Usually stats such as ball possession, shot on target, fouls can be predicted with about 98% accuracy, but referee decisions and unexpected occurrences during a match ( players injury and under performances thereof) can change the dynamics of machine learning and predictions.
Machine learning can be used to predict the results of football matches. Based on data about teams from the past, we model outcomes of football matches in order to predict future confrontations. Lets provide a little bit more information about what happens behind the scene.
How does Artificial Intelligence Software predict results?
AI uses a statistical model of football matches. With respect to most other models out there, two key differences make AI better.
Team strength dynamical Model
To predict future matches, a model needs to use data from the past. But how "far" in the past does it need to take data? Ideally, teams would have the same squad all the time and play frequently against each other. In practice, however, selected players change regularly and teams play only a few matches every year. To overcome this issue, TS model allows the strength of a team to change over time. This enables us to take advantage of the many matches played over almost a century, while taking into account that recent confrontations should be more important to predict upcoming matches.
Bayesian inference Model
This is a fancy way of saying that we are able to understand how confident we are about a particular prediction. As an example, take Argentina against Iceland. It is likely that Argentina will win—in fact, almost no one (with the possible exception of Icelanders) would claim that Iceland has better chances. But just how much more likely is an Argentina win than an Icelandic win? 60%? 90% Perhaps 99%?1 On the one hand, Argentina is clearly the better team on paper, but on the other hand Iceland has never taken part in a World Cup final tournament, and might perform over its usual level. Bayesian inference takes this (and much more) into account.
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