Cloud competition and sports analytics | Dekiutte Netherlands


Raising Everyone’s Game

Data analytics for amateur sports

As deep data analytics trickles down from top athletes to amateurs, sport will see many benefits

With more than 73 million users in 195 countries, Strava is one of the global standard-bearers for a fast growing market: sports analytics for everyone. People use the app to compare detailed data about their runs, their cycle rides, their swims and other workouts with those of friends and professionals. 
Strava is one of many. In almost every sport, multiple players, including some of the world’s biggest sportswear brands, are competing to meet the growing demand among amateurs for the same kind of detailed performance data and insights available to professional athletes. The falling cost and improving capabilities of lightweight cameras, GPS chips, accelerometers and other sensors are opening up sports analytics to hundreds of millions of people. 
Apps enable amateurs to easily compare their golf swing to that of Rory McIlroy, track their movement on a football pitch, analyse their tennis serve and the power they generate cycling up the hairpin bends of Alpe D’Huez. Most of these apps apply a “fremium” business model, attracting large numbers of users with free functionality, supplemented by a paid-for offering encompassing more detailed data and analysis. 
As well as generating direct revenues for a large band of innovators, the democratization of data analytics has several positive knock-on effects; greater engagement between professional and grassroots sport, a more inspired and active (and healthy) population and easier identification of talented individuals.

More engaged athletes at all levels

As cutting-edge tech trickles down from elite sport, ordinary people are benefitting from the insights available to top athletes. The pursuit of “marginal gains” by professional sports has helped to identify the metrics that really count in terms of performance. The falling cost of technology is enabling almost anyone to track those metrics. At the same time, these services help to build a stronger connection between fans and their idols – they give amateurs a greater appreciation of what is required to get to the top in a chosen sport. Through the gamification of training, they also help to keep people motivated. 
To use Strava and other similar apps, consumers just need a smartphone with GPS or they can buy a dedicated device, such as a fitness watch, a tracker mounted in a jacket or a cycle-computer. In other sports, such as golf, football or hockey, a video camera on a tripod can be used to capture footage that can then be analysed by specialist software running in the cloud. Increasingly, video footage will be combined with data captured by wearable sensors to provide a holistic performance analysis. Machine learning systems can help to detect the data patterns that are hallmarks of the best performers in specific sports, creating insights that amateurs can use to improve their performance. 
There are football apps, for example, that analyse video footage to generate statistical information, such as areas of pitch where most events occurred, expected goals, player-specific data, possession, number of fouls and assists. In most cases, the basic version is free, while users can subscribe to more advanced versions costing between €100 an €600 a year. Another approach is to use a dedicated vest and a GPS tracker. One such system from Playr claims to generate 1,250 data points per second, enabling players to track their tactical positioning, their distance covered, their top speed, how often they sprint and other performance data.  
Many of these services provide basic coaching. For example, they might advise a runner who is training very hard to ease off to avoid injury. One app uses computer vision software to analyse live video of people doing its home workout routines and flags if they need to bend their knees more or work harder. Some services even provide the customer with tips and advice from an expert coach who watches the video footage. In this way, they broaden access to the best coaches. In most cases, these services are making use of increasingly sophisticated machine learning capabilities provided by the major public cloud providers

A two-way shop window

Professional sports are, of course, the ideal shop window for these services and solutions. In its coverage of the Giro d’Italia three-week cycle race, Eurosport is now showing data collected from some of the leading riders. The data includes the rider’s average heart rate, how long they have slept and to what extent they will have recovered from the previous days exertions.  
But sports analytics services can also provide a shop window for professional teams looking to recruit new talent. Virtual cycle races run on the Zwift platform have, for example, helped pro cycling teams identify amateur athletes1 that could be capable of competing on the professional circuit. Of course, service providers need to be completely transparent about the data they are collecting and how it could be used. 
Not surprisingly, the leading companies in this fast-growing market are turning the heads of investors. In 2020, Strava raised US$110 million in new funding, in a Series F round led by TCV and Sequoia – two of Silicon Valley’s leading venture capitalists. As more money flows into sports analytics, the services will improve further still, raising everyone’s game. 

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