Sevilla
Sevilla operate as a fundamentally imbalanced side: their attacking output of 1.37 xG per match sits well below La Liga's top tier, yet they concede 1.49, suggesting structural defensive vulnerabilities that compound their limited offensive threat. Recent form has been turbulent, with just two wins across their last six matches, including three losses, indicating a team struggling for consistency. With no fixtures in the immediate window, attention turns to the underlying model: Bawler's Banker selections on Sevilla have maintained an 83% strike rate across six settled picks, a strong indicator of reliable predictive edge when the conditions align.
How to read this: the green bar shows the average goals Sevilla 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 Spain: La Liga average — if the bar extends past the diamond, Sevilla are above average there.
How to read this: the solid line is the goals Sevilla 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 Sevilla's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a Sevilla fixture, the model lands 6 out of 7 (86%). 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 Sevilla 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 Sevilla matches.