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In this paper, I introduce RisingBALLER, the first publicly available approach that leverages a transformer model trained on football match data to learn matchspecific player representations. Drawing inspiration from advances in language modeling, RisingBALLER treats each football match as a unique sequence in which players serve as tokens, with their embeddings shaped by the specific context of the match. Through the use of masked player prediction (MPP) as a pre-training task, RisingBALLER learns foundational features for football player representations, similar to how language models learn semantic features for text representations. As a downstream task, I introduce next match statistics prediction (NMSP) to showcase the effectiveness of the learned player embeddings. The NMSP model surpasses a strong baseline commonly used for performance forecasting within the community. Furthermore, I conduct an in-depth analysis to demonstrate how RisingBALLER's learned embeddings can be used in various football analytics tasks, such as producing meaningful positional features that capture the essence and variety of player roles beyond rigid x,y coordinates, team cohesion estimation, and similar player retrieval for more effective data-driven scouting. More than a simple machine learning model, RisingBALLER is a comprehensive framework designed to transform football data analytics by learning high-level foundational features for players, taking into account the context of each match. It offers a deeper understanding of football players beyond individual statistics. In recent years, the field of machine learning has been revolutionized by the introduction of the transformer architecture [1], which initially gained prominence in natural language processing (NLP) with models like BERT [2], RoBERTa [3], and more recently, the widespread use of large language models (LLMs). These models, often trained on seemingly simple tasks such as next token prediction or masked token prediction, have demonstrated remarkable performance in learning high-level features that effectively represent each word and model language intricately. They are capable of learning nuanced representations of the multiple meanings a word can have depending on its context.