How Machine Learning Is Being Leveraged in FIFA 2022?
Learn How Machine Learning Is Being Leveraged in FIFA 2022 In The Most Unique Ways | ProjectPro
![How Machine Learning Is Being Leveraged in FIFA 2022?](https://daxg39y63pxwu.cloudfront.net/images/blog/how-machine-learning-is-being-leveraged-in-fifa/FIFA_Machine_Learning.webp)
The FIFA World Cup 2022, which is the 22nd edition, is just getting started. Gianni Infantino, the president of FIFA, predicts that 5 billion people will watch the 2022 World Cup in Qatar. A total of 3.572 billion viewers, or more than half of the world's population aged four and over, watched the 2018 World Cup, according to the official broadcast coverage. This year's world cup event is more than simply an exceptional and fantastic opportunity for marketers to engage customers and make an impression. The 2022 FIFA World Cup focuses on implementing innovative AI and machine learning techniques in and outside the field. This blog will discuss how machine learning is being leveraged in FIFA 2022 while giving you an overview of the FIFA AI algorithm, including its applications in the tournament.
Linear Regression Model Project in Python for Beginners Part 2
Downloadable solution code | Explanatory videos | Tech Support
Start ProjectHow Machine Learning Is Being Leveraged in FIFA 2022?
The 2022 FIFA World Cup Tournament is leveraging artificial intelligence and machine learning in various ways. Let us look at how does FIFA use machine learning in the most innovative ways.
FIFA is deploying a brand-new, cutting-edge camera system using artificial intelligence to help referees make better decisions during the 2022 World Cup. The new method, developed in conjunction with the semi-automated offside technology, integrates with FIFA's existing Video Assistant Referee (VAR) technology, which made its debut at the 2018 FIFA World Cup.
With 12 dedicated tracking cameras on the underside of the stadium's roof, which keep a close eye on the ball, FIFA's AI referee software will serve as a special line of sight, digitizing the tournament. Up to 29 specific data points, including all key extremities and limbs required for offside calls, will be calculated 50 times per second for each player. When used in conjunction with the FIFA VAR system, the semi-automated technology will provide enhanced reactionary alerts when players violate Law 11. According to this law, a player is said to be in an offside position if their head, body, or feet are in the other team's half (aside from the halfway line) and their head, body, or feet are closer to the other team's line than the ball and the second-last opponent.
Even the official match ball, manufactured by Adidas (Al Rihla), will have an enhanced inertial measurement unit (IMU) sensor added to it, which would help referees spot any near-offside instances. The IMU will be able to communicate data 500 times per second from the ball's exact center to video operations for the most precise display of information with just a kick of the ball.
Furthermore, offside decisions can be made more efficiently and precisely since specific alerts will be relayed to the control room, where a validated decision will be made. The digital assets utilized in these decisions can be repurposed for animation effects exhibited on the various stadium screens and global TV broadcasts to ensure audiences comprehend why and how a certain call was made during the 2022 World Cup.
Want to know how Data Scientists work in the industry? Check out samples of real world data science project ideas!
FIFA Machine Learning Project Ideas
Here are two machine-learning project ideas that apply various machine-learning approaches to soccer datasets.
-
FIFA World Cup Prediction
In this FIFA machine learning project idea, you will employ machine learning to predict the 2018 FIFA World Cup winner. Throughout the competition, you will forecast each match's result. Additionally, you will simulate the following matches, including the semifinals, finals, and quarterfinals. Use the FIFA dataset from Kaggle to train the model. This FIFA CSV Kaggle dataset includes the FIFA Soccer Rankings and International Football Results from 1870 to 2018, Fixture Cup, and World Cup 2018. You will next apply some feature engineering techniques to determine the most relevant feature for prediction. After that, you will choose a machine learning model, manipulate the data, and eventually deploy it on the dataset.
Source Code: FIFA World Cup Prediction
Here's what valued users are saying about ProjectPro
![user profile](https://daxg39y63pxwu.cloudfront.net/images/testimonial/Abhinav Agarwal.webp)
Abhinav Agarwal
Graduate Student at Northwestern University
![user profile](https://daxg39y63pxwu.cloudfront.net/images/testimonial/ED.png)
Ed Godalle
Director Data Analytics at EY / EY Tech
Not sure what you are looking for?
View All Projects-
Build a Linear Regression Model using Soccer Data
With the help of this machine learning project, you will design a simple Python regression model and gain a basic introduction to the regression concepts. Use the soccer player dataset for this project. It gives data across ten attributes with the score as the target variable, and it has information on numerous players from different clubs. This project begins with an introduction to simple and multiple linear regression and a real-world example of regression analysis. You can get a quick overview of the regression formula by building the statistical basis for the regression.
Source Code: Build a Linear Regression Model using Soccer Data
If you want to explore more projects to explore various machine learning applications in sports industry, check out the ProjectPro repository that offers over 250 solved projects on Data Science and Big Data for industry professionals.
About the Author
Daivi
Daivi is a highly skilled Technical Content Analyst with over a year of experience at ProjectPro. She is passionate about exploring various technology domains and enjoys staying up-to-date with industry trends and developments. Daivi is known for her excellent research skills and ability to distill