FootballBERT introduces a new way of representing football players — not as static IDs or statistical aggregates that fluctuate wildly over short periods, but as contextual embeddings learned directly from match data. Built on a Transformer architecture and trained through a Masked Player Prediction (MPP) objective, FootballBERT captures how a player’s identity emerges from teammates, opponents, and coaches tactical demands — much like BERT learns word meaning from sentences. Openly released on Hugging Face, FootballBERT is a plug-and-play foundation model whose embeddings can be integrated into any downstream system, paving the way for player-aware analytics across performance modeling, recruitment and prediction.