Editorial Feature

How AI Helps Sports Professionals Maintain a Competitive Edge

Although it took a few years for professional sports to catch up with the worlds of finance, medicine, and research, data analysis is now fundamental to many aspects of athletic endeavor (and business).

How AI Helps Sports Professionals Maintain a Competitive Edge

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Now, with datasets growing from the statistical observations of scouts and coaches to huge caches of information from wearables and training ground sensors, artificial intelligence (AI) is putting all of this data to work in professional sports. This article provides key examples of AI being used to power elite performance in the world of sports.

Research on AI in Weight Training

In recent years, much research has highlighted the possibility of coaches and athletes employing AI to analyze their data and help them make decisions. A recent study looked at how neural networks (AI "brains") applied to weight training data can improve performance.

Feedback frameworks include sophisticated assessment techniques that have been automated by AI. Computers can then display appropriate interventions during the athlete's workout.

The study, published in the Journal of Sports Science and Medicine, used advanced sensor technologies attached to the training equipment to provide accurate and relevant data to the AI. The authors noted that advances in sensor technology – making sensors cheap, compact, and reliable – have enabled much more AI-supported performance gains.

Training and Performance Analysis

Professional sports is now awash with AI-supported training programs like the weight training AI discussed above. These have opened up whole new areas of data collection, building up a picture with more information than the standard metrics of runs scored, passes made, goals scored, distance covered, and so on.

Instead, AI can analyze numerous different data points and even create new metrics based on learning parameters fed in by human supervisors. AI can easily find correlations between varying qualitative traits and quantitative variables and provide novel training suggestions with a likely high impact.

For coaches, AI also helps analyze opposition teams and players. AI can help professional coaches and managers to learn about the tactics, strengths, and weaknesses of the teams they are about to face, and also suggest gameplans based on their computer assessment.

Turbo-Charging NASCAR Performance

NASCAR has clocked an average death toll of one fatality per year since 1950, and (much like boxcars on the track) the rate does not seem to be slowing down any time soon.

Ford Motor Company is working with AI leaders Argo AI to develop deep learning AI programs for self-driving consumer cars. Now, the partnership is looking to improve safety for NASCAR teams.

Engineers adapted the neural network used for Ford's self-driving cars to identify specific cars using image sensors. A dataset with thousands of images of cars trained the neural network to identify cars, and it worked even when images were blurry.

Identifying cars from blurry images is essential for NASCAR, as vehicles move at such high speeds that getting clear image data is impractical.

As the neural network grew, it became more proficient. The engineers reported that their system was more accurate than professional NASCAR drivers at identifying specific cars in a race. This AI could help drivers by warning them when a nearby car is malfunctioning, enabling the driver to prevent a disaster before it starts to happen.

Boxing Robots

Global boxing brand Everlast partnered with French sports robotics startup PIQ to develop what they claim is the first combat sports wearable powered by AI. The device uses GAIA Intelligence, a machine learning platform for analyzing sports metrics, to track and analyze variations in boxers' movements to make their workouts and training sessions as productive as possible.

Training data is linked to a smartphone app that allows users to track their activity and compare themselves against others on a leaderboard. PIQ has reportedly raised $5.5 million in funding from three Series A investors so far.

The Future of Smart Coaching with AI

In the NFL, AI is used to help teams develop and improve game strategies. A researcher at Oregon State University, Alan Fern, uses game videos and a deep learning AI to teach computers how to coach football plays.

Fern says that AI can help coaches by telling them how players move during a game and train to reach peak performance. AI can also pair receivers with the best cornerbacks and measure how each player contributes to individual plays.

Fern says he is using deep learning to reveal strategic insights that might otherwise never have been spotted. But, for now at least, those insights are strictly confidential.

AI for Everybody

Professional sports is full of data, and now amateurs can access the same amount of information as elite athletes. Wearables like the Apple Watch and FitBit, devices like power meters for cyclists, cheap heart monitors, and a host of other new technologies are putting much more data into the hands of hobbyists than they know what to do with.

This is where AI can step in to help. The eHealth and fitness market has been growing year on year for some time, with AI-powered products helping amateurs set training programs and achieve their goals.

The proliferation of cheap data acquisition and accessible AI-powered analysis worldwide will have a knock-on effect in the world of professional sports. As amateurs, youth teams, and smaller professional teams and academies access more data and data tools, a new generation of sports excellence may be in the making.

Continue reading: What is the Difference Between Artificial Intelligence and Robotics?

References and Further Reading

Joshi, N. (2019). Here's How AI Will Change The World Of Sports!. Forbes. [online] Available at: https://www.forbes.com/sites/cognitiveworld/2019/03/15/heres-how-ai-will-change-the-world-of-sports/?sh=52378f5b556b

Novatchkov, H., and A. Baca (2013). Artificial Intelligence in Sports on the Example of Weight Training. Journal of Sports Science and Medicine. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3761781/

Senaar, K. (2019). Artificial Intelligence in Sports – Current and Future Applications. EMERJ. [online] Available at: https://emerj.com/ai-sector-overviews/artificial-intelligence-in-sports/

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Ben Pilkington

Written by

Ben Pilkington

Ben Pilkington is a freelance writer who is interested in society and technology. He enjoys learning how the latest scientific developments can affect us and imagining what will be possible in the future. Since completing graduate studies at Oxford University in 2016, Ben has reported on developments in computer software, the UK technology industry, digital rights and privacy, industrial automation, IoT, AI, additive manufacturing, sustainability, and clean technology.

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