Limited launch offer

Create your account. Unlock Pro for free.

This launch window won’t stay open forever. Join now to explore the full Pro workspace and keep everything saved to your account.

  • Full Pro toolkit
  • No payment required
  • Workspace saved

No card required

Strategy guide · 7 min read

Prediction coverage: what it measures and why it matters

Prediction coverage describes which possible outcome paths remain represented in a plan. Card count is part of the picture, but useful coverage analysis also asks which scenarios are covered, how strongly they are represented, and what disappears when cards are removed.

Generate a coverage baseline

Separate raw card count from meaningful scenario coverage

Measure which outcomes remain represented after reduction

Compare equal and probability-weighted coverage

Coverage begins with the outcome space

The full outcome space contains every complete path allowed by the included matches. If four matches each have two modeled outcomes, the space contains 2⁴ = 16 paths. Generating all 16 gives complete coverage of that defined space. Selecting fewer cards creates partial coverage, even if each individual match outcome still appears somewhere.

Outcome coverage and scenario coverage differ

A reduced set may include both home and away outcomes for every match while still omitting many ways those outcomes can occur together. Outcome coverage asks whether an individual option appears at least once. Scenario coverage asks whether a complete joint path is represented. The second measure is stricter and usually more useful for card-level analysis.

Raw coverage percentage

For equally treated scenarios, raw coverage is retained cards divided by all valid cards. Keeping 24 of 96 cards gives 25% raw coverage. This figure is transparent but assumes each path carries equal importance. It does not say whether the retained quarter contains the more plausible or more profitable modeled outcomes.

Probability-weighted coverage

Weighted coverage adds the modeled probabilities of the scenarios represented by the retained cards. A set containing 25% of cards might cover more than 25% of modeled probability mass if it focuses on more likely paths. The result depends entirely on the probability estimates, so raw and weighted coverage should be shown together.

Coverage concentration

Two plans with the same coverage percentage can have different concentration. One may spread cards across several outcome families while another depends heavily on one favored team or pattern. Group cards by important outcomes, leagues, confidence levels, or scenarios to see whether one assumption controls too much of the plan.

Audit every reduction

Before and after using a card eliminator to reduce the size of your plan, compare raw coverage, weighted coverage, unique outcomes, scenario groups, estimated profit, and worst-case loss. Record which paths were removed and why. This audit makes the tradeoff reviewable and helps distinguish deliberate focus from an accidental coverage gap.