feat: add gameplay stats and restore Zenith rollout depth

- Add read-only gameplay statistics with filters and opponent comparisons.
- Report move-trace latency percentiles and external timeout counts.
- Pair engine latency with the preceding turn to separate timing stages.
- Adjust Zenith rollout deepening for its larger timeout reserve.
- Bump Zenith to 1.2.0 and document the fix and snake-stats command.
- Cover statistics, trace aggregation, rollout depth, and verdict budgets.

README.md

@@ -117,11 +117,32 @@ from the strategy budget and returns a legal-direction fallback instead of null
 when its request budget expires. This is best-effort deadline handling, not a
 hard real-time guarantee under CPU saturation.
 
+Version 1.2.0 fixes a search-depth regression from that larger reserve. Prism
+looks one turn deeper in 3-snake rollouts when more than 250 ms remain; with
+Zenith's 50 ms shorter budget that gate was never reached in production, so
+Zenith always stopped at depth 2. Zenith now lowers the gate by its extra
+reserve and deepens after the same elapsed time as Prism.
+
 Run it with:
 ```sh
 SNAKE=ZenithBattleSnake_ClaudeOpus5_5 python main.py
 ```
 
+Show read-only win statistics per own snake version and per opponent from the
+PostgreSQL gameplay database (`GAMEPLAY_DB_PG_DSN`):
+```sh
+just snake-stats --since-hours 24
+just snake-stats --snake-type ZenithBattleSnake --game-type duel --min-games 5
+just snake-stats --since-hours 48 --compare   # opponents side by side per own version
+just snake-stats --traces --since-hours 24    # move-trace latency per version and mode
+```
+
+`--traces` unpacks the newest `--trace-games` (default 300) finished replays and
+reports p50/p90/p99 for strategy time, server overhead (handler minus
+strategy), handler time, network/outside-handler time (engine latency minus
+handler) and engine latency. `ext.timeouts` counts engine timeouts where our
+handler had answered at least 100 ms before the deadline.
+
 Benchmark Apex and Prism against sampled positions from a gameplay database:
 ```sh
 python scripts/benchmark_snakes_from_db.py \

justfile

@@ -90,6 +90,15 @@ db *args:
   command -v psql >/dev/null || { echo "Install the PostgreSQL client (psql) first." >&2; exit 1; }
   exec psql --dbname="$GAMEPLAY_DB_PG_DSN" "$@"
 
+# Read-only win stats per own snake version and per opponent (extra args are passed through)
+[positional-arguments]
+snake-stats *args:
+  #!/usr/bin/env bash
+  set -euo pipefail
+
+  : "${GAMEPLAY_DB_PG_DSN:?Set GAMEPLAY_DB_PG_DSN in .env or the environment}"
+  PYTHONPATH="{{justfile_directory()}}" uv run python "{{justfile_directory()}}/scripts/gameplay_snake_stats.py" "$@"
+
 cleanup-gameplay-postgres older_than_days="30" execute="false" vacuum="false":
   #!/usr/bin/env bash
   set -euo pipefail

scripts/gameplay_snake_stats.py

@@ -0,0 +1,177 @@
+#!/usr/bin/env python3
+"""Read-only win statistics per own snake version and per opponent snake.
+
+Reads the PostgreSQL gameplay database (``GAMEPLAY_DB_PG_DSN``). The session is
+switched to read-only before any query runs, so it is safe against production.
+"""
+import argparse
+import asyncio
+import os
+from dataclasses import dataclass
+
+@dataclass(frozen=True)
+class StatsFilter:
+  since_hours: float | None = None
+  snake_type: str | None = None
+  snake_version: str | None = None
+  game_type: str | None = None
+  source: str | None = None
+
+def build_where(filters: StatsFilter, alias: str = "g") -> tuple[str, list]:
+  """Return a WHERE clause for finished games plus its positional arguments."""
+  clauses = [f"{alias}.status = 'finished'"]
+  args: list = []
+
+  def add(sql: str, value) -> None:
+    args.append(value)
+    clauses.append(sql.format(alias=alias, n=len(args)))
+
+  if filters.since_hours is not None:
+    add("{alias}.started_at > now() - make_interval(secs => ${n})", float(filters.since_hours) * 3600)
+  if filters.snake_type:
+    add("{alias}.your_snake_type = ${n}", filters.snake_type)
+  if filters.snake_version:
+    add("{alias}.your_snake_version = ${n}", filters.snake_version)
+  if filters.game_type:
+    add("{alias}.game_type = ${n}", filters.game_type)
+  if filters.source:
+    add("{alias}.source = ${n}", filters.source)
+  return "WHERE " + " AND ".join(clauses), args
+
+def own_snake_query(filters: StatsFilter) -> tuple[str, list]:
+  where, args = build_where(filters)
+  return f"""
+    SELECT g.your_snake_type AS snake, g.your_snake_version AS version,
+           g.game_type, count(*) AS games, sum(g.winner_you::int) AS wins,
+           round(avg(g.final_turn)) AS avg_turns, max(g.started_at) AS last_game
+    FROM games AS g
+    {where}
+    GROUP BY 1, 2, 3
+    ORDER BY 1, 2, 3
+  """, args
+
+def opponent_query(filters: StatsFilter, min_games: int) -> tuple[str, list]:
+  where, args = build_where(filters)
+  args.append(max(1, min_games))
+  return f"""
+    SELECT s.snake_name AS opponent, g.your_snake_type AS snake,
+           g.your_snake_version AS version,
+           count(*) AS games, sum(g.winner_you::int) AS wins,
+           sum((g.winner_name = s.snake_name)::int) AS their_wins,
+           round(avg(g.final_turn)) AS avg_turns
+    FROM games AS g
+    JOIN game_snakes AS s ON s.game_id = g.game_id AND NOT s.is_you
+    {where}
+    GROUP BY 1, 2, 3
+    HAVING count(*) >= ${len(args)}
+    ORDER BY count(*) DESC, 1
+  """, args
+
+def win_rate(wins: int, games: int) -> str:
+  return f"{100.0 * wins / games:5.1f}%" if games else "    -"
+
+def format_table(headers: list[str], rows: list[list]) -> str:
+  cells = [[str(value) for value in row] for row in rows]
+  widths = [max(len(h), *(len(row[i]) for row in cells)) if cells else len(h) for i, h in enumerate(headers)]
+  line = lambda values: "  ".join(v.ljust(w) for v, w in zip(values, widths)).rstrip()
+  return "\n".join([line(headers), line(["-" * w for w in widths]), *(line(row) for row in cells)])
+
+def own_rows(records: list[dict]) -> list[list]:
+  return [
+    [r["snake"], r["version"], r["game_type"], r["games"], r["wins"],
+     win_rate(r["wins"], r["games"]), r["avg_turns"],
+     r["last_game"].strftime("%Y-%m-%d %H:%M") if r["last_game"] else "-"]
+    for r in records
+  ]
+
+def opponent_rows(records: list[dict]) -> list[list]:
+  return [
+    [r["opponent"], f"{r['snake']} {r['version']}", r["games"], r["wins"],
+     win_rate(r["wins"], r["games"]), r["their_wins"],
+     win_rate(r["their_wins"], r["games"]), r["avg_turns"]]
+    for r in records
+  ]
+
+def pivot_rows(records: list[dict], min_games: int) -> tuple[list[str], list[list]]:
+  """One row per opponent, one ``wins/games win%`` column per own snake version."""
+  versions = sorted({f"{r['snake']} {r['version']}" for r in records})
+  table: dict[str, dict[str, tuple[int, int]]] = {}
+  for r in records:
+    table.setdefault(r["opponent"], {})[f"{r['snake']} {r['version']}"] = (r["wins"], r["games"])
+  rows = []
+  for opponent, cells in sorted(table.items(), key=lambda item: -sum(g for _, g in item[1].values())):
+    if len(cells) < 2 or any(games < min_games for _, games in cells.values()):
+      continue
+    rows.append([opponent, *(
+      f"{cells[v][0]}/{cells[v][1]} {win_rate(*cells[v]).strip()}" if v in cells else "-"
+      for v in versions
+    )])
+  return ["opponent", *versions], rows
+
+async def _read_only_connection(dsn: str):
+  import asyncpg  # noqa: PLC0415
+
+  connection = await asyncpg.connect(dsn=dsn)
+  await connection.execute("SET SESSION CHARACTERISTICS AS TRANSACTION READ ONLY")
+  return connection
+
+async def collect_traces(dsn: str, filters: StatsFilter, max_games: int):
+  from scripts.move_trace_stats import load_groups  # noqa: PLC0415
+
+  connection = await _read_only_connection(dsn)
+  try:
+    where, args = build_where(filters)
+    return await load_groups(connection, where, args, max_games)
+  finally:
+    await connection.close()
+
+async def collect(dsn: str, filters: StatsFilter, min_games: int, limit: int) -> tuple[list[dict], list[dict]]:
+  connection = await _read_only_connection(dsn)
+  try:
+    own_sql, own_args = own_snake_query(filters)
+    opp_sql, opp_args = opponent_query(filters, min_games)
+    own = [dict(row) for row in await connection.fetch(own_sql, *own_args)]
+    opponents = [dict(row) for row in await connection.fetch(opp_sql, *opp_args)]
+    return own, opponents if limit <= 0 else opponents[:limit]
+  finally:
+    await connection.close()
+
+def main() -> None:
+  parser = argparse.ArgumentParser(description=__doc__)
+  parser.add_argument("--dsn", default=os.environ.get("GAMEPLAY_DB_PG_DSN"))
+  parser.add_argument("--since-hours", type=float, default=None, help="Only games started within N hours")
+  parser.add_argument("--snake-type", help="Own snake type, e.g. ZenithBattleSnake")
+  parser.add_argument("--snake-version", help="Own snake version, e.g. 1.2.0")
+  parser.add_argument("--game-type", help="duel, standard, constrictor, ...")
+  parser.add_argument("--source", help="ladder, arena, custom, ...")
+  parser.add_argument("--min-games", type=int, default=3, help="Hide opponents with fewer games")
+  parser.add_argument("--limit", type=int, default=40, help="Maximum opponent rows (0 = all)")
+  parser.add_argument("--compare", action="store_true", help="Side-by-side win rates of own snake versions per opponent")
+  parser.add_argument("--traces", action="store_true", help="Move-trace latency stats per snake version and mode")
+  parser.add_argument("--trace-games", type=int, default=300, help="Newest finished games to unpack for --traces")
+  args = parser.parse_args()
+  if not args.dsn:
+    raise SystemExit("Set GAMEPLAY_DB_PG_DSN or pass --dsn")
+
+  filters = StatsFilter(args.since_hours, args.snake_type, args.snake_version, args.game_type, args.source)
+  if args.traces:
+    from scripts.move_trace_stats import summary_rows  # noqa: PLC0415
+
+    groups, games = asyncio.run(collect_traces(args.dsn, filters, args.trace_games))
+    print(f"Move traces from {games} newest finished games (ms; ext.timeouts = engine timeout while our handler answered >=100 ms early)")
+    print(format_table(*summary_rows(groups)))
+    return
+  limit = 0 if args.compare else args.limit
+  own, opponents = asyncio.run(collect(args.dsn, filters, args.min_games, limit))
+  print("Own snakes")
+  print(format_table(["snake", "version", "mode", "games", "wins", "win%", "turns", "last game (UTC)"], own_rows(own)))
+  if args.compare:
+    headers, rows = pivot_rows(opponents, args.min_games)
+    print("\nOpponents faced by several own snakes (our wins/games win%)")
+    print(format_table(headers, rows[:args.limit] if args.limit > 0 else rows))
+    return
+  print("\nOpponents (win% = our win rate against them)")
+  print(format_table(["opponent", "our snake", "games", "wins", "win%", "their wins", "their%", "turns"], opponent_rows(opponents)))
+
+if __name__ == "__main__":
+  main()

scripts/move_trace_stats.py

@@ -0,0 +1,110 @@
+"""Aggregate stored move traces into per-snake latency statistics.
+
+Each move stores ``my_thinking.move_trace`` with server-side stage timings. The
+engine-measured latency for turn ``T`` arrives with the board of turn ``T + 1``,
+so both are paired here to split total latency into strategy, server overhead
+and network/outside-handler time.
+"""
+from collections import defaultdict
+from dataclasses import dataclass, field
+
+from server.database.compact.replay_blob import unpack_replay
+from server.database.compact.thinking import MASK_KEY
+
+STAGES = ("strategy_ms", "overhead_ms", "handler_ms", "network_ms", "engine_ms")
+
+@dataclass
+class TraceGroup:
+  moves: int = 0
+  timeouts: int = 0
+  timeouts_fast_handler: int = 0
+  values: dict[str, list[float]] = field(default_factory=lambda: defaultdict(list))
+
+def _thinking_dict(thinking: object) -> dict:
+  if not isinstance(thinking, dict):
+    return {}
+  if thinking.get(MASK_KEY) == 0 and isinstance(thinking.get("_raw"), dict):
+    return thinking["_raw"]
+  return thinking
+
+def _number(value: object) -> float | None:
+  return float(value) if isinstance(value, (int, float)) and not isinstance(value, bool) else None
+
+def _latency(value: object) -> float | None:
+  # Compact storage keeps latency as a small int; "" (no response) is negative.
+  number = _number(value)
+  return number if number is not None and number >= 0 else None
+
+def add_game(groups: dict[tuple, TraceGroup], game: dict, you_id: str, turns: list, states: list) -> None:
+  """Add every traced move of one game to ``groups`` keyed by snake/version/mode."""
+  latency_by_turn = {s.turn: _latency(s.latency) for s in states if s.snake_id == you_id}
+  for record in turns:
+    thinking = _thinking_dict(record.thinking)
+    trace = thinking.get("move_trace")
+    if not isinstance(trace, dict):
+      continue
+    handler = _number(trace.get("handler_ms"))
+    strategy = _number(trace.get("strategy_ms"))
+    if handler is None:
+      continue
+    key = (game["your_snake_type"], game["your_snake_version"], thinking.get("mode") or "fallback")
+    group = groups.setdefault(key, TraceGroup())
+    group.moves += 1
+    group.values["handler_ms"].append(handler)
+    if strategy is not None:
+      group.values["strategy_ms"].append(strategy)
+      group.values["overhead_ms"].append(max(0.0, handler - strategy))
+    engine = latency_by_turn.get(record.turn + 1)
+    if engine is None:
+      continue
+    group.values["engine_ms"].append(engine)
+    group.values["network_ms"].append(max(0.0, engine - handler))
+    timeout_ms = (_number(trace.get("budget_ms")) or 450.0) + 50.0
+    if engine >= timeout_ms:
+      group.timeouts += 1
+      if handler < timeout_ms - 100:
+        group.timeouts_fast_handler += 1
+
+def percentile(values: list[float], quantile: float) -> float | None:
+  if not values:
+    return None
+  ordered = sorted(values)
+  return ordered[min(len(ordered) - 1, int(len(ordered) * quantile))]
+
+def _fmt(value: float | None) -> str:
+  return "-" if value is None else f"{value:.0f}"
+
+def summary_rows(groups: dict[tuple, TraceGroup]) -> tuple[list[str], list[list]]:
+  headers = ["snake", "version", "mode", "moves", "timeouts", "ext.timeouts"]
+  for stage in STAGES:
+    headers.append(f"{stage.removesuffix('_ms')} p50/p90/p99")
+  rows = []
+  for (snake, version, mode), group in sorted(groups.items(), key=lambda item: tuple(str(v) for v in item[0])):
+    row = [
+      snake, version, mode, group.moves,
+      f"{group.timeouts} ({100.0 * group.timeouts / group.moves:.1f}%)" if group.moves else "0",
+      group.timeouts_fast_handler,
+    ]
+    for stage in STAGES:
+      values = group.values.get(stage, [])
+      row.append("/".join(_fmt(percentile(values, q)) for q in (0.5, 0.9, 0.99)))
+    rows.append(row)
+  return headers, rows
+
+async def load_groups(connection, where: str, args: list, max_games: int) -> tuple[dict[tuple, TraceGroup], int]:
+  """Read the newest finished replays matching ``where``; never writes."""
+  args = [*args, max(1, max_games)]
+  rows = await connection.fetch(f"""
+    SELECT g.game_id, g.your_snake_id, g.your_snake_type, g.your_snake_version,
+           r.format, r.data
+    FROM games AS g
+    JOIN game_replays AS r ON r.gid = g.gid
+    {where}
+    ORDER BY g.started_at DESC
+    LIMIT ${len(args)}
+  """, *args)
+  groups: dict[tuple, TraceGroup] = {}
+  for row in rows:
+    turns, states = unpack_replay(row["data"], row["format"])
+    add_game(groups, dict(row), row["your_snake_id"], turns, states)
+  return groups, len(rows)

snakes/__init__.py

@@ -13,7 +13,7 @@ SNAKE_REGISTRATIONS = {
     "snakes.strategies.prism", "1.5.0"
   ),
   "ZenithBattleSnake_ClaudeOpus5_5": SnakeRegistration(
-    "snakes.strategies.zenith", "1.1.0"
+    "snakes.strategies.zenith", "1.2.0"
   ),
   "DummSnake": SnakeRegistration("snakes.legacy.DummSnake", "1.0.0"),
   "LogicSnake": SnakeRegistration("snakes.legacy.LogicSnake", "1.1.0"),

snakes/engine/survival.py

@@ -4,6 +4,10 @@ from time import perf_counter
 from snakes.engine.survival_search import CompactSurvivalSearch
 
 class BitboardSurvivalMixin:
+  # Remaining budget above which rollouts against at most two enemies may look
+  # one turn deeper. Snakes with a larger timeout reserve lower this threshold.
+  _rollout_deepen_ms = 250.0
+
   def _future_rollout_bonus(
       self, move: str, safe_moves: dict, my_body: list, other_snakes: list,
       food_set: set, is_constrictor: bool, width: int, height: int,
@@ -36,7 +40,7 @@ class BitboardSurvivalMixin:
       depth = min(self._planning_depth, 2 if len(other_snakes) > 1 else 3)
       if remaining < 90:
         depth = min(depth, 2)
-      elif remaining > 250 and len(other_snakes) <= 2:
+      elif remaining > self._rollout_deepen_ms and len(other_snakes) <= 2:
         depth = min(4, depth + 1)
       raw = self._survival_search_context.search_selected(
         my_body=my_body,

snakes/strategies/zenith.py

@@ -1,4 +1,4 @@
-"""ZenithBattleSnake_ClaudeOpus5_5 v1.1.0
+"""ZenithBattleSnake_ClaudeOpus5_5 v1.2.0
 
 Built on PrismBattleSnake_GPT_5_6_Sol v1.5.0. Prism's bitboard engine, duel
 search, and adversarial rollouts are inherited unchanged.
@@ -15,6 +15,11 @@ Z2: Proven-loss veto — a candidate whose completed search proves a loss is
     replaced by the best non-losing candidate.
 Z3: Proven-win override — a candidate whose completed search proves a win is
     played even if the heuristic preferred another move.
+Z4: Elapsed-time rollout gate — Zenith keeps a larger timeout reserve than
+    Prism. The inherited multiplayer "deepen when > 250 ms remain" gate is
+    lowered by that extra reserve, so 3-snake rollouts deepen after the same
+    elapsed time as Prism instead of never reaching depth 3. The duel gate is
+    left alone: the verdict pass already spends the duel budget.
 """
 from time import perf_counter
 
@@ -27,18 +32,22 @@ _PROVEN_WIN = BitboardDuelSearch.WIN / 2
 _PROVEN_LOSS = BitboardDuelSearch.LOSS / 2
 # Reserve kept for Apex's history bookkeeping after the verdict pass.
 _VERDICT_RESERVE_MS = 25.0
+_ZENITH_TIMEOUT_BUFFER_MS = 180
 
 class ZenithBattleSnake_ClaudeOpus5_5(ZenithSurvivalMixin, PrismBattleSnake_GPT_5_6_Sol):
-  VERSION = "1.1.0"
+  VERSION = "1.2.0"
 
   def __init__(self) -> None:
     super().__init__()
     self.name = "ZenithBattleSnake"
     self.version = self.VERSION
+    # Z4: deepen after the same elapsed time as the inherited Prism gates.
+    extra_reserve = self._get_timeout_buffer_ms() - super()._get_timeout_buffer_ms()
+    self._rollout_deepen_ms = max(120.0, self._rollout_deepen_ms - extra_reserve)
 
   def _get_timeout_buffer_ms(self):
     # Leave room for executor scheduling, history, JSON and network delivery.
-    return max(180, super()._get_timeout_buffer_ms())
+    return max(_ZENITH_TIMEOUT_BUFFER_MS, super()._get_timeout_buffer_ms())
 
   def _choose_duel_move(
     self, safe_moves, my_body, my_len, my_health, other_snakes, food_set,

tests/scripts/test_gameplay_snake_stats.py

@@ -0,0 +1,46 @@
+import unittest
+
+from scripts.gameplay_snake_stats import (
+  StatsFilter, build_where, format_table, opponent_query, pivot_rows, win_rate,
+)
+
+class TestGameplaySnakeStats(unittest.TestCase):
+
+  def test_filters_use_positional_arguments(self):
+    where, args = build_where(StatsFilter(since_hours=2, snake_type="ZenithBattleSnake", source="ladder"))
+
+    self.assertIn("g.status = 'finished'", where)
+    self.assertIn("make_interval(secs => $1)", where)
+    self.assertIn("g.your_snake_type = $2", where)
+    self.assertIn("g.source = $3", where)
+    self.assertEqual(args, [7200.0, "ZenithBattleSnake", "ladder"])
+
+  def test_opponent_query_appends_min_games_last(self):
+    sql, args = opponent_query(StatsFilter(game_type="duel"), min_games=5)
+
+    self.assertIn("HAVING count(*) >= $2", sql)
+    self.assertEqual(args, ["duel", 5])
+
+  def test_win_rate_handles_zero_games(self):
+    self.assertEqual(win_rate(1, 4), " 25.0%")
+    self.assertEqual(win_rate(0, 0), "    -")
+
+  def test_pivot_keeps_opponents_faced_by_every_listed_version(self):
+    records = [
+      {"opponent": "A", "snake": "Prism", "version": "1.5.0", "wins": 2, "games": 10},
+      {"opponent": "A", "snake": "Zenith", "version": "1.2.0", "wins": 3, "games": 6},
+      {"opponent": "B", "snake": "Prism", "version": "1.5.0", "wins": 1, "games": 9},
+    ]
+
+    headers, rows = pivot_rows(records, min_games=3)
+
+    self.assertEqual(headers, ["opponent", "Prism 1.5.0", "Zenith 1.2.0"])
+    self.assertEqual(rows, [["A", "2/10 20.0%", "3/6 50.0%"]])
+
+  def test_format_table_aligns_columns(self):
+    table = format_table(["name", "n"], [["long-name", 1]])
+
+    self.assertEqual(table.splitlines(), ["name       n", "---------  -", "long-name  1"])
+
+if __name__ == "__main__":
+  unittest.main()

tests/scripts/test_move_trace_stats.py

@@ -0,0 +1,61 @@
+import unittest
+
+from scripts.move_trace_stats import add_game, percentile, summary_rows
+from server.database.compact.records import SnakeState, TurnRecord
+
+GAME = {"your_snake_type": "ZenithBattleSnake", "your_snake_version": "1.2.0"}
+
+def _turn(turn: int, thinking: object) -> TurnRecord:
+  return TurnRecord(turn=turn, observed_at=None, move="up", thinking=thinking, food=None, hazards=None)
+
+def _state(snake_id: str, turn: int, latency: int | None) -> SnakeState:
+  return SnakeState(snake_id=snake_id, turn=turn, health=100, latency=latency, body=[])
+
+def _trace(handler: float, strategy: float) -> dict:
+  return {"handler_ms": handler, "strategy_ms": strategy, "budget_ms": 450.0}
+
+class TestMoveTraceStats(unittest.TestCase):
+
+  def test_pairs_engine_latency_of_next_turn(self):
+    groups = {}
+    turns = [
+      _turn(1, {"mode": "duel", "move_trace": _trace(200.0, 195.0)}),
+      _turn(2, {"mode": "duel", "move_trace": _trace(100.0, 90.0)}),
+    ]
+    states = [_state("you", 2, 260), _state("enemy", 2, 499), _state("you", 3, 500)]
+
+    add_game(groups, GAME, "you", turns, states)
+
+    group = groups[("ZenithBattleSnake", "1.2.0", "duel")]
+    self.assertEqual(group.moves, 2)
+    self.assertEqual(group.values["overhead_ms"], [5.0, 10.0])
+    self.assertEqual(group.values["engine_ms"], [260.0, 500.0])
+    self.assertEqual(group.values["network_ms"], [60.0, 400.0])
+    self.assertEqual((group.timeouts, group.timeouts_fast_handler), (1, 1))
+
+  def test_unwraps_raw_thinking_and_labels_missing_mode(self):
+    groups = {}
+    turns = [_turn(1, {"_derived": 0, "_raw": {"move_trace": _trace(5.0, 1.0)}}), _turn(2, "text")]
+
+    add_game(groups, GAME, "you", turns, [_state("you", 2, -32768)])
+
+    group = groups[("ZenithBattleSnake", "1.2.0", "fallback")]
+    self.assertEqual(group.moves, 1)
+    self.assertEqual(group.values["engine_ms"], [])
+
+  def test_summary_formats_percentiles(self):
+    groups = {}
+    add_game(groups, GAME, "you", [_turn(1, {"mode": "multi", "move_trace": _trace(20.0, 18.0)})], [])
+
+    headers, rows = summary_rows(groups)
+
+    self.assertEqual(headers[:4], ["snake", "version", "mode", "moves"])
+    self.assertEqual(rows[0][:6], ["ZenithBattleSnake", "1.2.0", "multi", 1, "0 (0.0%)", 0])
+    self.assertEqual(rows[0][6], "18/18/18")
+    self.assertEqual(rows[0][-1], "-/-/-")
+
+  def test_percentile_of_empty_list(self):
+    self.assertIsNone(percentile([], 0.5))
+
+if __name__ == "__main__":
+  unittest.main()

tests/snakes/test_ZenithBattleSnake_ClaudeOpus5_5.py

@@ -1,5 +1,8 @@
 import unittest
 from time import perf_counter
+from unittest import mock
+
+from snakes.engine.survival_search import CompactSurvivalSearch
 
 from snakes import SnakeBuilder, get_snake_version
 from snakes.strategies.prism import PrismBattleSnake_GPT_5_6_Sol
@@ -14,8 +17,8 @@ class TestZenithRegistration(unittest.TestCase):
     snake = ZenithBattleSnake_ClaudeOpus5_5()
 
     self.assertEqual(snake.name, "ZenithBattleSnake")
-    self.assertEqual(snake.version, "1.1.0")
+    self.assertEqual(snake.version, "1.2.0")
-    self.assertEqual(get_snake_version("ZenithBattleSnake_ClaudeOpus5_5"), "1.1.0")
+    self.assertEqual(get_snake_version("ZenithBattleSnake_ClaudeOpus5_5"), "1.2.0")
     self.assertIsInstance(snake, PrismBattleSnake_GPT_5_6_Sol)
     self.assertIsInstance(
       SnakeBuilder.build("ZenithBattleSnake_ClaudeOpus5_5"),
@@ -80,5 +83,56 @@ class TestZenithVerdicts(unittest.TestCase):
 
     self.assertEqual(verdicts, {})
 
+def _rollout_depth(snake, remaining_ms: float) -> int:
+  """Depth requested by a 3-snake rollout with ``remaining_ms`` left."""
+  snake._get_bb(11, 11)
+  snake._survival_search_context = None
+  enemies = [
+    {"body": [{"x": 8, "y": 8}, {"x": 8, "y": 9}, {"x": 8, "y": 10}]},
+    {"body": [{"x": 2, "y": 8}, {"x": 2, "y": 9}, {"x": 2, "y": 10}]},
+  ]
+  with mock.patch.object(CompactSurvivalSearch, "search_selected", return_value=0.0) as search:
+    snake._future_rollout_bonus(
+      move="up", safe_moves={"up": {"x": 5, "y": 3}},
+      my_body=[{"x": 5, "y": 2}, {"x": 5, "y": 1}, {"x": 5, "y": 0}],
+      other_snakes=enemies, food_set=set(), is_constrictor=False,
+      width=11, height=11, enemy_can_grow={},
+      deadline=perf_counter() + remaining_ms / 1000,
+    )
+  return search.call_args.kwargs["depth"]
+
+class TestZenithRolloutDepth(unittest.TestCase):
+
+  def test_gate_is_lowered_by_the_extra_timeout_reserve(self):
+    zenith = ZenithBattleSnake_ClaudeOpus5_5()
+    prism = PrismBattleSnake_GPT_5_6_Sol()
+    extra = zenith._get_timeout_buffer_ms() - prism._get_timeout_buffer_ms()
+
+    self.assertEqual(zenith._rollout_deepen_ms, prism._rollout_deepen_ms - extra)
+
+  def test_three_snake_rollout_deepens_after_same_elapsed_time_as_prism(self):
+    # Zenith's budget is 50 ms shorter, so 220 ms left equals 270 ms for Prism.
+    self.assertEqual(_rollout_depth(ZenithBattleSnake_ClaudeOpus5_5(), 220), 3)
+    self.assertEqual(_rollout_depth(PrismBattleSnake_GPT_5_6_Sol(), 270), 3)
+    self.assertEqual(_rollout_depth(PrismBattleSnake_GPT_5_6_Sol(), 220), 2)
+
+  def test_low_budget_still_caps_rollout_depth(self):
+    self.assertEqual(_rollout_depth(ZenithBattleSnake_ClaudeOpus5_5(), 150), 2)
+
+class TestZenithVerdictBudget(unittest.TestCase):
+
+  def test_verdict_pass_skips_when_only_reserve_remains(self):
+    snake = ZenithBattleSnake_ClaudeOpus5_5()
+
+    verdicts = snake._duel_verdicts(
+      safe_moves={"up": {"x": 0, "y": 2}, "right": {"x": 1, "y": 1}},
+      my_body=[{"x": 0, "y": 1}], enemy={"body": [{"x": 4, "y": 4}]},
+      my_health=100, food_set=set(), hazard_set=set(), hazard_damage=15,
+      hazard_count={}, previous_hazard_set=set(), width=5, height=5,
+      deadline=perf_counter() + 0.020,
+    )
+
+    self.assertEqual(verdicts, {})
+
 if __name__ == "__main__":
   unittest.main()