"""
Markov cricket transition engine.

Supports two transition table sources:
  1. Synthetic (default): data/transition_probs.json — keyed by (shot, phase)
  2. Cricsheet-derived: data/processed/cricket_transitions_v1.pkl — keyed by
     (over, wickets, score_band, phase, bowler_type)

When the Cricsheet table is present it is used; otherwise falls back to synthetic.
The engine is phase-aware and bowler-type-aware when richer data is available.
"""

import json
import os
import pickle
import random
from typing import Optional

try:
    from server.field_model import (
        DEEP_ZONE_FOR,
        get_field_layout,
        infer_trajectory,
        normalize_length,
        normalize_line,
        normalize_target_area,
        normalize_variation,
    )
except ImportError:
    from .field_model import (
        DEEP_ZONE_FOR,
        get_field_layout,
        infer_trajectory,
        normalize_length,
        normalize_line,
        normalize_target_area,
        normalize_variation,
    )

_DATA_DIR = os.path.join(os.path.dirname(__file__), "..", "data")
_SYNTHETIC_PATH = os.path.join(_DATA_DIR, "transition_probs.json")
_CRICSHEET_PATH = os.path.join(_DATA_DIR, "processed", "cricket_transitions_v1.pkl")

SHOT_INTENTS = ["leave", "defensive", "single", "rotate", "boundary", "six"]

SHOT_AGGRESSION = {
    "leave":     0.0,
    "defensive": 0.1,
    "single":    0.3,
    "rotate":    0.4,
    "boundary":  0.7,
    "six":       0.9,
}

# Bowler rotation probabilities by phase (pace_prob, spin_prob)
BOWLER_ROTATION = {
    "powerplay": (0.90, 0.10),
    "middle":    (0.45, 0.55),
    "death":     (0.80, 0.20),
}

BOWLER_TYPES = ["pace", "spin"]

# Extras rate: 5% of deliveries are wides/no-balls (ball replayed, 1 run added)
EXTRAS_RATE = 0.05


def over_to_phase(over: int, max_overs: int | None = None) -> str:
    """Return the phase label for a given over, respecting the match format.

    Without max_overs the old hardcoded thresholds (designed for ODI) would
    leave T20 overs 16-19 classified as "middle" instead of "death".  We now
    delegate to format_mapper.get_phase which reads the correct phase windows
    from data/format_rules.json.
    """
    try:
        from server.format_mapper import get_phase
    except ImportError:
        from .format_mapper import get_phase
    return get_phase(over, max_overs)


def sample_bowler_type(phase: str, rng: random.Random) -> str:
    pace_p, spin_p = BOWLER_ROTATION.get(phase, (0.60, 0.40))
    return rng.choices(BOWLER_TYPES, weights=[pace_p, spin_p], k=1)[0]


class MarkovCricketEngine:
    def __init__(self, rng: Optional[random.Random] = None):
        self._rng = rng or random.Random()
        self._cricsheet: Optional[dict] = None
        self._synthetic: dict[str, dict[str, list[tuple[int, bool, float]]]] = {}

        # Try Cricsheet table first
        if os.path.exists(_CRICSHEET_PATH):
            try:
                with open(_CRICSHEET_PATH, "rb") as f:
                    self._cricsheet = pickle.load(f)
            except Exception:
                self._cricsheet = None

        # Always load synthetic as fallback
        with open(_SYNTHETIC_PATH) as f:
            raw = json.load(f)
        for shot, phases in raw.items():
            self._synthetic[shot] = {}
            for phase, dist in phases.items():
                self._synthetic[shot][phase] = [(r, bool(w), p) for r, w, p in dist]

        self._using_cricsheet = self._cricsheet is not None

    @property
    def using_cricsheet(self) -> bool:
        return self._using_cricsheet

    # ------------------------------------------------------------------ #
    # Public API                                                           #
    # ------------------------------------------------------------------ #

    def step(
        self,
        over: int,
        shot_intent: str,
        wickets: int = 0,
        score: int = 0,
        bowler_type: str = "pace",
        field_setting: str = "Balanced",
        max_overs: int | None = None,
    ) -> tuple[int, bool, bool, str]:
        """Sample an outcome for one delivery.

        Returns (runs_scored, wicket_fell, was_extra, dismissal_type).
        dismissal_type is one of: "" | "bowled" | "lbw" | "caught" | "run_out" | "other"
        """
        if shot_intent not in SHOT_AGGRESSION:
            shot_intent = "defensive"

        # Extras check (influenced by bowling pressure, simplified for now)
        if self._rng.random() < EXTRAS_RATE:
            return 1, False, True, ""

        phase = over_to_phase(over, max_overs)

        if self._cricsheet is not None:
            runs, wicket = self._cricsheet_step(over, wickets, score, phase, bowler_type, shot_intent)
        else:
            runs, wicket = self._synthetic_step(shot_intent, phase, bowler_type)

        # Field setting modifier
        if not wicket:
            runs = _apply_field_modifier(runs, field_setting, self._rng)

        dismissal_type = _sample_dismissal_type(shot_intent, self._rng) if wicket else ""
        return runs, wicket, False, dismissal_type

    def step_with_plans(
        self,
        *,
        over: int,
        shot_plan: dict,
        delivery_plan: dict,
        batter_profile: dict | None = None,
        bowler_profile: dict | None = None,
        wickets: int = 0,
        score: int = 0,
        target: int | None = None,
        max_overs: int = 20,
        field_setting: str = "Balanced",
    ) -> tuple[int, bool, bool, str, str, dict]:
        """Sample an outcome conditioned on both batting and bowling plans.

        This is intentionally a lightweight physics layer: it starts from the
        existing transition table, then applies interpretable modifiers for
        player style, delivery/shot matchup, field, and chase pressure.

        Returns (runs, wicket, extra, shot_intent, dismissal_type, metadata).
        """
        shot_intent = shot_plan.get("shot_intent", "defensive")
        if shot_intent not in SHOT_AGGRESSION:
            shot_intent = "defensive"

        bowler_type = delivery_plan.get("bowler_type") or (bowler_profile or {}).get("type", "pace")
        line = normalize_line(delivery_plan.get("line", "outside off"))
        length = normalize_length(delivery_plan.get("length", "good length"))
        variation = normalize_variation(delivery_plan.get("delivery_type", "stock"), bowler_type)
        target_area = normalize_target_area(shot_plan.get("target_area", ""), shot_intent)
        risk = str(shot_plan.get("risk", "balanced")).lower()
        trajectory = infer_trajectory(shot_intent, risk, shot_plan.get("trajectory"))
        field_layout = get_field_layout(field_setting)

        metadata = {
            "event_type": "base_outcome",
            "base_runs": None,
            "base_wicket": None,
            "shot_intent": shot_intent,
            "target_area": target_area,
            "trajectory": trajectory,
            "delivery_features": {
                "bowler_type": bowler_type,
                "line": line,
                "length": length,
                "variation": variation,
            },
            "field_setting": field_setting,
            "field_zone": target_area,
            "field_layout": field_layout.positions,
            "fielder_count": field_layout.count(target_area),
            "boundary_rider": field_layout.boundary_rider(target_area),
            "close_catcher": field_layout.close_catcher(target_area),
            "fielder_effect": "none",
        }

        illegal = _illegal_delivery_event(line, length, variation, bowler_type, pressure=0.0, rng=self._rng)
        if illegal:
            metadata.update(illegal)
            return 1, False, True, shot_intent, "", metadata

        runs, wicket, extra, dismissal_type = self.step(
            over=over,
            shot_intent=shot_intent,
            wickets=wickets,
            score=score,
            bowler_type=bowler_type,
            field_setting="Balanced",
            max_overs=max_overs,
        )
        if extra:
            metadata.update({"event_type": "wide", "fielder_effect": "none", "base_runs": runs, "base_wicket": wicket})
            return runs, wicket, extra, shot_intent, dismissal_type, metadata

        pressure = _score_pressure(over=over, score=score, target=target, max_overs=max_overs)
        metadata["pressure"] = round(pressure, 3)
        matchup = _plan_matchup_modifier(
            shot_plan={**shot_plan, "target_area": target_area, "trajectory": trajectory, "risk": risk},
            delivery_plan={**delivery_plan, "line": line, "length": length, "delivery_type": variation},
            batter_profile=batter_profile or {},
            bowler_profile=bowler_profile or {},
            field_setting=field_setting,
            pressure=pressure,
        )
        fit = _shot_delivery_fit(shot_intent, target_area, trajectory, line, length, variation, bowler_type)
        field_pressure = _field_pressure(field_layout, target_area, trajectory)
        metadata.update({
            "base_runs": runs,
            "base_wicket": wicket,
            "matchup": round(matchup, 3),
            "shot_delivery_fit": round(fit, 3),
            "field_pressure": round(field_pressure, 3),
        })

        runs = _adjust_runs_for_matchup(runs, matchup + fit, self._rng)
        runs, field_effect = _apply_spatial_field_effect(runs, target_area, trajectory, field_layout, self._rng)
        metadata["fielder_effect"] = field_effect
        wicket, dismissal_type, event_type = _apply_wicket_and_exception_events(
            wicket=wicket,
            dismissal_type=dismissal_type,
            shot_intent=shot_intent,
            target_area=target_area,
            trajectory=trajectory,
            line=line,
            length=length,
            variation=variation,
            field_layout=field_layout,
            field_pressure=field_pressure,
            fit=fit,
            rng=self._rng,
        )
        metadata["event_type"] = event_type
        if wicket and event_type != "wicket":
            metadata["fielder_effect"] = event_type.replace("_", " ")
        if not wicket:
            runs, noise_event = _apply_run_noise(runs, field_pressure, self._rng)
            if noise_event:
                metadata["event_type"] = noise_event
        if wicket and not dismissal_type:
            dismissal_type = _sample_dismissal_type(shot_intent, self._rng)
        return runs, wicket, False, shot_intent, dismissal_type, metadata

    def simulate_batter_response(
        self,
        over: int,
        bowling_strategy: dict,
        field_setting: str = "Balanced",
        wickets: int = 0,
        score: int = 0,
        max_overs: int | None = None,
    ) -> tuple[int, bool, bool, str]:
        """Simulate an AI batter faced with the agent's bowling/fielding.

        Returns (runs, wicket, extra, shot_intent).
        """
        phase = over_to_phase(over, max_overs)
        
        # Decide AI batter's shot intent based on state and phase
        # Aggression increases in death overs or with wickets in hand
        aggression_prob = 0.3
        if phase == "death":
            aggression_prob = 0.7
        elif phase == "powerplay":
            aggression_prob = 0.5
            
        if wickets < 3:
            aggression_prob += 0.1
        
        choices = SHOT_INTENTS
        if aggression_prob > 0.6:
            weights = [0.05, 0.1, 0.1, 0.2, 0.35, 0.2] # Aggressive
        elif aggression_prob < 0.4:
            weights = [0.2, 0.4, 0.2, 0.1, 0.05, 0.05] # Defensive
        else:
            weights = [0.1, 0.2, 0.3, 0.2, 0.1, 0.1] # Balanced
            
        shot_intent = self._rng.choices(choices, weights=weights, k=1)[0]
        
        # Determine bowler type modifier from strategy
        bowler_type = bowling_strategy.get("bowler_type", "pace")
        
        runs, wicket, extra, dismissal_type = self.step(
            over=over,
            shot_intent=shot_intent,
            wickets=wickets,
            score=score,
            bowler_type=bowler_type,
            field_setting=field_setting,
            max_overs=max_overs,
        )

        return runs, wicket, extra, shot_intent, dismissal_type

    def expected_runs(self, over: int, shot_intent: str, bowler_type: str = "pace", max_overs: int | None = None) -> float:
        if shot_intent not in SHOT_AGGRESSION:
            return 0.0
        phase = over_to_phase(over, max_overs)
        if self._cricsheet:
            dist = self._get_cricsheet_dist(over, 3, 15, phase, bowler_type, shot_intent)
            if dist:
                return sum(r * p for r, _, p in dist)
        dist = self._synthetic[shot_intent][phase]
        return sum(r * p for r, _, p in dist)

    def wicket_probability(self, over: int, shot_intent: str, bowler_type: str = "pace", max_overs: int | None = None) -> float:
        if shot_intent not in SHOT_AGGRESSION:
            return 0.0
        phase = over_to_phase(over, max_overs)
        if self._cricsheet:
            dist = self._get_cricsheet_dist(over, 3, 15, phase, bowler_type, shot_intent)
            if dist:
                return sum(p for _, w, p in dist if w)
        dist = self._synthetic[shot_intent][phase]
        return sum(p for _, w, p in dist if w)

    def describe_last_ball(self, shot_intent: str, runs: int, wicket: bool, extra: bool = False, metadata: dict | None = None) -> str:
        metadata = metadata or {}
        event = metadata.get("event_type", "")
        zone = metadata.get("target_area", "")
        fielder_effect = metadata.get("fielder_effect", "none")
        if extra:
            if event == "no_ball":
                return "No-ball called — extra run added and the ball must be replayed."
            return "Wide delivery — extra run added. Ball to be replayed."
        if wicket:
            if event == "edge_to_catcher":
                return "A thin edge carries behind the wicket — taken cleanly. OUT!"
            if event.startswith("caught_in_"):
                return f"Lofted toward {zone} — fielder settles under it. OUT!"
            if event.startswith("run_out"):
                return f"Pushed into {zone}; sharp fielding creates a run-out. OUT!"
            if event == "beaten_by_yorker":
                return "Yorker at the stumps beats the swing and crashes into the base. OUT!"
            if event == "lbw_line_missed":
                return "Full and straight, no bat involved — LBW given. OUT!"
            templates = {
                "leave":     "Left alone — struck on the pad! LBW given.",
                "defensive": "Pushed at it — inside edge onto stumps. OUT!",
                "single":    "Pushed for a single — direct hit run-out!",
                "rotate":    "Turned to leg — sharp catch at short fine leg. OUT!",
                "boundary":  "Went for the boundary — top-edged to sweeper. OUT!",
                "six":       "Attempted a six — misread the length, caught at long-on. OUT!",
            }
            return templates.get(shot_intent, "Wicket falls!")
        run_word = {0: "dot ball", 1: "a single", 2: "two runs", 3: "three runs",
                    4: "a FOUR", 6: "a SIX"}.get(runs, f"{runs} runs")
        if event == "dropped_catch":
            return f"Lofted toward {zone}; chance goes down — {run_word}."
        if event in {"edge_safe", "edge_through_gap"}:
            return f"Edge runs toward {zone or 'third man'} — {run_word}."
        if event in {"misfield", "overthrow", "excellent_stop"}:
            return f"Played toward {zone}; {event.replace('_', ' ')} — {run_word}."
        if fielder_effect and fielder_effect != "none":
            return f"Played toward {zone}; {fielder_effect} — {run_word}."
        return {
            "leave":     f"Left outside off — {run_word}.",
            "defensive": f"Defended solidly — {run_word}.",
            "single":    f"Nudged into the gap — {run_word}.",
            "rotate":    f"Worked off the hips — {run_word}.",
            "boundary":  f"Driven through the covers — {run_word}!",
            "six":       f"Launched over long-on — {run_word}!",
        }.get(shot_intent, f"Played — {run_word}.")

    # ------------------------------------------------------------------ #
    # Private helpers                                                      #
    # ------------------------------------------------------------------ #

    def _cricsheet_step(
        self, over, wickets, score, phase, bowler_type, shot_intent
    ) -> tuple[int, bool]:
        score_band = min(score // 10, 49)
        dist = self._get_cricsheet_dist(over, wickets, score_band, phase, bowler_type, shot_intent)
        if dist is None:
            return self._synthetic_step(shot_intent, phase, bowler_type)
        outcomes = [(r, w) for r, w, _ in dist]
        weights = [p for _, _, p in dist]
        return self._rng.choices(outcomes, weights=weights, k=1)[0]

    def _get_cricsheet_dist(
        self, over, wickets, score_band, phase, bowler_type, shot_intent
    ) -> Optional[list]:
        """Try progressively less specific keys until we find data."""
        key5 = (over, wickets, score_band, phase, bowler_type)
        key4 = (over, wickets, score_band, phase, None)
        for key in (key5, key4):
            if key in self._cricsheet:
                entry = self._cricsheet[key]
                if entry.get("sample_size", 0) >= 50:
                    # Convert {run: prob} dict to [(runs, wicket, prob)] list
                    wicket_p = entry["wicket_prob"]
                    run_dist = entry["run_dist"]
                    dist = [(r, False, p * (1 - wicket_p)) for r, p in run_dist.items()]
                    dist.append((0, True, wicket_p))
                    return dist
        return None

    def _synthetic_step(self, shot_intent: str, phase: str, bowler_type: str) -> tuple[int, bool]:
        """Synthetic table step, with bowler_type modifier applied."""
        dist = list(self._synthetic[shot_intent][phase])
        if bowler_type == "spin":
            # Spin: fewer boundaries, fewer wickets, more dots/singles
            dist = _apply_spin_modifier(dist)
        outcomes = [(r, w) for r, w, _ in dist]
        weights = [p for _, _, p in dist]
        return self._rng.choices(outcomes, weights=weights, k=1)[0]


def _apply_spin_modifier(
    dist: list[tuple[int, bool, float]],
) -> list[tuple[int, bool, float]]:
    """Shift spin bowling distribution: -20% wickets, -30% boundaries, +dots/singles."""
    adjusted = []
    for runs, wicket, prob in dist:
        if wicket:
            adjusted.append((runs, wicket, prob * 0.80))
        elif runs >= 4:
            adjusted.append((runs, wicket, prob * 0.70))
        elif runs == 0:
            adjusted.append((runs, wicket, prob * 1.20))
        else:
            adjusted.append((runs, wicket, prob * 1.10))
    # Renormalize
    total = sum(p for _, _, p in adjusted)
    return [(r, w, p / total) for r, w, p in adjusted]


# Dismissal type probabilities by shot intent (bowled/lbw more likely on defensive shots)
_DISMISSAL_PROBS: dict[str, list[tuple[str, float]]] = {
    "leave":     [("lbw", 0.50), ("bowled", 0.20), ("caught", 0.20), ("other", 0.10)],
    "defensive": [("bowled", 0.35), ("lbw", 0.25), ("caught", 0.30), ("other", 0.10)],
    "single":    [("caught", 0.45), ("run_out", 0.20), ("bowled", 0.15), ("lbw", 0.10), ("other", 0.10)],
    "rotate":    [("caught", 0.50), ("run_out", 0.20), ("bowled", 0.10), ("lbw", 0.10), ("other", 0.10)],
    "boundary":  [("caught", 0.70), ("bowled", 0.10), ("lbw", 0.05), ("other", 0.15)],
    "six":       [("caught", 0.80), ("bowled", 0.05), ("lbw", 0.05), ("other", 0.10)],
}


def _sample_dismissal_type(shot_intent: str, rng: random.Random) -> str:
    probs = _DISMISSAL_PROBS.get(shot_intent, _DISMISSAL_PROBS["defensive"])
    types  = [t for t, _ in probs]
    weights = [p for _, p in probs]
    return rng.choices(types, weights=weights, k=1)[0]


def _apply_field_modifier(runs: int, field_setting: str, rng: random.Random) -> int:
    """Field settings can prevent runs (dots) or allow boundaries."""
    if runs == 0:
        return 0
    
    if field_setting == "Defensive":
        # 30% chance to turn a 1 or 2 into 0
        if runs <= 2 and rng.random() < 0.3:
            return 0
        # 10% chance to turn a 4 into 1 or 2 (cut off at boundary)
        if runs == 4 and rng.random() < 0.1:
            return rng.randint(1, 2)
    elif field_setting == "Aggressive":
        # 10% chance to turn a 0 into a 1 (gaps in the field)
        if runs == 0 and rng.random() < 0.1:
            return 1
            
    return runs


def _illegal_delivery_event(
    line: str,
    length: str,
    variation: str,
    bowler_type: str,
    pressure: float,
    rng: random.Random,
) -> dict | None:
    wide_p = 0.018
    no_ball_p = 0.006
    if line == "wide":
        wide_p += 0.09
    if length == "bouncer":
        wide_p += 0.025
        no_ball_p += 0.006
    if variation in {"slower", "googly"}:
        wide_p += 0.01
    if bowler_type == "spin":
        wide_p += 0.004
    wide_p += pressure * 0.015
    no_ball_p += pressure * 0.006

    roll = rng.random()
    if roll < no_ball_p:
        return {
            "event_type": "no_ball",
            "illegal_delivery": "no_ball",
            "fielder_effect": "illegal delivery; ball replayed",
        }
    if roll < no_ball_p + wide_p:
        return {
            "event_type": "wide",
            "illegal_delivery": "wide",
            "fielder_effect": "wide line; ball replayed",
        }
    return None


def _shot_delivery_fit(
    shot: str,
    zone: str,
    trajectory: str,
    line: str,
    length: str,
    variation: str,
    bowler_type: str,
) -> float:
    fit = 0.0
    if shot == "boundary" and zone in {"cover", "deep_cover"} and line == "outside_off" and length in {"full", "good"}:
        fit += 0.16
    if shot == "six" and zone in {"long_on", "deep_midwicket", "midwicket"} and length in {"full", "good"}:
        fit += 0.10
    if shot in {"single", "rotate"} and zone in {"midwicket", "square_leg", "fine_leg", "cover"}:
        fit += 0.08
    if shot in {"single", "rotate"} and trajectory == "ground":
        fit += 0.06
    if shot == "leave" and line in {"outside_off", "wide"}:
        fit += 0.08
    if shot in {"boundary", "six"} and variation == "slower":
        fit -= 0.11
    if shot in {"boundary", "six"} and length == "yorker":
        fit -= 0.16
    if shot in {"boundary", "six"} and length == "bouncer" and zone not in {"square_leg", "fine_leg", "deep_square_leg"}:
        fit -= 0.12
    if zone in {"square_leg", "fine_leg", "midwicket"} and line == "pads":
        fit += 0.08
    if zone in {"cover", "point", "third_man"} and line == "outside_off":
        fit += 0.06
    if bowler_type == "spin" and shot in {"sweep", "rotate"}:
        fit += 0.08
    if bowler_type == "spin" and variation in {"googly", "leg_spin"} and shot in {"boundary", "six"}:
        fit -= 0.06
    if trajectory in {"aerial", "lofted"}:
        fit += 0.04
    return max(-0.35, min(0.35, fit))


def _field_pressure(field_layout, zone: str, trajectory: str) -> float:
    pressure = field_layout.zone_pressure(zone)
    deep_zone = DEEP_ZONE_FOR.get(zone, zone)
    if trajectory in {"aerial", "lofted"} and field_layout.count(deep_zone):
        pressure += 0.25
    if trajectory == "edge" and (field_layout.count("slips") or field_layout.count("gully")):
        pressure += 0.25
    return max(0.0, min(1.0, pressure))


def _apply_spatial_field_effect(runs: int, zone: str, trajectory: str, field_layout, rng: random.Random) -> tuple[int, str]:
    pressure = field_layout.zone_pressure(zone)
    deep_zone = DEEP_ZONE_FOR.get(zone, zone)
    if runs >= 6 and trajectory in {"aerial", "lofted"} and field_layout.count(deep_zone) and rng.random() < 0.30 + 0.20 * pressure:
        return 4, f"boundary rider at {deep_zone} keeps aerial shot inside rope"
    if runs >= 4 and field_layout.count(deep_zone) and rng.random() < 0.35 + 0.25 * pressure:
        return rng.choice([1, 2]), f"deep fielder at {deep_zone} cuts off boundary"
    if runs in {1, 2} and field_layout.count(zone) and rng.random() < 0.18 + 0.18 * pressure:
        return max(0, runs - 1), f"inner fielder at {zone} saves one"
    if runs == 0 and pressure < 0.35 and zone in {"cover", "midwicket", "square_leg"} and rng.random() < 0.12:
        return 1, f"gap in {zone} allows a quick single"
    return runs, "none"


def _apply_wicket_and_exception_events(
    *,
    wicket: bool,
    dismissal_type: str,
    shot_intent: str,
    target_area: str,
    trajectory: str,
    line: str,
    length: str,
    variation: str,
    field_layout,
    field_pressure: float,
    fit: float,
    rng: random.Random,
) -> tuple[bool, str, str]:
    if wicket:
        if dismissal_type == "caught" and field_pressure < 0.25 and rng.random() < 0.18:
            return False, "", "dropped_catch"
        return True, dismissal_type, "wicket"

    edge_p = 0.01
    if line == "outside_off" and length == "good":
        edge_p += 0.035
    if variation in {"swing", "seam", "googly"}:
        edge_p += 0.02
    if shot_intent in {"boundary", "six"} and fit < 0:
        edge_p += 0.02
    if rng.random() < edge_p:
        if field_layout.count("slips") or field_layout.count("gully"):
            catch_p = 0.35 + min(0.35, 0.12 * (field_layout.count("slips") + field_layout.count("gully")))
            if rng.random() < catch_p:
                return True, "caught", "edge_to_catcher"
            return False, "", "edge_safe"
        return False, "", "edge_through_gap"

    aerial_catch_p = 0.0
    if trajectory in {"aerial", "lofted"}:
        aerial_catch_p = 0.025 + 0.08 * field_pressure
    if shot_intent == "six":
        aerial_catch_p += 0.015
    if rng.random() < aerial_catch_p:
        if rng.random() < 0.15:
            return False, "", "dropped_catch"
        return True, "caught", f"caught_in_{target_area}"

    run_out_p = 0.0
    if shot_intent in {"single", "rotate"} and field_layout.count(target_area):
        run_out_p = 0.01 + 0.02 * field_pressure
    if rng.random() < run_out_p:
        return True, "run_out", f"run_out_in_{target_area}"

    if length == "yorker" and line == "stumps" and shot_intent in {"boundary", "six"} and rng.random() < 0.025:
        return True, "bowled", "beaten_by_yorker"
    if length in {"full", "yorker"} and line in {"stumps", "pads"} and shot_intent == "leave" and rng.random() < 0.04:
        return True, "lbw", "lbw_line_missed"
    return False, "", "base_outcome"


def _apply_run_noise(runs: int, field_pressure: float, rng: random.Random) -> tuple[int, str | None]:
    if runs in {0, 1, 2} and rng.random() < 0.025:
        return min(6, runs + 1), "misfield"
    if runs in {1, 2} and rng.random() < 0.012:
        return min(6, runs + 1), "overthrow"
    if runs >= 4 and field_pressure > 0.7 and rng.random() < 0.02:
        return max(1, runs - 2), "excellent_stop"
    return runs, None


def _score_pressure(over: int, score: int, target: int | None, max_overs: int = 20) -> float:
    if target is None:
        return 0.0
    overs_left = max(max_overs - over, 1)
    required_rate = max(target - score, 0) / overs_left
    if required_rate >= 12:
        return 1.0
    if required_rate >= 9:
        return 0.6
    if required_rate >= 7:
        return 0.3
    return 0.0


def _plan_matchup_modifier(
    *,
    shot_plan: dict,
    delivery_plan: dict,
    batter_profile: dict,
    bowler_profile: dict,
    field_setting: str,
    pressure: float,
) -> float:
    """Positive favors batter; negative favors bowler."""
    modifier = 0.0
    shot = shot_plan.get("shot_intent", "defensive")
    risk = str(shot_plan.get("risk", "balanced")).lower()
    target_area = str(shot_plan.get("target_area", "")).lower()
    length = str(delivery_plan.get("length", "good length")).lower()
    line = str(delivery_plan.get("line", "outside off")).lower()
    variation = str(delivery_plan.get("delivery_type", "stock")).lower()
    batter_style = str(batter_profile.get("style", "balanced")).lower()
    bowler_style = str(bowler_profile.get("style", delivery_plan.get("delivery_type", "stock"))).lower()

    if batter_style in {"hitter", "finisher", "aggressive"} and shot in {"boundary", "six"}:
        modifier += 0.18
    if batter_style == "anchor" and shot in {"single", "rotate", "defensive"}:
        modifier += 0.12
    if bowler_style in {"death_specialist", "yorker"} and length in {"yorker", "full"}:
        modifier -= 0.18
    if bowler_style == "economy" and shot in {"six", "boundary"}:
        modifier -= 0.10

    if field_setting == "Defensive" and shot in {"boundary", "six"}:
        modifier -= 0.15
    if field_setting == "Aggressive" and shot in {"single", "rotate"}:
        modifier += 0.10
    if "wide" in line and "off" in target_area:
        modifier -= 0.08
    if variation in {"bouncer", "slower ball"} and risk == "high":
        modifier -= 0.08
    if pressure > 0.5 and shot in {"defensive", "leave"}:
        modifier -= 0.12
    if pressure > 0.5 and shot in {"boundary", "six"}:
        modifier += 0.08
    return max(-0.45, min(0.45, modifier))


def _adjust_runs_for_matchup(runs: int, matchup: float, rng: random.Random) -> int:
    if matchup > 0 and rng.random() < matchup:
        if runs == 0:
            return 1
        if runs in {1, 2}:
            return min(4, runs + 1)
        if runs == 4 and rng.random() < matchup / 2:
            return 6
    if matchup < 0 and rng.random() < abs(matchup):
        if runs >= 6:
            return 4
        if runs >= 4:
            return rng.choice([1, 2])
        if runs > 0:
            return max(0, runs - 1)
    return runs


def _adjust_wicket_for_matchup(wicket: bool, matchup: float, rng: random.Random) -> bool:
    if wicket and matchup > 0 and rng.random() < matchup / 2:
        return False
    if not wicket and matchup < 0 and rng.random() < abs(matchup) / 6:
        return True
    return wicket