# Wc Fitness Eval > Scores a FIFA World Cup Fantasy candidate squad/plan as risk-adjusted, rank-relative fitness under a stated rank objective (protect / gain / neutral). Sums fixture-scaled player xEV + captaincy uplift, adds clean-sheet correlation and progression-carry, applies an ownership-leverage term (cover chalk when protecting, fade it when gaining), and a variance term whose SIGN flips with the objective (penalty when protecting, reward when chasing) — the selection-pressure dial. Returns one fitness number plus the full decomposition. Use to score the population in the evolution engine, or to evaluate any single squad/plan. The objective makes the same candidate score differently for a leader vs a chaser; never returns raw expected points. - Skill: `lyndonkl/wc-fitness-eval` (Agent Skill) - Install (CLI): `npx skillmds add lyndonkl/wc-fitness-eval` - Raw SKILL.md: https://api.skillmd.com/api/skills/lyndonkl/wc-fitness-eval/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: lyndonkl (https://skillmd.com/u/lyndonkl) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/lyndonkl/wc-fitness-eval --- # wc-fitness-eval — risk-adjusted, rank-relative fitness Implements `footballfantasy/context/frameworks/fitness-function.md`. The whole evolution engine selects on this number, so it must encode the two things that make fantasy fitness *not* raw points: **rank is relative** (ownership matters) and **the rank objective sets the variance target** (the selection-pressure dial). ## The formula ``` fitness(c | θ) = raw_xEV(c) # Σ player xEV (fixture-scaled) + captaincy uplift + cs_corr_bonus(c) # within-team clean-sheet correlation (BB2 stacks) + progression_carry(c, horizon) # discounted future-round value of deep-team players + ownership_leverage(c | θ) # rank term — sign/shape set by θ − variance_term(c | θ) # selection pressure — sign flips with θ − feasibility_penalty(c) # 0 after clean repair; disqualifying if infeasible ``` ## Workflow ``` - [ ] 1. Read inputs: candidate (with block tags), round signals (player-ev, clean-sheet, fixture, ownership), θ and k - [ ] 2. raw_xEV: sum per-player xEV (from wc-player-ev signals); add captaincy uplift (from wc-captain-ladder) - [ ] 3. cs_corr_bonus: from wc-clean-sheet-model for each defensive stack - [ ] 4. progression_carry: Σ player_xEV_future × P(team advances)^rounds, discounted; weight up in KO phases - [ ] 5. ownership_leverage: apply θ (cover high-EO when protect; overweight low-EO ceilings when gain) - [ ] 6. variance_term: estimate candidate variance; multiply by ±k per θ - [ ] 7. feasibility_penalty: 0 if repair-clean; large if not - [ ] 8. Sum → fitness; emit fitness + the full decomposition ``` ## Step detail **raw_xEV** — sum the `xEV` field from each player's `player-ev` signal (already fixture-scaled, minutes-weighted, with the 60' tier and card/concede downside). Captaincy uplift = the expected *extra* from the captain ladder (from `wc-captain-ladder`), i.e. `E[best ladder-reachable captain outcome]`, not a naive 2× of the top player. For a squad-build (no fixed matchday yet), use the round-1 captaincy estimate. **cs_corr_bonus** — for each Clean-Sheet Spine stack (BB2), `wc-clean-sheet-model` returns the joint-distribution bonus capturing that a 3-defender + GK stack hauls *together*. Sign by θ: amplify the correlated-haul upside when `gain`; lightly discount when `protect` (they also blank together). **progression_carry** — `Σ_player xEV_typical(player) × Σ_{r=next..horizon} P(team alive at r) × discount^r`. A steady player on a likely semi-finalist accrues several future rounds; a coin-flip nation's player accrues little. Weight ≈0 in the final, high in early knockouts (this is what makes A3 Progression Theorist competitive on fitness). Advance probabilities from `wc-fixture-progression`. **ownership_leverage(c | θ)** — using effective ownership EO from `wc-ownership-meta`: - `θ=protect`: `− w · Σ_{high-EO players NOT owned} EO_haul_risk` → rewards covering chalk (penalise gaps in the template). - `θ=gain`: `+ w · Σ_{owned, low-EO, live-ceiling} (ceiling × (1−EO))` → rewards differentiation. - `θ=neutral`: small symmetric term; mostly defer to raw_xEV. **variance_term(c | θ)** — candidate variance from the spread of its players' point distributions + captaincy variance + stack correlation: | θ | term | |---|---| | protect | `+ k · variance` (penalty) — damp swings, bank the lead | | gain | `− k · variance` written as a *reward* (subtract a negative) — you need the swing | | neutral | small penalty | `k` scales with how extreme the standing is (big lead + few rounds → high k_protect; big deficit + few rounds → high k_gain). **feasibility_penalty** — 0 if the candidate passed repair (budget, nation cap, formation, 15-man shape). Large/disqualifying otherwise (keeps infeasible candidates out of the elite set; their blocks can still be harvested in crossover before they drop). **Revealed-preference soft terms** — add the small bonuses/penalties accrued in `manager-profile.md` (e.g. "+ minutes-secure mids", "− benching the favourite nation"). Soft: they tilt, never dominate. ## Output (the decomposition — always return it) ```yaml fitness: decomposition: raw_xEV: captaincy_uplift: cs_corr_bonus: progression_carry: ownership_leverage: <+/- n> # under θ variance: variance_term: <+/- n> # under θ, with k feasibility_penalty: objective: { theta: protect|gain|neutral, k: } variance_band: low|medium|high ``` The decomposition is mandatory — the board explains options through it, and the engine needs `variance_band` to mandate spectrum coverage in selection. ## Goodhart / substitution guard (`system-dynamics.md` §4, `invariants.md` §6) Fitness is **multi-term by design, and that is non-negotiable** — the systems-thinking digest's cautionary tale (held *"close before writing any agent-fitness metric"*): every "maximise X" rule produces a substitution effect at a node the rule did not name. Collapsing fitness to one dominant term is forbidden; if you ever find one term swamping the rest (one weight set so high the others are noise), that is the Goodhart failure in progress — re-balance. Emit, alongside the decomposition, a one-line **substitution-watch** per candidate when one term is doing most of the work: name *what the candidate gave up to score there.* Examples: - captaincy_uplift dominates → "ceiling-led; check the bench/minutes floor it traded away." - ownership_leverage dominates → "differential-led; check how many nailed starts it sacrificed." - raw_xEV (this round) dominates → "this-round-led; check the progression_carry it ignored." The thing that quietly degraded is usually the node the objective didn't name. Surfacing it on the artifact lets `round-review` run the substitution-watch across rounds before it compounds. ## Guardrails - **Never return raw points as fitness.** If θ isn't supplied, ask for it; do not default silently to neutral on a high-stakes round. - **Never collapse to a single metric.** Multi-term always (Goodhart). The decomposition ships every time. - **The same candidate must score differently under protect vs gain** — if it doesn't, the variance term is mis-wired. - Confirm the point values in `scoring-rules.md` are `confirmed: true` before trusting absolute magnitudes; otherwise flag every fitness as provisional.