RisingBALLER: A player is a token, a match is a sentence, A path towards a foundational model for football players data analytics

TL;DR

RisingBALLER introduces a transformer-based framework that models football matches as sequences with players as tokens, learning contextual player representations for improved analytics and performance prediction.

Contribution

It is the first approach to apply transformer models to football match data, creating foundational player embeddings that enhance various football analytics tasks.

Findings

NMSP model outperforms baseline performance forecasting methods.

Learned embeddings capture player roles and team cohesion.

Framework enables meaningful player similarity and role analysis.

Abstract

In this paper, I introduce RisingBALLER, the first publicly available approach that leverages a transformer model trained on football match data to learn match-specific 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 the learned embeddings by RisingBALLER 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.