
West Ham are a team caught between modest attack and leaky defence, averaging just 1.24 xG for whilst conceding 1.66 per match—a profile that leaves little margin for error. Their recent form has been brutal, dropping five consecutive fixtures without a draw or win, a sequence that compounds structural attacking limitations. With no imminent fixtures in the prediction window, the model's immediate focus remains on identifying when defensive vulnerabilities stabilise enough to offer value. Bawler's Banker selections have maintained a perfect 5/5 conversion rate on Hammers matches, suggesting the site's algorithms have effectively navigated their volatility.
How to read this: the green bar shows the average goals West Ham 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 England: Premier League average — if the bar extends past the diamond, West Ham are above average there.
How to read this: the solid line is the goals West Ham 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 West Ham's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a West Ham fixture, the model lands 5 out of 6 (83%). This is well above the cross-league baseline of ~65%. Every pick is logged before kickoff and settled publicly.
How to read this: each row groups settled Banker picks Bawler issued on West Ham 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 West Ham matches.