AI Agent for the football community
AI Agent for the football community
Product designer
Role
Product designer
Role
10+
Team
10+
Team
Overview
Overview
Overview
FAIR is an AI agent for the football that brings predictive analytics, real-time insights, and player performance predictions into a single experience for fans, scouts, and bettors. The goal was to design an MVP web platform that makes complex football data simple to navigate and compare while staying performant and accessible.
FAIR is an AI agent for the football that brings predictive analytics, real-time insights, and player performance predictions into a single experience for fans, scouts, and bettors. The goal was to design an MVP web platform that makes complex football data simple to navigate and compare while staying performant and accessible.
FAIR is an AI agent for the football that brings predictive analytics, real-time insights, and player performance predictions into a single experience for fans, scouts, and bettors. The goal was to design an MVP web platform that makes complex football data simple to navigate and compare while staying performant and accessible.
Problem & Context
Problem & Context
Problem & Context
Football clubs, scouts, and bettors increasingly rely on data, but traditional decision-making workflows are slow, manual, and fragmented across tools. There was no integrated, trustworthy AI platform where users could get odds, predict player performance, and find similar players in one place.
Football clubs, scouts, and bettors increasingly rely on data, but traditional decision-making workflows are slow, manual, and fragmented across tools. There was no integrated, trustworthy AI platform where users could get odds, predict player performance, and find similar players in one place.
Football clubs, scouts, and bettors increasingly rely on data, but traditional decision-making workflows are slow, manual, and fragmented across tools. There was no integrated, trustworthy AI platform where users could get odds, predict player performance, and find similar players in one place.
Designed an End-to-End Website for FAIR (Football AI Research): Led the design of a website (MVP) that included key services - FAIR Chat, FAIR Scout and FAIR Predictions. My design made sure that the platform's AI-driven insights were presented in an efficient and unified way for the user.
Designed an End-to-End Website for FAIR (Football AI Research): Led the design of a website (MVP) that included key services - FAIR Chat, FAIR Scout and FAIR Predictions. My design made sure that the platform's AI-driven insights were presented in an efficient and unified way for the user.
Designed an End-to-End Website for FAIR (Football AI Research): Led the design of a website (MVP) that included key services - FAIR Chat, FAIR Scout and FAIR Predictions. My design made sure that the platform's AI-driven insights were presented in an efficient and unified way for the user.
Created an Intuitive User Interface: Focused on creating an interface that was both intuitive and accessible, making complex data and analytics simple to navigate and comprehend. From casual football fans to professional scouts, the designs catered to a broad spectrum of users.
Created an Intuitive User Interface: Focused on creating an interface that was both intuitive and accessible, making complex data and analytics simple to navigate and comprehend. From casual football fans to professional scouts, the designs catered to a broad spectrum of users.
Created an Intuitive User Interface: Focused on creating an interface that was both intuitive and accessible, making complex data and analytics simple to navigate and comprehend. From casual football fans to professional scouts, the designs catered to a broad spectrum of users.
Validated the Platform Concept through MVP Design: By creating a user-friendly MVP, we helped validate the concept of FAIR and demonstrated its value to early adopters and stakeholders. Iterating on user feedback and improving the product to better meet user needs and business objectives were all part of this process.
Validated the Platform Concept through MVP Design: By creating a user-friendly MVP, we helped validate the concept of FAIR and demonstrated its value to early adopters and stakeholders. Iterating on user feedback and improving the product to better meet user needs and business objectives were all part of this process.
Validated the Platform Concept through MVP Design: By creating a user-friendly MVP, we helped validate the concept of FAIR and demonstrated its value to early adopters and stakeholders. Iterating on user feedback and improving the product to better meet user needs and business objectives were all part of this process.
Research
Research
Research
When I was tasked with designing the website for FAIR, I had the opportunity to work closely with the Product manager, Developers and marketing team, who provided crucial insights and direction for the project.
When I was tasked with designing the for FAIR, I had the opportunity to work closely with the Product manager, Developers and marketing team, who provided crucial insights and direction for the project.
When I was tasked with designing for FAIR, I had the opportunity to work closely with the Product manager, Developers and marketing team, who provided crucial insights and direction for the project.
Understanding the Vision:
Understanding the Vision:
FAIR is a platform that uniquely combines AI-driven chat, scouting, and predictive analytics tailored specifically for football. This combination of features is what sets FAIR apart in a market without direct competitors. The goal was to create a website that effectively showcases these innovative elements and demonstrates their value to users.
Understanding the Vision:
FAIR is a platform that uniquely combines AI-driven chat, scouting, and predictive analytics tailored specifically for football. This combination of features is what sets FAIR apart in a market without direct competitors. The goal was to create a website that effectively showcases these innovative elements and demonstrates their value to users.
FAIR is a platform that uniquely combines AI-driven chat, scouting, and predictive analytics tailored specifically for football. This combination of features is what sets FAIR apart in a market without direct competitors. The goal was to create a website that effectively showcases these innovative elements and demonstrates their value to users.
FAIR is a platform that uniquely combines AI-driven chat, scouting, and predictive analytics tailored specifically for football. This combination of features is what sets FAIR apart in a market without direct competitors. The goal was to create a website that effectively showcases these innovative elements and demonstrates their value to users.
FAIR(Football AI Research) also conducted a market survey, revealing that 70% of football fans, 91% of football fantasy fans, and 97% of football bettors were highly interested in AI-driven football insights. My goal was to build upon these insights to ensure the MVP addressed the specific needs and preferences of these user segments.
FAIR(Football AI Research) also conducted a market survey, revealing that 70% of football fans, 91% of football fantasy fans, and 97% of football bettors were highly interested in AI-driven football insights. My goal was to build upon these insights to ensure the MVP addressed the specific needs and preferences of these user segments.
FAIR(Football AI Research) also conducted a market survey, revealing that 70% of football fans, 91% of football fantasy fans, and 97% of football bettors were highly interested in AI-driven football insights. My goal was to build upon these insights to ensure the MVP addressed the specific needs and preferences of these user segments.
~86%
~86%
Highly interested in
AI-driven football
insights.
Highly interested in
AI-driven football
insights.
By focusing on these elements and leveraging the insights, I was able to design a product that met the visionary goals of FAIR while delivering a user-centered experience.
By focusing on these elements and leveraging the insights, I was able to design a product that met the visionary goals of FAIR while delivering a user-centered experience.
By focusing on these elements and leveraging the insights, I was able to design a product that met the visionary goals of FAIR while delivering a user-centered experience.
~86%
Highly interested in
AI-driven football
insights.
Define
Define
Define
After discussions with the founder and reviewing existing market data, it was clear that FAIR was tackling an untapped market segment. My aim was to create a seamless and intuitive user experience for football fans, fantasy players, and bettors, who lacked an integrated platform that combined AI chat, scouting, and predictive features.
After discussions with the founder and reviewing existing market data, it was clear that FAIR was tackling an untapped market segment. My aim was to create a seamless and intuitive user experience for football fans, fantasy players, and bettors, who lacked an integrated platform that combined AI chat, scouting, and predictive features.
After discussions with the founder and reviewing existing market data, it was clear that FAIR was tackling an untapped market segment. My aim was to create a seamless and intuitive user experience for football fans, fantasy players, and bettors, who lacked an integrated platform that combined AI chat, scouting, and predictive features.
I utilized the Insights and market survey data to define user personas. These personas represented the key user groups: football fans, fantasy players, and bettors. Mapping their journeys helped identify pain points and opportunities to enhance their experience on the platform.
I utilized the Insights and market survey data to define user personas. These personas represented the key user groups: football fans, fantasy players, and bettors. Mapping their journeys helped identify pain points and opportunities to enhance their experience on the platform.
I utilized the Insights and market survey data to define user personas. These personas represented the key user groups: football fans, fantasy players, and bettors. Mapping their journeys helped identify pain points and opportunities to enhance their experience on the platform.
Meet Lukas
Meet Lukas
21
21
21
Education
Education
Location
Location
Job title
Job title
University
University
UK
UK
Engineer
Engineer
Education
Location
Job title
University
UK
Engineer
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
Needs
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Needs
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Needs
Needs
To find a single platform for football insights, player scouting, and predictions.
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Overwhelmed by the time and effort required to gather football data from different sources.
Meet Jordan
Meet Jordan
35
35
35
Education
Education
Location
Location
Job title
Job title
University
University
Germany
Germany
Football
scout
Football
scout
Education
Location
Job title
University
UK
Engineer
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
I want a smarter way to follow football - something that gives me AI insights, player scouting, and predictions all in one place, without the usual hassle of switching between different apps.
Needs
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Needs
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Needs
Needs
To find a single platform for football insights, player scouting, and predictions.
To find a single platform for football insights, player scouting, and predictions.
To stay competitive in fantasy leagues and betting without extra hassle.
To stay competitive in fantasy leagues and betting without extra hassle.
Motivations
Motivations
Passion for football and desire to stay ahead in fantasy leagues.
Passion for football and desire to stay ahead in fantasy leagues.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Interest in using AI-driven tools to gain an edge in player analysis and predictions.
Pain Points
Pain Points
Frustrated with managing multiple apps for insights, scouting, and predictions.
Frustrated with managing multiple apps for insights, scouting, and predictions.
Overwhelmed by the time and effort required to gather football data from different sources.
Overwhelmed by the time and effort required to gather football data from different sources.
Defining the MVP
Defining the MVP
Defining the MVP
At this point, I was ready to begin defining the features, the website would include. I had a clear understanding of who my target users were and what obstacles they faced in organising their football insights, scouting reports, and predictions. With this knowledge in hand, I started working on defining the FAIR MVP (minimum viable product), concentrating on providing a simple and efficient solution to satisfy their requirements.
At this point, I was ready to begin defining the features, the website would include. I had a clear understanding of who my target users were and what obstacles they faced in organising their football insights, scouting reports, and predictions. With this knowledge in hand, I started working on defining the FAIR MVP (minimum viable product), concentrating on providing a simple and efficient solution to satisfy their requirements.
Mapping it out
Mapping it out
Mapping it out
The website for FAIR was starting to take shape. I had identified the core features to include and was beginning to visualize how the site would look and function. To refine the structure and architecture of the website, I developed an site map and user flow to clearly define the user journey and ensure a seamless experience across the platform.
The website for FAIR was starting to take shape. I had identified the core features to include and was beginning to visualize how the site would look and function. To refine the structure and architecture of the website, I developed an site map and user flow to clearly define the user journey and ensure a seamless experience across the platform.
Design
The actual design process for the MVP took place over several weeks, it involved many different parts including design of the landing page, designing the Scout report, players performance report, designing the compare player section and getting constant feedback from my team.
I started by developing low-fidelity wireframes to establish the basic layout and structure of the FAIR website. These wireframes focused on content flow and element placement, avoiding excessive detail. This approach enabled quick iteration and feedback on layouts, which was crucial for shaping the overall user experience. The wireframes served as a blueprint for the site, guiding the development of a functional, user-centered MVP.
Design
The actual design process for the MVP took place over several weeks, it involved many different parts including design of the landing page, designing the Scout report, players performance report, designing the compare player section and getting constant feedback from my team.
I started by developing low-fidelity wireframes to establish the basic layout and structure of the FAIR website. These wireframes focused on content flow and element placement, avoiding excessive detail. This approach enabled quick iteration and feedback on layouts, which was crucial for shaping the overall user experience. The wireframes served as a blueprint for the site, guiding the development of a functional, user-centered MVP.
…
and 150+
more :)
…and ~70
more :)
…
and 150+
more :)
Design
The actual design process for the MVP took place over several weeks, it involved many different parts including design of the landing page, designing the Scout report, players performance report, designing the compare player section and getting constant feedback from my team.
I started by developing low-fidelity wireframes to establish the basic layout and structure of the FAIR website. These wireframes focused on content flow and element placement, avoiding excessive detail. This approach enabled quick iteration and feedback on layouts, which was crucial for shaping the overall user experience. The wireframes served as a blueprint for the site, guiding the development of a functional, user-centered MVP.
Testing & Iteration
Testing & Iteration
Testing & Iteration
Testing revealed gaps such as difficulty viewing detailed statistics and friction in some flows on the FAIR platform. Iterative changes significantly improved how users accessed match predictions and interpreted the underlying data.
Testing revealed gaps such as difficulty viewing detailed statistics and friction in some flows on the FAIR platform. Iterative changes significantly improved how users accessed match predictions and interpreted the underlying data.
Testing revealed gaps such as difficulty viewing detailed statistics and friction in some flows on the FAIR platform. Iterative changes significantly improved how users accessed match predictions and interpreted the underlying data.
Scout List Comparison
Scout List Comparison
Scout List Comparison
Problem: In the scout list, users were unable to compare players effectively, as there was no functionality to view the statistics of similar players side-by-side. This limited the ability to assess potential player candidates against each other and made the decision-making process more difficult.
Problem: In the scout list, users were unable to compare players effectively, as there was no functionality to view the statistics of similar players side-by-side. This limited the ability to assess potential player candidates against each other and made the decision-making process more difficult.
Problem: In the scout list, users were unable to compare players effectively, as there was no functionality to view the statistics of similar players side-by-side. This limited the ability to assess potential player candidates against each other and made the decision-making process more difficult.
Solution: To resolve this issue, we implemented a player comparison feature within the scout list. This new functionality allows users to select up to four players and compare their key attributes directly in a side-by-side view. The comparison includes:
Solution: To resolve this issue, we implemented a player comparison feature within the scout list. This new functionality allows users to select up to four players and compare their key attributes directly in a side-by-side view. The comparison includes:
Solution: To resolve this issue, we implemented a player comparison feature within the scout list. This new functionality allows users to select up to four players and compare their key attributes directly in a side-by-side view. The comparison includes:
League Performance Report Bugs
League Performance Report Bugs
League Performance Report Bugs
Problem: During testing, multiple bugs were identified in the League Performance Report, particularly related to player ranking. Users were reporting that the rankings did not update correctly after applying filters or when viewing performance data across different time periods. Some players were not displaying in the correct order, which could mislead users when making data-driven decisions.
Problem: During testing, multiple bugs were identified in the League Performance Report, particularly related to player ranking. Users were reporting that the rankings did not update correctly after applying filters or when viewing performance data across different time periods. Some players were not displaying in the correct order, which could mislead users when making data-driven decisions.
Problem: During testing, multiple bugs were identified in the League Performance Report, particularly related to player ranking. Users were reporting that the rankings did not update correctly after applying filters or when viewing performance data across different time periods. Some players were not displaying in the correct order, which could mislead users when making data-driven decisions.
Solution: Implemented real-time ranking updates when filters or dates change
Solution: Implemented real-time ranking updates when filters or dates change
Solution: Implemented real-time ranking updates when filters or dates change
Fixed sorting logic to ensure rankings reflect key performance metrics accurately.
Fixed sorting logic to ensure rankings reflect key performance metrics accurately.
Fixed sorting logic to ensure rankings reflect key performance metrics accurately.
Validated fixes across multiple devices and browsers.
Validated fixes across multiple devices and browsers.
Validated fixes across multiple devices and browsers.
These improvements significantly enhanced decision-making, data reliability, and overall platform usability.
These improvements significantly enhanced decision-making, data reliability, and overall platform usability.
These improvements significantly enhanced decision-making, data reliability, and overall platform usability.
Final UI
Final UI
Final UI
Scout Report
Scout Report
Scout Report
Player performance report
Player performance report
Player performance report
From Team Selection to Payer performance report to player Comparison (video)
From Team Selection to Payer performance report to player Comparison (video)
From Team Selection to Payer performance report to player Comparison (video)
Match predictions
Match predictions
FAIR chat
FAIR chat
FAIR chat
Scout list
Design system
Design system
Typography
Typography
Inputs
Buttons
Buttons
Buttons
Components
Components
Typography
Icons
Icons
Icons
Dropdowns
Dropdowns
Dropdowns
Results and next steps
Results and next steps
The project contributed to securing a €300,000 investment, validating both the product vision and market potential. This milestone enabled the team to accelerate product development, expand platform capabilities, and strengthen its position within the football analytics ecosystem.
The project contributed to securing a €300,000 investment, validating both the product vision and market potential. This milestone enabled the team to accelerate product development, expand platform capabilities, and strengthen its position within the football analytics ecosystem.