Artificial intelligence (AI) in sports is rapidly transforming the face of our favorite pastimes. From unlocking the secrets of athletic success to changing coaching tactics, preventing injury, and even assisting in analyzing and predicting athletes’ performance, we’ve got you covered.
But how did we get from stopwatches and chalkboards to predictive algorithms and machine learning in such a short period?
As technology advanced and processing power increased, programmers worked tirelessly to create algorithms to incorporate sports into this new generation of computing.
With the addition of cutting-edge technology, such as wearables and athlete tracking systems, AI has the potential to transform a simple piece of equipment into a multifunctional tool.
Gorilla Bonuses examined how artificial intelligence (AI) reshapes our understanding of sports and enables athletes and coaches to capitalize on the next information age to transform the global sports industry.
The Most Significant Advantages of Artificial Intelligence in Sports
The introduction of AI into sports represents a significant advancement in how we measure and comprehend the massive amounts of data available to us.
Because AI can process and interpret sporting data at incredible speeds and with spectacular accuracy, we can finally delve into minute detail and use it to optimize and perfect our performances.
The advantages of AI in sports are undeniable and far-reaching. Aside from the obvious benefits that athletes can gain from learning more about their abilities and limitations than ever before, coaches can now make data-informed decisions about training and performances, and fans can finally be a part of the action as well.
AI in Sports – Benefits for Athletes and Coaches
Professional, college and amateur coaches find AI extremely useful. It enables them to make critical strategic decisions based on unquestionable data about their teams or individual players.
AI-linked cameras, sensors, and trackers also provide access to data before, during, and after a game, allowing coaches to make informed decisions in the best interests of their players.
AI also aids in developing coaching tactics and methods based on data collected during competition or even over a season.
Artificial intelligence in sports training and performance enhancement
Training is essential for improving performance. The most recent advancements in artificial intelligence in sports mean that players can use apps like HomeCourt, which use computer vision and machine learning to assess skills and provide accurate feedback.
This enables athletes to identify areas for improvement, focus on them during training, and track their progress. Simple but efficient.
AI in sports analytics and prediction of outcomes
When it comes to sports analytics, nothing beats AI. AI in sports analytics, powered by advanced programming and backed by powerful learning capacity, can be used in various ways.
Athlete results analysis: While keeping track of on-field activity is important, understanding training results and progress can often be the difference between success and failure. Several software solutions on the market can help you understand sports performance.
Svexa, a training platform that tracks and analyzes athlete movement, for example, uses a deep understanding of technology and molecular profiling to generate tailored exercise and training recommendations.
Another popular solution is Sparta Science, which the NFL’s scouting program uses to evaluate and identify top college players by tracking each candidate’s real-life movement.
Predicting match outcomes: We’ve all wondered if AI can predict sports outcomes. Not quite yet, but it’s on the verge of doing so. Because of the unpredictable nature of sports, anything can happen on any given day, and upsets are unavoidable, but AI is already getting good at predicting those as well.
When large amounts of sporting data are available, ML models can frequently predict the outcome while the action is taking place on the field.
Students in the IPL, for example, used AI techniques to accurately predict the outcome of a match while it was still in progress! This model demonstrated how teams could use AI during a game to improve their performance and increase their chances of winning.
Advanced customized training
Athlete tracking systems powered by AI can do more than just measure on-field performance. They can also keep track of everything that happens away from it. Wearables and other technology can measure everything from stress loads to distances traveled, while artificial intelligence (AI) can supplement off-season training schedules and recovery performance.
This enables doctors, physiotherapists, coaches, and athletes to better manage their workloads.
As a result, they can create customized training programs that maximize exercise returns while lowering the risk of injury or even preventing it entirely.
Enhanced Team Analytics
Coaches can now use AI to make data-driven, unbiased decisions about players, teams, and even skill levels. In addition, AI can detect things that most coaches would miss while providing them with accurate, real-time data.
Analytics in real-time and retrospectively, including those of competing athletes.
Coaches and athletes can now track their performance in real-time, making decisions on the fly rather than waiting for results after the game. This means that strategies and plays can be changed in real-time, while the opposition’s tactics can also be tracked and responded to.
AI in Sports – Benefits for Fans and the General Public
Greater Loyalty and Support
We tend to love our sports stars more when we better understand their careers and feel like we’re a part of their journey. AI sports developments enable current and prospective fans to better understand athletic performances, trends, injury updates, and overall form, resulting in a more devoted fan base.
AI’s main strength is its ability to process massive amounts of data and apply algorithms to predict what will happen- impressive accuracy. While AI cannot predict the future, it can predict the most likely outcome of a race, match, or competition.
This adds flavor to the sports betting and fantasy league scenes, making for a more exciting overall experience.
These are just a few of AI’s incredible benefits to sports, and the trend is already resulting in massive improvements in athlete capabilities and training returns. This invariably leads to greater success on the field and a higher return on investment for athletes, coaches, and fans alike.
Predictions for the game
For years, bettors have been attempting to process massive amounts of data to predict the outcome of future matches and win large sums of money. In an attempt to predict sports outcomes, they studied first and second-serve percentages in tennis, as well as the number of aces and backhand winners.
However, a human cannot process as much data as an AI-driven football algorithm prediction, nor can they accurately predict enough matches to become millionaires (ha). Therefore, they are always constrained by their human limitations, and thus the majority will not become millionaires.
AI is also incapable of accurately predicting the outcome of every single match. However, a predicting algorithm can get much closer than a human can.
Computer vision, for example, is used to determine ball possession time.
Researchers ran multiple configurations of the pos-N-M model on a manually labeled set of 4,000 frames and achieved an 85.5% accuracy on a test. This was despite several issues, such as the ball being small and difficult to see, or the ball being covered by a player at times.
When computer vision is properly trained as a ball possession model, it can aid in predicting future match results.
Computer vision can also be used to collect and analyze data based on:
- The number of passes made by teammates
- The formation of a team
- Total number of goals scored
- Opportunities created
- Important passes that resulted in a scoring opportunity
Using this data, the model can forecast whether a team will win, lose, or draw a future match.
AI in Sports – Benefits for Sports Journalism
AI sports are well-suited to sports journalism. With more teams competing every day and so many statistics and facts available, automated reporting is gaining popularity.
Journalists can now cover far more sports, leagues, and matches than they used to, thanks to pre-defined templates, writing styles, and advanced predictive models.
Wordsmiths, for example, can now take complex data or metrics and use natural language generation to turn numbers, statistics, and results into narratives.
Sports journalism is only now beginning to capitalize on this new power, with the Associated Press expanding its reporting capacity to cover 13 leagues and 14 MLB-affiliated teams.
Reporting on Matches
AI bots can now track ongoing matches in real-time and report on key events, scores, and statistics as they occur.
As a result, followers are frequently surprised to learn that the reporter is actually an AI-powered program capable of reporting the game’s ebb and flow in real-time, complete with excitement and emotion.
Faster and Less Expensive
Sports journalism organizations, such as the Associated Press’s sports division, quickly discover that an AI-powered reporter is far less expensive and more capable than human scribes. Capable of tracking multiple events and using structured data to generate compelling content, AI-powered sports journalism is becoming faster, less biased, and more accurate than ever before.
AI solutions for sports journalism are gaining traction in some of the world’s most popular sports. AI is already being used to manage live reporting in sports such as tennis, soccer, baseball, and basketball, and journalists are scrambling to keep up.
In fact, Automated Insights and Narrative Science, two of the United States’ leading natural language generation technology companies, began as attempts to automatically generate game recaps.
AI in Sports – Benefits for Broadcasting and Streaming
As the streaming industry grows, AI in broadcasting and streaming services is becoming increasingly important in understanding audiences, giving them exactly what they want, and thus increasing ratings.
The same can be said for sports streaming.
Approximately 30% of sports fans already stream live sports content on mobile devices, and highlights are becoming more interactive, accessible, and engaging – all thanks to artificial intelligence.
A more enjoyable AI sports streaming experience
The live sporting experience has also become more interactive, immersing audiences in action with on-demand replays, camera angle selection, and multiple commentary language options.
By offering paid advertising opportunities and leveraging location-specific features, providers are capitalizing on people’s willingness to pay more for sports streaming services.
AI in sports – use cases with real-world examples
While AI can be incorporated into almost any sporting code, some are already doing so successfully. These sports industries are seeing lucrative returns from their AI in sports industry investments, with basketball, football, and horse racing performing particularly well.
Artificial intelligence in football
Football, also known as soccer, is the most popular and widely followed sport in the world. This AI in the sports industry is larger than many others, with over 3.5 billion global fans and over 130,000 professional players. Football artificial intelligence has been around for a while, but the most impressive advances have only recently been made.
So, how exactly is AI used in football? Football artificial intelligence tools and techniques are being used to power various applications to evaluate and improve overall sports performances by leveraging some innovative strategies:
The apps use biomechanical sensors to capture and represent player movements as they occur.
The AI automatically analyzes the playing strategy and provides accurate real-time feedback to coaches.
This enables them to advise players on when to increase performance outputs and how to use various decision-making strategies while playing.
Instead of simply ranking players, 3D visualizations of game information show how they play in their own style.
This football artificial intelligence capability means coaches and players can develop customized strategies based on individual players’ specific strengths, rather than just looking at shots taken or comparative player performance statistics.
SciSports provides deep learning AI solutions for football.
SciSports is an excellent example of a football artificial intelligence tool. The company provides AI-powered player tracking performance solutions that analyze players’ movements and decision-making patterns.
The company uses machine learning and advanced analytics to provide intelligence data to both professional and amateur football teams. On an ongoing basis, this information can be used for scouting, team strategies, and player analysis.
SciSports, with the ability to track over 500,000 players worldwide, is the perfect example of how artificial football intelligence can be used for more than just performance tracking and monitoring. Its findings can be expanded to help with talent acquisition, recruiting, and ensuring responsible investing.
Artificial intelligence in horse racing
Horse racing has always been associated with large sums of money, science, and pure entertainment. So it’s no surprise that AI technology is already making inroads into the horse racing industry.
While experienced followers have spent years developing a sense of which horse to bet on, new or inexperienced punters frequently struggle to bet correctly.
However, as artificial intelligence (AI) becomes smarter and more capable of predicting the outcome of events, tech startups and companies have been using horse racing data accumulated over decades. They’re working on advanced machine learning-powered capabilities that can predict race outcomes with surprising accuracy.
Winners will be predicted using advanced analysis
For some time, programs like Beth Bet have used AI to develop advanced algorithms to predict the outcome of horse races. Beth Bet is able to predict which horses will do well and which will not by analyzing a wide range of available data. Algorithms analyze pedigree, form, weather, and even the composition of the turf, among other factors, to determine which horses will win.
To narrow the field, use neural networking.
The data engineers at Vermont-based tech startup AI Certain Inc. have used groundbreaking AI technology to create innovative AI sports solutions. They used their deep learning neural network system and predictive algorithm-based programming to reduce the field from 20 horses to just two possible winners.
They were then able to correctly predict the winner of the Kentucky Derby by selecting the favorite with a 1% advantage over the next best horse.
Who said you couldn’t generate your own luck?
Basketball artificial intelligence
Basketball is a fast-paced, high-impact action sport, and keeping up with the amount of data available can be difficult.
However, AI is already changing how coaches and players manage their training programs and how game strategies evolve. According to a 2021 study report from Georgetown University and the Washington Institute for Health Sciences, AI has the potential to prevent sports injuries and improve the overall enjoyment of basketball games.
Despite these encouraging results, the artificial intelligence basketball realm remains poorly understood and researched outside of professional basketball team settings. This is bound to change soon, as the undeniable benefits of artificial intelligence basketball begin to show more mainstream practical applications, particularly in the AI in the sports industry.
So, how exactly is AI used in basketball? First, let us look at some examples.
The NBA employs advanced AI-powered tracking devices.
Since 2017, Second Spectrum has provided NBA tracking technology.
Tracking players in the NBA is a tried-and-true method for keeping coaches and fans up to date on different athlete performances and form trends. However, artificial intelligence (AI) has recently enabled tracking devices to do much more than simply measure distance and speed. They can now recognize when specific plays are in progress and analyze which actions worked and which did not.
With this data at their disposal, coaches can use it to eliminate bad plays and get the most out of their top players. Second Spectrum, the NBA’s tracking technology provider, believes this technological revolution’s possibilities are limitless.
Live and post-game analytics
If you’ve ever watched a LA Clippers home game, you’ll be familiar with the Courtvision experience. This artificial intelligence basketball tool elevates the broadcast by utilizing machine learning, specialized data visualization, and augmented reality.
Fans can enjoy everything from watching the game with in-arena sound enhancements to replicating the live atmosphere in their own home, from the comfort of their sofa, thanks to a variety of camera angles and audio commentary options managed and run by CourtVision’s AI.
Fans can choose between several CourtVision modes:
- Player – immerses you in action by providing real-time player tracking stats.
- Coach – gives you access to play charts and information that coaches require.
- Mascot – a character that unlocks graphics features that allow you to get the most out of your entertainment experience.
CourtVision also offers a wealth of information on probabilities and statistics, giving punters a competitive advantage during the game by integrating ongoing post-game analysis-style stats with the selected players.
AI Applications and Use Cases in Sports
We’ve talked a little bit about the industry and how AIs are used. Now, look at some AI applications and use cases in the sports industry.
Create Player Performance Prediction Models
Predictive modeling is a type of artificial intelligence used to forecast future events. This type of AI is commonly used in marketing and finance. It is, however, used in sports to predict player performance.
Predictive models can help identify players who are prone to injury or underperformance. This data can be used by coaches and managers to make decisions about player rotations and game strategy.
Predictive modeling can also be used to identify players who are poised to have breakout seasons. Teams can use this information to make trades or sign new players.
Using machine learning algorithms to analyze data about past performances is one way AI is used to create predictive models of player performance. This information includes player statistics, injuries, and disciplinary history. By analyzing this data, AI can develop models that predict how a player will perform in the future.
This type of AI can be used to make predictions about a variety of topics, including:
- The likelihood of a player being injured
- The likelihood that a player will underperform.
- How likely is it for a player to have a breakout season?
Examine game footage for patterns and trends
There are several advantages to using AI to analyze game footage. It can assist teams in identifying patterns and trends they were unaware of. This can help them make better strategic decisions during games, giving them an advantage.
Machine learning algorithms can identify and track specific players or objects on the field, which is how AI analyzes game footage. This can assist teams in better understanding player movement and game flow.
AI can also be used to assess player performance. This can assist teams in determining which players are performing well and which require improvement.
Many athletes are now using artificial intelligence to help them train more effectively. AI can be used to create personalized training programs tailored to each athlete’s needs. AI can also track an athlete’s progress and provide feedback. This data can be used to make changes to the training program.
Some of the advantages of using AI to assist athletes in training include:
- enhanced performance
- Less injuries
- faster recovery times
Improve Sporting Equipment
AI is now being used by businesses to create better sports equipment. Adidas, for example, has created a soccer ball that uses AI to adjust its flight path to improve accuracy. Wilson has also developed a tennis racket that employs artificial intelligence to assist players in hitting the ball with greater power and precision.
AI has improved sports equipment by assisting in the development of more accurate and powerful products. Furthermore, AI has aided in the development of products that are tailored to the needs of individual athletes. Athletes can now train more effectively and improve their performance as a result.
Other examples of artificial intelligence-enhanced sports equipment include:
- Golf clubs’ swing weights can be adjusted based on the user’s preferences. They function by using sensors to track the user’s swing. This information is then used to adjust the swing weight of the club to better suit the individual user.
- Running shoes that automatically adjust the amount of cushioning based on the user’s level of fatigue. The shoes will detect the user’s level of fatigue by monitoring their movement and heart rate. The shoes will then adjust the amount of cushioning based on this information.
- Bicycles that can calculate the most efficient route to the user’s destination. This is accomplished by combining GPS and traffic data.
Enhance Fan Experiences
Many sports teams are now utilizing artificial intelligence to enhance fan experiences. The Golden State Warriors, for example, use AI to create virtual reality experiences for their fans. Furthermore, the Los Angeles Dodgers use AI to assist fans in finding their seats in the stadium.
AI is also being used to improve fan experiences in a variety of other ways. Among these methods are:
- Provide fans with virtual reality experiences. This gives fans the impression that they are right in the middle of the action.
- Assist fans in finding their seats in the stadium. This is accomplished by scanning fans’ faces with a camera and matching them with their ticket information.
- Make personalized content available to fans. This includes showing game highlights tailored to the individual fan’s preferences.
- Assist fans in interacting with their favorite players. This is accomplished through chatbots that can answer fan questions and provide information about the player.
- Keep an eye on social media to see what fans say about their favorite team. This enables teams to quickly address any negative sentiment.
- Follow the movements of the fans in the stadium. This data is then used to optimize the stadium’s layout and traffic flow.
- Create experiences based on biometrics. One example is using a fan’s heart rate to determine when they are most excited during a game.
- Assist fans in purchasing game tickets. This is accomplished by using a chatbot to ask about the fan’s preferences and recommend games to them.
- Offer fans discounts on products and services. A chatbot determines a fan’s interest in a product or service.
- Assist fans in finding parking. It accomplishes this by scanning drivers’ faces with a camera and matching them with their vehicle’s license plate information.
Injured players can suffer significant financial consequences. For example, if a player is injured and misses significant time, the team may be required to pay his salary during his absence.
Furthermore, the team may suffer financial losses if he cannot play in games. This is especially damaging if the team is in the playoffs or fighting for a division title.
Many teams are now utilizing artificial intelligence to monitor player health in order to avoid these costly injuries. The Chicago Cubs, for example, use AI to monitor player fatigue levels. The Cleveland Indians also use AI to track their players’ sleep patterns. These systems can help identify and prevent injuries in players at risk of injury.
The Seattle Seahawks have created software that uses machine learning to identify players who are in danger of injuring themselves. This data can be used to make player rotation and game strategy decisions.
In addition, the NFL is utilizing AI to create a system that can detect concussions. Again, this data can be used to increase player safety.
Many sports leagues are now utilizing artificial intelligence to assist with officiating. The NBA, for example, uses AI to call fouls. Additionally, MLB is utilizing artificial intelligence to track the location of balls and strikes. This data can be used to make game strategy decisions.
AI is also used to assist officials in other ways. The NHL, for example, uses AI to determine whether or not goals should be counted. In addition, the PGA employs artificial intelligence to detect penalties. This data can be used to make game strategy decisions.
Furthermore, many sports leagues are now utilizing this technology to assist in rule enforcement. The NFL, for example, is using AI to review close calls. This data can be used to make game strategy decisions.
Summary of AI in Sports
AI in sports is expanding at a breakneck pace. Wearables and athlete tracking systems are now the norm, and machine learning is rising. AI development services are taking center stage in technology and information technology.
Fans, sports management agencies, professional recruiters, and AI sports representatives are all capitalizing on the growing popularity of this revolutionary advancement in data processing.
As artificial intelligence in fitness grows and evolves alongside AI in sports, the industry is poised for rapid growth, benefiting both professional athletic organizations and the general public.
Likewise, as the world becomes smarter and more connected, artificial intelligence in sports is paving the way for things like artificial intelligence basketball gains and artificial football intelligence that advances faster than we can keep up with.
So, if you’re considering investing in AI in the sports revolution, now is a good time to do so. You will not be let down.