In sports analytics, machine learning models are used for players’ appointment analysis, strategic planning, etc. Multiple rating systems were developed to rank the player performance based on statistical measurements, machine learning, and deep learning analysis of actions and sequences. However, capturing the complex relationships between actions within open play sequences, affected by tactical game planning and team deployment, is a major challenge. Inspired by language models, we developed SoccerTransformer, a transformer-based deep learning framework tailored to soccer analytics to address this challenge. The proposed framework includes (1) self-supervised pre-training to capture generic in-game dynamics, (2) a downstream sequence classification task to predict the attack phase outcomes, and (3) a player rating system. Empirical results demonstrate that SoccerTransformer accurately captures player roles and predicts goals with F1 of 0.814 to 0.862 on previously unseen games. SoccerTranformer framework stands out by providing a greater correlation with market values than other state-of-the-art rating systems.