AI in Sports: Exciting Computer Vision & NLP Applications
The Netflix flick “Moneyball: The Art of Winning an Unfair Game” is the perfect anecdote to start this article. It highlights how the Oakland Athletics Baseball team’s manager Billy Beane streared his focus on an analytical evidence-based approach to compile a competitive baseball team. The team’s front office took advantage of the analytical gauges of the player performance to build a team that could compete with Major League Baseball.
All of this was telecasted in 2011 and today almost 10 years later this analytical horizon is expanding and the world of AI is ready to revolutionize every decision that is ever taken.
Artificial Intelligence (AI) made its mark in the world of Gaming when for the first time ever Googles AlphaGo defeated a Go champion. Check out that exciting game-changing match!
AI is revolutinizing sports while its true that analytics and qualitative statistics have played a critical role in the sports arena for a long time, but AI is a leap ahead by introducing predictions. Right from how a game is played to engaging the audience AI is doing a fantastic job. Soon, it will be predicting results even before a match begins! – Yes the world is moving towards Artificial Super Intelligence and I believe Gaming and Sports will be much impacted by it.
AI has penetrated into the locker room discussions across baseball, tennis, soccer, football, cricket, baseball, basketball, and many others. Sports coaches have been able to share better advice with the team and the highlights flash with better insights on viewers’ screens in a blaze.
Talking about gaming, the companies that were launched twenty years ago are all vying for the same thing: increased gameplay and skyrocketing revenues. AI has taken a plunge in live game streaming, gameplay, in-app purchase prediction and is significantly growing customer engagement. It is basically strengthening the gaming community.
Use Cases for AI in Sports and Gaming
U.S.A’s sports industry has been generating billions of dollars in revenue each year. Spectator sports are churning that kind of revenue which is directly impacting their GDP in a huge way. Lets take a plunge and understand the current applications of AI technologies in professional sports.
Conversational AI and Virtual Assistants
Here is an interesting thought by Howard Katz, M.D., a psychiatry instructor at Harvard Medical School that might interest you. Read on…
Sports give the feeling of oneness, it unities the entire nation and make it come alive. Conversational AI can scale up the interaction sports teams/ clubs have with their fans and supporters.
Sports brands can share a fixture list of upcoming matches through chatbots. Making it more intelligent, AI can render information about matches happening near you and just at the click of a button help you buy the ticket as well. These pro-active activities will scale up the fan-base and more fans on the move will be able to enjoy sports.
Chatbots or virtual assistants can share the match highlights, news or any relevant information about a player in near-real-time. It’s like chatting up with a friend about games.
A chatbot can be your “Google-Friend” it can share all the information a fan needs like who won the first Olympics Gold Medel for the US or Who is the world record holder for the long jump.
Chatbot screen can be personalized for every viewer, based on the pattern of interests showcased they can be shown the relevant ads or content they would not switch over from during the recesses.
Computer Vision for Player Tracking
Player tracking is a method to detect the position of all players at a given time. This information is vital for the team management and coach to help in improving their team’s performance and allows them to analyze the ways in which individual players movies on the field and get insights into how a team’s field formation is typically strategized.
The most advanced computer vision applications are used to automate segmentation techniques to identify regions that are likely to correspond to players.
Post the generation of key elements in an image or video frame are detected semantic information can be generated this can further create context on what actions the players are performing.
This information can be vital to a team’s success as it will help them formulate a counter-strategy beforehand, basically revolutionizing training and scouting for players in sports.
Here is an interesting video by Prof. Moeslund on how computer vision is currently used in sports and an in-depth understanding of computer vision-based researches in the sports industry.
Real-Time and Automated Sports Journalism
Automated Insights, a North Carolina based sports journalism startup is working with Automated Insights to expand the media outlet’s coverage of games in Minor League Baseball.
Minor League Baseball hard data are converted into narratives using Natual Language Processing (NLP). This has resulted in an increase in the broadcasting channels reporting capacity to cover 13 leagues and 142 MLB teams. “3,700 quarterly earnings stories – a 12-fold increase over [AP’s] manual efforts.”
Since sports statistics are number based automated journalism fits into it really well. All the hard data can be structured to make articles simple to read and faster to publish.
Data Collection and Annotation for AI in Sports
All games are captured on videos and that becomes the basis for building a computer vision model. The clips of goals, penalties, near misses, player placements, field environment, fatal accidents are very useful in building predictive models. The information can be further classified into sport-specific groups by assigning them labels.
The action recognition and classification can automatically generate performance stats in a match or in the training session. Data annotation can also be applied to index videos based on their contents to be able to browse through footage with ease and generate highlight movies automatically.
The use cases are marvelous but one bottleneck remains. The high-quality labeled dataset to train your deep learning models to adhere to any one of the above-mentioned use-cases.
Are you encountering a similar problem? Training a Computer vision model requires a voluminous amount of high-quality labeled data. Which in turn needs a sophisticated enterprise-scale, cloud-based annotation system.
Where you can plug your data stream from the cloud/on-prem-based databases and get labeled data with confidence scores for training the computer vision-enabled image segmentation models and accounting for a cent percent data privacy.
If yes. I got your back. Let me introduce you to a one-stop solution to all your machine learning needs. Labellerr
Labellerr is a data-annotation platform that provides simple, clear and easy to use UI with seamless UX to perform annotation on different types of data catering to a wide array of industries, Retail, Health care, E-commerce, Hospitality, Businesses to name a few.
For your, AI needs Labellerr provides pre-trained models and products that can deliver Plug n-Play APIs with basic functionality to reach clients and can be modified on request easily and swiftly.
Leverage the auto-label feature on Labellerr to annotate your data with 10x speed and save crucial man-hours.
Automated Data Annotation with Labellerr
Experience the truly automated machine learning experience with Labellerr’s complete ML suite. Just plug in your data via a range of connectors as FTP, Local storage, Google Drive, AWS S3, Azure Blob etc.
Allow our inbuilt Auto ML feature to suggest annotations based on your requirement. Leverage the Auto-label feature to annotate your data with 10x speed and save crucial man-hours. Get a list of confidence scores of the assigned labels and verify only those with a low score.
Connect Labellerr to the Cloud-Based Compute service of your choice with assured data privacy and train your machine learning models on the go. Without the hassle of downloading the data, Service-specific data conversion formats, Data Leakage to name a few.
Labellerr’s community service initiative
As a part of our community service initiative, we have created a GitHub repository, wherein we list the implementations and walkthrough guides of tools, technologies, state of the art algorithms catering to the latest developments in the field of Deep Learning and do our bit in building a strong community of deep learning enthusiasts.
Head over to our blog where we regularly write about the industrial and corporate use-cases of Deep Learning. The recent advancements in the field and how the current industry is accepting them, building over them in pursuit of solutions that were once deemed unachievable.
Have any other use case in mind. Visit our website and mention your use case in brief and our customer engineer will contact you and help you prepare the plan and get you running on a trial with us to validate.