Global

Get to grips with the 'big data' phenomenon and understand how it can be applied to the sports industry in this in-depth report.

The specific nature of on-field metrics has enabled sports analytics to come a long way in a comparatively short time: from goal expectancy and earned run average to strike rate and line breaks, almost every aspect of play and performance is now being tracked, benchmarked and fed into tactics, training programmes and transfer strategies by almost every major team in every major league worldwide.

Man has been collecting and analysing data for centuries. From Dr. John Snow’s mapping of the London Cholera Outbreak of 1854 to statistical visualisations of Florence Nightingale after the Crimean War, we have a long history of counting, observing and collating data to solve problems and make new discoveries in everything from healthcare and education to commerce and defence.

The datasets most organisations will be examining as part of their big data initiatives fall into the categories of structured and unstructured information

Big data can often – if not always correctly – be seen as requiring big investment, meaning it needs to demonstrate the value it can add to business planning and performance. Consulting firm McKinsey identifies five broad areas in which big data can create this value:

Accessing data is not an issue for most businesses – more typically the issue is drawing out the analysis that will deliver its value.

From ticketing and merchandise to sponsorship and broadcast rights, many of the areas in which big data promises sports organisations improvement and success have previously been targeted through customer relationship management (CRM). So does the advent of all-singing, all-dancing, all-powerful big data spell the end of CRM?

In Chapter 1 we talked about ‘right data’ as much as ‘big data’, reflecting the consensus that bigger is not always better and that the best data is the type that is most relevant to your business goals.

Successful teams wear different kits, but are often underpinned by similar strengths. Successful data-driven sports organisations are no different: they may pursue different objectives through widely differing means, but there exists a core of behaviours and characterisations that are broadly common to all.

How does data-driven strategy evolve? The answer is slowly – and the comparative youth of the discipline is the principal reason for that.

One issue at the heart of the cultural challenges facing big data adoption in sport is the perceived threat it poses to established command structures, and the value of experience on which the authority of senior management is primarily based.

Stressing the collaborative role of big data in the commercial sports strategy mix should not overlook its huge potential to identify new opportunities, as well as confirm or disprove established business hypotheses.

In 2015, British horseracing set out to become a data-driven sport to achieve its ambition of growing attendances from six million to seven million by 2020.

In 2013, racecourse operator The Jockey Club launched the first retail bond in UK sport as a means of funding a major redevelopment of Cheltenham.

The age of big data has been created by the ability of technology to track almost everything we do, not just digitally but in the ‘real-world’ too; consumers are not necessarily producing more data, we are just able to capture more of it than ever before and do so from across an ever-widening range of sources. ‘Datafication’ is the term most used to describe this shift.

Internal data is the easiest type of big data for sports organisations to source as it is all information they already possess, own and control access to.