Add Monte Carlo simulator, game metrics and sim CLI (Phase 5)

`dojo sim` plays seeded games over a scenario matrix (variant, players,
Kiai bonus, start position, bot lineup) in parallel, prints a summary per
scenario and optionally writes one CSV row per game. Metrics are replayed
from the event log. Plan updated with the Phase 5 baseline.

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

src/dojo/report.py

@@ -0,0 +1,53 @@
+"""Simulation output: a plain-text summary per scenario and one CSV row per game."""
+
+import csv
+import dataclasses
+from collections.abc import Iterable
+from pathlib import Path
+
+from dojo.events import Source
+from dojo.metrics import GameMetrics, Spread, Summary
+
+
+def format_summary(summary: Summary) -> str:
+    income = summary.income_per_turn
+    seats = ", ".join(f"{seat} {rate:.0%}" for seat, rate in enumerate(summary.win_rate_by_seat))
+    bots = ", ".join(f"{bot} {rate:.0%}" for bot, rate in summary.win_rate_by_bot.items())
+    lines = [
+        f"== {summary.scenario}  ({summary.games} games, {summary.finished:.0%} finished)",
+        f"Rounds        {_spread(summary.rounds)}",
+        f"Minutes       {_spread(summary.minutes)}",
+        f"Kumite        {summary.kumite_per_round:.2f}/round; upsets {summary.upset_rate:.0%}, "
+        f"higher beats lower {summary.expected_rate:.0%}",
+        f"Longest chain {_spread(summary.max_chain)}  (Kumite in one turn, per game)",
+        f"Exam          {summary.exam_attempts_per_game:.2f} attempts/game, "
+        f"{summary.exam_success:.0%} passed",
+        f"Stuck on 14   {summary.turns_on_14_per_game:.2f} turns/game, "
+        f"longest streak {summary.longest_14_streak} turns",
+        f"Income/turn   square {income[Source.SQUARE]:+.2f} "
+        f"(home {summary.square_income_home_per_turn:+.2f}, "
+        f"away {summary.square_income_away_per_turn:+.2f}), "
+        f"card {income[Source.CARD]:+.2f}, kumite {income[Source.KUMITE]:+.2f}, "
+        f"exam {income[Source.EXAM]:+.2f}",
+        f"Lead changes  {_spread(summary.lead_changes)}",
+        f"Final spread  {_spread(summary.final_spread)}",
+        f"Wins by seat  {seats}",
+        f"Wins by bot   {bots}",
+    ]
+    return "\n".join(lines)
+
+
+def write_games_csv(path: Path, games: Iterable[GameMetrics]) -> None:
+    fields = [field.name for field in dataclasses.fields(GameMetrics)]
+    with path.open("w", encoding="utf-8", newline="") as file:
+        writer = csv.DictWriter(file, fieldnames=fields)
+        writer.writeheader()
+        for game in games:
+            writer.writerow(dataclasses.asdict(game))
+
+
+def _spread(spread: Spread) -> str:
+    return (
+        f"mean {spread.mean:.1f}  p10 {spread.p10:.1f}  median {spread.median:.1f}  "
+        f"p90 {spread.p90:.1f}"
+    )