top of page
  • Writer's pictureRob Bates

Optimising the sports viewer's experience

It’s often claimed that big data analytics is for the benefit of the customer as much as the provider but cynics almost equally counter that it’s really there just to improve profit margins. Sport is a great example of both sides benefiting hugely. This is a big money business where the smallest of margins deliver vastly different results. One goal conceded for a Premier League team at the end of the season could result in millions of pounds of lost prize money and a millisecond lost in qualifying in Formula One could be the difference from starting 1st or 4th. With so much at stake across such minute differences it should be no surprise that teams and athletes across the world, in all sports, constantly seek ways to gain that edge – and analytics now plays a key role.

F1 offers a prime example: each car is fitted with 200+ sensors, each transmitting data back to the trackside garage and the team’s headquarters. Teams of analysts work from this data to build on race strategies as the race is progressing. In the 2017 season, Mercedes-AMG Petronas had up to 30 analysts trackside and 200 more back at their Brackley headquarters. Sensors monitor everything from speeds, car temperatures, engine performance, reliability and downforce to tyre wear. Intel reports that each car can transmit over 2GB of data per lap: that’s 3TB of data over a full race. Add this to the data accumulated during qualifying, 3 practice sections and previous races and we are taking about huge datasets.

F1 Data Gathered - Source: Inte

It’s this data – which obviously benefits the teams – that underpins the enhanced viewer experience. Viewers at home can see lap times, sector times, speed traps and expected race strategies amongst many other statistics, all of which makes the experience impressively more direct. One of the more recent inventions has been the likelihood of an overtake statistic. Built using Amazon Web Services machine learning capabilities and using live in race data, this has the ability to predict which driver will come out on top in a duel, giving the viewer a % likelihood of passing. But with so many data points available, the point comes when we need to know which ones enhance the experience and at what point is the viewer just overwhelmed and the experience is diminished.

Football provides another great example. Although some purists argue the game is too fluid and raw to be analysed in such a way, the evidence is to the contrary. Most professional teams now have a team of analysts and most players wear some form of tracking device, monitoring heart rate and distance run, amongst other factors. Using machine learning algorithms analysts can now track player movements and make predictions on the actions the players will take. Based on studying spaces on the pitch, data scientists at FC Barcelona found that Messi was able to make more space for himself in certain positions by standing still or walking rather than sprinting.

The TV football viewer benefits hugely: Opta Sports track every Premier League game, each creating over 2,000 data points, which are then used to enhance TV coverage. This is how we now see predictions such as Expected Goals (xG) and Expected Assists (xA). Based on numerous variables such as the quality of a shot, type of assist, the angle of the shot, and distance from the goal, xG gives an indication of how many goals a player or team should have scored given the chances they had. But, as with F1, what is the optimum range and depth of analysis for the viewer? What mix brings the game alive, gives insight and increases engagement and at what point will the viewer suffer from over supply and just tune out, thus undermining the whole experience.

Of course, sport is simply a great example of how data – understood, analysed and used properly – can not only benefit the provider but also deliver an experience for the viewer that informs, educates, engages and makes the event so exciting they can’t wait for the next time round.

The big question, to which we at Shoppercentric can provide clear and precise guidance, is what mix of information and analysis optimises the experience.

Rob Bates

Follow us on Twitter and LinkedIn.

Source -


Commenting has been turned off.
bottom of page