As we are moving toward a technology-rich future, we see the world of sports is evolving by leaps. While statistical data has always played a central role in the sports industry, one technology has significantly increased the level of audience engagement and strategic gaming. We are talking about Artificial Intelligence in Sports.
Over the past two decades, Artificial Intelligence has completely transformed the way we consume and analyze sports. AI is making the world smarter for athletes, broadcasters, advertisers, and at last the viewers with real-time statistics. Not to mention the role of AI in sports forecasting and improved decision making, amongst other benefits, is one of the top applications of modern technology.
The applications of AI in sports have become a common sight even though not many experts talk about it. However, we don’t limit the potential of AI when integrated into businesses and enterprises. Considering the positive impact and precision this technology brings to the ground, there’s not an iota of doubt that AI in sports will flourish immensely in the future.
Speaking of which, let’s discuss the undiscussable. This article talks about the transformation AI is bringing to the sports industry, the uses and applications of computational intelligence in sports, and the future of AI technology in the sports business. So sit through; it’s going to be an exciting ride.
Here’s the roadmap to content ahead of you:
- Statistical AI facts in sports and gaming
- Introduction of Artificial Intelligence framework for sports
- Applications/use cases of AI in the sports business
- Future of the Sports AI industry
- How can Appinventiv help your business with AI development services?
AI Statistics in Sports and Gaming
- As per recent studies, the global artificial intelligence sports market was worth $1.8 billion in 2021. It is forecasted to reach $19.9 billion by the year 2030.
- This would make a total Compound Annual Growth Rate (CAGR) of 30.4% during the forecast period from 2022 to 2030.
- Some of the factors influencing the market growth are :
- Increase in demand for player monitoring and tracking
- The rise in demand for real-time data analytics
- Growing demand for AI for forecasts and sports predictions
- Demand for virtual assistants and Chatbots to interact with fans and sports enthusiasts.
Another study suggests that mobile applications such as HomeCourt, ESPN, AI SmartCoach, etc., are used to assess players’ skills, giving them a good medium to improve.
The above data proves how AI influences the sports industry to be data and information-rich. Not just popular sports but certain Sports enterprises rely entirely on AI and machine learning to drive their business. If you are one of them, you might want to know the whereabouts of AI before we jump to its use cases. Let’s take a quick glance at AI for sports.
Introduction of Artificial Intelligence framework for sports
Artificial Intelligence is an umbrella term covering a variety of what we refer to as “smart” technologies. If you are new to the whole concept of AI, check out the Artificial Intelligence in business guide.
AI collects the information and responds to it without any manual support. The technology can take mass amounts of data and analyze it for a better experience and learning. At the most complex level, we are talking about drones and self-driving automobiles; however, in our daily sports life, it boils down to screen monitors, AI-based chatbots in mobile apps, and a lot more.
The adoption of AI and statistical modeling in sports has become more prominent with recent developments in professional sports analytics. This is probably because the applicability of machine learning algorithms combined with computer processing power has made the sports audience hungry for new strategies and applications.
The primary objective of AI in sports is to make competitions more fierce on and off the field. There are certain areas where AI and machine learning have left a solid footprint in the world of sports. Let’s see what are the top AI uses:
- Talent identification and acquisition – This includes biomechanics, player performance measurement, player recruitment, etc.
- Fan and business management – This includes modeling demand for event attendance, sensor and wearable design, ticket pricing optimization (variable and dynamic), virtual and augmented reality sports watch, measurement of players’ economic value, etc.
- Game analytics – This includes umpire assistance, match event, match outcome, ball/player tracking, sports betting, etc.
- Training and Coaching – This includes tactical planning, player injury modeling, team formation assessment, etc.
The field of AI, particularly machine learning, has proven to be beneficial for all the sports challenges mentioned above. To talk about them in detail, here is a range of applications and use cases of Artificial Intelligence in the sports industry.
Applications and use cases of AI in the sports business
The sports business is at a point where it is ready to adopt every AI strategy and improve its decision-making by performing data-driven objectives. As a matter of fact, from 2015 to 2018, the NBA reviewed over 25,000 games and found over 2,000 missed or incorrect actions. This amounts to 1.49% wrong decisions in the finals of each close game.
This deciding factor has now been rescued by AI-dependent technologies where the officials can watch every close game with probability and visual data. Not only that, AI-based technologies serve the sports industry in a thousand other scenarios.
Below are a few of the significant AI applications in sports with real use examples:
1. Player performance
Thanks to predictive analytics, AI in sports is used to boost performance and health. With the help of wearable technology, the athletes can gather information on strain and tear levels and can further avoid serious injuries. This also helps the team shape strong tactics and strategies and maximize their strength.
The analysis of player performance is even more sophisticated, thanks to AI. Even the coaches can gain insights using visuals and data to work on the strength and weaknesses of the players and make alterations in the game strategies.
From football to tennis, this is true of all sports. A powerful AI technology, Computer Vision is used for human motion sensing and tracking using video sequences. This brings out three results:
- Motion tracking and detection
- Color tracking and
- Color and template combination tracking
One popular real use example of AI in sports is determining the swimmer’s performance below water filters using human pose estimation. This method takes over the ancient quantitative evaluation method by manually annotating the swimmer’s body.
2. Personalized training and diet plans
AI is on a track to winning in sports; this is concluded by how AI has taken personal training to the next level. An AI diet plan uses machine learning to customize different meal plans for different players based on their needs and schedule. And that’s just the beginning.
Not to forget about the featured AI-based fitness apps that have flooded the market. These tools and techniques can now train algorithms to detect human poses in real-time.
One popular example is women’s fitness app development, where keypoint skeleton models are used to identify human joints for online yoga and pilates.
3. Scouting and recruitment
Sports teams are making competitions more rigid and fierce by adding Artificial Intelligence to the scouting and recruitment box of tricks. Everything that takes place on the field, from the player’s movements to the orientation of their body, is tracked to make the right decision.
Further, machine learning algorithms are put to aggregate data and evaluate players’ skills and overall potential in various game categories.
Not only the recruitment choices are improved this way, but countries also get a strong and healthy team to achieve the impossible.
In often scenarios, the audience struggles to get inside stadiums on time for the match at big sporting events. Nothing could resolve the crowd issue until now when the AI struck in.
Recently, Columbus Crew adapted the AI-based face recognition technology to allow the fans to enter the stadium without having to check their tickets. This prevented bottlenecks and made the stadium entryway more efficient.
Apart from this, predictive and cognitive analytics is used to forecast what attendance is likely to be at the stadium along with the timing schedule. This helps the officials to keep up with the demand without much effort. Moreover, the merchandise and food arrangements are on time.
5. Sports predictions
It’s not hidden that officials have been trying to princess a heap of data in a bid to predict outcomes and win money for years. However, examining the first half of the match or the number of aces and scores it’s a pseudo prediction if you are just using a probability experience.
AI in sports can not predict the exact outcomes either, but it can get much closer with the algorithms than the human prediction.
Over 40% of the sports categories can now predict match outcomes using AI based on the below factors:
- A team’s formation
- Number of goals scored
- Key passes for goal scoring opportunity
- A team’s formation
- The number of passes between teammates etc.
AI analytic tools can analyze the closest match prediction scores possible using the above data.
6. Automated sports journalism
Sports journalism is a big business where every highlight needs to be covered. These details and updates are strictly heard, especially when it comes to data and statistics in tournaments and minor leagues. AI has simplified and made sports journalism a little easier.
For example, AI-driven platforms can hard score data into narratives using natural language. The platforms are based on automated insights that sync intelligently with computer vision and perform the journal score hearing.
This is a fascinating take on AI sports where technologies can cover even the local matches without the officials standing on the field.
This is a short and underrated benefit of AI in sports. Artificial Intelligence can be used to identify opportunities and present more relevant ads based on demographics. Brands this way get better advertising based on top highlights of the game identified by AI.
The cherry on the cake is the automated learning algorithms of AI and Machine learning in sports that monitor players’ actions and audiences’ emotions during matches.
Isn’t it fascinating how Artificial Intelligence in sports has redefined the concept of watching and playing games in the most efficient way possible? If you ask about the future of AI in the sports business, we’d say it’s bright and shining. AI is everywhere, and there’s no going back from drones and big sports monitors now, so we might as well invest in the AI sports industry.
Future of AI in the sports industry: What to expect
AI has already increased competitiveness by a huge margin. With effective sensors and algorithms, AI has all in hand for game strategists, sports companies, advertisers, franchise owners, and spectators. With such a broad scope of implementations, businesses are likely to invest in health and sports fitness application development, AI development services, and similar technical sports opportunities.
Therefore make sure you are not too late for the AI in sports party.
How can Appinventiv help you with AI development services?
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