Signal Noise, The Economist and Siemens have worked together to visualise the fan energy in FC Bayern Munich’s Allianz Arena.
The visualisation includes: game timelines; fan energy; highlights; players; and social ripple. The visualisation provides the user with a rich array of options.
I think this is a great example of the analytic turn in sport and highlights the data expertise available to sport.
Earlier this year, Signal Noise hosted a Data Obscura exhibition that explored the relationship between data and truth. The exhibition was launched with a panel discussion that considered whether transparency and truth should be the ultimate aim online, and asked “how much is ‘true enough’?”.
This interplay between practice, epistemology and ontology is fundamental to anyone contemplating a career in sport analytics at a time when:
Multiple filters are applied to the information that we see: algorithms distill a world of opinions to give us a distinct view of events, and authenticity is becoming an increasingly scarce commodity. (My emphasis) (Data Obscura, 2018)
This contemplation could lead to a consideration of epistemic cultures and the machineries of knowledge construction. Karin Cetina (1999) writes:
Everyone knows what science is about: it is about knowledge, the ‘objective’ and perhaps ‘true’ representation of the world as it really is. The problem is that no one is quite sure how scientists and other experts arrive at this knowledge. The notion of epistemic culture is designed to capture these interiorised processes of knowledge creation. It refers to those sets of practices, arrangements and mechanisms bound together by necessity, affinity and historical coincidence which, in a given area of professional expertise, make up how we know what we know. Epistemic cultures are cultures of creating and warranting knowledge.
This process involves what Maurizio Ferraris (2006) defines as ‘documentality’. For Maurizio, documents are social objects (such that they involve at least two persons) “characterised by the fact of being written: on paper, in a computer file, or simply in people’s heads”.
His theory develops in three different directions:
- an ontology (“What is a document?”)
- a technology (an explaination of how documents are distributed)
- a pragmatics (an understanding of the efficient distribution of documents)
Sharing the Signal Noise, The Economist and Siemens venture into the Allianz Stadium here has led me to reflect on learning journeys.
The volume and quality of data analysis opportunities positions this generation of data analysts in sport in a very important ontological and pragmatic space.
There are more ways to share primary data and analysis than ever before. Each of us can make an informed and transparent decision about the machineries we choose to construct information sharing and stimulate conversations about knowledge and understanding.
In my case, I use the WordPress blog platform to connect ideas that strike me as important. I discovered news of the Signal Noise project on Twitter. The tweet came as I was re-reading Maurizio Ferraris and editing the Ethical Issues page of the wikiEducator course Sport Informatics and Analytics. In sharing this process openly, I am hopeful that readers can make informed decisions about authenticity and contemplate these issues as worthy of consideration.
Photo Credits
Frame grab Reimagine the Game
FC Bayern (Twitter)
