The FIFA World Cup 2026 Master Dataset: A Normalized 3NF Relational Benchmark of the Expanded 48-Team International Football Tournament

The 2026 FIFA World Cup expanded the international tournament format from 32 to 48 national teams, increasing the schedule from 64 to 104 matches across 16 host venues in Canada, Mexico, and the United States (concluding on July 19, 2026, with Spain defeating Argentina 1-0 in the Final). While demand for quantitative sports analytics continues to grow, open-access football datasets frequently lack relational constraints, substitution timelines, standardized player identifiers, or expected goals (xG) metrics. Here, we present the FIFA World Cup 2026 Master Dataset, a 3rd Normal Form (3NF) relational database capturing all 104 matches, 48 qualified national teams, 1,248 registered players, 2,704 minute-by-minute lineup records, tactical team statistics, expected goals (xG), and pre-engineered predictive feature matrices. Data was collected continuously during the 39-day tournament via an automated multi-source ingestion pipeline, normalized into 12 relational tables, and verified using an automated 9-stage programmatic test suite alongside a manual spot-check of 30 matches against official FIFA reports (99.87% empirical accuracy). Distributed in CSV, Apache Parquet, and SQLite formats (sqlite_fifa_world_cup_2026.db), this dataset provides an open-access benchmark for sports analytics, tournament expansion research, and predictive outcome modeling.