to provide different possible outcomes.

Because Monte Carlo Simulation software runs thousands of simulations, it can give you a good look at the possibilities and risks of different outcomes. If you are working with a massive wager, this method can help build confidence in your wager. So, here is an example of how it works in different gambling events.

Sports Betting

Presuming you want to wager on an NFL match between the Green Bay Packers and Philadelphia Eagles. The Monte Carlo Method will simulate the match many times to determine the possibility of each team winning, drawing, or losing. All you need to do is alter the variables like injuries, team ability, and track record so you can see the effect on the probabilities.

Casino Gaming

You can use the Monte Carlo method to determine the likelihood of your wagered hand combinations paying out in a Blackjack or Baccarat game. If you prefer, you can also simulate the likelihood of the ball landing on a specific pocket in roulette or the probability of rolling a six in a dice game.

Information You Need for a Monte Carlo Simulation

For a Monte Carlo Simulation to work correctly, it needs three major components. These include the variables and mathematical models the computer simulates to provide results. Read on to learn more about these components:

Input Variables: These are random values that influence the result of the Monte Carlo Simulation. For instance, in sports betting, this could include the history of the team, past injuries, number of goals, etc.

Output Variable: This is your result after conducting Monte Carlo experiments. It displays this historical data in a graph or histogram, properly distributed in a continuous range.

Mathematical Model: This helps to mathematically describe the relationship between the input and output models. The Simulation can run for long hours if many random variables exist.

How to Carry Out a Monte Carlo Simulation

Carrying out a Monte Carlo Simulation is easy. If you don’t know how to go about it, here’s a step-by-step guide involved in a Monte Carlo Simulation:

  • Collect your Data. This is perhaps the most important step in running a Monte Carlo simulation. The wrong data can leave you with the wrong results.
  • Determine the problem you want the system to model. For instance, who will win the match between the Packers and the Eagles?
  • Enter your variables. For example, past team performance, team strength, player injuries, and goals.
  • The system constantly simulates the game and generates random samples using each variable.
  • Change variables as you desire to see how they impact the outcome.
  • Analyze and interpret the result.

Commonly Used Monte Carlo Methods

You’ll find plenty of models that work seamlessly with Monte Carlo Simulations, all with varying extensiveness. However, if you want to use this Simulation for gambling, you can choose from one of the three types below.

Deterministic Model

This is the easiest model to use and is ideal for betting on sports. This is because you already have all the variables you need before the game starts, like the team records, injuries, past scores, etc. With this information, calculating the outcome is easy. A good example is the match between the Green Bay Packers and Philadelphia Eagles mentioned earlier. The match could end with the home team winning, the away team winning, or a tie.

Stochastic Model

This is a more complex Monte Carlo method. The Stochastic Model uses randomly generated numbers to figure out the likely outcomes. In this case, you have plenty of random variables instead of one function – and your goal is to get numerous results.

Unlike the Deterministic Model, you’ll need to pick the right outcome yourself using the trends in the result. In sports betting, a great way to apply this model is to determine which teams will make it to the world cup final or win the league.

Dynamic Model

This model is an extension of the Stochastic Model. Here, the parameters improve as the model is simulated. For instance, if you placed a wager on Chelsea to win the premier league, you update the model after each match to ensure accuracy as the season progresses. While this model offers more accuracy, the odds might not be great on sportsbooks since you are practically placing live bets – which have lower odds.

How is the Monte Carlo Method Used in Gambling?

Gambling is an activity that is based on luck and randomness. Since the Monte Carlo Method can predict outcomes of events that are meant to be random, it can be quite helpful. With the Monte Carlo Method, you can predict the most likely hand in a game of blackjack, the team that will probably win a football game, and more.

Is the Monte Carlo Method Applicable to Any Sport?

The Monte Carlo Method applies to any sport with variables you can enter. For instance, Football, Basketball, and Baseball. So long as the sport can fall under any of the Monte Carlo methods we have covered above, you’ll have no problem using it.

When to Use This System

Ideally, we recommend you use this system when you want a range of outcomes from a wager. This shortens the time you spend analyzing as the model quickly gives you the likely outcomes and their odds.

However, as you must have observed, this system can be difficult to understand and requires skill to get the best result. So, we recommend you first practice by wagering for free first using bonuses. Once you have gotten around it, you can use it as often as possible.

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Pros & Cons of the Monte Carlo Method

The Monte Carlo Method is awesome, with numerous benefits. It is great for generating predictions and determining sportsbook odds while considering numerous variables.

But like every great strategy and method, Monte Carlo integration has a few drawbacks. It is important to weigh both before you use this method, so you know if it is the right call for you. We have put together all you need in the table below.

Can help calculate sports betting oddsNeeds accurate data. Wrong data means wrong results
Useful for quickly solving complex gambling problemsIt can be expensive and time-consuming, especially with a large number of variables
It can be used to predict the impact of changes on an eventIt is complicated and has a steep learning curve
Ideal for most sports events


Is Monte Carlo simulation used in gambling?

The Monte Carlo Simulation is a model that works for various fields, including engineering, finance, and iGaming. Punters can use the Monte Carlo Simulation to determine the likelihood of their wagers.

What is an example of the Monte Carlo method?

There are many commonly used Monte Carlo methods. But when it comes to gambling, the most popular options include the Deterministic, Stochastic, and Dynamic Model.

Why not use the Monte Carlo simulation?

While the Monte Carlo simulation is great, it is a complex system. Moreover, the outcome depends on inputting accurate information in the variables. Failing to do this means you get the wrong information. If you are unsure of the variables needed for the Simulation, this Simulation may not be ideal.

What is the success rate of the Monte Carlo simulation?

The Monte Carlo simulation has a reasonable success rate, but it is not flawless. Your success with this system depends on the variables you use during the Simulation. For instance, using reliable data and properly accounting for key factors. You may sometimes need to forecast and make assumptions during a simulation which can impact the overall result.