Big data is the biggest buzzword in world sports today. A well placed argument, also applicable to many other industries, is the fact that data analytics witnessed gradual invasion within the sports industry much before big data as a mainstream theme caught on with the market participants. Nevertheless, it is undeniable that as of today, big data is an important core agenda for sports marketers.
In sports, big data is relevant for all the three significant stakeholders, namely, the fans, the teams and the team owners. The fans are constantly craving for more and more relevant data and its data analysis in the sports live feed. Fans are also impatient as to why we are not using more technology in making their experience better, whether it is related to adjudicating referee decisions or using more cams to present the best view on the field. For the teams, the blend of technology and big data has meant that they now have tools to track and monitor parameters which were unthinkable in the real-time, both related to team strategies and individual player’s health and performance improvement strategies. For the team owners, fans are the data asset that can be better monetized using fast improving consumer analytics data tools. In this story, I have highlighted five emerging and distinct ways in which data is changing the market landscape.
#1 Building better strategies and machines in motorsport
This is more of an evolution than a disruption. In Formula 1 sport, every tactical pit stop move is a competitive decision making. Teams track the heath of their car, the strategies of the competitors and measure the odds of winning by comparing the strategies available. So far, team directors and drivers, while relying on the technology for smart skimming, favour manual decision-making for pit stop strategies. But with big data enabling more information collection the many parts and systems contained within the car, we are likely to see an evolution of sorts where all the possible outcomes of any strategy can be evenly mapped out with all the possible variables analysed. Even outside the circuit, big data will help analyse drive performance for better results. Formula 1 will soon become a competition of only the machines.
#2 For in-depth and real-time player performance and health tracking
Technology has been the main catalyst here. Devices such as GPS systems, heart rate monitors, motion capture sensor technology mapping can track athlete performance in real time. Aside that, regular medical diagnostic tests on athletes also adds to the data repository. For coaches, trainers, sports medics, scientists and athletes, capture and analysis of this data is truckload of worthy information. Information analysis of this data can help on issues ranging from injury prevention, rehabilitation and performance improvement.
#3 Drawing the fans to the game venues
Big data, combined with technology, can play a strong role here. Off late, we’ve also seen several team owners’ induction technocrats at critical positions in their management team. The objective is twofold. The first objective is to draw the fans by offering them an experience at the game venue that outscores the in-home large HD screen viewing. This will involve real-time analytics and analysis available at the venue. For example, The Tampa Bay Lightning give its season-ticket holders a smart jersey. This jersey, with implanted chip in the sleeve, offers live food and concession discounts to the patron. Secondly, big data can help to draw the fan to the game. This will involve using the fan (customer) data to develop customized marketing appeal to her to watch the game live. A lot of it also involves remain connected with the fan via social networks, ticketing sites and forums to instil and sustain the values of real-life game viewing.
#4 For efficient nurturing of upcoming and new talent
In colleges, tracking emerging athletes’ health records, academic performance and keeping a tab on issues and future prospects can help teams and coaches identify and nurture talent far more effectively.
#5 For building accurate predictive models for sports
In baseball, a new buzzword in “expected possession value”. Its creators from the Harvard University claim that it can predict the resulting points at any given time in a game depending on who has the ball and where do they have it on the court. We might see an extension of this analogy to a lot many games in the future.
Some of the companies/startups active in the business are:
- Sportvision Pitchf/x technology is already in use in baseball to track pitches during games. The company also offers solutions for football and motor sports. The PITCHF/x system tracks data on pitch type, speed and movements and correlates the same with the player performance and health status. Such a tool can go a long way in building predictive models in the future.
- Adidas miCoach is one of the many wearable devices that capture and analyse real-time health and performance data such as speed and heart rate of the athlete.
- Zebra Technologies has developed a RFID tag that can be attached to equipment, balls or even players to track its motion.
- SportVU has installed six cameras at each NBA arena that tracks the movements of each player and the ball at more than 25 frames per second for big data analysis. Similar to Pitchf/x, it tracks vital movement indicators in the playing arena.
- iBeacon from Apple iOS is a Bluetooth technology in iOS 7 from Apple that allows team management to track fans who have the apps installed on their iPhones and are in the stadium. The team then uses this app to help the fans with live information relevant to them such as freebies and offers at the concession stand to what is the queue status in the washrooms.
- Australia-based company Catapult has developed a system called OptimEye. Equipped with gyroscope and accelerometer, this systems provides real-time data on player movements such as movement intensity, elevations, jumping ranges, etc.
- Teams might want to be tight-lipped about proprietary technology for competitive advantages reducing probability of collaborative technology development.
- While data collection and visualization is good, using all the data to develop sensible predictive models is another ball game and requires talented data scientists. Talent is going to an immediate shortage for this industry.
- The technology is still very expensive and team owners, if not satisfied with the utility of the products and returns generated in the short term, might get impatient and discontinue the belief in big data.
- Cost of talented big data scientists could further increase the investment costs in the industry.
- Player objections could be another hurdle. While it is true that performance analytics has the potential to keep a player injury free and maybe prolong his career, the same system also unearths the potential hidden physical problems of the players. In this scenario, team owners might want to distance themselves from aggrieved players sooner than expected.