There’s a great article in the Financial Times describing the use of data in football. The focus is on the Everton manager David Moyes who is the likely candidate to replace Sir Alex Ferguson at Manchester United. But for this post, the interest is in how Moyes used data to improve performance.
The background into the club highlighted how it is one of the most under-funded football clubs in the Premier league yet consistently out-performs wealthier rivals. Data is used in a range of different ways, but the value has come from when it is personalised to the individual situation.
Rather than have a signature style of game plan like most football managers, Moyes analyses data to determine the specific tactics for each game. If the data isn’t available, the in-house team gathers it from video coverage of football matches. Capturing statistics about the performance of each player and the movement of the ball. Identifying patterns but also putting the statistics within context to help predict behaviours for the next game.
The same approach is applied to talent recruitment. The largest signing at Everton to date has been for Belgian player Marouane Fellaini. Everton needed a specific style of player to replace one who had been sold on. Fellaini was not well known at the time, short of being sent-off early during the 2008 Olympics. From the article:
There were few match stats for Fellaini, because there was then no data available for Belgian league matches. And so Everton watched videos of him to compile their own stats, using key performance indicators that seemed relevant.
The tactics worked and, as a result of his performances since joining Everton, he is expected to be transferred this summer for a far bigger fee than Everton paid. It’s a great article – link at the end of this post to read in full.
Using data to improve performance is not a new science. If you want to read up on the subject, there are plenty of books to choose from. One of my preferred is ‘Competing on Analytics’. (Yep, the title of this post is not the most original.) But I am surprised how little analytics are used to innovate within business, compared to sport. The difference is in the level of personalisation. Within sport, the use of data is highly contextual to the individual scenario to maximise potential within that moment. In business, it tends to be more generalised to look at the ‘big picture’ but often loses value in the process. Much like attempts to capture and re-use knowledge.
General predictions tend to lead to general results (read ‘average’). To compete requires raising performance above average and that means doing something differently to everyone else. Personalisation enables that difference.
References
- Everton: how the blues made good – by Simon Kuper, Financial Times, 3 May 2013 (may require subscription to access)
- Competing on Analytics – by Thomas H. Davenport and Jeanne G. Harris, published 2007 by Harvard Business School Press
Side note: the image is me 🙂 a sports-related post is too good an excuse for the occasional horsey pic
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