The sports world is an entertainment business at its core, and technology is starting to play a more important role in the expansion of this business. From the NHL to the NFL, MLB, NBA and NASCAR, virtually every major U.S. sports league is now incorporating AI to expand its bottom line.
The North American sports industry is expected to rake in $73.5 billion in revenue by 2019 in the form of gate revenues, merchandising, sponsorships, and media rights.
There are a number of key areas in the sports industry in which AI now has an important role, including computer vision, automated journalism, marketing, and wearable technology. It can track athlete performance and improve their health by offering recommendations on how to prevent fatigue or injury.
But these use cases are only scratching the surface of what’s possible in athletics, as we could soon see smart assistants aiding football coaches on their decision-making and game planning. Technologies such as smart process automation can help to digitize up to 90% of the brunt work in a variety of industries, including collegiate and professional sports.
Here’s what you should know about the four newest applications of AI in sports.
1. Computer Vision in NASCAR
NASCAR is designed to provide high-speed entertainment for millions around the U.S., but safety is still the number one concern in the stock car racing league. The sport has experienced an average of more than one death annually since 1950, with the trend appearing to continue in the past five years. Plus, a single race car is worth around $300,000, plus repair, maintenance, and labor costs—tires alone are $500 a pop. AI is now using deep learning to create self-driving cars, while it’s also helping to enhance the safety measures in the car racing world.
This technology employs a deep learning neural neural network that can identify specific cars using images. The neural net is trained on a dataset with thousands of images of cars in different conditions, which ultimately helps to more quickly and accurately identify a vehicle that’s malfunctioning before the driver is at risk. This then signals to maintenance crews to fix malfunctioning cars as quickly as possible.
2. Automated Journalism in MiLB
Minor League Baseball (MiLB) is quickly becoming a trendsetter in the world of sports journalism. The Associated Press has been working with an AI startup to expand the media’s coverage of minor league baseball games. A platform named Wordsmith translates statistics and other hard data from MiLB games into narratives using natural language.
The platform is now capable of covering 13 leagues and 142 MLB-affiliated teams, resulting in 3,700 quarterly earnings stories, which is a 12-fold increase over the press’ manual efforts.
Wordsmith creates about 1.5 billion pieces of content annually. The platform uses data that’s structured in a manner that makes it easy for AI to generate articles.
Virtually every major sports league in the U.S. could use additional marketing at any given point to maximize that league’s resources and bolster their ROI. It will always be necessary to have the human creative muscle to make a marketing campaign tick, and AI can help these workers achieve their goals.
Many of the basic human tasks can be replaced by technology, including the study of customer data in order to generate campaigns that fit the context of where consumer interests are trending.
Content generation serves as another marketing tool that AI can play a role in. Additionally, the technology can track content performance in order to determine how well a campaign is doing.
4. Wearable Tech
Fitness devices such as Fitbit have been around for a while now, but more sophisticated devices are also being developed to fit the needs of specific sports.
PIQ and Everlast developed the first AI-powered wearable for combat sports, which uses a machine learning platform for sports analytics. The device is capable of tracking and analyzing small variations in boxing movements in order to maximize how efficient a workout or training session is. Boxing data is then recorded and stored through a phone app, which can be used to track activity and compare it to other boxers based on a leaderboard.
We also have connected sneakers now, as Boltt Sports Technologies developed special sneakers with a stride sensor that can be synced to Bluetooth through the company’s app.
Machine learning allows the app to track performance data and offer recommendations based on user goals. The app also has a nutrition guide, workout library, and custom training programs.
There are a number of other ways professional sports leagues are leveraging AI and machine learning to improve performance, entertainment value, and more. Here are a few additional uses cases to check out: