Artificial intelligence (AI) is rapidly transforming the world around us, and sports are no exception. AI is already being used in a variety of ways to improve performance, enhance fan engagement, and even create new sports altogether.
Improving Performance
One of the most significant impacts of AI on sports is its potential to improve performance. AI can be used to analyze data from games and training sessions to identify areas where athletes can improve. For example, AI can be used to track a player’s movements and identify patterns that could lead to injuries. AI can also be used to create personalized training programs that are tailored to each athlete’s individual needs.
Enhancing Fan Engagement
AI is also being used to enhance fan engagement. For example, AI can be used to create personalized experiences for fans, such as suggesting games to watch or providing insights into their favorite players. AI can also be used to create new ways for fans to interact with the sport, such as through virtual reality or augmented reality.
Creating New Sports
AI is even being used to create new sports altogether. For example, the sport of “RoboCup” is a competition between teams of robots that play soccer. RoboCup is a great example of how AI can be used to create new and exciting ways to play sports.
The Future of AI in Sports
The future of AI in sports is bright. As AI technology continues to develop, we can expect to see even more innovative and impactful applications of AI in the sports world. AI has the potential to revolutionize the way we play, watch, and enjoy sports.
Here are some specific examples of how AI is being used in sports today:
- In football, AI is being used to track the movement of players and the ball. This data can be used to make better decisions about plays and strategies.
- In baseball, AI is being used to analyze pitch data. This data can be used to identify pitchers who are most likely to give up hits or walks.
- In basketball, AI is being used to track player movement and shooting accuracy. This data can be used to identify players who are most likely to score points.
As AI technology continues to develop, we can expect to see even more innovative and impactful applications of AI in the sports world. AI has the potential to revolutionize the way we play, watch, and enjoy sports.
Now that we are familiar with the concept of artificial intelligence in sports, let’s take a closer look at it and provide some examples. All of these examples have been discussed and operational solutions have been developed in the AI Think Tank of the Kish AI Association.
Artificial Intelligence as a Sports Coach Assistant
In almost all sports (individual and team), such as football, basketball, volleyball, wrestling, weightlifting, etc., many people play a role, including head coach, assistant coach, strength coach, goalkeeper coach, analyst, etc. These people sometimes take actions to achieve success for the team based on their scientific and academic knowledge, and sometimes based on their experience.
Some of the actions and responsibilities of sports coaches based on global standards include:
- Providing a time-bound training program for players
- Managing and implementing training and preparing the team for competitions
- Defining team strategies and the responsibilities of each player on the field
- Analyzing the needs of the team and making decisions on buying and selling players to meet the needs of the team
- Analyzing the strengths and weaknesses of the team after each game and providing scientific, experiential, and operational solutions
- Analyzing the mental and emotional state of players and providing solutions to improve these factors
These are just some of the responsibilities of sports coaches, and in this article, we will discuss them and provide operational solutions for artificial intelligence in each one.
Providing a Time-Bound Training Program for Players
The training programs for a sports team are prepared by different coaches based on factors such as the physical condition of the players, the goals of the team, the available resources, the upcoming competitions, etc., and the head coach summarizes them as the main leader of the team.
Machine learning models in artificial intelligence have the ability to find the relationship between x and y by receiving hundreds, thousands, and millions of rows and columns of data through a mathematical function whose simple form is f(x) = x or in other words y = x. Then, based on this relationship, it can provide solutions. In large sports teams, training planning is considered by the coach based on many factors, and these factors are usually stored in a database and are constantly updated.
For example, age, height, weight, blood pressure, muscle volume of the legs and other parts of the body, the amount of accumulated fat, the physical fitness status in the previous few games, the number of goals scored, the number of healthy and unhealthy passes, playing position, and thousands of other raw data are among the factors that will affect a successful and effective training program. It is clear that reviewing all of this data, which is unique for each player and also varies over different time periods, is a very difficult and complex task for a human. Apart from the complexity of reviewing data that have been collected once, all the numbers must be updated in a short period of time and new decisions must be made based on the updated data.
Now we will see the peak of the surprise and the impact of artificial intelligence in sports in this way that a machine learning model with thousands of rows and columns of data over different time periods can decide which program A is more suitable for which players and which program B is more suitable for which players!
| Player | Height | Weight | Goal | Correct Pass | Wrong Pass | Exercise |
|---|---|---|---|---|---|---|
| P1 | 187 | 92 | 12 | 130 | 20 | A |
| P2 | 176 | 68 | 1 | 150 | 30 | B |
| P3 | 185 | 89 | 15 | 20 | 160 | A |
| P4 | 168 | 71 | 4 | 160 | 30 | B |
| P5 | 172 | 73 | 2 | 40 | 110 | B |
In the table above, we see sample information from 5 players of a football team, and we have 5 types of data about each of them in our database. It is clear that these data are very simple and are simply a 55 matrix. In reality, this matrix can be 30400 or even larger.
Here, artificial intelligence comes into play and detects the relationship between the data and the previous successful and unsuccessful programs. With the detection of this relationship, which is similar to a formula like y=wx+c in simple form, we can always ask for the best choice of training program from this model and be sure that this model will be with the head coach and coaches with very high accuracy (usually above 90%).
Of course, we have not yet seen a program with the above features and capabilities. In the event of investment by reputable companies and international sports clubs, the KishAI Association announces its readiness to develop such a program.