What I learned about Big Data and Machine Learning from trying to predict football matches.
Ola Lidmark Eriksson
332

It’s true that football is a more of a random sport than other sports due to its low scoring nature, but from a predictive modeling standpoint I would say football analytics has really reached the potential of the tools available.

At this point it seems like the biggest gains are still at the feature/data engineering level, because a lot of potentially revealing metrics have not been been defined or standardized. Caley’s description of creating features for tracking fast breaks exemplifies this. When a lot of domain or technical concepts in soccer like ‘pressing’, zone coverage, or ball-carrying are tracked by metrics designed to track such concepts, we will start to be able to align the pedagogical views of football theory with empirical observations of them from a modeling perspective.

Re using ‘big data’ or tracking data, I would look to see whether IoT is making meaningful discoveries because they seem much more invested in analyzing and using that information than sports.

Show your support

Clapping shows how much you appreciated rames’s story.