Swansea operate as a narrow, cautious outfit: they're generating just 1.17 xG per match whilst conceding 1.26, indicating a side that creates sparingly and defends with modest solidity. Recent form has been mixed—one win across five games with two draws—suggesting inconsistency despite the underlying metrics remaining relatively balanced. With no fixtures in the immediate window, attention turns to their next assignment, where the model will assess whether this measured approach continues to frustrate. Bawler's banker picks on Swansea have maintained a perfect 100% hit rate across five selections, indicating strong predictive clarity on their most likely outcomes.
How to read this: the green bar shows the average goals Swansea 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: Championship average — if the bar extends past the diamond, Swansea are above average there.
How to read this: the solid line is the goals Swansea 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 Swansea's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a Swansea 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.
How to read this: each row groups settled Banker picks Bawler issued on Swansea 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 Swansea matches.