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Risk-Tolerance-Weighted Cross Optimization

Passing MetricsLevel 3 — Advanced

What It Is

Combining cross-completion probability with an xG reward metric via a tunable risk-tolerance parameter (λ) to recommend the optimal delivery zone given a coach's situational risk appetite. Conservative (high λ) favors completion probability; aggressive (low λ) favors xG potential. The key insight: 33% cross-completion rate isn't inherently wasteful — the question is where to place them, and that depends on risk tolerance. Crosses that lead to shots two actions later are undervalued by immediate-shot-chain metrics.

Correct Execution

For each cross situation: compute P(completion) and E(xG|completion) for multiple target zones. Combined score = λ × P(completion) + (1-λ) × E(xG). Vary λ by game state: protecting a lead → high λ; chasing a game → low λ. The optimal target zone shifts with λ.

Diagnostic Tree

Edges

Conventional Wisdom Is Wrong

33% Cross Completion Rate Isn't Bad — The Question Is Where They Land

Crosses have a league-average completion rate of ~33%, which sounds wasteful. But a 33% cross to the far post that generates 0.12 xG when completed is better expected value than a 60% cross to the near post that generates 0.02 xG when completed: 0.33 x 0.12 = 0.040 vs 0.60 x 0.02 = 0.012. Raw completion rate is the wrong metric for evaluating crossing — expected value per cross (completion probability x reward if completed) is the correct one. Additionally, crosses that lead to shots two actions later (second-ball conversions) are undervalued by immediate-shot-chain metrics.

What most people do
Evaluate crossing by completion rate. "Low completion = bad crosser."
What the best do
Compute expected value per cross = P(completion) x E(xG|completion) for each target zone. Optimize target zone by game state: when protecting a lead, target high-completion zones; when chasing, target high-xG zones despite lower completion.
Why it's an edge: Teams that eliminate crossing because of "low completion rate" are removing one of their highest expected-value actions. The math says: keep crossing, but cross to the right zone for the situation.
How to exploit: Build a cross target-zone optimizer with a tunable risk tolerance parameter. Conservative (protecting lead) = near post, high completion. Aggressive (chasing) = far post/cutback zone, higher xG. Present to coaches with game-state-specific recommendations.
Caitlan Krasinski, StatsBomb Conference, 2022-10-03. Risk-tolerance cross optimization model.

Sources

  • Caitlan Krasinski, StatsBomb Conference 2022, YouTube, 2022-10-03