EvalStatX is a Football Prediction Engine built by First and Second Year students. Powered by Machine Learning in Python, it predicts outcomes and suggests roles. Bringing data-driven insights to the beautiful game. ⚽📊
Hey There I am Ankita!!
Our football data analysis project aims to uncover deep insights from the game using the power of data science. Motivated by our passion for football and analytics, we set out to build a tool that merges both. Our team brought together diverse strengths — from coding to strategy. The project includes player performance metrics, team comparisons, and predictive models to assist in smarter football decision-making.
Hi Traveller, I am Arka, Let me introduce you to our Website
We approached the project by first collecting and cleaning reliable football datasets, followed by exploratory data analysis to uncover patterns. Our goal was to build models that are both insightful and user-friendly. We developed predictive models for player market value, best XI selection, and match outcome forecasting. Each model is backed by statistical rigor and tailored to provide meaningful football insights at a glance.
Hello, I am Ankan!!
Our dataset was built by sourcing data from trusted football databases, including player stats, match outcomes, and transfer history. We ensured data quality through cleaning and normalization. Feature selection was guided by domain knowledge and correlation analysis — focusing on key metrics like goals, assists, pass accuracy, age, and market trends to ensure our models captured the most impactful variables.
Hey!! I am Mayukh, I will be your Guide in our First Model
Our Transfer Market Analysis tool dives deep into player performance data, age, market trends, and team needs to predict potential value shifts. By tracking recent transfers and comparing similar player profiles, it identifies undervalued talents and potential transfer targets. The model adapts to current season trends, making it a valuable asset for clubs and analysts seeking data-driven transfer decisions.
Hola!!! I am Ritwapriya, Let me take you through our Second Model
Our Player Comparison Model uses radar plots to visually compare players across multiple performance metrics such as goals, assists, pass accuracy, and defensive contributions. By plotting these attributes on a single graph, users can quickly assess strengths and weaknesses side by side. This intuitive tool helps scouts, analysts, and fans make informed decisions based on data-driven player evaluations.
Hi Visitor, I am Rajdeep, Come Let's explore our Best 11 Predictor Model
Our Best XI Predictor intelligently selects an optimal starting lineup by analyzing player performance metrics, match statistics, and tactical requirements. The model evaluates key factors like form, consistency, positional strengths, and team chemistry. By aligning individual stats with formation needs, it suggests a well-balanced squad tailored to maximize on-field efficiency — helping coaches and fans envision the strongest possible XI.
Hi, I am Rounak
Our project offers a comprehensive approach to football data analysis, delivering insights into player value, team selection, and performance comparisons. It highlights how data can transform decision-making in the sport. Looking ahead, we plan to integrate live match data for real-time analysis, add intelligent scouting suggestions, and expand support for fantasy football team optimization — making the tool even more dynamic and versatile for users.