Traditionally, people had to be present at a sporting event to experience a game. There was no other way to experience it unless somebody told you the highlights of the match. Essentially, sports broadcasting was developed from discussing and retellings of sporting events. Subsequently, the hype expanded more from the radio, and television, culminating in the explosion of sports media we experience today.
Sports production shepherds the planning, setting-up, directing, announcing, and editing involved with producing an event. However, it is becoming harder and far more expensive for sports broadcasters to engage with the fan base and increase the viewership. Hence, AI and Big Data can play a crucial role in overmastering viewer engagement. By automating the entire process of producing, streaming, editing, and distributing, sports broadcasters can target a huge market. Read and understand how AI and Big Data are used in Sports Broadcasting.
Leveraging Big Data in Business of Sports
Broadcast Production and Digital Distribution
Big data has a crucial role in broadcast production and digital distribution. Big sporting events like the Indian Premier League (IPL) are storing and leveraging big data for spectator programming such as replays, ongoing game statistics, displaying game facts, and other relevant real-time data. Eventually, the broadcasters source these use cases which will help in increasing the viewership metrics.
Similarly, the distributors leverage the user-generated data to promote across multiple broadcast channels and social media platforms. The same data can also provide innovative trends ongoing in the sports world.
Exploiting Big Data for Advertising
Distribution without advertisement is like a half-match played. Broadcasters target consumer-generated content data to understand their audience in a meaningful way. Once broadcasters understand their audience, they advertise the sporting events to gain followers and build a brand.
Hitting the shot with Viewer Engagement
Spectators are the highlight of the sporting world whether they’re watching the sporting event at the stadium or home. Moreover, social media channels are giving fans a platform to share videos and tweets about their experiences. Broadcasters utilize these experiences to make audience-oriented content. To illustrate, just before the 2014 Cricket World Cup, broadcasters launched a one-minute trailer. The video ignited a spark among the cricket fans and increased the bandwidth to 12.6 TB. Additionally, more than a total of 73,531 viewers watched the trailer video in the span of two days.
Interested in learning more about AI in Sports: Current Use Cases and Future Challenges
Computer Vision: Gamechanger for Sports Production
Pixellot is a world leader in AI sports broadcasting. The company has produced around 100,000 hours of sports matches every month. In 2019, the company produced more than 220,000 live matches – from tennis, basketball to soccer and handball. It has developed a camera using Computer Vision.
Computer Vision, a subset of Artificial Intelligence, trains computers to interpret and understand the contents inside images and videos. In fact, it seeks to understand and automate complexities in human vision systems and visual perception by applying deep learning models. Interestingly, the models can accurately detect and classify objects from the dynamic and varying physical world.
After installing the Pixellot camera, it provides a panoramic production of a sporting event that can be live-streamed. The cameras capture varied angles with different focus settings. Moreover, it eliminates the concept of a cameraman or production staff operating complex and expensive cameras. Once placed, the standalone cameras do their job with the help of computer vision and AI algorithms and do not require any further assistance.
In the final analysis, AI and Big Data have a long road to travel ahead to outshine the sports industry. However, the adoption rate of new technologies will grow soon. New technologies will not only affect viewers but also advertisers, broadcasters—and even the athletes themselves. It will enrich video content with better insights and better recommendations. Most importantly, the personalization of sports content will transform the way the sports industry operates. In the future, we can even see higher viewer engagement with innovations.
Revti Vadjikar is a digital marketing associate who creates and distributes compelling stories about data science. She creates engaging blogs, case studies, visual and video content for US-based businesses operating in a variety of industries. She is an engineer who is passionate about reading non fiction stories