Build Profitable Neural Networks In Soccer Betting
After understanding the issue, the real deal is to read a lot about it. There is a lot of valuable information online that helps to kick start projects. And to say the least, machine learning isn’t new to the world of soccer. The internet is full of people ready to teach you how they predict match outcomes. Most are mistaking random chance for skill although. The misleading often lies in the size of the test sample : 10 matches isn’t enough to get an accurate accuracy of the model.
Before building our own model, let’s first see how well people are doing on this matter, at least from the people who share about their results online. According to the articles I read on the subject, the best “human expert level” spikes at 48% accuracy. This is better than the “always home win” strategy. Playing home is a significant advantage : 46% of games are won by the home team. These numbers are average and not league specific. Please note that they can vary (+/- 2%) depending on the league you are looking at.
Next is the Elo rating system, a method for calculating relative skill levels of teams. Each team’s rating is represented by a number that changes according to outcomes of past games. The difference in the ratings between two teams can be used to predict outcomes. The Elo system is an effective method that is widely used in sports and games in general. Although in soccer, its accuracy pikes at 48%.
sTruth is, soccer is hard to predict and there is multiple reasons to it. The most obvious one being there is not only win and defeat but also draw, an additional outcome makes it harder. But the biggest one is how low soccer’s scores are. The more points are played during a game and the most likely the better team or player will come out on top. With scores low, random chance can have a big impact since a single goal can change it all between a win, a draw or a defeat. This makes soccer one of, if not the most difficult sport to predict.
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