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Can AI predict football matches?

kickoff.ai - About us – Kickoff.ai: predicting football matches

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Kickoff.ai uses machine learning to predict the results of football matches. Based on data about national teams from the past, we model outcomes of football matches in order to predict future confrontations. This page provides a little bit more information about what is happening behind the scenes.

How does Kickoff.ai predict results?

Kickoff.ai uses a statistical model of football matches. With respect to most other models out there, two key differences make Kickoff.ai better.

We model team strength dynamically

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, our 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. We use Bayesian inference 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.

1 Our model says 80%...

The football predictions that you see on this site are one particular application of the models and methods that we investigate in our research. In fact, we hope that some of the lessons we learned while building Kickoff.ai will be useful in improving some of the general machine learning techniques that we make use of.

Relevant publications

L. Maystre, V. Kristof, M. Grossglauser, Pairwise Comparisons with Flexible Time-Dynamics, Knowledge Discovery and Data Mining (KDD), 2019. L. Maystre, V. Kristof, A. J. G. Ferrer, M. Grossglauser, The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions, Machine Learning and Data Mining for Sports Analytics (MLSA), 2016.

L. Maystre, Efficient Learning From Comparisons, PhD thesis, EPFL, 2018.

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