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Uses agnostic model as baseline with skill-based adjustments. Skills are added DIRECTLY to probability (no weighting in prediction). Weights are only used for distributing updates, not for prediction.

Usage

calculate_expected_wicket_skill(
  agnostic_wicket,
  batter_wicket_skill,
  bowler_wicket_skill,
  venue_perm_wicket_skill,
  venue_session_wicket_skill,
  format = "t20",
  gender = "male"
)

Arguments

agnostic_wicket

Numeric. Base wicket probability from agnostic model.

batter_wicket_skill

Numeric. Batter's wicket skill index (positive = gets out more).

bowler_wicket_skill

Numeric. Bowler's wicket skill index (positive = takes more wickets).

venue_perm_wicket_skill

Numeric. Venue permanent wicket skill index.

venue_session_wicket_skill

Numeric. Venue session wicket skill index.

format

Character. Match format (unused, kept for API consistency).

gender

Character. Gender (unused, kept for API consistency).

Value

Numeric. Adjusted expected wicket probability.

Details

Formula: expected_wicket = clamp(baseline + batter_wicket_skill + bowler_wicket_skill + venue_perm_wicket_skill + venue_session_wicket_skill, 0.001, 0.50)

NOTE: Wicket skills use OPPOSITE sign convention from run skills. Run skills: positive = good for entity (batter scores more / bowler restricts more) Wicket skills: positive = higher wicket probability for BOTH (bad for batter, good for bowler) This is intentional - each skill represents DIRECT contribution to wicket probability.

Batter wicket skill is positive if they get out MORE often (bad for batter). Bowler wicket skill is positive if they take MORE wickets (good for bowler).

Examples

calculate_expected_wicket_skill(0.054, 0.01, 0.02, 0.005, 0.0)
#> [1] 0.089