AC Milan's attacking profile sits marginally above league average at 1.49 xG per match, but their underlying issue is defensive fragility—1.22 xG conceded suggests structural vulnerabilities in transition. Recent form has been volatile (2W-1D-2L across five fixtures), reflecting this inconsistency between moments of control and costly lapses. With no settled fixtures immediately ahead, the model will recalibrate as the next match emerges. Bawler's 80% banker hit rate on Milan suggests strong predictive edge on their games when opportunities arise.
How to read this: the green bar shows the average goals AC Milan were expected to score per match (their xG output). The red bar shows what opponents are expected to score against them. The diamond on each bar marks the Italy: Serie A average — if the bar extends past the diamond, AC Milan are above average there.
How to read this: the solid line is the goals AC Milan actually scored each match. The dashed line is the goals the model expected them to score (xG). When the solid line is above the dashed, they overperformed — they finished better than the chances they created suggested. When it's below, they underperformed. Persistent underperformance often regresses; a one-off gap usually doesn't.
How to read this: each tile is one settled match, most recent first. Green = win, amber = draw, red = loss. Numbers show the actual scoreline from AC Milan's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a AC Milan fixture, the model lands 4 out of 6 (67%). Every pick is logged before kickoff and settled publicly.
How to read this: each row groups settled Banker picks Bawler issued on AC Milan fixtures by market type, so you can see where the model has the strongest read on this team. Higher hit rate = more reliable category for AC Milan matches.