Artificial Intelligence
Consulting
Development ServicesBig Data is referred to as the analysis of voluminous historical data in order to find key trends and make better business decisions. The use of Big Data and predictive analytics in sports has driven many sports organizations to build data analytics departments to foresee potential teams success, surmount salary cap restraints, and improve fans engagement.
Sports teams have come up with truly sophisticated ways of monitoring and capturing ever growing data volumes. Cameras, sensors and actuators record nearly every aspect of player performance; managers, athletes and coaches are using this data to monitor calories intake, training levels and fans engagement.
Since Oakland Athletics general manager Billy Beane became illustrious to use analytics to turn around his MLB team, development of data perspectives towards sports has kept on growing precipitately. By applying predictive modelling, he elevated process engineering and analytics to surmount salary cap restraints. Beane also appeals to the Moneyball theory stating that the primary percentage of players is consequential in foreseeing team success and salaries.
97% of MLB teams and 80% of NBA teams take on analytics professionals (source: Innovation Enterprise, 2015)
Dallas Mavericks coach Rick Carlisle has recently announced that its analytics team had been broadened with new data scientists: “We’re going to be a better team this year – we know that by the analytics.”
Technological changes help push sports activity research forward by monitoring people’s attitude towards sport. For instance, STATS SportVu tracking technology is widely used by NBA to obtain player and team statistics.
The broad usage of statistics proves Big Data can be a perfect tool for sports as it gives the opportunity for teams to collect enormous amounts of data through various channels, and more to the point, to turn it to their advantage.
Since referees often make judgements in seconds flat, there is always a possibility for a mistake. So, it is beyond controversy that Big Data technology can come practicable for referees to make better decisions. The Pitchf/x technology from Spotsvision has been set up in all 30 MLB playing fields to track pitches and to easily determine if a throw is a strike or a ball.
Now sports games can be observed more profoundly than before as the immense amount of information becomes available. Such Big Data products as IBM’s Slam Tracker gives a heads up to analytics of tennis matches to give analysts the pleasure of inquiring into every stroke and point.
Besides, many technology implementations are attempting to make their way to the wearable technology market and interested in devices like Google Glass and fitness trackers.
The UK's Premier League soccer team Arsenal has allegedly invested millions of dollars in building their own data analytics team to make better use of data. Important data is provided by the 8 cameras installed around the stadium and tracking every player and their interactions during a game. Arsenal uses Big Data solution provided by Prozone to track 10 data points per second for each player or 1.4 million data points per game. The system also monitors 12,000 soccer matches globally by using specific automated algorithms and coding every interaction with the ball to increase the value and accuracy and analytics.
The wearable application that Intersog has recently built for racket sports enthusiasts and professionals allows for live scoreboard broadcasting, personal stats and achievements through gasification, local and social ranking and a powerful data mining engine to generate more insights into players' individual performance and track their success / progress over time.
Not only Big Data and predictive analytics can influence the on-court activity; it also provides a better understanding of fan engagement and their in-game experience. This information is invaluable for sports marketing teams that can significantly improve the outreach of their marketing programs and fans experience during matches.
And what other Big Data use cases in sports can you add here?
