Do you want to make more money from betting on horse races? Horse betting has been a popular pastime for centuries, and today, it’s just as easy to join in!
Thanks to modern technology, you can find numerous betting sites offering a variety of deposit and withdrawal methods, so you won’t have to waste time waiting or worrying about where your money goes. Whether you’re an experienced bettor or someone new to the game, there are plenty of resources at your fingertips to get all the information you need to make informed bets.
However, have you wondered if there’s an approach rooted in data science and machine learning that would help increase your returns? While it may sound like something far outside of the reach of most people, predicting horse race results is increasingly becoming accessible, thanks to the combination of advanced analytics and automated computing power.
In this blog post, we’ll discuss how leveraging recent advances in technology can help increase your success on the track. By exploring technical methods and applicable examples, you will hopefully gain insight to land a winning bet!
What is Data Science?
If you are reading this article, it’s safe to assume you are interested in horse betting and wonder if using data science can give you an edge to win more bets. Before continuing, it’s necessary to understand exactly what data science is.
In its most basic definition, data science is extracting information from data to answer questions. It does this using scientific methods, scientific computing, algorithms, statistics, processes, and systems. The information extracted is either from unstructured, structured or noisy data, and the end goal is to draw insight from the information extracted.
What is Machine Learning?
One of the more interesting forms of study is machine learning, and machine learning is essential to predicting successful horse racing bets. Machine learning is the study of computer algorithms, but one that, through experience, can improve and learn by using data. There are many videos online if people having conversations with “AI”; this is machine learning.
A good comparison to understand machine learning would be that a computer is used to write an article, but machine learning would be teaching a computer to write the article itself. In terms of betting on horse racing, it would be giving the computer or program enough data to learn how to make profitable bets.
How is this Possible?
At this point, you might wonder, “how is it possible to use data science and machine learning to make successful bets?” Well, the first thing you will need to understand is that horse betting is essentially researching information and then drawing conclusions from the information as to which horses are more likely to win.
At the end of the day, any horse can win a race, depending on which other horses are running the race. Since data science is used to extract information, and machine learning requires massive sets of data pools to learn, the two work hand in hand. With enough information and the processing power of AI, successful bets will be made.
Many Variables are Needed
First, as mentioned above, massive data pools are necessary for data science and machine learning to make successful bets. More importantly, parameters need to be set for the machine to make reasonable guesses and to successfully compare the various data sets to make a good bet.
For example, the horse’s name, the track, the going, the race type, the number of horses, the distance, the name of the jockey, the name of the trainer, the rating of the horses, the gear on the horse, the age of the horse, the weight of the horse, weight change of horse from previous race, the rating change since previous race, and more. The variables go on and on but are necessary for the machine to determine a pattern or influential changes in data that could indicate which horse is more likely to win.
Is Human Input Needed?
Yes, human input is still necessary; however, one could argue that it’s only necessary for two reasons. The first reason human input is necessary is to choose the data parameters needed for the machine to learn since the input of specific information allows the machine to learn.
The second reason it is still necessary is to determine how successful the bets were and to analyse if most of the bets are incorrect and where the issue could be. Is it with the information provided? Is there too little? Is it not relevant to predicting the outcome?
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