From thinking-frameworks-skills
Scores FIFA World Cup Fantasy squad plans with risk-adjusted, rank-relative fitness, factoring ownership, variance, and rank objective. Useful for evolutionary selection or evaluating candidate squads.
How this skill is triggered — by the user, by Claude, or both
Slash command
/thinking-frameworks-skills:wc-fitness-evalThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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).
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).
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
- [ ] 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
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.
fitness: <number>
decomposition:
raw_xEV: <n>
captaincy_uplift: <n>
cs_corr_bonus: <n>
progression_carry: <n>
ownership_leverage: <+/- n> # under θ
variance: <n> variance_term: <+/- n> # under θ, with k
feasibility_penalty: <n>
objective: { theta: protect|gain|neutral, k: <n> }
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.
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:
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.
scoring-rules.md are confirmed: true before trusting absolute magnitudes; otherwise flag every fitness as provisional.npx claudepluginhub lyndonkl/claude --plugin thinking-frameworks-skillsComputes a single player's expected FIFA World Cup Fantasy points per round (xEV) by combining start probability, minute-based scoring tiers, fixture-scaled npxG/xA, defensive floors, set-piece premium, and downside risks.
Builds a tactical game plan by analyzing opponent weaknesses, team strengths, and match context using performance indicators and decision rules.
Calculate derived football metrics such as xG, xGOT, PPDA, expected threat, and passing networks from raw event data. Useful for football analytics and model building.