Most people’s ideas about analytics in sport are probably based on the book and film Moneyball, about Billy Beane, manager of the Oakland Athletics baseball team in 2002, and his efforts to find a way to build a winning team without overspending. His analytical approach set the foundation for the use of analytics in sport, but today’s uses go far beyond the original idea.

Just a few examples show that sports teams are now using analytics in a wide range of ways. British Rowing is drawing on analytical approaches to improve athlete performance. SciSports in the Netherlands is using analytics to help teams select the right players. Motion-tracking wearables are improving player safety and preventing injuries on the pitch, including concussion detection and treatment.

All these uses – performance, selection and safety – are related to athletes. The New York Mets, however, are harnessing analytics to help them engage more effectively with fans. This is particularly interesting because it is a much more business-oriented approach, focusing on customers – the fans – rather than inputs like players, and recognising that customer experience is key to success as a business, including for sports teams.

Addressing challenges in using analytics

There are, of course, a number of challenges to the use of analytics in sport. Two cultural issues are particularly interesting. First, if analytics takes the “luck” out of sport, is it as much fun for either players or fans? If sport becomes more predictable, fans may turn away from it to other areas. We are seeing the same challenges about the use of artificial intelligence and algorithms in other arenas. Is your business more about the skills of your people, and will they resist being augmented by analytics?

The second cultural issue is how to embed the use of analytics. This is, perhaps, less of a problem for athletes because they are usually very keen to find anything that will improve their performance. It may, however, be more difficult for managers to accept. They are less used to the idea of data-driven decisions, and more likely to depend on instinct and the reputation of their skilled and experienced subject matter experts.

There are also practical challenges to overcome, such as bringing together multiple sources of data. It is fair to say that the challenges in sports analytics mirror those in business: how to gather data, assure its quality, and manage and store it. Timeliness is also an issue, and particularly in sport, with the need to win NOW, rather than in two or three seasons’ time.

Broader lessons for midmarket businesses

Is an analytical approach new? Maybe not. The Oakland A’s were, after all, using a rudimentary form of analytics back in 2002. British Cycling have been trying to analyse everything for years, with a view to “incremental improvements.”

What has changed, perhaps, is the range of uses, the volume of data, and the computing power available at a relatively low price. These have combined to make analytics much more ubiquitous, and also more useful to smaller-budget teams and organisations.

The majority of sports teams are small to medium businesses, trying to find a way to outplay their peers. They want a competitive advantage that will help them to punch above their weight in a crowded field. Analytics is likely to offer that advantage, and as AI comes on board, even more so. How each team chooses to use the tools available is highly individual.

Find out the different applications of analytics in sports

SciSport innovative project will be illustrated during the SAS Roadshow, Road to Artificial Intelligence, which will take place in many EMEA cities. Join the conversation on Twitter with the hashtag #Road2AI and choose the city closest to you on the website.