There is no denying the fact that technology has played an indispensable role in the world of sports right from the beginning. Over the last decade, new-age technologies such as AI have found several applications in the entire sports life cycle, right from recruitment, training and performance analysis, to ticketing, journalism and match prediction.
AI-based tools are being used in all major sports disciplines such as football, lawn tennis, cricket, yoga and many more. Computer vision-powered cameras, sensors, and wearables are used to collect data on the performance of players. This data is processed using machine learning systems to create forecasting models and help coaches, managers, and referees in decision-making, or to automate a process (e.g. reporting).
In this blog, we will have a look at the applications and use cases of AI in sports to understand how AI is helping revolutionize the industry.
Recruitment
AI can affect a player’s career right from the time of their recruitment into the team. AI can process historical data related to players’ performance, allowing managers to assess their potential before they decide to invest in them.
Everything that happens on the field, be it players’ movements or orientation of their body during a match is captured to make the right decision. AI models can aggregate and process this data to evaluate the skills and overall potential of players and rank them accordingly. Computer vision can be used to identify specific attributes in a player that are likely to determine their performance in the future.
So, all in all, AI can reduce bias during recruitment and help build competent and successful teams.
Personalized Training and Diet
Thanks to AI, the training and diet plans of players can be personalized to the extent needed. Machine learning algorithms can be used to customize meal plans for players depending on their dietary requirements and practice schedule. Apps such as Strongr, Fastr and Fitgenie with several advanced customization options can be used to fine-tune the meal plans for athletes.
AI-assisted fitness apps have also found their way into the market. Using a technique called human pose estimation, it is possible to train algorithms to detect human poses in real-time. In fields such as yoga, such algorithms can spot human joints and guide the user on how to exercise the right way.
Performance Analysis
AI can be leveraged for improving the performance of players using the technique of predictive analytics. Wearables can be used to gather data on the strain and tear levels of athletes to help them avoid serious injuries.
AI can also help teams refine their strategies and tactics. With the help of AI models, a high-level analysis of a player’s performance can be carried out. Coaches can use data and visuals to gain insights into the strengths and weaknesses of their players and tweak their tactics and strategy as required.
In several sports including football, handball and tennis, computer vision is used to track and analyze the movement of players. Using the data collected through cameras, spatio-temporal trajectories are generated for every player. These trajectories provide coaches with valuable insights into a player’s skill and performance. They can identify which players are putting in the required efforts and which aren’t, and which areas they need to work upon.
AI Referees
AI can also mitigate one of the most controversial elements in sports i.e. poor referee decisions. Virtual assistant referees have been used in sports for many years now to provide slow-motion highlights to human referees, and have met with considerable success.
With the advances made in the realm of AI, these virtual assistant referees have only become faster and more accurate. This means that decisions that can alter the outcome of a game and swing the game one way or the other can now be judged with higher accuracy. This can reduce errors and avoid controversies.
For instance, offside detection from overhead cameras can help virtual referees in football to make sure goals aren’t given or denied incorrectly. Likewise, in lawn tennis, on-field tracking systems help spot the ball so that any line call can be judged with higher accuracy.
Ticketing
We often come across scenarios where the audience struggles to get inside a sports stadium for a match. AI can help resolve this issue.
Of late, AI-powered facial recognition technology is being used to allow sports fans to enter stadiums, eliminating the need to appoint an individual to check their tickets. This not only prevents the creation of bottlenecks and avoids stampedes but also entices fans who don’t want to be stuck in long queues.
Predictive analytics can also forecast the attendance at a stadium on any given day. This can help officials assess the demand for food and merchandise and make the required arrangements.
Sports Journalism
Every single day, hundreds of matches take place in different parts of the world. Covering most of these matches would be a near-impossible feat for a sports journalist, if it weren’t for AI. Media groups are using AI bots to write detailed reports for these matches. These reports include an accurate description of major events, statistics and data, and are interesting enough to keep the reader engaged. This way, the workload of sports journalists is being eased off to a considerable degree. Plus, a majority of local matches find their way into newspapers, and teams and players that would otherwise be lost in oblivion are getting the recognition they deserve.
For instance, Wordsmith is an AI-powered platform that converts hard data (e.g. statistics, numbers, and data) from Minor League Baseball (MiLB) into narratives. Thanks to the platform, AP has enhanced its reporting capacity to cover 13 leagues and 14 MiLB-affiliated teams.
Match Predictions
Sports enthusiasts interested in betting try to process massive amounts of data in an attempt to predict the outcome of a match and win money. Needless to say, humans have their limitations when it comes to processing large amounts of data or accurately predicting the outcome of a sufficient number of matches. And that’s where the role of AI comes into the picture. Though AI cannot predict the outcome of each and every match with 100% accuracy, it can do much better than a human being.
In several sports, computer vision is used to collect data on the number of passes between teammates, team’s formation, number of goals scored, major passes that created a goal-scoring opportunity, etc. AI models can harness this data to forecast whether a team will win or lose a match.
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