
Tottenham's underlying profile reveals a side hamstrung by both blunt attack and defensive fragility: they're generating just 1.22 xG per match whilst conceding 1.52, a combination that has yielded one win, two draws and two losses across their last five settled fixtures. This thin margins profile—neither clinical nor defensively assured—suggests volatility will remain their calling card until the underlying metrics shift materially. With no upcoming fixtures in the current window, focus shifts to the underlying model's stability: Bawler's banker picks on Spurs have hit at 80 per cent across five recent selections, a strike rate that reflects the predictability of their xG-driven ceiling and floor.
How to read this: the green bar shows the average goals Tottenham 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, Tottenham are above average there.
How to read this: the solid line is the goals Tottenham 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 Tottenham's perspective. Tap a tile to see Bawler's full prediction for that match.
When Bawler issues a Banker pick on a Tottenham 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 Tottenham 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 Tottenham matches.