Wolverhampton
Wolves operate with a notable defensive vulnerability, conceding 1.55 xG per match against an attacking output of just 1.06—a profile that explains their recent winless run of two draws and two losses across the last four settled fixtures. Their underlying metrics suggest a team caught between defensive fragility and blunt finishing, making them vulnerable to sides that can exploit wide spaces. With no immediate fixture scheduled, the model's focus remains on identifying value in their next outing. Bawler's Banker selections on Wolves matches have maintained a perfect 100% hit rate across four picks, signalling strong predictive calibration for this fixture set.
How to read this: the green bar shows the average goals Wolverhampton 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, Wolverhampton are above average there.
How to read this: the solid line is the goals Wolverhampton 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 Wolverhampton's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a Wolverhampton fixture, the model lands 5 out of 5 (100%). This is well above the cross-league baseline of ~65%. Every pick is logged before kickoff and settled publicly.