What inspired me to explore football data analytics?
I’ve been obsessed with football since I was a youngster.
To this day, the way that professional teams, managers and coaches use football data analytics to turn raw numbers into tangible player and game stats really fascinates me.
However, current approaches (the best-known being Opta sports data), can be a little bit convoluted. They require large-scale operations, meticulous hand-eye coordination and attention to detail that can only be achieved by a full team of people.
As a developer, I wanted to challenge myself:
Can recent trends in machine learning be leveraged to make this level of insight financially viable for the average consumer, as well as naturally ergonomic for football data analysts?
I spent my week of Innovation Time exploring these trends in machine learning to see if I could develop an AI that translates football commentary into statistics.
I was lucky enough to bump into the AFC Bournemouth team recently. As a Cherries fan myself, it’s no surprise I was inspired to take on this project!
Introducing StatChat: The football data analytics platform
Analysing football commentary:
An audio interface lets users stream spoken input that they want to analyse; converting speech to text and translating sentences, phrases and keywords into a language that the computer can understand, define and record.
Translating raw data into statistics:
The system is configured to understand pre-defined “micro-events” within a match. This should cover every conceivable occurrence, including substitutions, throw-ins, offsides, fouls, free-kicks, yellow and red cards, even additional time at the end of each half.
Visualising football data on a dashboard:
StatChat then feeds this data from the API to its dashboard, allowing a bunch of different systems to present the same data in various ways. This could be as simple as including images for each stat, or as complex as presenting patterns and trends over time.