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Option-Aware Pass Decision Evaluation

Player EvaluationLevel 3 — Advanced

Prerequisites

Pass Risk-Gain DecompositionPlayer Decision-Making Risk Preference Profiling

What It Is

Evaluating a player's passing decisions by comparing the pass they chose to all the passes they could have chosen. For every passing moment, compute risk and gain for the actual pass AND for hypothetical passes to every other reachable teammate. Then measure: (1) Risk Decision Parameter — what percentage of the time does the player choose the lowest-risk option? (2) Gain Decision Parameter — what percentage of the time does the player choose the highest-gain option? These two parameters capture the player's decision-making style independently of their execution quality. The key insight: "when you are valuing actions of a player you should take into account the options the player had."

Correct Execution

(1) For each passing moment, identify all reachable teammates from 360 data. (2) Compute risk and gain for a hypothetical pass to each teammate (using the kinematic simulation from pass-risk-gain-decomposition). (3) Rank the options by risk (lowest risk = safest) and by gain (highest gain = most attacking). (4) Check: did the player choose the lowest-risk option? → increment risk decision counter. Did they choose the highest-gain option? → increment gain decision counter. (5) Average across all passes for the player: Risk Decision Parameter = % of times chose lowest risk. Gain Decision Parameter = % of times chose highest gain.

Key findings from Barcelona (37 matches, 2020-21):

  • Average risk decision = 36% (players choose the safest option about a third of the time)
  • Most conservative (high risk decision): Dest, Dembélé, Elijah Marie
  • Most risk-taking (low risk decision): Riqui Puig, Messi, Busquets
  • Highest gain decision: Messi, Jordi Alba, Sergio Roberto (choosing the most dangerous option most often)
  • Temporal shift: In the second half, risk decision decreases and gain decision increases (players take more chances as the game progresses)

Progression Levels

Diagnostic Tree

Coaching Cues

  • "The best pass isn't always the most dangerous one."
  • "There's no single 'right pass' metric. There's the right pass for this moment."
  • "Before you judge the decision, check what else was available."
  • "Messi chooses the highest-gain option more often than anyone — and completes it."

Common Errors

  1. Combining risk and gain decision into one score: They're independent dimensions that should stay separate. A conservative player (high risk decision) can also be a high-gain player if the safest option also happens to be the most dangerous.
  2. Blaming players without checking their options: A player who always chooses low-gain passes may not have had any high-gain options available. Check the alternative options before judging.
  3. Ignoring body orientation: A player physically facing backward can't see or play a forward penetrative pass even if it's the highest-gain option. 360 data doesn't capture body orientation.

Edges

🔑 Hidden Causal Lever

Second-Half Decision Profiles Shift Toward Risk-Seeking — This Is Exploitable

player-evaluationoption-aware-pass-decision-evaluation →

Across a full season of Barcelona data, risk decision parameter decreases and gain decision parameter increases in the second half. Players systematically take more chances as the game progresses — even controlling for score line. This temporal shift is measurable and exploitable.

What most people do
Analyze decisions as static across the match. Use full-match aggregates.
What the best do
Split decision analysis by half. Identify which players shift most toward risk-seeking. Time defensive intensity accordingly.
Why it's an edge: Pressing harder when opponents are taking more chances (and making more errors) is more efficient than constant pressing.
How to exploit: Build per-player temporal decision profiles. Time your pressing intensity to coincide with opponents' decision quality decline. Monitor your own team's second-half risk escalation.
Complex Systems Group, Madrid, StatsBomb Conference, 2021-11-04
💎 Elite-Only Behavior

The Added Value of a Decision Is Measured Against What Was Available — Not Against Zero

player-evaluationoption-aware-pass-decision-evaluation →

EPV of a chosen action minus EPV of the best alternative gives the "added value" of the decision. If a player's best alternative was a 0.10 EPV pass and they chose a 0.12 EPV pass, their added value is 0.02 — not 0.12. A player who consistently finds the 0.02 improvement over the obvious option is elite. A player whose choices match the best obvious option has adequate but not exceptional decision-making. A player who consistently chooses below the best alternative is costing the team.

What most people do
Measure action value as the EPV delta of what happened — ignoring what COULD have happened.
What the best do
Model all available options at each decision point. Compute added value = chosen action EPV - best alternative EPV. This reveals whether the player's contribution was truly creative or merely adequate.
Why it's an edge: A player on a great team may have high EPV per action because the team creates good options — the best available option is already high, and the player just has to take it. Their added value may be near zero. A player on a weak team who consistently finds 0.03 above the best alternative in difficult situations is genuinely creative — but their raw EPV will be low because the team context is weak.
How to exploit: Compute added value per player. Use it as a team-context-independent creativity metric. Identify players on weak teams with high added value — they're demonstrating individual quality in adverse conditions.
Javier Fernandez, FC Barcelona, 2019-10-22. Option-aware evaluation as the pinnacle of the EPV framework.

Sources

  • Complex Systems and Sports Analytics Group, Madrid, StatsBomb Conference 2021, YouTube, 2021-11-04 — presented option-aware pass decision evaluation with risk and gain decision parameters; showed Barcelona player rankings; identified temporal shift in decision-making between halves; stressed impossibility of single "best pass" metric