In this week’s Data Day, Luke Barratt is joined by Matteo Moschella to discuss the use of data in sport journalism.
Data is omnipresent in the reporting of sport, particularly recently. The closing of the Barclays Premier League January transfer window has prompted a glut of visualisations on the month’s top stories.
Check out some of the code used by the Guardian on their Github.
Athletes and sports teams are using more and more data nowadays to optimise their performance. But crucially for journalists, the vast audiences drawn by sports demand extensive data.
Opta provides detailed data feeds on a number of different sports.
While providing this data to users in raw format is common, there is also great scope for journalists to use data to analyse issues in sport.
Here, Rob Minto uses data to defend a potential increase of the number of teams taking part in the FIFA World Cup.
One crucial area where this kind of journalism has flourished is in predictions. Nate Silver, now renowned as a polling expert, made his name using data to predict the results of baseball games.
Visit his site, FiveThirtyEight, which still applies its methods to sport.
Similarly, the Financial Times has built a complicated statistical model to predict the outcome of the 2016/17 Premier League.
Daniel Finkelstein has a weekly column in The Times using similar methods to analyse football. Here, he uses sport to teach his readers a lesson about probability through a parable about the likelihood of giant-killing in the FA Cup.
We’ve also seen data used for in-depth investigations into sporting issues. Buzzfeed used data from betting markets to uncover indications that certain players had been guilty of match-fixing.
The Sunday Times, meanwhile, in a more traditional piece of data journalism, made use of data from a whistleblower to find evidence of doping throughout the world of athletics.