BUILT BY
YASH MORI
AI/ML Engineer · Bangalore, India
Built this project to explore the intersection of sports analytics and machine learning — from raw match data to a fully simulated 2026 World Cup using an XGBoost + Random Forest ensemble with ELO and EA FC squad ratings.
PROJECT STATS
TECH STACK
What powers the predictions, the simulations, and the frontend.
DATA SOURCES
Four independent datasets, combined to give the model a complete picture.
35,304 matches from 1884 to 2024 — every FIFA-recognized international result including friendlies, qualifiers, and tournament matches.
Historical ELO ratings for every team at the time of each match. Updated after every result, weighted by opponent strength and match importance.
Squad-level player ratings from EA FC (FIFA 15 through FC 26). Scout-assessed attributes covering pace, shooting, passing, defending, and physicality.
Player attributes for 209 nationalities — calibrated to EA scale to fill coverage gaps for nations not in the EA FC database.