Before the match starts, this model picks the winning lineup: joint player-proficiency and team-congruency modeling reaches 61.33% accuracy
The outcome of a temporary team is often foreshadowed before play begins, yet a single player rating says neither whether that player fits the current role nor whether the five assignments complement one another. This work puts both questions into one deep-learning model: it first reads each player's earlier matches against the current champion and role, then compares the two ten-player lineups as a whole. On a chronologically split validation set, the model predicts the winner before play at 61.33% accuracy, above the TrueSkill, CatBoost, GRU and DraftRec comparisons.