Building separate directed passing networks for each type of possession outcome — goals, shots on target, shots off target, and lost possession — then learning how much each outcome type predicts winning using logistic regression. Each player becomes a node; each pass creates a directed, weighted edge. The network is built by iterating through all passages of play, accumulating edge weights by outcome type. Player influence is then computed as network centrality (sum of incoming and outgoing edge weights) within each outcome-specific network, weighted by the learned importance of that outcome.
(1) Iterate through all passages of play in the dataset. (2) For each passage, classify the terminal event: goal, shot on target, shot off target, or lost possession. (3) Build a directed graph: for each pass in the sequence, add weight to the edge between passer and receiver in the network corresponding to that outcome type. (4) After processing all passages, compute each player's centrality per outcome network: V(player, event) = sum of all adjacent edge weights (incoming + outgoing). (5) Learn outcome weights using logistic regression: train on per-match player centrality values as features, with match result (win/loss/draw) as target. The learned coefficients (alpha values) represent how much each outcome type contributes to winning. (6) Final player value = weighted sum across all outcome types: Σ(alpha_k × V(player, event_k)).