This tool allows you to scout players from all over the world." - Nicole
Emma-Louise Amanshia speaks to Nicole Kozlova, forward for Glasgow City Football Club and lead striker for the Ukrainian National Women's Team. Watch the film to find out how AI is changing the world of football.
EMMA-LOUISE: Today, I'm at Glasgow City Football Club to meet Nicole Kozlova, forward for the team and lead striker for the Ukrainian National Women's Team. And when she's not on the pitch, she's working on cutting-edge AI tech that is changing the world of football. How did you become a footballer and a data analyst?
NICOLE: Football just stuck. At about 13, 14, I knew that's what I wanted to do. I knew I wanted to go to the States. I had visited Virginia Tech and they had a new degree called Computer Modelling and Data Analytics.
Going out of college, I found this part-time opportunity with Twelve Football, a Swedish company, and have been with them now for three years.
EMMA-LOUISE: Can we have a look?
NICOLE: Yeah. In football, like for example, it's really important to scout players.
So if I ask it, "Can you tell me the best wingers?" It's now given me a massive list of all the wingers kind of playing in the Premier League and kind of ranked them based off these metrics. If I change the metrics—if I change the age, the height, the value—the list will update. And then, once you've now scouted a player, you can also now look into the player: the age of the player, how many minutes he's played, how many games, how many he's scored, how many assists.
EMMA-LOUISE: Wow.
NICOLE: Data analytics has been around for a while in football, but not on this scale and being able to do it so expansively, so quickly—and also that prediction part of things—is kind of the big thing currently right now.
EMMA-LOUISE: Who actually uses this tool?
NICOLE: For the most part, I would say clubs use it. Clubs use it either to scout players, because, like I said, this tool allows you to scout players from all over the world. And we use it to scout teams prior to playing them.
EMMA-LOUISE: How could a player use this to improve their own game?
NICOLE: So basically, you're able to pull up your own stats. So I could also ask about myself. It says I'm a versatile striker for Glasgow City and I showcase my finishing skills, high ability to hold up the ball, and kind of be effective with my teammates. And then it kind of goes into strengths and weaknesses. For example, it says I'm a clinical finisher, often showing that I can convert my chances. But I do use it to see things I've done well and things I can improve on and kind of track it throughout the season. And also, I'm quite competitive and I do want to be the best in the league, so I'm always kind of comparing and seeing what things I need to add to my game.
EMMA-LOUISE: It's quite a good review.
NICOLE: If you ask it to rank the strikers in the league, I did do quite well.
Nicole's journey
The Bitesize Guide to AI team spoke to Nicole to find out more about her experience of AI.
How has AI impacted your job?
I do use it to see things I've done well and things I can improve on and kind of track it throughout the season. And also, I'm quite competitive and I do want to be the best in the league, so I'm always kind of comparing and seeing what things I need to add to my game.
AI has enabled us to use football data on a much larger scale, allowing us to build powerful language models that translate complex insights into clear, human language—making data in football accessible, understandable, and useful for everyone.
Where do you see the AI going in the future?
This is already starting to happen, but AI is increasingly being used to collect tracking data — capturing information on every player on the pitch, whether they’re on or off the ball. It can track things like body shape, movement speed, positioning, and more.
While this technology already exists, it’s expected to keep evolving and become more accessible to teams and leagues at all levels. The data collected through AI will make analysis even more detailed, enabling the development of deeper and more advanced models.
What was your route into football and data analytics?
Football just stuck. At about 13, 14, I knew that's what I wanted to do. I knew I wanted to go to the States. I had visited Virginia Tech and they had a new degree called Computer Modelling and Data Analytics.
Going out of college, I found this part-time opportunity with Twelve Football, a Swedish company, and have been with them now for three years.
How the AI tool works
- Nicole works with Swedish AI company Twelve Football. They use data collected from thousands of football games around the world to create an AI machine learning system called Earpiece. The system identifies patterns in player’s performance data and combines intelligence to help teams win.
- The AI uses the data to analyse a player’s performance and create reports on individual players, producing rankings of players at any position. The AI system can also interpret and transform data into sentences that are easy to read and understand.
- Other modern AI systems use computer vision to automatically analyse video footage from matches. The technology tracks a player's movement, speed, passing accuracy, and tactical decisions. It captures data that is difficult to see with the naked eye.
How else is AI used in the sport industry?
| Player and athlete performance | As well as analysing the performance of players and athletes and tracking their progress, AI tools can be used to design personalised training programmes to improve their performance, reduce their injury risk and optimise rest and recovery times. |
| Sports reporting | Generative AI is used in sports reporting to write sports articles, reports, recaps of games and to deliver personalised sports content. |
| Fan experiences | AI is used to create immersive VR experiences for fans and to streamline ticketing. |
| Sport equipment innovation | From AI-powered footballs and bicycles to AI-designed running shoes and golf clubs, AI is used to boost performance. |
Did you know?
Player analytics data is mostly collected from TV broadcasts. As more men's football matches are televised than women's, there can be some bias in the data. This could affect decisions made by talent scouts and managers if they don’t have the full picture of a player’s performance. Now that more and more women's football games are being televised, more accurate data on women players is becoming available. As technology improves, the data will get better and better.
Other industries can be affected by data bias too. For example, data bias in healthcare, where there is lack of representation of certain groups, can affect diagnosis, and data bias in recruitment may promote stereotypes in job advertisements or unfairly discriminate against age, race or gender. So it's important for AI tool developers to combat it.
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