Machine learning is a study of computer algorithms that scour massive amounts of data in search of structure and patterns. The algorithms learn from the data and improve independently over time, without additional programming from their creators. As such, machine learning algorithms can extrapolate predictions or decisions on their own.
Machine learning is a subset of artificial intelligence and is responsible for some of the biggest strides in this field. At its core, machine learning mimics what humans have done for centuries. You upload the data, and it finds a pattern and attempts to predict future events based on those patterns. Many core fields, like economics, rely on these sorts of predictions.
Machine learning is human learning on steroids, with computers able to process infinitely more data and more efficiently detect patterns, creating better predictions. Today, much of the world relies on machine learning. Services we use every day, like Netflix and YouTube utilize machine learning in order to provide recommendations. Most online search engines depend on machine learning as well.
Machine Learning and Sports
So, what about using machine learning to predict the outcome of sports matches? In recent years, this has become a popular field of study. This comes as no surprise, as humans have been doing it since the birth of professional sports and betting. Data like historical results, previous team and player performances, or whether a team is playing home or away have been used to create betting odds and develop match strategies for decades.
When applying machine learning to sport result predictions, algorithms are fed exactly this type of data in order to try and make a prediction. Many machine learning techniques use the number of goals scored in order to evaluate a team’s performance and therefore its chances of winning.
Picking the Right Metrics
As you can imagine, there are several obstacles to making accurate predictions. The first one is obvious – no sports event is entirely predictable. The famous German football coach Sepp Herberger put it best: “The ball is round, the game lasts 90 minutes. Everything else is pure theory.” Unexpected results and random events always occur, and these moments are an important part of the charm in sports. The possibility of the unbelievable is why people love to bet and what makes betting so profitable.
Of course, people working on machine learning predictions do not expect to predict 100% or 90% of the matches. But they strive for the highest degree of accuracy. Unfortunately, it’s not as easy as piling up all the relevant data and making the algorithm learn from it. Identifying the correct metrics for determining team performance is a problem in itself.
We’ve already mentioned how the number of goals scored in a football match is a metric that’s often used to measure team performance and predict outcomes. However, the number of goals possesses a random element that makes predictions difficult. Determining the key features is crucial and arguably one of the most important aspects of making better quality machine learning predictions. You also have to take into consideration the specific leagues you’re making predictions for, as biased referees and match-fixing can also greatly impact results.
Artificial Neural Networks
Most newer studies of machine learning predictions for sports matches rely on artificial neural networks. ANN’s are computing systems designed to simulate the way the human brain functions. It’s modeled after the human brain with its interconnected nodes, containing inputs (the data you have the computer analyze) and outputs (the predictions) resembling neurons.
This technique allows an even greater amount of factors to be taken into account by the computing system when making predictions. Still, some problems persist, as there’s always the question of which factors to take into account. Naturally, the more tests the algorithm runs – ie, the more matches are played, the better it becomes at making predictions. This is how machines learn.
Some studies, like the one conducted by Paolo Giuliodori in 2018 that offered a prediction model for matches involving underdog NBA teams, take into account bookmaker odds as an additional factor. This is to be expected, as bookmaker markets have so far shown to be very precise.
What does the future hold?
So, can we expect machine learning to get so good at predicting matches that crashes the betting market? Probably not, due to the presence of random elements. But that doesn’t mean that the predictions won’t get better over time. A growing number of people are increasingly reliant on machine learning and welcome advances in this field. Bookmakers can make their odds around their predictions, coaches and managers can make better tactical decisions, and it has a positive effect on player development and sports medicine as well.
Author Bio: Ilija Acimovic, a researcher and a writer currently investigating tech topics such as machine learning and blockchain technologies, and their impact on society and businesses.