'use strict';

/**
 * CHALLENGER PROJECTION (Layer 3, Step 2) — archetype-aware, measured not claimed.
 *
 * The CHAMPION (`probabilityEstimator` → `p_win`) keeps serving and grading
 * users, completely unchanged. This computes a SECOND probability from the same
 * inputs at the same instant, retained beside the champion and joined to the
 * same outcome and the same close, so the ledger can decide which is better.
 *
 * NOTHING HERE IS CLAIMED. Running a challenger is honest beta; asserting it is
 * better before the settled ledger says so is not. Promotion is a separate,
 * later decision gated on Brier + calibration over sufficient segmented volume.
 *
 * ── WHY INTERPRETABLE, NOT A RE-ESTIMATION ───────────────────────────────
 * The challenger is the champion's probability ADJUSTED by the Layer-2 axes,
 * never a black-box re-derivation. That buys three things:
 *   1. Every difference is attributable to a named axis and a signed nudge —
 *      we can see exactly what the archetype changed and where.
 *   2. Where the archetype is absent or unremarkable the challenger is
 *      BYTE-IDENTICAL to the champion, so the A/B differs only where
 *      archetype-awareness could possibly help or hurt. That is the clean
 *      experiment: no dilution from rows the treatment never touched.
 *   3. A bad adjustment is removable without touching the base projection.
 *
 * ── ISOLATION ────────────────────────────────────────────────────────────
 * The champion is read, never written. `adjust()` is pure: same inputs → same
 * output, no shared state, no feedback. A contaminated A/B measures nothing.
 *
 * ── THE ADJUSTMENT ───────────────────────────────────────────────────────
 * Applied in LOG-ODDS space, so a nudge cannot push a probability past 0 or 1
 * and the same nudge means the same thing at p=0.5 and p=0.9 (an additive
 * probability bump does neither). Magnitudes are deliberately SMALL: this is a
 * lean on a real signal, not a re-forecast.
 */

const CHALLENGER_VERSION = 'arch-v1';

/** Log-odds nudges. `elite` = the Layer-2 p90 tier, `hi` = p75. Capped, and the
 *  total is clamped, so no stack of axes can run away with the projection. */
const NUDGE = Object.freeze({ elite: 0.22, hi: 0.11 });
const MAX_TOTAL_NUDGE = 0.45; // ≈ 10 pts at p=0.5 — a lean, never a re-forecast

/**
 * Which axis speaks to which stat, and in which direction.
 * `+1` = the trait makes the stat MORE likely, `-1` = less likely.
 * Only relationships that are mechanically obvious are encoded — a speculative
 * mapping would be the same guessing this whole layer exists to replace.
 */
const BATTER_MAP = Object.freeze({
  home_runs:    { power: +1, launch: +1, swing_miss: -1, contact: 0 },
  total_bases:  { power: +1, launch: +1, swing_miss: -1 },
  hits:         { contact: +1, line_drive: +1, swing_miss: -1, power: 0 },
  rbi:          { power: +1 },
  runs:         { patience: +1 },
  doubles:      { line_drive: +1, power: +1 },
  strikeouts:   { swing_miss: +1, contact: -1, aggression: +1, patience: -1 },
  walks:        { patience: +1, aggression: -1 },
  stolen_bases: {},
});

const PITCHER_MAP = Object.freeze({
  strikeouts:       { strikeout: +1, chase: +1, velocity: +1, contact_allowed: -1 },
  pitcher_strikeouts: { strikeout: +1, chase: +1, velocity: +1, contact_allowed: -1 },
  hits_allowed:     { strikeout: -1, contact_allowed: +1, ground_ball: -1 },
  earned_runs:      { contact_allowed: +1, wild: +1, strikeout: -1 },
  outs_recorded:    { control: +1, ground_ball: +1, wild: -1 },
  innings_pitched:  { control: +1, ground_ball: +1, wild: -1 },
  walks_allowed:    { wild: +1, control: -1 },
});

const clamp = (v, lo, hi) => (v < lo ? lo : v > hi ? hi : v);
const toLogOdds = (p) => Math.log(p / (1 - p));
const fromLogOdds = (l) => 1 / (1 + Math.exp(-l));

function num(v) {
  if (v == null || v === '') return null;
  const n = typeof v === 'number' ? v : Number(v);
  return Number.isFinite(n) ? n : null;
}

/**
 * adjust({ pWin, direction, statType, classification }) — PURE.
 *
 * Returns { p_win_challenger, delta, adjustments[], reason }.
 * When there is nothing to say, `p_win_challenger === pWin` EXACTLY and
 * `adjustments` is empty — the challenger is the champion on those rows, by
 * construction, and the comparison stays clean.
 */
function adjust({ pWin, direction, statType, classification } = {}) {
  const p = num(pWin);
  const identical = (reason) => ({
    p_win_challenger: p, delta: 0, adjustments: [], reason, version: CHALLENGER_VERSION,
  });

  if (p == null || p <= 0 || p >= 1) return identical('no_champion_probability');
  if (!classification || !classification.sufficient) return identical('archetype_absent_or_thin');

  const vector = classification.vector || {};
  const stat = String(statType || '').toLowerCase();
  const map = (classification.role === 'pitcher' ? PITCHER_MAP : BATTER_MAP)[stat];
  if (!map) return identical('stat_not_mapped');

  // Direction: a trait that raises the stat raises P(over) and lowers P(under).
  const dirSign = String(direction || 'over').toLowerCase() === 'under' ? -1 : 1;

  const adjustments = [];
  let total = 0;
  for (const [axisKey, sign] of Object.entries(map)) {
    if (!sign) continue;
    const hit = vector[axisKey];
    // null = measured and unremarkable, or no data. Either way: no signal, no
    // nudge. Only a DISTINCTIVE trait (>= p75) moves anything.
    if (!hit) continue;
    const mag = NUDGE[hit.tier] || 0;
    if (!mag) continue;
    const signed = mag * sign * dirSign;
    total += signed;
    adjustments.push({ axis: axisKey, label: hit.label, tier: hit.tier, nudge: Math.round(signed * 1000) / 1000 });
  }

  if (!adjustments.length) return identical('no_distinctive_axis_for_stat');

  const capped = clamp(total, -MAX_TOTAL_NUDGE, MAX_TOTAL_NUDGE);
  const challenger = clamp(fromLogOdds(toLogOdds(p) + capped), 0.01, 0.99);
  const rounded = Math.round(challenger * 1000) / 1000;

  return {
    p_win_challenger: rounded,
    delta: Math.round((rounded - p) * 1000) / 1000,
    adjustments,
    capped: capped !== total,
    reason: null,
    version: CHALLENGER_VERSION,
  };
}

/**
 * attachChallenger(grades, classifyFor) — map a slate's grades to the same
 * grades plus challenger fields. `classifyFor(playerName, statType)` returns a
 * Layer-2 classification or null; injected so this never does its own I/O and
 * tests stay hermetic.
 *
 * The champion field (`p_win`) is NEVER written here. Read-only by design.
 */
function attachChallenger(grades, classifyFor) {
  return (grades || []).map((g) => {
    if (!g) return g;
    const cls = typeof classifyFor === 'function'
      ? classifyFor(g.player || g.player_name, g.stat_type || g.stat)
      : null;
    const out = adjust({
      pWin: g.p_win,
      direction: g.direction,
      statType: g.stat_type || g.stat,
      classification: cls,
    });
    return {
      ...g,
      p_win_challenger: out.p_win_challenger,
      challenger_delta: out.delta,
      challenger_adjustments: out.adjustments.length ? out.adjustments : null,
      challenger_version: CHALLENGER_VERSION,
      challenger_reason: out.reason,
    };
  });
}

module.exports = {
  adjust,
  attachChallenger,
  CHALLENGER_VERSION,
  NUDGE,
  MAX_TOTAL_NUDGE,
  BATTER_MAP,
  PITCHER_MAP,
  __internals: { toLogOdds, fromLogOdds, clamp, num },
};