Sport has gone digital from end-to-end. Not just in terms of e-commerce to drive ticket sales and technology to drive so-called VAR (Video Assisted Referee) playback systems, but also in areas covering player performance analytics, provision of stadium WiFi systems and even facial recognition systems serving venue security services.  

As we consider how big data analytics is being applied to sport, it’s interesting to examine just how far we will push the analytical envelope. If the German national soccer team uses SAP data analytics to track player movement heatmaps, monitor diets and calculate detailed metrics on player aggression…then where do we stop? 

Where previously we found that data analytics stopped “before” we got down to the level of analyzing how fast the grass on the field grows, that statistic is very much a part of what sports enterprises are analyzing today. Think of a seemingly insignificant (but still potentially quantifiable) metric related to sports (such as air quality, bathroom breaks, or time of sunrise and sunset perhaps) and we’re probably now measuring it. 

This topic of sports data analytics and its wider discussion was the subject of the ”Leaders Meet Innovation” sports technology summit held in London, England in January 2018. Speakers included representatives from SAP, Cisco, the AS Roma soccer club, Boston Celtics, Instagram, and the National Basketball Association (NBA).

365-day 360-degree Fan Experience 

According to James ”Jim” Pallotta, owner of the AS Roma soccer team, “A lot of teams have information on their season ticket holders, but very little information on their total fan base. Roma has built out an application called Fan Manager to start building a database of fans’ names—those that openly list themselves on social media, those that are happy to join loyalty fan club lists and those that are happy to subscribe to newsletters and other services such as dedicated club Internet radio programs. In this way, we can start to build out a 365-day 360-degree fan experience, even when the season is on a break.”

Representing SAP at this event was Greg McStravick, president of database and database management. McStravick’s presentation was entitled ”How Machine Learning Will Shape Sport.”  

“As data starts to become an asset that needs to be recorded on the financial books of every trading company, we are seeing companies that are figuring out how to use data effectively—and, conversely and equally, we can see those that are literally asleep at the switch,” said McStravick.  

“There is no industry that will be sacrosanct from the effects of technology. What is non-negotiable is that data will be a part of all transactions at the core. But what we also have to realize is that 90% of the data being produced today will not come from ‘traditional’ sources that traditionally feed corporate databases in an area we call Mode 1. This is where we find transactional systems that work with corporate systems of record.”  

McStravick’s description of Mode 2 data is the stream of data that comes from places such as phone data, geospatial data, and time series data. So logically then, we can say that most sports analytics data relating to teams and their business models sits in Mode 1 and most player performance data resides in Mode 2. The sum result of this new even bigger world of big data is a world where operational business models start to change.  

”What the Hell?” Business Models  

“I pay to park my car to not use it. What the hell is wrong with this business model? Digital natives will not accept this reality in the next future, which is here now already—this is why you will, soon, not be paying for insurance on the days you are not driving—this is another business model about to be shaken up,” said SAP’s McStravick.  

He suggests that the toughest play to make here is getting hold of Mode 2 data and making sure firms can put it to productive use inside the organization, whether that firm operates in the sports business or some completely different vertical.  

“SAP started in Mode 1 and has invested heavily in Mode 2 to get to the future. As a business starts to move into dual mode data analytics, it’s also important to know that the data you use tomorrow may not necessarily always be your data, that is—it’s the weather, it’s social media, it’s IoT device data and everything in between,” said McStravick.  

Sports Lessons for all Industries  

We must come to a point here where we ask whether the sports industry provides lessons for general business across other verticals. The answer is almost certainly yes and the point of interest comes out of the way sports organizations react with their customers, who, crucially, they typically refer to as fans. If pharmaceutical firms, construction businesses, or Fast Moving Consumer Goods (FMCG) manufacturers and other businesses can start to develop the same kind of loyalty in their corporate customer connections, then we will exist in a more integrated society.  

SAP thinks that we have yet to really see any tangible concrete examples of machine learning in the sports industry. We need to get way past the point where we see companies (in fact not just in sports) say, “hey look, we’ve got a bot.” Simplistic software bots that exhibit some basic form of artificial intelligence (AI) when interacting with users are a long way from authentic or complete examples of machine learning.  

“In the SAP world of enterprise level machine learning, we need to look for more tangible examples such as models that can look at the relationships between Accounts Receivable (AR) and which companies pays when, etc.,” concluded SAP’s McStravick.  

Long-term developments could even see us allow users to create their own user bots to be able to personalize their preferences and behavioral traits so that when firms start to build machine learning and AI-based interactions with us as users, we can also influence the way these bots interact with us.  

Sportserization of Business  

If there has been a consumerization of IT (and there very much has) as enterprise users now accept certain previously consumer-level-only applications and devices as standards in the workplace, then could we be on the edge of seeing the ”sportserization” of business? It could well be so.