ML (short for Machine Learning) is a field of computer science that provides systems that can learn on their own from experience – like how we teach computers to understand speech, recognize objects, or play games. ML provides the ability for computers to learn in an automated fashion, without the need for manual intervention – this is in contrast to traditional methods of data-handling where decisions are made by human analysts and data scientists.
Even though ML algorithms can be very effective at learning, it is still important to understand the fundamental concepts behind this field – and how they relate to betting. Below we will discuss the basic ideas behind ML and how you can integrate these into your betting strategy.
What Is ML and What Does It Mean?
ML is a field of mathematics that provides a framework for machines to “learn” from experience (i.e., data). Specifically, ML describes the theoretical procedures whereby a machine can predict the outcome of a given situation (e.g., throw of the dice, stock market forecast, weather prediction, etc.) based on information about past outcomes and other relevant variables (i.e., input variables).
Generally, you can think of ML as the systematic study of how to make accurate predictions based on existing data – and the science of statistics applies to ML in much the same way it does to any other field of scientific research.
It is important to note that while ML algorithms can be very effective at learning and making predictions based on existing data, it is still necessary to understand the basic concepts behind this field – and how they relate to betting. Without this basic knowledge, you may end up predicting outcomes incorrectly (i.e., due to faulty reasoning or analytical errors) and potentially suffer from gambling-related repercussions.
The Need For Data
While it is possible to gain a good understanding of ML by studying online tutorial videos or reading academic papers, nothing compares to experiencing it first-hand via a demo account. This is why it is advisable for prospective bettors to do some research into ML prior to investing any real money – particularly if they intend to use an automated system to follow their strategy (as opposed to doing all the trading themselves).
The reason for this is that while it is possible to program an automated system to follow a specific trading strategy, it is still necessary to input the relevant data into the system (e.g., the specific symbols you want to trade, how much money you want to risk per trade, etc.). This is because most automated systems are built on the assumption that you are providing them with ready-made data (e.g., from a broker’s API) – and not creating the model yourself (using, for example, an online neural network training tool).
In other words, you would be better served by building up a demo account and experimenting with different ML algorithms (such as gradient boosting, logistic regression, and random forests) – and using the results to determine which algorithm performs best for your particular strategy (e.g., a long-short equity strategy, or “hedge fund” strategy, etc.).
The Goal Of ML
When developing strategies for automated trading, the general objective is to create a model that can predict future outcomes accurately – based on past experience. To put it another way, the goal of ML is to develop a tool that can learn from experience and recognize patterns that help it make more accurate predictions.
Once you have achieved this, you can use the tool to make intelligent decisions about the future – such as whether or not to enter into a particular trade (or which type of trade to enter into), or whether or not to continue holding a position (e.g., in a trending market), etc.
This may seem like a tall order, but developing a model that can accurately predict the future is a process that can be very challenging – particularly since there are so many different variables that may impact the outcome (i.e., input variables that can change the result).
To give you an example, let’s say you develop a model that can predict the outcome of NBA games. This model may include statistics such as points scored, field goal percentage, and rebounds obtained by each of the players on each team. In addition, it could include “advanced” stats such as “effective field goal percentage,” or the “True Shooting Percentage” that takes into account all the pertinent factors (i.e., scoring, field goal percentage, and 2-point shots). So, at this point, your model can predict the outcome of an NBA game accurately – but what if I told you there was another statistic that the models couldn’t account for? Say, the quality of the officiating?
In this example, your model may be predicting the outcome accurately – but you would be ignoring something important that would otherwise impact your decision-making process. In other words, if you want to consistently make money from betting, it is important to actively seek out new information, whether it is available via a formal publication or a blog post on social media.