Fantasy Football Data


I have been introduced to the world of fantasy football thanks to Julian Zipparo.
Last season I followed his fortunes as manager of Ranieri’s Ghost team in the English Premier Fantasy Football (FPL)  and learned a great deal about forensic insights into performance.

Fantasy Football

Julian is writing guest blog posts for FourFourTwo during the 2013-14 FPL Season.
In his first post, he notes:

This blog will give readers regular updates throughout the season, to help you to keep up to date with what is happening in the FPL world. The aim is to assist you with your weekly team decisions, without having to dedicate ALL of your free time to the game…and of course, without having to watch all of those matches at 2am on a Sunday or Monday morning!

The ALL in capitals in the quote is a very interesting point. The amount of data and social media chat available make voluntary performance prediction a consuming life interest. Particularly if you are following the FPL from Australia in real time.
Julian’s second post gives a feel for the kind of research that goes on. He draws upon the resources of FourFourTwo’s StasZone with data provided by Opta to look at strikers.

Source: Frame Grab
Source: Frame Grab

Julian looks at mid-to-high priced strikers who in the first two weeks of the season “have been getting the ball in the penalty box, having the most looks on goal, and maximising their chances of scoring by getting their shots on target”. An example is Edin Dzeko (valued at $7.7M).
I am fascinated by this kind of secondary data use.
I am very interested in how FPL managers distinguish signal from noise in these data in sufficient time to identify trends to support their team selection.
The volume and quality of performance data available to Fantasy managers was the subject of Bob Roble’s post on NFL ‘big data’. He provided some insights into the work of STATS, Intel and the Harvard College Sports Analysis Collective. Bob reported:

Whether you are a veteran player or a first-time team owner Intel reports that 75 percent of you demand real-time, detailed big data in order to upgrade your draft and team management formulas. In fact, two-thirds of those polled shared that technology is the key component to assisting with managing your team or teams and winning.

Julian’s success with his Ranieri’s Ghost team has encouraged me to think about an invisible army of football performance analysts that are very sophisticated collectors and users of data. Bob indicates just how many there are looking at NFL too.

Fantasy Community Studies

Many years ago, I read Robert and Helen Lynd’s Middletown and William Whyte’s Street Corner Society. Both of these classic sociological texts use key informants to share community stories. Geoff and Judy Payne point out that key informants:

are those whose social positions in a research setting give them specialist knowledge about other people, processes or happenings that is more extensive, detailed or privileged than ordinary people, and who are therefore particularly valuable sources of information to a researcher, not least in the early stages of a project.

Julian is my key informant in the world of Fantasy Football. I am looking forward to following his work and thinking carefully for what this means for a wider community of practice in performance analysis.

Photo Credits

Julian Zipparo (FourFourTwo)
Edin Dzeko (Frame Grab)


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