How To Make A Betting Model
- How To Make A Sports Betting Model
- How To Make A Betting Model In Python
- How To Make An Nfl Betting Model
- How To Make A Baseball Betting Model
- How To Make A Betting Model In Word
Betting as a process of placing money on a chosen result of a sporting event has a simple aim, which is to help you generate profit on regular basis.
Clearly, one does not set out to create a sports betting model without an initial goal or basic knowledge of mathematical concepts. There's a reason betting models are only common for sportsbooks or full-time bettors. They take time to create and then you have to tweak them if something is off. It's an ongoing process that changes by the season. In principle replacing decisions by specific humans with betting markets should work – the contractor could, when facing a decision like where to put the subway line, make a betting market that pays out if the value of the ConTracked goes up upon that decision being made – but in practice this would be hugely complicated, require massive.
Consistent betting wins are difficult to achieve, but not impossible. This is where a betting model kicks in as a system that is intended to help you identify elements with which you can predetermine if a certain outcome is profitable or not.
What’s a Betting Model and how to Build It?
A Betting Model is a mathematical system which determines the probability for all outcomes in a particular match, without a biased look at the teams involved. A betting model will assess a team’s ability to win rather more accurately and help you get an extra edge on bookmakers.
Betting Models are not easy to build and they often require some understanding of mathematics and programming. In order to fully understand them, you need to have insights in quantitative risk assessment methods such as Actuarial Control Cycle, which is used by big insurance companies around the globe.
Similar to basic elements of the risk assessment method, a betting model will contain a couple of different features. In order to have a successful betting method built you need to:
- Define the problem
- Build the solution
- Monitor results
- Be Professional
- Be Aware of External Forces
Eight-Step Subdivision
The aforementioned five elements that help you form your betting model are divided into the total of eight steps you need to take in order to make sure you have a working betting model.
- Aim of the betting model
The most common mistake a punter can make is to adopt a generic aim of the betting model, as it can often lead to players getting stuck under the numbers and getting their vision blurred, losing focus of their more specific goals.
Narrow focus is required and a specific aim of the betting model will only help you be more successful with other steps of the way.
- Select the metric
Same step is applied in the Actuarial Control Cycle when you need to make your investigation fit in a quantifiable metric.
- Collecting data
This step is self-explanatory as each model depends on the collected data you will subsequently put into your algorithms to analyses. You can of course do data collecting yourself, but there are too many of the services which are providing data solutions that you can easily be saved of all the trouble.
- Form of the Model
Interchangeable with step three, format of the model is where the math come into play. One basic model is the one that looks at the past three matches, a simple but effective way to give you valuable insight before you go all complicated.
- Assumptions
Self-questioning is imperative and if you don’t make assumptions of your model and try to foresee the possible problems and issues, they will hit you hard and even before you know it.
- Build the working model
This step is about the actual building process through tools and calculators which are provided through Excel, Java, VBA and others.
- Test the model
The efficiency of a model needs to be put to a test so that you will be able to see if any of the previous steps should be reconsidered or edited.
- Monitor the results
Once you have found an adequate model which works, it needs to be constantly monitored and maintained through time so that you can get valuable information of its success rate and efficiency.
How do you build a sports betting model? What steps are involved? What do you need to consider? Follow these steps to build your own quantitative model, and take your betting to the next level.
What is a betting model?
In it's simplest form a sports betting model is a system that can identify unbiased reference points from where you can determine the probability for all outcomes in a particular game.
The model will ultimately be able to highlight profitable betting opportunities, by judging a team's true ability more accurately than a bookmaker.
However, building a sports betting model can be difficult and time consuming. There are various instructions and orders advised for you to follow when creating a model, which can complicate the process.
With that said, once you have created a successful betting model, it can show you opportunities that the general betting public simply wouldn't consider.
Let's begin.
For this example we use an approach similar to the Actuarial Control Cycle – a quantitative risk assessment employed by insurance companies. There are five main features:
- Defining the problem
- Building the solution
- Monitoring results
- Professionalism
- External forces
Step 1: Specify the aim of your betting model
This appears simple, but many sports bettors miss the point their betting model is trying to accomplish.
Once you have created a successful betting model, it can show you opportunities that the general betting public simply wouldn't consider.
Without an aim you could be overwhelmed with numbers and lose focus of your overall goals.
Although you may argue you can get the data first to see if there are any patterns, this would still need to be tested against a number of hypothesis, each with a different aim.
Therefore starting with a specific, rather than a generic aim, is strongly recommended.
Step 2: Select the metric
The next step is to formalise your investigation into numerical form by selecting a quantifiable metric.
These first two steps relate to defining the problem stage of the Actuarial Control Cycle.
Step 3: Collect, group and modify data
Every model needs data so you can integrate it into your algorithm. There are two ways of collecting data – by yourself, or by using other published data online.
Luckily, there is a plethora of data available on the Internet, some of which is free, while some websites offer a paid service.
Once you have the data, you may realise that there are queries that need to be taken care of.
If we are looking at Premier League teams for instance, should you consider all matches or just their league games? It's possible to make adjustments if the team in question had players missing, or had a mid-week Champions’ League clash.
This is where you can exercise your judgement, determined by what your aim is.
Step 4: Choosing the form of your model
This is where the mathematics comes into play given there are so many models to choose from or invent.
There is a plethora of data available on the Internet, some of which is free, while some websites offer a paid service.
We have proposed a number of models in the past and they can be as complex or as simple as you wish. Our recommendation is not to overcomplicate.
This step can be interchanged with step 3 as the data may lead you to use a particular model, or a particular model may require specific data.
Step 5: Dealing with assumptions
How To Make A Sports Betting Model
Each model will have a number of assumptions, and you should be aware of their limitations. You may forget to do this, but it's absolutely vital.
For example a significant contributor to the financial crisis in 2007-08 was the misuse of derivatives caused by a misunderstanding of assumptions in contracts such as Collateralised Debt Obligations and Credit Default Swaps.
Previously in this article we highlighted how averages and standard deviations assume events are normally distributed. This for example would need be tested.
Step 6: Build the sports betting model
The next step is to actually build the sports betting model. There are numerous tools to use including online calculators, Excel, MatLab, Java, R programming and VBA.
You don’t have to be a wiz at programming to build a sports betting model, but the more you understand the functionality, the better equipped you will become when testing and analysing the data.
Step 7: Test the model
You don’t have to be a wiz at programming to build a sports betting model, but the more you understand the functionality, the better equipped you will become when testing and analysing the data.
It's paramount that you test the efficiency of any sports betting model to understand how sensitive it is to the results.
In any case the results of the model may lead us to reconsider any of the previous steps.
The key question as always is whether or not the model is making a profit? Therefore you’d need to test that – leading you to running through the cycle again.
Step 8: Monitor results
Assuming that an adequate model has been built and tested, it needs to be maintained as time progresses. This leads us back to the starting point – defining future aims.
Applied knowledge
How To Make A Betting Model In Python
Understanding the processes involved is paramount when learning how to build a sports betting model.
How To Make An Nfl Betting Model
Quantitative modelling isn’t just about taking a model and applying it, there are a number of processes – not necessarily in the order stated – which should be completed.
Following this process won't guarantee a profit-making model, but it will ensure you are considering the fundamental aspects that are needed to build a new sports betting model.
How To Make A Baseball Betting Model
For an example of how to build a betting model, click here.
How To Make A Betting Model In Word
Dominic Cortis is a lecturer with the Department of Mathematics at The University of Leicester; and an assistant lecturer at The University of Malta. He is an associate actuary and his research focuses on sports analytics as well as financial and betting derivatives.