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Tariq Elnaggar

Tariq Elnaggar

Data Analysis & Data Scientist.

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Tanta, Egypt

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Tanta, Egypt

šŸ† I built a live AI-powered match predictor for the 2026 FIFA World Cup ā€” and it updates itself every hour, automatically. 48 teams. 104 matches. The biggest sporting event on the planet. So I asked myself: can machine learning predict what no one can? šŸ¤– š—§š—›š—˜ š— š—¢š——š—˜š—Ÿ Not just another ELO-based predictor. This one combines: āš” Live ELO ratings ā€” recalculated after every WC match šŸ“ˆ Recent form ā€” last 5 & 10 official matches āš”ļø Head-to-head history ā€” including World Cup-only records šŸ‘„ Squad strength ā€” EA FC 26 player ratings šŸ’° Market value ā€” Transfermarkt squad valuations šŸŽÆ Penalty shootout win rate ā€” the "clutch factor" most models ignore šŸ”„ Knockout pressure ā€” encoded as a binary feature āš ļø Upset detection ā€” flags weaker teams in better form Dual-model ensemble: XGBoost + Poisson Regression 22 engineered features across 9 categories Trained on 49,477 international matches (1872ā€“2026) šŸ”§ š—§š—›š—˜ š—„š—˜š—”š—Ÿ š—˜š—”š—šš—œš—”š—˜š—˜š—„š—œš—”š—š š—–š—›š—”š—Ÿš—Ÿš—˜š—”š—šš—˜ Building the model wasn't the hard part ā€” keeping it LIVE was. Results change daily, teams get eliminated mid-tournament, and shootouts aren't always recorded immediately. The solution: an update engine that runs every hour ā€” fetching results, detecting eliminated teams, recalculating ELO, and handling shootouts with smart fallback logic. Fully automated. šŸ“Š š—§š—›š—˜ š—•š—œš—šš—šš—˜š—¦š—§ š—§š—›š—œš—”š—š š—œ š—Ÿš—˜š—”š—„š—”š—˜š—— Early versions suffered from "ELO Dominance" ā€” assuming the stronger team almost always wins. Statistically wrong. The fix: convert raw ELO difference into a calibrated win probability using the standard logistic formula. Now a +300 ELO advantage means ~88% win probability, not 100%. Because football is unpredictable. That's what makes it beautiful. šŸ–„ļø š—§š—›š—˜ š——š—”š—¦š—›š—•š—¢š—”š—„š—— A 6-page Streamlit app featuring: šŸ”® Match prediction ā€” Win/Draw/Loss probabilities šŸ”µ Group stage simulation ā€” Monte Carlo standings šŸ† Championship probability per team šŸ“„ Downloadable PDF report šŸŒ™ Dark/Light mode šŸ‡øšŸ‡¦ Arabic / šŸ‡¬šŸ‡§ English support šŸŽ¬ Full walkthrough in the video below šŸ‘‡ šŸ”“ Live App ā†’ https://lnkd.in/e8BuSUh7 (https://lnkd.in/e8BuSUh7)šŸ™ GitHub ā†’ https://lnkd.in/ej8w_cUq (https://lnkd.in/ej8w_cUq)āš ļø Built for educational & portfolio purposes. Predictions are probabilistic ā€” not guarantees šŸ˜„ What feature would YOU add to improve upset prediction? Drop it below šŸ‘‡ #MachineLearning (https://www.linkedin.com/search/results/all/?keywords=%23machinelearning&origin=HASH_TAG_FROM_FEED) #DataScience (https://www.linkedin.com/search/results/all/?keywords=%23datascience&origin=HASH_TAG_FROM_FEED) #Streamlit (https://www.linkedin.com/search/results/all/?keywords=%23streamlit&origin=HASH_TAG_FROM_FEED) #SportsAnalytics (https://www.linkedin.com/search/results/all/?keywords=%23sportsanalytics&origin=HASH_TAG_FROM_FEED) #FIFA2026 (https://www.linkedin.com/search/results/all/?keywords=%23fifa2026&origin=HASH_TAG_FROM_FEED)
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Cover image for Ł‚ŲµŲ© Ų§Ł„Ų«Ł„Ų§Ų«Ų© Ų§Ł„Ų°ŁŠŁ† Ų­ŲØŲ³ŲŖŁ‡Ł… ŲµŲ®Ų±Ų©! ŁˆŁ‡Ł„ Ł†Ų¬ŁˆŲ§ Ł…Ł† Ų§Ł„Ł…ŁˆŲŖŲŸ (Ų­ŲÆŁŠŲ« Ł†ŲØŁˆŁŠ ŲµŲ­ŁŠŲ­)
Ł‚ŲµŲ© Ų§Ł„Ų«Ł„Ų§Ų«Ų© Ų§Ł„Ų°ŁŠŁ† Ų­ŲØŲ³ŲŖŁ‡Ł… ŲµŲ®Ų±Ų©! ŁˆŁ‡Ł„ Ł†Ų¬ŁˆŲ§ Ł…Ł† Ų§Ł„Ł…ŁˆŲŖŲŸ (Ų­ŲÆŁŠŲ« Ł†ŲØŁˆŁŠ ŲµŲ­ŁŠŲ­)
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