In the 2015 movie “Concussion” starring Will Smith, the protagonist, a forensic pathologist, investigates multiple cases of severe mental disorders among former football players to arrive at the conclusion that they were all caused by the athletes’ sport careers in the first turn, but also – and to a very considerable extent – by inefficient safety measures taken by the NFL to protect players from systematic head injuries and blatant neglect of their deteriorating health conditions. Although the movie was set in a relatively near past, technologies that existed at that time could hardly have prevented any of this. Today, however, advancements in wireless sensor technologies, RFID tagging, player tracking, real-time visualization, computer analysis and, of course, big data as we know it, have given us everything it takes to send a distress call even before something is about to go wrong.
At the same time, big data is in no way limited to health and athlete performance monitoring only. These days, sports are a global multi-billion-dollar industry made up of leagues, teams, clubs, players, managers, fans, fan clubs, service providers, merchants, sponsors and businesses of all sorts. All these actors constantly interact in countless ways, creating a massive and ever-growing amount of data, some apparently useful and some seemingly useless, that can be utilized to improve virtually every aspect of professional sports, both inside and outside. From player tracking and performance analysis to devising optimal game strategies and all the way through to fan base management and extra services at sport arenas – the era of big data has come and the game is on.
Analysis of athletes’ behavior is the first idea that pops up in everyone’s mind when it comes to big data in sports. Each game can be represented as a colossal array of digital data and the number of conclusions and deductions drawn from this data is nearly limitless. Coaches can validate the correctness of their strategic decisions, match the team’s performance against historic values, track individual progress, identify trends in opponents’ playing styles to come up with counter-tactics, and do a lot more things based on actual data, not hunches and guesswork. Some of this is still being done using more conventional methods, but companies like Stats, Zebra and Sportvision are already offering state-of-the-art solutions that take sports analytics to a whole new level, both in terms of the scope of collected data, its detail, quality of visualization and depth of analysis.
The most serious challenge of leveraging big data in sports is not its collection, but processing and further application, including monetization. To that end, companies can use readily available BI products or build custom BI solutions and Big Data analytic tools. This software enables users to consolidate and normalize heterogeneous data sets, add complex mapping and visualize the results so as to obtain meaningful and actionable insights. The latter, in turn, can be used for a variety of purposes – from preventing cumulative injuries among athletes or adjusting their training programs to changing the overall strategy/composition of a team based on competitor analysis.
Although sports are all about athletic achievements, they are also all about money. According to competent sources, the global sports market will generate over 90 billion dollars of revenue in 2017, and this number will continue to grow. With so much money at stake, every party involved will push the envelope to score a home run – and this is much easier to do if you know what to do. With big data feeding companies with countless hints and tips about fans’ preferences, attendance trends, social network activities and even seating patterns, companies are now able to target their ads, products and online offers with unprecedented accuracy, spawning a multitude of new monetization opportunities. IBM, for instance, offers the cloud-based “Fan Insight” platform that uses big data analysis to help teams and venues to forecast fan base dynamics and generally form the image of a typical fan. This, in turn, makes it possible to look their fans’ needs from the right perspective, enhance their experience, lower attrition and increase sales.
On-site assistance and extra services
Big data comes in small bits and pieces flowing from ticket booths, online ticket services, point-of-sale terminals installed at arenas and other venues, gift shops, social media like Facebook or Foursquare, fan sites, and even form in-stadium beacons monitoring crowds of fans by interacting with their mobile devices. All of this information, if used properly, may and will help businesses provide sports fans with additional perks like advanced interactivity with elements of augmented reality, the ability to watch instant replays of the most notable moments of the game, assistance with in-venue navigation, provision of detailed information about WC queues or vacant parking lot spaces, possibility to order food to be delivered right to their seats and much more. In other words, fans will benefit from the increased comfort, while businesses will generate more revenue by bringing in more people, saving their time, sending them in the right direction and giving them exactly what they want at the right moment.
As progress ploughs onward, it becomes apparent that the sports industry is getting more and more dependent on big data. Imminent advancements in wearable technologies will inevitably redefine the strategic decision-making process and healthcare in sports, reducing traumatism and helping athletes to consistently demonstrate their very best. Big data analytics will help build better-integrated and efficient teams and train individual athletes for optimal performance. Finally, companies will enjoy the benefits of having a highly loyal customer base eagerly consuming merchandise and services offered to them at home and during sport events. The future of big data in sports looks bright and promising, and we are yet to see its most impressive parts.