With the continued growth of online gambling, finding a way to stay relevant has become critical for any sportsbook. One way of doing this is using Machine Learning (ML) to its advantage.
An ML model is a computer program that can learn from data. The data is typically loaded into a computer from a database or spreadsheet, and the model is trained on this data.
Once trained, the model can then be used to make predictions or recommendations on new data.
There are many different types of models, and each one has its own specialty and purpose. One of the more popular use cases of ML is for betting. We’ll go over what makes ML so special and unique in relation to sports betting, and dive into the world of ML in betting.
What is ML in relation to Sports Betting?
In a nutshell, ML is what allows a sportsbook or any other type of betting operation to analyze past performance and determine what style of betting to implement going forward.
This analysis is usually done using statistics and probability, and much like any other type of betting, these are applied to the odds given for each sporting event.
For example, let’s say that a sportsbook is offering odds on a certain sporting event. For some reason, this particular betting operation decides that they don’t like oddsmakers who give them even odds (2.0) on their games, because they think that these types of odds result in profits that aren’t much higher than those of an oddsmaker with a long history of success. In this case, the sportsbook would most likely train a model that favors oddsmakers with longer track records of success, and gives them a slight edge (usually 1.0 or 2.0 points) over their competitors.
ML is not meant to be a perfect science, and many times a model will be trained using a combination of data from several different sportsbooks. This way, the model will have the best chance at being accurate.
Why is it beneficial to use ML in relation to betting?
There are several unique advantages to using ML in relation to sports betting. To begin with, ML allows for a much greater degree of precision when calculating winning chances than is usually possible with human bettors. This is important for a number of reasons, not the least of which is that it allows for more accurate predictions and recommendations on betting games.
Additionally, using ML in relation to sports betting provides a greater degree of transparency to the betting public. Since most sportsbooks use a standardized method of computing odds, the results of any given calculation will be the same for all gamblers. Therefore, there is no hidden math going on behind the scenes that could result in different payouts to different bettors.
One of the most significant benefits of using ML in relation to sports betting is that it allows for greater flexibility. It is well established that the odds given for any sporting event can vary significantly from one venue to the next, and often change from one week to the next at a number of different sportsbooks. This is largely due to the fact that a lot of the information used to generate these odds is not readily available until a few days before the event.
For instance, let’s say that the Denver Broncos play the Oakland Raiders in the NFL on Sunday, September 8th. The game will be played in Oakland, California, and the over/under is set at 44 points. If the sportsbook for whatever reason doesn’t have access to all of the information necessary to generate accurate odds until Thursday, September 12th, then they’ll have to make do with whatever odds they have access to, which may or may not be accurate.
The problem with this scenario is that between the time the game is played and the time the odds are updated, there is usually a significant amount of movement in the odds, and this could put the gambler at a significant disadvantage. This is why it is preferable to use a model that is already trained to take into account the variations in odds that may exist between different sportsbooks and locations, so that the gambler does not have to worry about constantly re-training the model.
There are many different variations of the above scenario, but the point is that using ML in relation to sports betting provides a significant degree of flexibility, and allows for much higher winnings than with traditional methods of betting. This is probably one of the reasons why so many sportsbooks turned to ML in the first place.
Types of Models that can be used to generate betting odds
In sports betting, there is no set formula for how to generate betting odds, and this is one of the reasons why so many different models exist. A lot of the time, the choice of which model to use will depend on the needs of the operator. Not all models are created equal though, and some are more suitable for certain types of operations than others.
A neural network model is what is most often used in AI to process and make decisions about large amounts of data, and much like the human brain, these models are able to learn from experience and continually improve at an incredible rate. While these types of models are extremely powerful when it comes to fitting data, they can also be incredibly difficult to train, and require a lot of samples to achieve acceptable results. They are also great at adjusting to new situations and are not as prone to overfitting as some of the other models available.
An example of a neural network model that can be used to generate betting odds is the XNORM neural network, which was developed by Ellevest. This model is fully trainable, and can be used to make accurate predictions and recommendations on the outcome of various sports and betting events, with a degree of flexibility that allows for much greater winnings than with other models. This model has the added advantage of being able to generate betting lines for more than one event at the same time, which means that it can be used to make multiple predictions without having to input the data for each event separately.
Another model that has become very popular in recent years is the gradient boosted tree. This is a type of “ensemble” model that combines the accuracy of a statistical model with the flexibility of a decision tree. The great thing about this model is that it is extremely easy to use, and requires little to no data pre-processing to achieve optimal results. This model is great for getting started, and although it is not always the best choice, it has the potential to become very accurate when trained on a significant amount of data.
The downside to both of these models is that they require a lot of computational power to run, which most online casinos and sportsbooks don’t have access to 24/7, so they’ll need to make use of their servers’ idle time to generate betting lines. Even then, this won’t be a quick process, and it could take hours or even days to get a decent amount of predictive data.
How can ML be applied to improve my sports betting experience?
As we’ve established, ML allows for much greater flexibility when it comes to sports betting, but this also means that it can be used to improve your overall betting experience. A lot of the time, the choice of which model to use will depend on the needs of the operator, but in most cases, the goal is to create the best possible betting experience for the user.
One of the ways that ML can be applied to sports betting is to create a more accurate experience for the bettor. There are a number of issues that can arise from the inherent inaccuracies in the odds that are currently available for any given event. For example, let’s say that you are interested in betting on the Denver Broncos to win the Super Bowl this year. Currently, the Broncos have an over/under of 21 points, and you look at the odds which currently show that the Patriots have a 1.7 edge in the win. While this may not seem like a significant point differential to you, keep in mind that the goal is to make your betting experience as accurate as possible. In this case, you could ask the operator whether or not they are willing to make the Denver Broncos – New England Patriots odds more in your favor.