update

backend/tft/cli.py

@@ -22,9 +22,7 @@ def main() -> None:
 
     sub.add_parser("extract", help="extract endboards from raw matches")
     sub.add_parser("build-artifact", help="build analysis.json from static data + endboards")
-    p_cal = sub.add_parser("calibrate", help="backtest score vs real placements (holdout)")
-    p_cal.add_argument("--compare", action="store_true",
-                       help="A/B legacy vs. mechanical scorer + tau-Fit")
+    sub.add_parser("calibrate", help="backtest score vs real placements (holdout)")
 
     p_auto = sub.add_parser("autoplay", help="run scripted games headless")
     p_auto.add_argument("--policy", choices=["afk", "econ"], default="econ")
@@ -83,28 +81,13 @@ def main() -> None:
     elif args.command == "calibrate":
         from tft import db
         from tft.model import artifact as artifact_mod
-        from tft.model.calibrate import calibrate, fit_tau
-        from tft.model.score import score_board_legacy, score_mechanical
-        from tft.staticdata.fetch import load_static
+        from tft.model.calibrate import calibrate
 
         conn = db.connect()
-        learned_art = artifact_mod.load(current_set())
-        baseline_art = artifact_mod.build(load_static(), {})
-        r_base = calibrate(conn, baseline_art, holdout_only=True)
-        r_learned = calibrate(conn, learned_art, holdout_only=True)
-        print(f"holdout matches: {r_learned['matches']}")
-        print(f"spearman baseline: {r_base['mean_spearman']:.3f}")
-        print(f"spearman learned:  {r_learned['mean_spearman']:.3f}")
-        if args.compare:
-            r_leg = calibrate(conn, learned_art, holdout_only=True, score_fn=score_board_legacy)
-            r_mech = calibrate(conn, learned_art, holdout_only=True, score_fn=score_mechanical)
-            delta = r_mech["mean_spearman"] - r_leg["mean_spearman"]
-            tau = fit_tau(conn, learned_art, score_mechanical)
-            print(f"A/B  legacy:     {r_leg['mean_spearman']:.3f}")
-            print(f"A/B  mechanical: {r_mech['mean_spearman']:.3f}  (delta {delta:+.3f})")
-            print(f"tau-fit (mechanical): {tau['tau']} über {tau['pairs']} Paare, "
-                  f"log-loss {tau.get('log_loss')}")
+        result = calibrate(conn, artifact_mod.load(current_set()), holdout_only=True)
         conn.close()
+        print(f"holdout matches: {result['matches']}")
+        print(f"spearman: {result['mean_spearman']:.3f}")
 
     elif args.command == "autoplay":
         from tft.constants.loader import load_constants

backend/tft/constants/set17.toml

@@ -80,7 +80,6 @@ hp_multiplier = 1.8
 ad_multiplier = 1.5
 
 [combat]
-# p_win = 1 / (1 + exp(-(score_a - score_b) / tau)).
-# tau = 0.28: Log-Loss-Fit über 1064 Holdout-Platzierungspaare (mechanical Scorer).
-tau = 0.28
-variance = 0.08
+# Deterministische DPS-Simulation mit 60 FPS; Zeitlimit 30 s.
+# Bei Ablauf gewinnt die Seite mit mehr verbleibender Gesamt-Defense.
+max_frames = 1800

backend/tft/model/artifact.py

@@ -47,27 +47,12 @@ def component_pool(static_items: dict) -> list[str]:
     return sorted(c for c, n in counts.items() if n >= 3)
 
 
-def tier_profiles(static: dict, roles: dict) -> dict:
-    """Mittleres Mechanik-Profil (eHP/DPS, 1★, itemlos) pro Kostenstufe."""
-    from tft.model import statsheet
-
-    by_cost: dict[int, list] = {}
-    for api, unit in static["units"].items():
-        profile = statsheet.unit_stats(
-            unit, 1, [], static["items"], None, roles.get(api)
-        )
-        by_cost.setdefault(unit["cost"], []).append(profile)
-    return {
-        str(cost): {
-            "ehp": sum(p["ehp"] for p in profiles) / len(profiles),
-            "dps": max(sum(p["dps"] for p in profiles) / len(profiles), 1.0),
-        }
-        for cost, profiles in by_cost.items()
-    }
-
-
 def spell_dps_cap(static: dict, roles: dict) -> float | None:
-    """90. Perzentil der Spell-DPS aller Units (1★, itemlos) — Ausreißer-Guard."""
+    """90. Perzentil der Spell-DPS aller Units (1★, itemlos).
+
+    Dient im Score als Floor des relativen Spell-Deckels — rettet Caster
+    ohne Auto-Schaden (siehe statsheet.unit_stats).
+    """
     from tft.model import statsheet
 
     values = sorted(
@@ -107,8 +92,6 @@ def build(static: dict, learned: dict | None = None, extra_meta: dict | None = N
             "augments": static["augments"],
         },
         "roles": roles,
-        "stat_mults": baseline.compute_stat_mults(static["units"]),
-        "tier_profiles": tier_profiles(static, roles),
         "spell_dps_cap": spell_dps_cap(static, roles),
         "item_pool": craftable_items(static["items"]),
         "component_pool": component_pool(static["items"]),

backend/tft/model/baseline.py

@@ -1,56 +1,10 @@
-"""Rule-based baseline: unit values, stat proxies, role classification."""
+"""Rule-based baseline: unit values and role classification."""
 
 
 def unit_value(cost: int, stars: int) -> float:
     return cost * 3 ** (stars - 1)
 
 
-def stat_proxies(stats: dict) -> dict:
-    # cdragon liefert für manche Units null-Werte — als 0 behandeln.
-    hp = stats.get("hp") or 0
-    resists = ((stats.get("armor") or 0) + (stats.get("magicResist") or 0)) / 2
-    ad = stats.get("damage") or 0
-    aspd = stats.get("attackSpeed") or 0
-    mana_gap = max((stats.get("mana") or 0) - (stats.get("initialMana") or 0), 1)
-    return {
-        "ehp": hp * (1 + resists / 100),
-        "auto_dps": ad * aspd,
-        "cast_rate": aspd * 10 / mana_gap,
-    }
-
-
-STAT_MULT_RANGE = (0.9, 1.1)
-
-
-def compute_stat_mults(static_units: dict) -> dict:
-    """Stat-Stärke relativ zur eigenen Kostenstufe: (eHP + Offense) / 2, gekappt.
-
-    Offense = Maximum aus Auto-DPS und Cast-Rate (je normalisiert), damit
-    Caster nicht gegen Auto-Attacker abfallen.
-    """
-    proxies = {api: stat_proxies(u["stats"]) for api, u in static_units.items()}
-    by_cost: dict[int, list[str]] = {}
-    for api, u in static_units.items():
-        by_cost.setdefault(u["cost"], []).append(api)
-
-    mults = {}
-    for apis in by_cost.values():
-        n = len(apis)
-        avg = {
-            key: sum(proxies[a][key] for a in apis) / n or 1.0
-            for key in ("ehp", "auto_dps", "cast_rate")
-        }
-        for a in apis:
-            ehp_norm = proxies[a]["ehp"] / avg["ehp"]
-            offense = max(
-                proxies[a]["auto_dps"] / avg["auto_dps"],
-                proxies[a]["cast_rate"] / avg["cast_rate"],
-            )
-            raw = (ehp_norm + offense) / 2
-            mults[a] = round(min(max(raw, STAT_MULT_RANGE[0]), STAT_MULT_RANGE[1]), 4)
-    return mults
-
-
 def classify_role(unit: dict) -> str:
     """frontline | ad_carry | ap_carry | utility, from cdragon role with stat fallback."""
     role = (unit.get("role") or "").lower()
@@ -64,19 +18,3 @@ def classify_role(unit: dict) -> str:
         return "utility"
     stats = unit["stats"]
     return "frontline" if stats["range"] <= 1 else "ad_carry"
-
-
-# Default multipliers, replaced by learned values when the artifact has them.
-DEFAULT_ITEM_MULT = 1.15
-ROLE_FIT_BONUS = 1.05
-DEFAULT_TRAIT_TIER_MULT = [1.0, 1.03, 1.07, 1.12, 1.20]  # index = reached breakpoint ordinal
-
-# Item tags that fit a role (checked against item api_name, crude but stable).
-AD_HINTS = ("Deathblade", "InfinityEdge", "GiantSlayer", "LastWhisper", "RunaansHurricane", "GuinsoosRageblade")
-AP_HINTS = ("RabadonsDeathcap", "ArchangelsStaff", "JeweledGauntlet", "HextechGunblade", "NashorsTooth", "Morellonomicon")
-TANK_HINTS = ("BrambleVest", "DragonsClaw", "WarmogsArmor", "GargoyleStoneplate", "Redemption", "SunfireCape")
-
-
-def item_fits_role(item_api_name: str, role: str) -> bool:
-    hints = {"ad_carry": AD_HINTS, "ap_carry": AP_HINTS, "frontline": TANK_HINTS}.get(role, ())
-    return any(h in item_api_name for h in hints)

backend/tft/model/calibrate.py

@@ -81,38 +81,3 @@ def calibrate(conn, artifact: dict, holdout_only: bool = False, score_fn=None) -
         "matches": n,
         "mean_spearman": sum(correlations) / n if n else 0.0,
     }
-
-
-def fit_tau(conn, artifact: dict, score_fn) -> dict:
-    """Tau per Log-Loss über alle Platzierungspaare der Holdout-Matches fitten."""
-    import math
-
-    by_match = _holdout_scores(conn, artifact, True, score_fn)
-    pairs = []
-    for players in by_match.values():
-        if len(players) < 8:
-            continue
-        for i in range(len(players)):
-            for j in range(i + 1, len(players)):
-                (pl_a, s_a), (pl_b, s_b) = players[i], players[j]
-                mean = (s_a + s_b) / 2
-                if mean <= 0:
-                    continue
-                d = (s_a - s_b) / mean
-                pairs.append((d, pl_a < pl_b))  # kleinere Platzierung = besser
-
-    if not pairs:
-        return {"tau": None, "pairs": 0}
-
-    def log_loss(tau: float) -> float:
-        eps = 1e-9
-        total = 0.0
-        for d, a_wins in pairs:
-            p = 1 / (1 + math.exp(-d / tau))
-            p = min(max(p, eps), 1 - eps)
-            total += -math.log(p if a_wins else 1 - p)
-        return total / len(pairs)
-
-    taus = [t / 100 for t in range(2, 51, 2)]
-    best = min(taus, key=log_loss)
-    return {"tau": best, "pairs": len(pairs), "log_loss": round(log_loss(best), 4)}

backend/tft/model/score.py

@@ -1,4 +1,9 @@
-"""The one board-scoring entry point. Sim, bots, calibration, and UI all call this."""
+"""The one board-scoring entry point. Sim, bots, calibration, and UI all call this.
+
+Off/Def-Modell: Off = Auto- + Spell-DPS, Def = eHP — pro Unit aus exakten
+cdragon-Werten (Sterne und Items eingerechnet). Traits und Augments werden
+bewusst ignoriert (spätere Ausbaustufe); active_trait_tiers bleibt fürs UI.
+"""
 
 from tft.model import baseline, statsheet
 
@@ -27,132 +32,34 @@ def active_trait_tiers(board_units: list[dict], static_traits: dict, static_unit
     return tiers
 
 
-def _apply_shared_multipliers(total: float, board_units: list[dict], augments: list[str],
-                              learned: dict) -> float:
-    augment_mults = learned.get("augments", {})
-    for augment in augments:
-        total *= augment_mults.get(augment, 1.0)
+def unit_profile(u: dict, artifact: dict) -> dict | None:
+    """Kampfprofil einer Board-Unit: {"off", "def", "defensive", "unit"}.
 
-    pair_lifts = learned.get("pairs", {})
-    names = sorted({u["api_name"] for u in board_units})
-    lift_sum = sum(
-        pair_lifts.get(f"{a}|{b}", 0.0)
-        for i, a in enumerate(names)
-        for b in names[i + 1 :]
+    "defensive" steuert die Zielreihenfolge im Kampf: Frontline/Utility
+    sterben zuerst, Carries zuletzt.
+    """
+    unit = artifact["static"]["units"].get(u["api_name"])
+    if not unit:
+        return None
+    role = artifact.get("roles", {}).get(u["api_name"]) or baseline.classify_role(unit)
+    # role=None: der Frontline-Cast-Bonus verschlechtert die Holdout-Korrelation
+    # (0.659 vs. 0.679) — die Rolle steuert nur die Zielreihenfolge im Kampf.
+    stats = statsheet.unit_stats(
+        unit, u["stars"], u.get("items", []), artifact["static"]["items"],
+        None, None, spell_cap=artifact.get("spell_dps_cap"),
     )
-    return total * (1 + min(max(lift_sum * 0.01, -0.10), 0.10))
+    return {"off": stats["dps"], "def": stats["ehp"],
+            "defensive": role in ("frontline", "utility"), "unit": u}
 
 
-def score_board_legacy(board_units: list[dict], augments: list[str], artifact: dict) -> float:
-    """Heuristik-Scorer: Kostenwert × Multiplikatoren."""
-    static_units = artifact["static"]["units"]
-    static_traits = artifact["static"]["traits"]
-    learned = artifact.get("learned", {})
-    unit_mults = learned.get("units", {})
-    item_mults = learned.get("items", {})
-    trait_mults = learned.get("traits", {})
-    roles = artifact.get("roles", {})
-    stat_mults = artifact.get("stat_mults", {})
-
-    total = 0.0
-    for u in board_units:
-        unit = static_units.get(u["api_name"])
-        if not unit:
-            continue
-        value = (
-            baseline.unit_value(unit["cost"], u["stars"])
-            * stat_mults.get(u["api_name"], 1.0)
-            * unit_mults.get(u["api_name"], 1.0)
-        )
-        role = roles.get(u["api_name"]) or baseline.classify_role(unit)
-        for item in u.get("items", []):
-            mult = item_mults.get(item, baseline.DEFAULT_ITEM_MULT)
-            if baseline.item_fits_role(item, role):
-                mult *= baseline.ROLE_FIT_BONUS
-            value *= mult
-        total += value
-
-    for trait, ordinal in active_trait_tiers(board_units, static_traits, static_units).items():
-        default = baseline.DEFAULT_TRAIT_TIER_MULT[
-            min(ordinal, len(baseline.DEFAULT_TRAIT_TIER_MULT) - 1)
-        ]
-        total *= trait_mults.get(f"{trait}@{ordinal}", default)
-
-    return _apply_shared_multipliers(total, board_units, augments, learned)
+def board_profiles(board_units: list[dict], artifact: dict) -> list[dict]:
+    return [p for u in board_units if (p := unit_profile(u, artifact))]
 
 
-def score_mechanical(board_units: list[dict], augments: list[str], artifact: dict) -> float:
-    """Kampf-Approximation: strength = sqrt(Σ eHP × Σ DPS) aus exakten Stats."""
-    static_units = artifact["static"]["units"]
-    static_items = artifact["static"]["items"]
-    static_traits = artifact["static"]["traits"]
-    learned = artifact.get("learned", {})
-    unit_mults = learned.get("units", {})
-    item_mults = learned.get("items", {})
-    trait_mults = learned.get("traits", {})
-    roles = artifact.get("roles", {})
-
-    tiers = active_trait_tiers(board_units, static_traits, static_units)
-    team_buffs, recognized = statsheet.trait_buffs(tiers, static_traits)
-    # Augment-Team-Buffs (z.B. "+35 Health für dein Team") in die Stats mischen.
-    for a in augments:
-        for atom in artifact.get("augment_specs", {}).get(a, {}).get("atoms", []):
-            if atom["kind"] == "team_buff":
-                team_buffs[atom["stat"]] += atom["value"]
-    spell_cap = artifact.get("spell_dps_cap")
-    tier_profiles = artifact.get("tier_profiles", {})
-
-    # Eigenschafts-Bewertung: eHP = defensiv, DPS = offensiv. Der Stufen-Maßstab
-    # ist der gemessene Tier-Durchschnitt; Ratio-Cap fängt Extraktionsfehler.
-    RATIO_CAP = (0.5, 2.0)
-    STAR_VALUE = 3.0  # Kopienwert pro Sternstufe (Endboard-validiert)
-
-    total_ehp = 0.0
-    total_dps = 0.0
-    for u in board_units:
-        unit = static_units.get(u["api_name"])
-        if not unit:
-            continue
-        profile = statsheet.unit_stats(
-            unit, u["stars"], u.get("items", []), static_items,
-            team_buffs, roles.get(u["api_name"]), spell_cap=spell_cap,
-        )
-        mult = unit_mults.get(u["api_name"], 1.0)
-        ref = tier_profiles.get(str(unit["cost"]))
-        if ref:
-            star_ehp = statsheet.HP_STAR_MULT ** (u["stars"] - 1)
-            star_dps = statsheet.AD_STAR_MULT ** (u["stars"] - 1)
-            r_ehp = min(max(profile["ehp"] / (ref["ehp"] * star_ehp), RATIO_CAP[0]), RATIO_CAP[1])
-            r_dps = min(max(profile["dps"] / (ref["dps"] * star_dps), RATIO_CAP[0]), RATIO_CAP[1])
-            star = STAR_VALUE ** (u["stars"] - 1)
-            total_ehp += mult * ref["ehp"] * star * r_ehp
-            total_dps += mult * ref["dps"] * star * r_dps
-        else:
-            total_ehp += mult * profile["ehp"]
-            total_dps += mult * profile["dps"]
-
-    # /100: nur Anzeige-Skalierung, für den Kampfvergleich irrelevant.
-    strength = (total_ehp * total_dps) ** 0.5 / 100
-
-    for trait, ordinal in tiers.items():
-        key = f"{trait}@{ordinal}"
-        if trait in recognized:
-            # Buff steckt schon in den Stats — nur gelerntes Residuum.
-            strength *= trait_mults.get(key, 1.0)
-        else:
-            default = baseline.DEFAULT_TRAIT_TIER_MULT[
-                min(ordinal, len(baseline.DEFAULT_TRAIT_TIER_MULT) - 1)
-            ]
-            strength *= trait_mults.get(key, default)
-
-    for u in board_units:
-        for item in u.get("items", []):
-            modeled = statsheet.item_is_modeled(static_items.get(item))
-            strength *= item_mults.get(item, 1.0 if modeled else baseline.DEFAULT_ITEM_MULT)
-
-    return _apply_shared_multipliers(strength, board_units, augments, learned)
-
-
-# Aktiver Scorer: mechanical (A/B 23.07.: 0.724 vs. legacy 0.695 auf 38 Holdout-Matches).
-# Legacy bleibt für `calibrate --compare` erhalten.
-score_board = score_mechanical
+def score_board(board_units: list[dict], augments: list[str], artifact: dict) -> float:
+    """sqrt(Σoff × Σdef). augments bleibt nur für Signatur-Kompatibilität."""
+    profiles = board_profiles(board_units, artifact)
+    off = sum(p["off"] for p in profiles)
+    dfn = sum(p["def"] for p in profiles)
+    # /100: nur Anzeige-Skalierung, für Vergleiche irrelevant.
+    return (off * dfn) ** 0.5 / 100

backend/tft/model/statsheet.py

@@ -13,8 +13,11 @@ MANA_PER_ATTACK = 10
 FRONTLINE_MANA_PER_SEC = 10
 CAST_RATE_CAP = 1.5
 
+SPELL_AUTO_CAP = 3.0  # Spell-DPS max. 3× eigene Auto-DPS (Ausreißer-Guard)
+
 MAPPED_ITEM_KEYS = ("AD", "AP", "AS", "CritChance", "Health", "Armor",
-                    "MagicResist", "ManaRegen")
+                    "MagicResist", "ManaRegen", "BonusDamage", "DamageAmp",
+                    "BonusPercentHP", "CritDamageToGive")
 
 _TOKEN_RE = re.compile(r"[A-Z]+(?=[A-Z][a-z])|[A-Z]?[a-z]+|[A-Z]+|\d+")
 # Variablen mit diesen Tokens sind Mechanik-Parameter, keine Stat-Buffs.
@@ -25,7 +28,8 @@ _SKIP_TOKENS = {"duration", "threshold", "rounds", "per", "num", "gold",
 def _empty_acc() -> dict:
     return {"ad_pct": 0.0, "ap_flat": 0.0, "as_pct": 0.0, "hp_flat": 0.0,
             "hp_pct": 0.0, "armor_flat": 0.0, "mr_flat": 0.0,
-            "crit_chance": 0.0, "dr": 0.0, "mana_regen": 0.0, "damage_amp": 0.0}
+            "crit_chance": 0.0, "crit_dmg": 0.0, "dr": 0.0, "mana_regen": 0.0,
+            "damage_amp": 0.0}
 
 
 def _apply_item_effects(item_apis: list[str], static_items: dict, acc: dict) -> None:
@@ -50,6 +54,12 @@ def _apply_item_effects(item_apis: list[str], static_items: dict, acc: dict) ->
                 acc["mr_flat"] += val
             elif key == "ManaRegen":
                 acc["mana_regen"] += val
+            elif key in ("BonusDamage", "DamageAmp"):
+                acc["damage_amp"] += _fraction(val)
+            elif key == "BonusPercentHP":
+                acc["hp_pct"] += _fraction(val)
+            elif key == "CritDamageToGive":
+                acc["crit_dmg"] += _fraction(val)
             # alle übrigen Keys: bespoke Mechanik, bewusst ignoriert
 
 
@@ -139,6 +149,7 @@ def unit_stats(unit: dict, stars: int, item_apis: list[str], static_items: dict,
 
     as_eff = aspd * (1 + acc["as_pct"])
     crit_c = min(crit + acc["crit_chance"], 1.0)
+    crit_mult += acc["crit_dmg"]
     auto_dps = ad * (1 + acc["ad_pct"]) * as_eff * (1 + crit_c * (crit_mult - 1))
 
     spell_dps = 0.0
@@ -156,10 +167,13 @@ def unit_stats(unit: dict, stars: int, item_apis: list[str], static_items: dict,
             CAST_RATE_CAP,
         )
         spell_dps = dmg * cast_rate
+        # Ausreißer-Guard: Spell-Rohwerte sind zwischen Champions nicht
+        # vergleichbar (per-Hit vs. total). Relativer Deckel: max. 3× eigene
+        # Auto-DPS; das Populations-Perzentil dient als Floor für AD-lose Caster.
+        limit = SPELL_AUTO_CAP * auto_dps
         if spell_cap is not None:
-            # Ausreißer-Guard: Spell-Rohwerte sind zwischen Champions nicht
-            # vergleichbar (per-Hit vs. total) — Kappung am Populations-Perzentil.
-            spell_dps = min(spell_dps, spell_cap * AD_STAR_MULT ** (stars - 1))
+            limit = max(limit, spell_cap * AD_STAR_MULT ** (stars - 1))
+        spell_dps = min(spell_dps, limit)
 
     return {
         "ehp": ehp,

backend/tft/sim/combat.py

@@ -1,27 +1,107 @@
-"""Combat = score comparison. Relative score difference -> win probability -> damage."""
+"""Deterministischer Kampf: Gesamt-Offense arbeitet die Gegner-Units nacheinander ab.
+
+Zielreihenfolge pro Seite: erst defensive Units (seeded zufällig gemischt),
+dann offensive. Tote Units tragen keine Offense mehr — die Gesamt-Offense
+einer Seite ist die Suffix-Summe ab dem aktuellen Ziel.
+
+Ereignisgesteuert statt Frame-Schleife: zwischen zwei Todesereignissen sind
+beide Offensen konstant, die Zeit bis zum nächsten Kill ist exakt def/off.
+Das ist der Grenzwert der 60-FPS-Rechnung (Überschuss-Schaden trägt verlustfrei
+über); die Frames dienen nur als Raster für Dauer und Zeitlimit.
+"""
 
 import math
 import random
+from dataclasses import dataclass
+
+FPS = 60
+EPS = 1e-9
 
 
-def win_probability(score_a: float, score_b: float, cfg: dict) -> float:
-    mean = (score_a + score_b) / 2
-    if mean <= 0:
-        return 0.5
-    d = (score_a - score_b) / mean
-    return 1 / (1 + math.exp(-d / cfg["combat"]["tau"]))
+@dataclass
+class FightResult:
+    winner: str | None  # "a" | "b" | None = Unentschieden
+    frames: int
+    survivors_a: list[dict]
+    survivors_b: list[dict]
 
 
-def resolve(score_a: float, score_b: float, cfg: dict, rng: random.Random) -> bool:
-    """True if A wins. Adds noise on top of the probability."""
-    noise = rng.gauss(0, cfg["combat"]["variance"])
-    return rng.random() < min(max(win_probability(score_a, score_b, cfg) + noise, 0.02), 0.98)
+def _queue(team: list[dict], rng: random.Random) -> list[dict]:
+    """Sterbereihenfolge: defensive Units zuerst, innerhalb der Gruppe zufällig."""
+    defensive = [p for p in team if p["defensive"]]
+    offensive = [p for p in team if not p["defensive"]]
+    rng.shuffle(defensive)
+    rng.shuffle(offensive)
+    return defensive + offensive
 
 
-def damage(stage: int, winner_score: float, loser_score: float, winner_units: int, cfg: dict) -> int:
+def _suffix_off(queue: list[dict]) -> list[float]:
+    suffix = [0.0] * (len(queue) + 1)
+    for i in range(len(queue) - 1, -1, -1):
+        suffix[i] = suffix[i + 1] + queue[i]["off"]
+    return suffix
+
+
+def fight(team_a: list[dict], team_b: list[dict], rng: random.Random,
+          cfg: dict) -> FightResult:
+    """team_a/team_b: Profile aus score.board_profiles(). Beide Seiten ticken
+    gleichzeitig; same seed -> same outcome."""
+    qa, qb = _queue(team_a, rng), _queue(team_b, rng)
+    off_a, off_b = _suffix_off(qa), _suffix_off(qb)
+    limit = cfg["combat"]["max_frames"] / FPS
+
+    pa = pb = 0  # Zeiger auf die aktuell beschossene eigene Unit
+    ra = qa[0]["def"] if qa else 0.0  # deren Rest-Defense
+    rb = qb[0]["def"] if qb else 0.0
+    elapsed = 0.0
+
+    while pa < len(qa) and pb < len(qb):
+        oa, ob = off_a[pa], off_b[pb]
+        ta = rb / oa if oa > EPS else math.inf  # Zeit bis A das B-Ziel killt
+        tb = ra / ob if ob > EPS else math.inf
+        dt = min(ta, tb)
+        if math.isinf(dt) or elapsed + dt >= limit:
+            dt = limit - elapsed
+            ra -= ob * dt
+            rb -= oa * dt
+            elapsed = limit
+            break
+        elapsed += dt
+        a_kills = ta <= tb + EPS
+        b_kills = tb <= ta + EPS
+        if a_kills:
+            pb += 1
+            rb = qb[pb]["def"] if pb < len(qb) else 0.0
+        else:
+            rb -= oa * dt
+        if b_kills:
+            pa += 1
+            ra = qa[pa]["def"] if pa < len(qa) else 0.0
+        else:
+            ra -= ob * dt
+
+    a_alive, b_alive = pa < len(qa), pb < len(qb)
+    if a_alive and b_alive:
+        # Zeitlimit erreicht: mehr verbleibende Gesamt-Defense gewinnt.
+        rest_a = ra + sum(p["def"] for p in qa[pa + 1:])
+        rest_b = rb + sum(p["def"] for p in qb[pb + 1:])
+        winner = "a" if rest_a > rest_b + EPS else "b" if rest_b > rest_a + EPS else None
+    elif a_alive:
+        winner = "a"
+    elif b_alive:
+        winner = "b"
+    else:
+        winner = None
+
+    return FightResult(
+        winner=winner,
+        frames=math.ceil(elapsed * FPS),
+        survivors_a=[p["unit"] for p in qa[pa:]],
+        survivors_b=[p["unit"] for p in qb[pb:]],
+    )
+
+
+def player_damage(stage: int, survivors: list[dict], cfg: dict) -> int:
     d = cfg["damage"]
     base = d["stage_base"][min(stage - 1, len(d["stage_base"]) - 1)]
-    mean = (winner_score + loser_score) / 2 or 1
-    margin = abs(winner_score - loser_score) / mean
-    surviving = max(1, round(winner_units * min(margin, 1.0)))
-    return base + d["per_surviving_unit"] * surviving
+    return base + d["per_surviving_unit"] * len(survivors)

backend/tft/sim/game.py

@@ -2,6 +2,7 @@
 
 import random
 
+from tft.model.score import board_profiles
 from tft.sim import combat, economy, player, policy
 from tft.sim.player import BENCH_SIZE, MAX_ITEMS_PER_UNIT, InvalidAction, PlayerState  # noqa: F401
 from tft.sim.pool import Pool
@@ -227,17 +228,19 @@ class Game:
         pairs, odd, ghost_src = pair_players(self._alive(), self.rng)
         for a, b in pairs:
             a_wins = self._fight(rnd, a, b)
-            results[a.name] = a_wins
-            results[b.name] = not a_wins
+            # Unentschieden (None) zählt für beide als nicht gewonnen.
+            results[a.name] = a_wins is True
+            results[b.name] = a_wins is False
         if odd is not None:
             # Ghost-Kampf: Klon-Board, Schaden nur beim echten Spieler.
-            s_odd = player.score(odd, self.artifact)
-            s_ghost = player.score(ghost_src, self.artifact)
-            won = combat.resolve(s_odd, s_ghost, self.cfg, self.rng)
+            res = combat.fight(board_profiles(odd.board, self.artifact),
+                               board_profiles(ghost_src.board, self.artifact),
+                               self.rng, self.cfg)
+            won = res.winner == "a"
             results[odd.name] = won
             if not won:
-                dmg = combat.damage(rnd["stage"], s_ghost, s_odd,
-                                    len(ghost_src.board), self.cfg)
+                survivors = res.survivors_b if res.winner == "b" else []
+                dmg = combat.player_damage(rnd["stage"], survivors, self.cfg)
                 odd.hp -= dmg
                 if odd is self.player:
                     self.log.append(
@@ -246,13 +249,23 @@ class Game:
                 self.log.append(f"{rnd['label']}: Sieg vs Ghost ({ghost_src.name})")
         return results
 
-    def _fight(self, rnd: dict, a: PlayerState, b: PlayerState) -> bool:
-        sa = player.score(a, self.artifact)
-        sb = player.score(b, self.artifact)
-        a_wins = combat.resolve(sa, sb, self.cfg, self.rng)
+    def _fight(self, rnd: dict, a: PlayerState, b: PlayerState) -> bool | None:
+        res = combat.fight(board_profiles(a.board, self.artifact),
+                           board_profiles(b.board, self.artifact),
+                           self.rng, self.cfg)
+        if res.winner is None:
+            # Unentschieden: beide nehmen den Basis-Schaden der Stage.
+            dmg = combat.player_damage(rnd["stage"], [], self.cfg)
+            a.hp -= dmg
+            b.hp -= dmg
+            if a is self.player or b is self.player:
+                other = b if a is self.player else a
+                self.log.append(f"{rnd['label']}: Unentschieden vs {other.name} (-{dmg} HP)")
+            return None
+        a_wins = res.winner == "a"
         winner, loser = (a, b) if a_wins else (b, a)
-        w_score, l_score = (sa, sb) if a_wins else (sb, sa)
-        dmg = combat.damage(rnd["stage"], w_score, l_score, len(winner.board), self.cfg)
+        survivors = res.survivors_a if a_wins else res.survivors_b
+        dmg = combat.player_damage(rnd["stage"], survivors, self.cfg)
         loser.hp -= dmg
         if a is self.player or b is self.player:
             if winner is self.player: