diff --git a/experiments/elo/.gitignore b/experiments/elo/.gitignore
index 37467a5fdebb60747ecc0c953087ff65ae282416..d0d2a5f79370c8cd8db890461e090861c8847782 100644
--- a/experiments/elo/.gitignore
+++ b/experiments/elo/.gitignore
@@ -1,4 +1,4 @@
 .env
 .jobs/
-results/
+results*/
 *.jl
diff --git a/experiments/elo/BGA Elo.py b/experiments/elo/BGA Elo.py
new file mode 100644
index 0000000000000000000000000000000000000000..0659e7bdf1bfec9b712eb727383ecd4ca13e5a20
--- /dev/null
+++ b/experiments/elo/BGA Elo.py	
@@ -0,0 +1,116 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import itertools
+import jupyter_black
+import networkx as nx
+import polars as pl
+from elo.elo_ratings import RankOrderedLogitElo
+from elo.optimal_k import approximate_optimal_k
+from tqdm import tqdm
+
+jupyter_black.load()
+pl.Config.set_tbl_rows(100)
+
+num_matches_regulars = 25
+game_id = 1741  # Ark Nova
+
+# %%
+data = pl.read_ipc(f"results/arrow/matches/{game_id}.arrow", memory_map=False)
+num_all_matches = len(data)
+data.shape
+
+# %%
+graph = nx.Graph()
+for player_ids in tqdm(data["player_ids"]):
+    graph.add_edges_from(itertools.combinations(player_ids, 2))
+num_all_players = graph.number_of_nodes()
+graph.number_of_nodes(), graph.number_of_edges()
+
+# %%
+largest_community = max(nx.connected_components(graph), key=len)
+num_players = len(largest_community)
+num_players
+
+# %%
+data = data.filter(
+    pl.col("player_ids").list.eval(pl.element().is_in(largest_community)).list.any()
+)
+num_matches = len(data)
+data.shape
+
+# %%
+data.sample(10)
+
+# %%
+data.describe()
+
+# %%
+players = (
+    data.lazy()
+    .select(pl.col("player_ids").explode().value_counts())
+    .unnest("player_ids")
+)
+player_stats = players.describe(
+    percentiles=(0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95, 0.99)
+)
+num_matches_max = int(player_stats["count"][-1])
+player_stats
+
+# %%
+num_regular_players = (
+    players.filter(pl.col("count") >= num_matches_regulars)
+    .select(pl.len())
+    .collect()
+    .item()
+)
+num_regular_players
+
+# %%
+print(f"Number of matches: {num_all_matches} (all) / {num_matches} (connected only)")
+print(
+    f"Number of players: {num_all_players} (all) / {num_players} (connected only) / {num_regular_players} (regulars only)"
+)
+print(f"Maximum number of plays by a single player: {num_matches_max}")
+
+# %%
+matches = (
+    dict(zip(player_ids, payoffs))
+    for player_ids, payoffs in zip(data["player_ids"], data["payoffs"])
+)
+
+# %%
+elo = RankOrderedLogitElo()
+elo.update_elo_ratings_batch(
+    matches,
+    full_results=True,
+    progress_bar=True,
+    tqdm_kwargs={"total": len(data)},
+)
+len(elo.elo_ratings)
+
+# %%
+sorted(elo.elo_ratings.items(), key=lambda x: -x[1])[:100]
+
+# %%
+# This will take very very long if there's a large number of matches
+optimal_k = approximate_optimal_k(
+    matches=matches,
+    min_elo_k=0,
+    max_elo_k=200,
+    elo_scale=400,
+)
+optimal_k
diff --git a/experiments/elo/K investigations.py b/experiments/elo/K investigations.py
new file mode 100644
index 0000000000000000000000000000000000000000..c865feccfb0b84f464a9daad6dc9f26bd1e8b94c
--- /dev/null
+++ b/experiments/elo/K investigations.py	
@@ -0,0 +1,77 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import jupyter_black
+import numpy as np
+import polars as pl
+import seaborn as sns
+from matplotlib import pyplot as plt
+from scipy.optimize import curve_fit
+
+jupyter_black.load()
+
+# %%
+data = (
+    pl.scan_csv("results/p_deterministic*.csv")
+    # .filter(pl.col("num_matches") > 1000)
+    .with_columns(matches_per_player=pl.col("num_matches") / pl.col("num_players"))
+    .sort("p_deterministic")
+    .collect()
+)
+data.shape
+
+
+# %%
+def power_law(x: np.ndarray, a: float, b: float, c: float) -> np.ndarray:
+    return a / (x**b) + c
+
+
+def fit_and_plot(data):
+    data = data.filter(pl.col("matches_per_player") > 10)
+
+    if len(data) < 10:
+        print(f"Only {len(data)} row(s) provided, skipping…")
+        return
+
+    x = data["matches_per_player"].to_numpy()
+    y = data["elo_k"].to_numpy()
+    popt, pcov = curve_fit(power_law, x, y, bounds=(0, np.inf))
+    a, b, c = popt
+    print(f"Fitted parameters: a={a:.4f}, b={b:.4f}, c={c:.4f}")
+    x_fit = np.linspace(x.min(), x.max(), 100)
+    y_fit = power_law(x_fit, *popt)
+
+    _, ax = plt.subplots()
+    sns.scatterplot(
+        data=data,
+        x="matches_per_player",
+        y="elo_k",
+        hue="players_per_match",
+        size="num_players",
+        ax=ax,
+    )
+    sns.lineplot(x=x_fit, y=y_fit, ax=ax)
+
+    return ax
+
+
+# %%
+for (p_deterministic,), group in data.group_by("p_deterministic", maintain_order=True):
+    print(f"{p_deterministic=}")
+    fit_and_plot(group)
+    plt.show()
+
+# %%
diff --git a/experiments/elo/Matches.py b/experiments/elo/Matches.py
new file mode 100644
index 0000000000000000000000000000000000000000..528af25776bbfe1465b2f2230dd4d0765cf532f8
--- /dev/null
+++ b/experiments/elo/Matches.py
@@ -0,0 +1,68 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.0
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import jupyter_black
+import polars as pl
+
+jupyter_black.load()
+pl.Config.set_tbl_rows(100)
+seed = 13
+
+# %%
+matches = (
+    pl.scan_ndjson("matches.jl")
+    .drop("id", "scraped_at")
+    .with_columns(pl.col("timestamp").cast(pl.Int64))
+    .with_columns(
+        pl.from_epoch("timestamp").dt.convert_time_zone(time_zone="UTC"),
+        num_players=pl.col("players").list.len(),
+    )
+    .filter(pl.col("num_players") > 1)
+    .filter(
+        pl.col("players")
+        .list.eval(pl.element().struct.field("place").is_not_null())
+        .list.all()
+    )
+)
+
+# %%
+matches.head(10).collect()
+
+# %%
+games_info = pl.scan_ndjson("games.jl").select(
+    pl.col("id").alias("game_id"),
+    pl.col("name").alias("game_slug"),
+    "bgg_id",
+    "display_name_en",
+    "games_played",
+)
+games_matches = matches.group_by("game_id").agg(
+    num_matches=pl.len(),
+    min_players=pl.col("num_players").min(),
+    max_players=pl.col("num_players").max(),
+    first=pl.col("timestamp").min(),
+    last=pl.col("timestamp").max(),
+)
+all_games = games_info.join(games_matches, on="game_id", how="full").collect()
+all_games.shape
+
+# %%
+all_games.describe()
+
+# %%
+all_games.filter(pl.col("min_players") == 2).filter(
+    pl.col("max_players") == 2
+).describe()
diff --git a/experiments/elo/Process matches.py b/experiments/elo/Process matches.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a1e6bb47d93baff84bc64327a26e911cb7eb8ff
--- /dev/null
+++ b/experiments/elo/Process matches.py	
@@ -0,0 +1,101 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import jupyter_black
+import polars as pl
+from datetime import datetime
+from itertools import batched
+from pathlib import Path
+from tqdm import tqdm
+
+jupyter_black.load()
+pl.Config.set_tbl_rows(100)
+
+# %%
+in_dir = Path("results").resolve()
+out_dir = in_dir / "arrow"
+match_dir = out_dir / "matches"
+out_dir.mkdir(parents=True, exist_ok=True)
+match_dir.mkdir(parents=True, exist_ok=True)
+in_dir, out_dir, match_dir
+
+# %%
+file_batch_size = 10
+ts = datetime.now().replace(microsecond=0).isoformat().replace(":", "-")
+suffix = f"{ts}-euler-"
+file_batch_size, suffix
+
+# %%
+schema = {
+    "id": pl.String,
+    "timestamp": pl.String,
+    "game_id": pl.Int64,
+    "players": pl.List(
+        pl.Struct(
+            {
+                "player_id": pl.Int64,
+                "place": pl.Int64,
+                "score": pl.Float64,
+            }
+        )
+    ),
+    "scraped_at": pl.String,
+}
+
+# %%
+for i, batch in enumerate(batched(tqdm(in_dir.glob("matches-*.jl")), file_batch_size)):
+    df = (
+        pl.scan_ndjson(list(batch), schema=schema)
+        .with_columns(
+            pl.col("id").cast(pl.Int64),
+            pl.col("timestamp").cast(pl.Int64),
+        )
+        .with_columns(
+            pl.from_epoch("timestamp").dt.convert_time_zone(time_zone="UTC"),
+            pl.col("scraped_at").str.to_datetime(time_zone="UTC"),
+        )
+    )
+    df.sink_ipc(out_dir / f"matches-{suffix}{i:05d}.arrow")
+
+# %%
+matches = pl.scan_ipc(out_dir / "matches-*.arrow")
+game_ids = matches.select(pl.col("game_id").unique()).collect()["game_id"]
+game_ids.shape
+
+# %%
+for game_id in tqdm(game_ids):
+    match_data = (
+        matches.filter(pl.col("game_id") == game_id)
+        .sort("scraped_at", "timestamp")
+        .unique("id", keep="first")
+        .select(
+            num_players=pl.col("players").list.len(),
+            player_ids=pl.col("players").list.eval(
+                pl.element().struct.field("player_id")
+            ),
+            places=pl.col("players").list.eval(pl.element().struct.field("place")),
+        )
+        .filter(pl.col("num_players") >= 2)
+        .filter(pl.col("player_ids").list.eval(pl.element().is_not_null()).list.all())
+        .filter(
+            pl.col("places")
+            .list.eval(pl.element().is_not_null() & (pl.element() >= 1))
+            .list.all()
+        )
+        .with_columns(payoffs=pl.col("places").list.eval(pl.len() - pl.element()))
+        .filter(pl.col("payoffs").list.eval(pl.element() >= 0).list.all())
+    )
+    match_data.sink_ipc(match_dir / f"{game_id}.arrow")
diff --git a/experiments/elo/elo/bga_spider.py b/experiments/elo/elo/bga_spider.py
index 0ec41709e955ed543a20e80900440804438634d8..6ff9563c3ee08c55547a20933663d57a5a5005d5 100644
--- a/experiments/elo/elo/bga_spider.py
+++ b/experiments/elo/elo/bga_spider.py
@@ -1,13 +1,20 @@
-from collections.abc import Generator
-from datetime import datetime, timezone
+from __future__ import annotations
+
 import json
 import math
 import re
-from typing import Any
+import time
+from datetime import datetime, timezone
+from typing import Any, TYPE_CHECKING
 
-from scrapy import FormRequest, Request, Spider
+import funcy
+import jmespath
+from scrapy import FormRequest, Request, Selector, Spider
 from scrapy.http import Response, TextResponse
 
+if TYPE_CHECKING:
+    from collections.abc import Generator, Iterable
+
 
 class BaseFilter:
     type_name: str
@@ -29,14 +36,22 @@ class RankingFilter(BaseFilter):
     type_name = "ranking"
 
 
+class MatchFilter(BaseFilter):
+    type_name = "match"
+
+
 class BgaSpider(Spider):
     name = "bga"
-    start_urls = ("https://en.boardgamearena.com/gamelist?allGames=",)
+    base_url = "https://en.boardgamearena.com/"
+    start_urls = (base_url,)
 
     custom_settings = {
-        "DOWNLOAD_DELAY": 1,
-        "CONCURRENT_REQUESTS_PER_DOMAIN": 4,
-        "FEED_EXPORT_BATCH_ITEM_COUNT": 10_000,
+        "COOKIES_ENABLED": True,
+        "COOKIES_DEBUG": False,
+        "DOWNLOAD_DELAY": 10,
+        "CONCURRENT_REQUESTS_PER_DOMAIN": 16,
+        "LOG_FORMATTER": "scrapy_extensions.QuietLogFormatter",
+        "FEED_EXPORT_BATCH_ITEM_COUNT": 100_000,
         "FEEDS": {
             "results/games-%(time)s-%(batch_id)05d.jl": {
                 "format": "jsonlines",
@@ -50,20 +65,83 @@ class BgaSpider(Spider):
                 "overwrite": False,
                 "store_empty": False,
             },
+            "results/matches-%(time)s-%(batch_id)05d.jl": {
+                "format": "jsonlines",
+                "item_filter": MatchFilter,
+                "overwrite": False,
+                "store_empty": False,
+            },
         },
         "JOBDIR": ".jobs",
     }
 
+    request_token_url = "/account/auth/getRequestToken.html"
+    request_token_path = jmespath.compile("data.request_token")
+    request_token: str | None = None
+
+    game_list_url = "/gamelist?allGames="
+    game_list_regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE)
+    game_keys: Iterable[str] | None = None
+
+    scrape_rankings = False
+    ranking_url = "/gamepanel/gamepanel/getRanking.html"
+    ranking_path = jmespath.compile("data.ranks")
     max_rank_scraped = None
-    regex = re.compile("globalUserInfos=(.+);$", flags=re.MULTILINE)
+    ranking_keys: Iterable[str] | None = None
+
+    scrape_matches = True
+    base_match_url = "/message/board"
+    match_path = jmespath.compile("data.news")
+    max_matches_per_page = 9500
+    match_keys: Iterable[str] | None = frozenset(
+        (
+            "id",
+            "timestamp",
+            "game_id",
+            "players",
+            "scraped_at",
+            "type",
+        )
+    )
+
+    ordinal_regex = re.compile(r"(\d+)(st|nd|rd|th)|winner|loser", re.IGNORECASE)
+    integer_regex = re.compile(r"(\d+)")
+
+    def parse(self, response: Response) -> Request:
+        return FormRequest(
+            url=response.urljoin(self.request_token_url),
+            formdata={"bgapp": "bga"},
+            callback=self.parse_request_token,
+            method="POST",
+        )
 
-    def parse(self, response: Response) -> Generator[dict | Request]:
+    def parse_request_token(self, response: Response) -> Request | None:
+        if not isinstance(response, TextResponse):
+            self.logger.warning("Response <%s> is not a TextResponse", response.url)
+            return
+
+        payload = response.json()
+        request_token = self.request_token_path.search(payload)
+
+        if not request_token:
+            self.logger.error("Request token not found in response <%s>", response.url)
+            return
+
+        self.request_token = request_token
+
+        return Request(
+            url=response.urljoin(self.game_list_url),
+            callback=self.parse_games,
+            headers={"X-Request-Token": request_token},
+        )
+
+    def parse_games(self, response: Response) -> Generator[dict | Request]:
         now = datetime.now(timezone.utc).isoformat(timespec="seconds")
 
         for text in response.xpath(
             "//script[@type = 'text/javascript']/text()"
         ).getall():
-            match = self.regex.search(text)
+            match = self.game_list_regex.search(text)
             if not match:
                 continue
 
@@ -78,20 +156,34 @@ class BgaSpider(Spider):
             for game in payload["game_list"]:
                 game["type"] = "game"
                 game["scraped_at"] = now
-                yield game
-
-                yield FormRequest(
-                    url=response.urljoin("/gamepanel/gamepanel/getRanking.html"),
-                    method="POST",
-                    formdata={
-                        "game": str(game["id"]),
-                        "start": "0",
-                        "mode": "elo",
-                    },
-                    callback=self.parse_ranking,
-                    meta={"game_id": game["id"]},
-                    priority=0,
-                )
+                yield funcy.project(game, self.game_keys) if self.game_keys else game
+
+                if self.scrape_rankings:
+                    yield FormRequest(
+                        url=response.urljoin(self.ranking_url),
+                        method="POST",
+                        formdata={
+                            "game": str(game["id"]),
+                            "start": "0",
+                            "mode": "elo",
+                        },
+                        callback=self.parse_ranking,
+                        meta={"game_id": game["id"]},
+                        priority=0,
+                        headers={"X-Request-Token": self.request_token},
+                    )
+
+                if self.scrape_matches:
+                    games_played = int(game.get("games_played", 0))
+                    priority = games_played // (2 * self.max_matches_per_page)
+                    yield Request(
+                        url=self.build_match_url(response, game["id"]),
+                        method="GET",
+                        callback=self.parse_matches,
+                        meta={"game_id": game["id"]},
+                        priority=priority,
+                        headers={"X-Request-Token": self.request_token},
+                    )
 
     def parse_ranking(self, response: Response) -> Generator[dict | Request]:
         if not isinstance(response, TextResponse):
@@ -103,13 +195,21 @@ class BgaSpider(Spider):
         game_id = response.meta["game_id"]
         payload = response.json()
 
+        entries = self.ranking_path.search(payload)
+
+        if not entries:
+            self.logger.error("Ranking not found in response <%s>", response.url)
+            return
+
         rank_nos = []
 
-        for entry in payload["data"]["ranks"]:
+        for entry in entries:
             entry["game_id"] = game_id
             entry["type"] = "ranking"
             entry["scraped_at"] = now
-            yield entry
+            yield (
+                funcy.project(entry, self.ranking_keys) if self.ranking_keys else entry
+            )
             rank_nos.append(int(entry["rank_no"]))
 
         max_rank_no = max(rank_nos, default=math.inf)
@@ -122,5 +222,106 @@ class BgaSpider(Spider):
                     "mode": "elo",
                 },
                 meta={"game_id": game_id},
-                priority=-max_rank_no,
+                priority=response.request.priority - 1,
             )
+
+    def build_match_url(
+        self,
+        response: Response,
+        game_id: int,
+        from_time: int = None,
+        from_id: int = None,
+    ) -> str:
+        params = {
+            "type": "lastresult",
+            "id": str(game_id),
+            "social": "false",
+            "per_page": str(self.max_matches_per_page),
+            "dojo.preventCache": str(int(time.time() * 1000)),
+        }
+
+        if from_time is not None and from_id is not None:
+            params["page"] = "0"
+            params["from_time"] = str(from_time)
+            params["from_id"] = str(from_id)
+
+        query = "&".join(f"{k}={v}" for k, v in params.items())
+        url = f"{self.base_match_url}?{query}"
+        return response.urljoin(url)
+
+    def parse_matches(self, response: Response) -> Generator[dict[str, Any] | Request]:
+        if not isinstance(response, TextResponse):
+            self.logger.warning("Response <%s> is not a TextResponse", response.url)
+            return
+
+        now = datetime.now(timezone.utc).isoformat(timespec="seconds")
+
+        game_id = response.meta["game_id"]
+        payload = response.json()
+
+        matches = self.match_path.search(payload)
+
+        if not matches:
+            self.logger.error("Matches not found in response <%s>", response.url)
+            return
+
+        last_id = None
+        last_timestamp = None
+
+        for match in matches:
+            match["game_id"] = game_id
+            match["type"] = "match"
+            match["scraped_at"] = now
+            try:
+                match["players"] = list(self.parse_match_html(match["html"]))
+            except Exception:
+                match["players"] = None
+            last_id = match.get("id")
+            last_timestamp = match.get("timestamp")
+            yield funcy.project(match, self.match_keys) if self.match_keys else match
+
+        if last_id and last_timestamp:
+            yield Request(
+                url=self.build_match_url(response, game_id, last_timestamp, last_id),
+                method="GET",
+                callback=self.parse_matches,
+                meta={"game_id": game_id},
+                priority=response.request.priority - 1,
+                headers={"X-Request-Token": self.request_token},
+            )
+
+    def parse_match_html(self, html: str) -> Generator[dict[str, Any]]:
+        selector = Selector(text=html, type="html")
+
+        player_divs = selector.css("div.board-score-entry")
+        num_players = len(player_divs)
+
+        for player_div in player_divs:
+            rank_text = player_div.xpath("text()[1]").get() or ""
+            match = self.ordinal_regex.search(rank_text)
+            if not match:
+                place = None
+            elif match.group(1):
+                place = int(match.group(1))
+            elif match.group(0).lower() == "winner":
+                place = 1
+            elif match.group(0).lower() == "loser":
+                place = num_players
+            else:
+                raise ValueError(f"Invalid place: {place}")
+
+            player_link = player_div.css("a.playername")
+            player_href = player_link.xpath("@href").get() or ""
+            player_id = int(player_href.split("=")[-1]) if "=" in player_href else None
+
+            player_name = player_link.xpath("text()").get()
+            score_text = player_link.xpath("following-sibling::text()[1]").get() or ""
+            match = self.integer_regex.search(score_text)
+            score = int(match.group(1)) if match else None
+
+            yield {
+                "player_id": player_id,
+                "player_name": player_name,
+                "place": place,
+                "score": score,
+            }
diff --git a/experiments/elo/elo/data.py b/experiments/elo/elo/data.py
index d374336103b54d3243333a55a1412b193d5b7c2b..0ccfb0d9fe06be7622b71af48ff126a00fcfe761 100644
--- a/experiments/elo/elo/data.py
+++ b/experiments/elo/elo/data.py
@@ -86,6 +86,51 @@ def merge_rankings(
     )
 
 
+def merge_matches(
+    matches_path: str | Path | list[str] | list[Path],
+    output_path: str | Path,
+    overwrite: bool = False,
+    progress_bar: bool = False,
+) -> None:
+    schema = {
+        "id": pl.String,
+        "timestamp": pl.String,
+        "game_id": pl.Int64,
+        "players": pl.List(
+            pl.Struct(
+                {
+                    "player_id": pl.Int64,
+                    "player_name": pl.String,
+                    "place": pl.Int64,
+                    "score": pl.Float64,
+                }
+            )
+        ),
+        "scraped_at": pl.String,
+    }
+
+    config = MergeConfig(
+        schema=pl.Schema(schema),
+        in_paths=matches_path,
+        out_path=output_path,
+        key_col="id",
+        latest_col=pl.col("scraped_at").str.to_datetime(time_zone="UTC"),
+        sort_fields=[
+            "game_id",
+            "timestamp",
+            "id",
+        ],
+    )
+
+    merge_files(
+        merge_config=config,
+        overwrite=overwrite,
+        drop_empty=True,
+        sort_keys=True,
+        progress_bar=progress_bar,
+    )
+
+
 def load_data(
     *,
     bgg_games_path: str | Path,
@@ -194,3 +239,9 @@ if __name__ == "__main__":
         overwrite=True,
         progress_bar=True,
     )
+    merge_matches(
+        matches_path="results/matches-*.jl",
+        output_path="matches.jl",
+        overwrite=True,
+        progress_bar=True,
+    )
diff --git a/experiments/elo/elo/elo_ratings.py b/experiments/elo/elo/elo_ratings.py
index ccc3c01a46d91a15ebd776c3fd9edcc5030aac6d..a662123fc4be5570b82b1f07aa4f2a14f101837d 100644
--- a/experiments/elo/elo/elo_ratings.py
+++ b/experiments/elo/elo/elo_ratings.py
@@ -1,64 +1,316 @@
 from __future__ import annotations
+
+import itertools
+import warnings
+from abc import ABC, abstractmethod
 from collections import defaultdict
-from collections.abc import Iterable, Mapping
-from typing import TypeVar
+from collections.abc import Mapping
+from typing import TYPE_CHECKING, override
+
+import numpy as np
 
-ID_TYPE = TypeVar("ID_TYPE")
+if TYPE_CHECKING:
+    from collections.abc import Iterable, Generator
+    from typing import Any
 
 
 def elo_probability(diff: float, scale: float = 400) -> float:
     return 1 / (1 + 10 ** (-diff / scale))
 
 
-def calculate_elo_ratings(
-    *,
-    player_1_ids: Iterable[ID_TYPE],
-    player_2_ids: Iterable[ID_TYPE],
-    player_1_outcomes: Iterable[float],
-    init_elo_ratings: Mapping[ID_TYPE, float] | None = None,
-    elo_initial: float = 0,
-    elo_k: float = 32,
-    elo_scale: float = 400,
-    full_results: bool = False,
-    progress_bar: bool = False,
-) -> (
-    defaultdict[ID_TYPE, float]
-    | tuple[defaultdict[ID_TYPE, float], list[float], list[float]]
-):
-    elo_ratings = defaultdict(
-        lambda: elo_initial,
-        init_elo_ratings if init_elo_ratings is not None else {},
-    )
-
-    player_1_win_probs: list[float] = []
-    elo_updates: list[float] = []
-
-    if progress_bar:
-        from tqdm import tqdm
-
-        player_1_ids = tqdm(player_1_ids, desc="Processing games")
-
-    for player_1_id, player_2_id, player_1_outcome in zip(
-        player_1_ids,
-        player_2_ids,
-        player_1_outcomes,
-        strict=True,
-    ):
-        elo_1 = elo_ratings[player_1_id]
-        elo_2 = elo_ratings[player_2_id]
+class EloRatingSystem[ID_TYPE](ABC):
+    elo_initial: float = 0
+    elo_k: float = 32
+    elo_scale: float = 400
+
+    elo_ratings: defaultdict[ID_TYPE, float]
+
+    def __init__(
+        self,
+        *,
+        init_elo_ratings: Mapping[ID_TYPE, float] | None = None,
+        elo_initial: float = 0,
+        elo_k: float = 32,
+        elo_scale: float = 400,
+    ) -> None:
+        self.elo_initial = elo_initial
+        self.elo_k = elo_k
+        self.elo_scale = elo_scale
+
+        self.elo_ratings = defaultdict(
+            lambda: elo_initial,
+            init_elo_ratings if init_elo_ratings is not None else {},
+        )
+
+    @abstractmethod
+    def update_elo_ratings(
+        self,
+        players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE],
+    ) -> np.ndarray: ...
+
+    @abstractmethod
+    def update_elo_ratings_batch(
+        self,
+        matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+        *,
+        full_results: bool = False,
+        progress_bar: bool = False,
+        tqdm_kwargs: dict[str, Any] | None = None,
+    ) -> np.ndarray | None: ...
+
+    @abstractmethod
+    def probability_matrix(
+        self,
+        players: Iterable[ID_TYPE],
+    ) -> np.ndarray: ...
+
+    @abstractmethod
+    def expected_outcome(
+        self,
+        players: Iterable[ID_TYPE],
+        *,
+        rank_payoffs: np.ndarray | None = None,
+    ) -> np.ndarray: ...
+
+
+class TwoPlayerElo[ID_TYPE](EloRatingSystem[ID_TYPE]):
+    @override
+    def expected_outcome(
+        self,
+        players: Iterable[ID_TYPE],
+        *,
+        rank_payoffs: np.ndarray | None = None,
+    ) -> np.ndarray:
+        if rank_payoffs is not None:
+            warnings.warn("rank_payoffs are ignored for two-player elo")
 
+        player_1, player_2 = players
+        elo_1 = self.elo_ratings[player_1]
+        elo_2 = self.elo_ratings[player_2]
         diff = elo_1 - elo_2
-        player_1_win_prob = elo_probability(diff, elo_scale)
-        player_1_update = elo_k * (player_1_outcome - player_1_win_prob)
+        prob = elo_probability(diff, self.elo_scale)
+        return np.array([prob, 1 - prob])
+
+    @override
+    def probability_matrix(
+        self,
+        players: Iterable[ID_TYPE],
+    ) -> np.ndarray:
+        expected_outcome = self.expected_outcome(players)
+        return np.array([expected_outcome, 1 - expected_outcome])
+
+    @override
+    def update_elo_ratings(
+        self,
+        players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE],
+    ) -> np.ndarray:
+        if isinstance(players, Mapping):
+            player_ids = tuple(players.keys())
+            outcomes = np.array(list(players.values()), dtype=float)
+        else:
+            player_ids = tuple(players)
+            outcomes = np.array([1, 0], dtype=float)
+
+        diffs = outcomes - self.expected_outcome(player_ids)
+        for player_id, diff in zip(player_ids, diffs):
+            self.elo_ratings[player_id] += self.elo_k * diff
+
+        return diffs
+
+    def _update_elo_ratings_batch(
+        self,
+        matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+    ) -> Generator[np.ndarray]:
+        for match in matches:
+            yield self.update_elo_ratings(match)
+
+    @override
+    def update_elo_ratings_batch(
+        self,
+        matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+        *,
+        full_results: bool = False,
+        progress_bar: bool = False,
+        tqdm_kwargs: dict[str, Any] | None = None,
+    ) -> np.ndarray | None:
+        if progress_bar:
+            from tqdm import tqdm
+
+            tqdm_kwargs = tqdm_kwargs or {}
+            matches = tqdm(matches, desc="Updating elo ratings", **tqdm_kwargs)
+
+        if full_results:
+            return np.array(list(self._update_elo_ratings_batch(matches)))
+
+        for _ in self._update_elo_ratings_batch(matches):
+            pass
+        return None
+
+
+class RankOrderedLogitElo[ID_TYPE](EloRatingSystem[ID_TYPE]):
+    """
+    Rank-ordered-logit Elo with Monte-Carlo fallback for large fields.
+    """
 
-        # Update ratings
-        elo_ratings[player_1_id] += player_1_update
-        elo_ratings[player_2_id] -= player_1_update
+    def __init__(
+        self,
+        *,
+        init_elo_ratings: Mapping[ID_TYPE, float] | None = None,
+        elo_initial: float = 0,
+        elo_k: float = 32,
+        elo_scale: float = 400,
+        max_exact: int = 6,
+        mc_samples: int = 5_000,
+    ) -> None:
+        super().__init__(
+            init_elo_ratings=init_elo_ratings,
+            elo_initial=elo_initial,
+            elo_k=elo_k,
+            elo_scale=elo_scale,
+        )
+
+        self.max_exact = max_exact
+        self.mc_samples = mc_samples
+
+    def _calculate_probability_matrix_two_players(
+        self,
+        players: tuple[ID_TYPE, ID_TYPE],
+    ) -> np.ndarray:
+        rating_a = self.elo_ratings[players[0]]
+        rating_b = self.elo_ratings[players[1]]
+        diff = rating_a - rating_b
+        prob_a = elo_probability(diff, self.elo_scale)
+        prob_b = 1 - prob_a
+        return np.array([[prob_a, prob_b], [prob_b, prob_a]])
+
+    def _calculate_probability_matrix_from_simulations(
+        self,
+        players: tuple[ID_TYPE, ...],
+    ) -> np.ndarray:
+        # TODO: implement Monte Carlo fallback
+        raise NotImplementedError
+
+    def _calculate_probability_matrix_from_permutations(
+        self,
+        players: tuple[ID_TYPE, ...],
+    ) -> np.ndarray:
+        num_players = len(players)
+        ratings = np.array([self.elo_ratings[p] for p in players], dtype=float)
+        exp_ratings = 10 ** (ratings / self.elo_scale)
+        probs = np.zeros((num_players, num_players))
+
+        for perm in itertools.permutations(range(num_players)):
+            prob = 1
+            denom = np.sum(exp_ratings)
+            for i in range(num_players - 1):
+                prob *= exp_ratings[perm[i]] / denom
+                denom -= exp_ratings[perm[i]]
+            for position, player in enumerate(perm):
+                probs[player, position] += prob
+
+        assert np.allclose(probs.sum(axis=0), 1), "Probabilities must sum to 1"
+        assert np.allclose(probs.sum(axis=1), 1), "Probabilities must sum to 1"
+
+        return probs
+
+    @override
+    def probability_matrix(
+        self,
+        players: Iterable[ID_TYPE],
+    ) -> np.ndarray:
+        players = tuple(players)
+
+        if len(players) < 2:
+            raise ValueError("At least 2 players are required")
+
+        if len(players) == 2:
+            return self._calculate_probability_matrix_two_players(players)
+
+        if len(players) > self.max_exact:
+            return self._calculate_probability_matrix_from_simulations(players)
+
+        return self._calculate_probability_matrix_from_permutations(players)
+
+    @override
+    def expected_outcome(
+        self,
+        players: Iterable[ID_TYPE],
+        *,
+        rank_payoffs: np.ndarray | None = None,
+    ) -> np.ndarray:
+        players = tuple(players)
+        if rank_payoffs is None:
+            rank_payoffs = np.arange(len(players) - 1, -1, -1, dtype=float)
+
+        assert rank_payoffs.ndim == 1, "Rank payoffs must be a 1D array"
+        assert len(rank_payoffs) == len(players), (
+            "Rank payoffs must be the same length as the number of players"
+        )
+        probs = self.probability_matrix(players)
+        return probs @ rank_payoffs
+
+    @override
+    def update_elo_ratings(
+        self,
+        players: Mapping[ID_TYPE, float] | Iterable[ID_TYPE],
+    ) -> np.ndarray:
+        if isinstance(players, Mapping):
+            player_ids = tuple(players.keys())
+            payoffs = np.array(list(players.values()), dtype=float)
+        else:
+            player_ids = tuple(players)
+            payoffs = np.arange(len(player_ids) - 1, -1, -1, dtype=float)
+
+        assert np.all(payoffs >= 0), "Payoffs must be non-negative"
+        assert np.any(payoffs > 0), "At least one payoff must be positive"
+
+        max_payoff = payoffs.max()
+        expected_payoffs = self.expected_outcome(player_ids, rank_payoffs=payoffs)
+        diffs = (payoffs - expected_payoffs) / max_payoff
+        for player_id, diff in zip(player_ids, diffs):
+            self.elo_ratings[player_id] += self.elo_k * diff
+
+        return diffs
+
+    def _update_elo_ratings_batch(
+        self,
+        matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+    ) -> Generator[np.ndarray]:
+        for match in matches:
+            yield self.update_elo_ratings(match)
+
+    @override
+    def update_elo_ratings_batch(
+        self,
+        matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+        *,
+        full_results: bool = False,
+        progress_bar: bool = False,
+        tqdm_kwargs: dict[str, Any] | None = None,
+    ) -> np.ndarray | None:
+        if progress_bar:
+            from tqdm import tqdm
+
+            tqdm_kwargs = tqdm_kwargs or {}
+            matches = tqdm(matches, **tqdm_kwargs)
 
         if full_results:
-            player_1_win_probs.append(player_1_win_prob)
-            elo_updates.append(player_1_update)
+            return _padded_numpy_array(self._update_elo_ratings_batch(matches))
 
-    if full_results:
-        return elo_ratings, player_1_win_probs, elo_updates
-    return elo_ratings
+        for _ in self._update_elo_ratings_batch(matches):
+            pass
+        return None
+
+
+def _padded_numpy_array(
+    arrays: Iterable[np.ndarray],
+    *,
+    pad_value: float = np.nan,
+    dtype: np.dtype | None = None,
+) -> np.ndarray:
+    arrays = tuple(arrays)
+    max_len = max((a.shape[0] for a in arrays), default=0)
+    result = np.full((len(arrays), max_len), pad_value, dtype=dtype or np.float64)
+    for i, a in enumerate(arrays):
+        result[i, : a.shape[0]] = a
+    return result
diff --git a/experiments/elo/elo/game_stats.py b/experiments/elo/elo/game_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..521868123bef1828048759117b395796cef6ab2c
--- /dev/null
+++ b/experiments/elo/elo/game_stats.py
@@ -0,0 +1,147 @@
+from __future__ import annotations
+
+import itertools
+import json
+import logging
+import sys
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+import networkx as nx
+import polars as pl
+
+
+if TYPE_CHECKING:
+    from typing import Generator, Any
+
+LOGGER = logging.getLogger(__name__)
+
+
+def game_stats(
+    matches_path: Path | str,
+    *,
+    threshold_matches_regulars: int = 25,
+    progress_bar: bool = False,
+) -> dict[str, int]:
+    matches_path = Path(matches_path).resolve()
+    LOGGER.info("Reading matches from %s", matches_path)
+
+    data = pl.read_ipc(matches_path, memory_map=False)
+    num_all_matches = len(data)
+
+    graph = nx.Graph()
+
+    player_ids_col = data["player_ids"]
+    if progress_bar:
+        from tqdm import tqdm
+
+        player_ids_col = tqdm(player_ids_col)
+
+    for player_ids in player_ids_col:
+        graph.add_edges_from(itertools.combinations(player_ids, 2))
+
+    num_all_players = graph.number_of_nodes()
+    largest_community = max(nx.connected_components(graph), key=len)
+    num_players = len(largest_community)
+
+    data = data.filter(
+        pl.col("player_ids").list.eval(pl.element().is_in(largest_community)).list.any()
+    )
+    num_matches = len(data)
+
+    result = (
+        data.lazy()
+        .select(pl.col("player_ids").explode().value_counts())
+        .unnest("player_ids")
+        .select(
+            num_matches_max=pl.col("count").max(),
+            num_regular_players=(pl.col("count") >= threshold_matches_regulars).sum(),
+        )
+        .collect()
+        .to_dicts()[0]
+    )
+
+    result["num_all_matches"] = num_all_matches
+    result["num_matches"] = num_matches
+    result["num_all_players"] = num_all_players
+    result["num_players"] = num_players
+
+    return result
+
+
+def _games_stats(
+    *,
+    games_path: Path | str,
+    matches_dir: Path | str,
+    threshold_matches_regulars: int = 25,
+    progress_bar: bool = False,
+) -> Generator[dict[str, Any]]:
+    games_path = Path(games_path).resolve()
+    matches_dir = Path(matches_dir).resolve()
+
+    LOGGER.info("Reading games from %s", games_path)
+    LOGGER.info("Reading matches from %s", matches_dir)
+
+    games = pl.read_ndjson(games_path)
+    games_dicts = games.to_dicts()
+
+    for game in games_dicts:
+        LOGGER.info("Processing game %s", game["display_name_en"])
+        game_id = game["id"]
+        matches_path = matches_dir / f"{game_id}.arrow"
+        if not matches_path.exists():
+            LOGGER.warning("Matches file %s does not exist", matches_path)
+            yield game
+            continue
+
+        try:
+            stats = game_stats(
+                matches_path,
+                threshold_matches_regulars=threshold_matches_regulars,
+                progress_bar=progress_bar,
+            )
+        except Exception:
+            LOGGER.exception("Error processing game %s", game["display_name_en"])
+            yield game
+        else:
+            yield game | stats
+
+    LOGGER.info("Done.")
+
+
+def games_stats(
+    *,
+    games_path: Path | str,
+    matches_dir: Path | str,
+    output_path: Path | str,
+    threshold_matches_regulars: int = 25,
+    progress_bar: bool = False,
+):
+    output_path = Path(output_path).resolve()
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    LOGGER.info("Writing games stats to %s", output_path)
+
+    with output_path.open("w") as file:
+        for game in _games_stats(
+            games_path=games_path,
+            matches_dir=matches_dir,
+            threshold_matches_regulars=threshold_matches_regulars,
+            progress_bar=progress_bar,
+        ):
+            file.write(json.dumps(game) + "\n")
+
+
+def _main():
+    logging.basicConfig(level=logging.INFO, stream=sys.stdout)
+    games_stats(
+        games_path="games.jl",
+        matches_dir="results/arrow/matches",
+        output_path="games_stats.jl",
+        threshold_matches_regulars=25,
+        progress_bar=True,
+    )
+
+
+if __name__ == "__main__":
+    _main()
diff --git a/experiments/elo/elo/optimal_k.py b/experiments/elo/elo/optimal_k.py
index c4b1cb34290666b05906c4fdee98418164b20839..af5590a44d3d7fe2fc43c43ab38c3163853864ea 100644
--- a/experiments/elo/elo/optimal_k.py
+++ b/experiments/elo/elo/optimal_k.py
@@ -1,65 +1,56 @@
 from __future__ import annotations
 
-from collections.abc import Iterable
-from typing import TypeVar
+import functools
+from collections.abc import Mapping
+from typing import TYPE_CHECKING
 
 import numpy as np
 from scipy.optimize import minimize_scalar
 
-from elo.elo_ratings import calculate_elo_ratings
+from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo
 
+if TYPE_CHECKING:
+    from collections.abc import Iterable
 
-ID_TYPE = TypeVar("ID_TYPE")
 
-
-def calculate_loss(
+def calculate_loss[ID_TYPE](
     *,
-    player_1_ids: Iterable[ID_TYPE],
-    player_2_ids: Iterable[ID_TYPE],
-    player_1_outcomes: Iterable[float],
-    elo_k: float,
-    elo_scale: float,
+    elo: EloRatingSystem[ID_TYPE],
+    matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
     progress_bar: bool = False,
 ) -> np.float64:
-    if not isinstance(player_1_outcomes, np.ndarray):
-        player_1_outcomes = np.array(list(player_1_outcomes))
-
-    _, player_1_win_probs_list, _ = calculate_elo_ratings(
-        player_1_ids=player_1_ids,
-        player_2_ids=player_2_ids,
-        player_1_outcomes=player_1_outcomes,
-        elo_k=elo_k,
-        elo_scale=elo_scale,
+    errors = elo.update_elo_ratings_batch(
+        matches,
         full_results=True,
         progress_bar=progress_bar,
     )
+    assert errors is not None
+    return np.nanmean(errors**2)
 
-    player_1_win_probs = np.array(player_1_win_probs_list)
-    errors = player_1_outcomes - player_1_win_probs
-
-    return np.mean(errors**2)
 
-
-def approximate_optimal_k(
+def approximate_optimal_k[ID_TYPE](
     *,
-    player_1_ids: Iterable[ID_TYPE],
-    player_2_ids: Iterable[ID_TYPE],
-    player_1_outcomes: Iterable[float],
+    matches: Iterable[Mapping[ID_TYPE, float] | Iterable[ID_TYPE]],
+    two_player_only: bool = False,
     min_elo_k: float = 0,
-    max_elo_k: float = 160,
+    max_elo_k: float = 200,
     elo_scale: float = 400,
 ) -> np.float64:
-    player_1_ids = list(player_1_ids)
-    player_2_ids = list(player_2_ids)
-    player_1_outcomes = np.array(list(player_1_outcomes))
-
+    match_list: list[Mapping[ID_TYPE, float]] = [
+        dict(zip((m := tuple(match)), range(len(m) - 1, -1, -1)))
+        if not isinstance(match, Mapping)
+        else match
+        for match in matches
+    ]
+
+    @functools.cache
     def loss(elo_k: float) -> np.float64:
         return calculate_loss(
-            player_1_ids=player_1_ids,
-            player_2_ids=player_2_ids,
-            player_1_outcomes=player_1_outcomes,
-            elo_k=elo_k,
-            elo_scale=elo_scale,
+            elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale)
+            if two_player_only
+            else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale),
+            matches=match_list,
+            progress_bar=False,
         )
 
     result = minimize_scalar(
diff --git a/experiments/elo/elo/p_deterministic.py b/experiments/elo/elo/p_deterministic.py
index a62942c9b7bfd19e11945e2bb08f5df8c0fc933f..3da89c0e434bd67bff5e97bfac737c3bb60f079b 100644
--- a/experiments/elo/elo/p_deterministic.py
+++ b/experiments/elo/elo/p_deterministic.py
@@ -1,83 +1,233 @@
 from __future__ import annotations
-from collections import defaultdict
+
+import dataclasses
+import itertools
+import time
+from collections.abc import Iterable
+from concurrent.futures import ProcessPoolExecutor, as_completed
+from typing import TYPE_CHECKING
+
 import numpy as np
 
-from elo.elo_ratings import calculate_elo_ratings
+from elo.elo_ratings import EloRatingSystem, TwoPlayerElo, RankOrderedLogitElo
+from elo.optimal_k import approximate_optimal_k
+
+if TYPE_CHECKING:
+    from collections.abc import Generator
+    from concurrent.futures import Future
 
 
-def simulate_p_deterministic_games(
+def simulate_p_deterministic_matches(
+    *,
     rng: np.random.Generator,
     num_players: int,
-    num_games: int,
+    num_matches: int,
+    players_per_match: int = 2,
     p_deterministic: float,
-) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
+) -> np.ndarray:
     assert 0 <= p_deterministic <= 1, "p_deterministic must be between 0 and 1"
     assert num_players > 1, "num_players must be greater than 1"
-    assert num_games > 0, "num_games must be greater than 0"
+    assert num_matches > 0, "num_games must be greater than 0"
+    assert 2 <= players_per_match <= num_players, (
+        "players_per_match must be between 2 and num_players"
+    )
 
-    players_a = rng.integers(low=0, high=num_players, size=num_games)
-    # Sample offset for second player, ensuring it's not equal to the first
-    offset = rng.integers(low=1, high=num_players, size=num_games)
-    players_b = (players_a + offset) % num_players
-    assert np.all(players_a != players_b), "Players A and B must be different"
+    players = np.arange(num_players)
+    random_outcomes = np.array(
+        [
+            rng.choice(
+                players,
+                size=players_per_match,
+                replace=False,
+            )
+            for _ in range(num_matches)
+        ]
+    )
 
     if p_deterministic == 0:
-        # If p_deterministic is 0, all games are probabilistic
-        return players_a, players_b, rng.random(size=num_games) < 0.5
+        return random_outcomes
+
+    deterministic_outcomes = np.sort(random_outcomes, axis=1)
 
     if p_deterministic == 1:
-        # If p_deterministic is 1, all games are deterministic
-        return players_a, players_b, players_a < players_b
+        return deterministic_outcomes
 
-    # Otherwise, mix deterministic and probabilistic outcomes
-    prob_player_a_wins = rng.random(size=num_games) < 0.5
-    det_player_a_wins = players_a < players_b
+    mask = rng.random(size=num_matches) < p_deterministic
+    return np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes)
 
-    deterministic = rng.random(size=num_games) < p_deterministic
-    player_a_wins = np.where(deterministic, det_player_a_wins, prob_player_a_wins)
 
-    return players_a, players_b, player_a_wins
+def update_elo_ratings_p_deterministic(
+    *,
+    rng: np.random.Generator,
+    elo: EloRatingSystem | None = None,
+    num_players: int,
+    num_matches: int,
+    players_per_match: int = 2,
+    p_deterministic: float,
+    progress_bar: bool = False,
+) -> EloRatingSystem:
+    if elo is None:
+        elo = TwoPlayerElo() if players_per_match == 2 else RankOrderedLogitElo()
+
+    matches = simulate_p_deterministic_matches(
+        rng=rng,
+        num_players=num_players,
+        num_matches=num_matches,
+        players_per_match=players_per_match,
+        p_deterministic=p_deterministic,
+    )
 
+    payoffs = np.arange(players_per_match - 1, -1, -1, dtype=float)
 
-def update_elo_ratings_p_deterministic(
+    elo.update_elo_ratings_batch(
+        matches=(dict(zip(match, payoffs)) for match in matches),
+        full_results=False,
+        progress_bar=progress_bar,
+        tqdm_kwargs={"total": num_matches},
+    )
+
+    return elo
+
+
+@dataclasses.dataclass(frozen=True, kw_only=True)
+class ExperimentResult:
+    num_players: int
+    num_matches: int
+    players_per_match: int
+
+    p_deterministic: float
+    elo_k: float
+    elo_scale: float
+
+    mean: float
+    std_dev: float
+    p00: float
+    p01: float
+    p25: float
+    p50: float
+    p75: float
+    p99: float
+    p100: float
+
+
+def p_deterministic_experiment(
+    *,
     rng: np.random.Generator,
-    elo_ratings: np.ndarray,
-    num_games: int,
+    num_players: int,
+    num_matches: int,
+    players_per_match: int = 2,
     p_deterministic: float,
-    elo_initial: float = 0,
-    elo_k: float = 32,
     elo_scale: float = 400,
-    inplace: bool = True,
     progress_bar: bool = False,
-) -> np.ndarray:
-    assert elo_ratings.ndim == 1
-    num_players = len(elo_ratings)
+) -> ExperimentResult:
+    matches = simulate_p_deterministic_matches(
+        rng=rng,
+        num_players=num_players,
+        num_matches=num_matches,
+        players_per_match=players_per_match,
+        p_deterministic=p_deterministic,
+    )
 
-    if not inplace:
-        elo_ratings = np.copy(elo_ratings)
+    elo_k = approximate_optimal_k(
+        matches=matches,
+        two_player_only=players_per_match == 2,
+        min_elo_k=0,
+        max_elo_k=elo_scale / 2,
+        elo_scale=elo_scale,
+    )
 
-    players_a, players_b, player_a_wins = simulate_p_deterministic_games(
+    elo = update_elo_ratings_p_deterministic(
         rng=rng,
+        elo=TwoPlayerElo(elo_k=elo_k, elo_scale=elo_scale)
+        if players_per_match == 2
+        else RankOrderedLogitElo(elo_k=elo_k, elo_scale=elo_scale),
         num_players=num_players,
-        num_games=num_games,
+        num_matches=num_matches,
+        players_per_match=players_per_match,
         p_deterministic=p_deterministic,
+        progress_bar=progress_bar,
     )
+    elo_ratings = np.array(list(elo.elo_ratings.values()))
 
-    results = calculate_elo_ratings(
-        player_1_ids=players_a,
-        player_2_ids=players_b,
-        player_1_outcomes=player_a_wins,
-        init_elo_ratings=dict(enumerate(elo_ratings)),
-        elo_initial=elo_initial,
+    return ExperimentResult(
+        num_players=num_players,
+        num_matches=num_matches,
+        players_per_match=players_per_match,
+        p_deterministic=p_deterministic,
         elo_k=elo_k,
         elo_scale=elo_scale,
-        full_results=False,
-        progress_bar=progress_bar,
+        mean=elo_ratings.mean(),
+        std_dev=elo_ratings.std(),
+        p00=elo_ratings.min(),
+        p01=np.percentile(elo_ratings, 1),
+        p25=np.percentile(elo_ratings, 25),
+        p50=np.percentile(elo_ratings, 50),
+        p75=np.percentile(elo_ratings, 75),
+        p99=np.percentile(elo_ratings, 99),
+        p100=elo_ratings.max(),
     )
 
-    assert isinstance(results, defaultdict)
-
-    for player_id, elo_rating in results.items():
-        elo_ratings[player_id] = elo_rating
 
-    return elo_ratings
+def _make_iterable[T](value: T | Iterable[T]) -> Iterable[T]:
+    return value if isinstance(value, Iterable) else (value,)
+
+
+def _p_deterministic_experiment_futures(
+    *,
+    executor: ProcessPoolExecutor,
+    seed: int,
+    num_playerss: Iterable[int] | int,
+    num_matchess: Iterable[int] | int,
+    players_per_matchs: Iterable[int] | int,
+    p_deterministics: Iterable[float] | float,
+    elo_scales: Iterable[float] | float,
+) -> Generator[Future[ExperimentResult]]:
+    for i, (
+        num_players,
+        num_matches,
+        players_per_match,
+        p_deterministic,
+        elo_scale,
+    ) in enumerate(
+        itertools.product(
+            _make_iterable(num_playerss),
+            _make_iterable(num_matchess),
+            _make_iterable(players_per_matchs),
+            _make_iterable(p_deterministics),
+            _make_iterable(elo_scales),
+        )
+    ):
+        rng = np.random.default_rng(seed + i)
+        yield executor.submit(
+            p_deterministic_experiment,
+            rng=rng,
+            num_players=num_players,
+            num_matches=num_matches,
+            players_per_match=players_per_match,
+            p_deterministic=p_deterministic,
+            elo_scale=elo_scale,
+            progress_bar=False,
+        )
+
+
+def p_deterministic_experiments(
+    *,
+    seed: int | None = None,
+    num_players: Iterable[int] | int,
+    num_matches: Iterable[int] | int,
+    players_per_match: Iterable[int] | int = 2,
+    p_deterministic: Iterable[float] | float,
+    elo_scale: Iterable[float] | float = 400,
+) -> Generator[ExperimentResult]:
+    with ProcessPoolExecutor() as executor:
+        futures = _p_deterministic_experiment_futures(
+            executor=executor,
+            seed=seed or time.time_ns(),
+            num_playerss=num_players,
+            num_matchess=num_matches,
+            players_per_matchs=players_per_match,
+            p_deterministics=p_deterministic,
+            elo_scales=elo_scale,
+        )
+        for future in as_completed(futures):
+            yield future.result()
diff --git a/experiments/elo/p-deterministic distribution.py b/experiments/elo/p-deterministic distribution.py
index b0ee9d650df5a4c21e471880397e6c077a05408a..c6992b048b97a2ece7fcb93d8085247d4c40ef33 100644
--- a/experiments/elo/p-deterministic distribution.py	
+++ b/experiments/elo/p-deterministic distribution.py	
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.17.0
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -14,96 +14,37 @@
 # ---
 
 # %%
+import dataclasses
 import jupyter_black
 import numpy as np
 import polars as pl
 import seaborn as sns
-from elo.optimal_k import approximate_optimal_k
-from elo.p_deterministic import simulate_p_deterministic_games, update_elo_ratings_p_deterministic
+from elo.p_deterministic import p_deterministic_experiments
+from tqdm import tqdm
 
 jupyter_black.load()
 
+seed = 13
 
 # %%
-def generate_distributions(
+experiments = p_deterministic_experiments(
+    seed=seed,
     num_players=1000,
-    num_games=1_000_000,
-    p_deterministic_min=0.01,
-    p_deterministic_max=0.99,
-    num_steps=99,
+    num_matches=1_000_000,
+    players_per_match=2,
+    p_deterministic=np.linspace(start=0.01, stop=0.99, num=99),
     elo_scale=400,
-    seed=None,
-    progress_bar: bool = False,
-):
-    rng = np.random.default_rng(seed)
-
-    p_deterministics = np.linspace(
-        start=p_deterministic_min,
-        stop=p_deterministic_max,
-        num=num_steps,
-    )
-
-    if progress_bar:
-        from tqdm import tqdm
-
-        p_deterministics = tqdm(p_deterministics)
-
-    for p_deterministic in p_deterministics:
-        player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games(
-            rng=rng,
-            num_players=num_players,
-            num_games=num_games,
-            p_deterministic=p_deterministic,
-        )
-
-        elo_k = approximate_optimal_k(
-            player_1_ids=player_1_ids,
-            player_2_ids=player_2_ids,
-            player_1_outcomes=player_1_outcomes,
-            min_elo_k=0,
-            max_elo_k=elo_scale / 2,
-            elo_scale=elo_scale,
-        )
-
-        elo_ratings = update_elo_ratings_p_deterministic(
-            rng=rng,
-            elo_ratings=np.zeros(num_players),
-            num_games=num_games,
-            p_deterministic=p_deterministic,
-            elo_k=elo_k,
-            elo_scale=elo_scale,
-            inplace=True,
-            progress_bar=False,
-        )
-
-        yield p_deterministic, elo_k, elo_ratings
-
-
-# %%
-p_deterministics, elo_ks, elo_ratingss = zip(
-    *generate_distributions(seed=13, progress_bar=True)
 )
-
-# %%
-elo_ratingss_array = np.array(elo_ratingss)
-elo_ratingss_array.shape, elo_ratingss_array.dtype
-
-# %%
-elo_ratings_stds = np.std(elo_ratingss_array, axis=1)
-elo_ratings_stds.shape, elo_ratings_stds.dtype
-
-# %%
-results = pl.DataFrame(
-    data=elo_ratingss_array,
-    schema=[f"elo_{i:03d}" for i in range(elo_ratingss_array.shape[1])],
-).select(
-    pl.Series("p_deterministic", p_deterministics),
-    pl.Series("elo_k", elo_ks),
-    pl.Series("elo_ratings_std", elo_ratings_stds),
-    pl.all(),
+results = (
+    pl.LazyFrame(dataclasses.asdict(experiment) for experiment in tqdm(experiments))
+    .sort("num_players", "num_matches", "players_per_match", "p_deterministic")
+    .collect()
 )
 results.shape
 
+# %%
+results.sample(10, seed=seed)
+
 # %%
 results.write_csv("results/p_deterministic.csv", float_precision=5)
 
@@ -114,7 +55,7 @@ sns.scatterplot(data=results, x="p_deterministic", y="elo_k")
 sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_k"]))
 
 # %%
-sns.scatterplot(data=results, x="p_deterministic", y="elo_ratings_std")
+sns.scatterplot(data=results, x="p_deterministic", y="std_dev")
 
 # %%
-sns.scatterplot(x=results["p_deterministic"], y=np.log(results["elo_ratings_std"]))
+sns.scatterplot(x=results["p_deterministic"], y=np.log(results["std_dev"]))
diff --git a/experiments/elo/p-deterministic games.py b/experiments/elo/p-deterministic games.py
index 5d500d9a36c569cfda957dd987254a7adc280b92..f4edde1d62a70ab544afd84657b49604d58ad90b 100644
--- a/experiments/elo/p-deterministic games.py	
+++ b/experiments/elo/p-deterministic games.py	
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.17.0
+#       jupytext_version: 1.17.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -19,8 +19,11 @@ import numpy as np
 import polars as pl
 import seaborn as sns
 from elo.optimal_k import approximate_optimal_k
-from elo.p_deterministic import simulate_p_deterministic_games, update_elo_ratings_p_deterministic
-from elo.elo_ratings import elo_probability
+from elo.p_deterministic import (
+    simulate_p_deterministic_matches,
+    update_elo_ratings_p_deterministic,
+)
+from elo.elo_ratings import RankOrderedLogitElo, TwoPlayerElo, elo_probability
 
 jupyter_black.load()
 
@@ -28,21 +31,25 @@ jupyter_black.load()
 seed = 13
 rng = np.random.default_rng(seed)
 num_players = 1000
-num_games = 1_000_000
+num_matches = 1_000_000
+players_per_match = 2
 p_deterministic = 0.5
 elo_scale = 400
 
 # %%
-player_1_ids, player_2_ids, player_1_outcomes = simulate_p_deterministic_games(
+matches = simulate_p_deterministic_matches(
     rng=rng,
     num_players=num_players,
-    num_games=num_games,
+    num_matches=num_matches,
+    players_per_match=players_per_match,
     p_deterministic=p_deterministic,
 )
+matches.shape
+
+# %%
 elo_k = approximate_optimal_k(
-    player_1_ids=player_1_ids,
-    player_2_ids=player_2_ids,
-    player_1_outcomes=player_1_outcomes,
+    matches=matches,
+    two_player_only=players_per_match == 2,
     min_elo_k=0,
     max_elo_k=elo_scale / 2,
     elo_scale=elo_scale,
@@ -50,20 +57,22 @@ elo_k = approximate_optimal_k(
 elo_k
 
 # %%
-elo_ratings = np.zeros(num_players)
-elo_ratings.shape, elo_ratings.dtype
-
-# %%
-elo_scores = update_elo_ratings_p_deterministic(
+elo_kwargs = {"elo_k": elo_k, "elo_scale": elo_scale}
+elo = (
+    TwoPlayerElo(**elo_kwargs)
+    if players_per_match == 2
+    else RankOrderedLogitElo(**elo_kwargs)
+)
+elo = update_elo_ratings_p_deterministic(
     rng=rng,
-    elo_ratings=elo_ratings,
-    num_games=num_games,
+    elo=elo,
+    num_players=num_players,
+    num_matches=num_matches,
+    players_per_match=players_per_match,
     p_deterministic=p_deterministic,
-    elo_k=elo_k,
-    elo_scale=elo_scale,
-    inplace=True,
     progress_bar=True,
 )
+elo_ratings = np.array(list(elo.elo_ratings.values()))
 elo_ratings.shape, elo_ratings.dtype
 
 # %% jp-MarkdownHeadingCollapsed=true
diff --git a/experiments/elo/p-deterministic multi.py b/experiments/elo/p-deterministic multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ff9fce26dd1dccc516889df48cf4432c4c3e2cc
--- /dev/null
+++ b/experiments/elo/p-deterministic multi.py	
@@ -0,0 +1,63 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.1
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import jupyter_black
+import numpy as np
+from elo.elo_ratings_multi import RankOrderedLogitElo
+from elo.optimal_k import approximate_optimal_k_multi
+
+jupyter_black.load()
+rng = np.random.default_rng()
+
+# %%
+p_deterministic = 0.5
+elo_scale = 400
+num_players = 1000
+num_matches = 100_000
+player_per_match = 4
+players = np.arange(num_players)
+
+# %%
+random_outcomes = np.array(
+    [
+        rng.choice(
+            players,
+            size=player_per_match,
+            replace=False,
+        )
+        for _ in range(num_matches)
+    ]
+)
+deterministic_outcomes = np.sort(random_outcomes, axis=1)
+mask = rng.random(size=num_matches) < p_deterministic
+outcomes = np.where(mask[:, np.newaxis], deterministic_outcomes, random_outcomes)
+random_outcomes.shape, deterministic_outcomes.shape, outcomes.shape
+
+# %%
+optimal_k = approximate_optimal_k_multi(
+    matches=outcomes,
+    elo_scale=elo_scale,
+    min_elo_k=0,
+    max_elo_k=elo_scale / 2,
+)
+optimal_k
+
+# %%
+elo = RankOrderedLogitElo(elo_k=optimal_k, elo_scale=elo_scale)
+elo.update_elo_ratings_batch(matches=outcomes, progress_bar=True)
+
+# %%
+np.std(tuple(elo.elo_ratings.values()))
diff --git a/experiments/elo/poetry.lock b/experiments/elo/poetry.lock
index 0538c890d55df56e6323203a942e8451a27da8da..ffb7a3394f101194081a9cff9af8520d4790ad84 100644
--- a/experiments/elo/poetry.lock
+++ b/experiments/elo/poetry.lock
@@ -2,13 +2,13 @@
 
 [[package]]
 name = "anyio"
-version = "4.9.0"
-description = "High level compatibility layer for multiple asynchronous event loop implementations"
+version = "4.10.0"
+description = "High-level concurrency and networking framework on top of asyncio or Trio"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"},
-    {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"},
+    {file = "anyio-4.10.0-py3-none-any.whl", hash = "sha256:60e474ac86736bbfd6f210f7a61218939c318f43f9972497381f1c5e930ed3d1"},
+    {file = "anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6"},
 ]
 
 [package.dependencies]
@@ -17,8 +17,6 @@ sniffio = ">=1.1"
 typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""}
 
 [package.extras]
-doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"]
-test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1)", "uvloop (>=0.21)"]
 trio = ["trio (>=0.26.1)"]
 
 [[package]]
@@ -34,60 +32,55 @@ files = [
 
 [[package]]
 name = "argon2-cffi"
-version = "23.1.0"
+version = "25.1.0"
 description = "Argon2 for Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 argon2-cffi-bindings = "*"
 
-[package.extras]
-dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
-docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
-tests = ["hypothesis", "pytest"]
-typing = ["mypy"]
-
 [[package]]
 name = "argon2-cffi-bindings"
-version = "21.2.0"
+version = "25.1.0"
 description = "Low-level CFFI bindings for Argon2"
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.9"
 files = [
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-
-[package.dependencies]
-cffi = ">=1.0.1"
-
-[package.extras]
-dev = ["cogapp", "pre-commit", "pytest", "wheel"]
-tests = ["pytest"]
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+]
+
+[package.dependencies]
+cffi = {version = ">=1.0.1", markers = "python_version < \"3.14\""}
 
 [[package]]
 name = "arrow"
@@ -181,6 +174,22 @@ files = [
 [package.extras]
 dev = ["backports.zoneinfo", "freezegun (>=1.0,<2.0)", "jinja2 (>=3.0)", "pytest (>=6.0)", "pytest-cov", "pytz", "setuptools", "tzdata"]
 
+[[package]]
+name = "bayesian-optimization"
+version = "1.4.0"
+description = "Bayesian Optimization package"
+optional = false
+python-versions = "*"
+files = [
+    {file = "bayesian-optimization-1.4.0.tar.gz", hash = "sha256:5e7b5ada7826a8a342932d67f8ae5e638ade588a12b7f7df058fb7a2defe5866"},
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+]
+
+[package.dependencies]
+numpy = ">=1.9.0"
+scikit-learn = ">=0.18.0"
+scipy = ">=1.0.0"
+
 [[package]]
 name = "beautifulsoup4"
 version = "4.13.4"
@@ -284,13 +293,13 @@ tqdm = "*"
 
 [[package]]
 name = "certifi"
-version = "2025.1.31"
+version = "2025.8.3"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.7"
 files = [
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+    {file = "certifi-2025.8.3.tar.gz", hash = "sha256:e564105f78ded564e3ae7c923924435e1daa7463faeab5bb932bc53ffae63407"},
 ]
 
 [[package]]
@@ -374,119 +383,137 @@ pycparser = "*"
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.1"
+version = "3.4.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [[package]]
 name = "click"
-version = "8.1.8"
+version = "8.2.1"
 description = "Composable command line interface toolkit"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.10"
 files = [
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+    {file = "click-8.2.1-py3-none-any.whl", hash = "sha256:61a3265b914e850b85317d0b3109c7f8cd35a670f963866005d6ef1d5175a12b"},
+    {file = "click-8.2.1.tar.gz", hash = "sha256:27c491cc05d968d271d5a1db13e3b5a184636d9d930f148c50b038f0d0646202"},
 ]
 
 [package.dependencies]
 colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
+[[package]]
+name = "cma"
+version = "4.3.0"
+description = "CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python"
+optional = false
+python-versions = "*"
+files = [
+    {file = "cma-4.3.0-py3-none-any.whl", hash = "sha256:65ad8799e6438b8b82c82c11aad070727e5887915ea6f2c17d7ca0eea940c57a"},
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+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+constrained-solution-tracking = ["moarchiving"]
+plotting = ["matplotlib"]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -500,18 +527,15 @@ files = [
 
 [[package]]
 name = "comm"
-version = "0.2.2"
+version = "0.2.3"
 description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc."
 optional = false
 python-versions = ">=3.8"
 files = [
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-    {file = "comm-0.2.2.tar.gz", hash = "sha256:3fd7a84065306e07bea1773df6eb8282de51ba82f77c72f9c85716ab11fe980e"},
+    {file = "comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417"},
+    {file = "comm-0.2.3.tar.gz", hash = "sha256:2dc8048c10962d55d7ad693be1e7045d891b7ce8d999c97963a5e3e99c055971"},
 ]
 
-[package.dependencies]
-traitlets = ">=4"
-
 [package.extras]
 test = ["pytest"]
 
@@ -528,135 +552,152 @@ files = [
 
 [[package]]
 name = "contourpy"
-version = "1.3.2"
+version = "1.3.3"
 description = "Python library for calculating contours of 2D quadrilateral grids"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.11"
 files = [
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 [[package]]
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 ]
 
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 [[package]]
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 version = "2.2.0"
@@ -788,65 +843,57 @@ typing = ["typing-extensions (>=4.12.2)"]
 
 [[package]]
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+python-versions = ">=3.8"
 files = [
-    {file = "h11-0.14.0-py3-none-any.whl", hash = "sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761"},
-    {file = "h11-0.14.0.tar.gz", hash = "sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d"},
+    {file = "h11-0.16.0-py3-none-any.whl", hash = "sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86"},
+    {file = "h11-0.16.0.tar.gz", hash = "sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1"},
 ]
 
 [[package]]
 name = "httpcore"
-version = "1.0.8"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "httpcore-1.0.8-py3-none-any.whl", hash = "sha256:5254cf149bcb5f75e9d1b2b9f729ea4a4b883d1ad7379fc632b727cec23674be"},
-    {file = "httpcore-1.0.8.tar.gz", hash = "sha256:86e94505ed24ea06514883fd44d2bc02d90e77e7979c8eb71b90f41d364a1bad"},
+    {file = "httpcore-1.0.9-py3-none-any.whl", hash = "sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55"},
+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
 ]
 
 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"
 
 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
@@ -973,46 +1030,46 @@ scripts = ["click (>=6.0)"]
 
 [[package]]
 name = "ipykernel"
-version = "6.29.5"
+version = "6.30.1"
 description = "IPython Kernel for Jupyter"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "ipykernel-6.29.5-py3-none-any.whl", hash = "sha256:afdb66ba5aa354b09b91379bac28ae4afebbb30e8b39510c9690afb7a10421b5"},
-    {file = "ipykernel-6.29.5.tar.gz", hash = "sha256:f093a22c4a40f8828f8e330a9c297cb93dcab13bd9678ded6de8e5cf81c56215"},
+    {file = "ipykernel-6.30.1-py3-none-any.whl", hash = "sha256:aa6b9fb93dca949069d8b85b6c79b2518e32ac583ae9c7d37c51d119e18b3fb4"},
+    {file = "ipykernel-6.30.1.tar.gz", hash = "sha256:6abb270161896402e76b91394fcdce5d1be5d45f456671e5080572f8505be39b"},
 ]
 
 [package.dependencies]
-appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+appnope = {version = ">=0.1.2", markers = "platform_system == \"Darwin\""}
 comm = ">=0.1.1"
 debugpy = ">=1.6.5"
 ipython = ">=7.23.1"
-jupyter-client = ">=6.1.12"
+jupyter-client = ">=8.0.0"
 jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
 matplotlib-inline = ">=0.1"
-nest-asyncio = "*"
-packaging = "*"
-psutil = "*"
-pyzmq = ">=24"
-tornado = ">=6.1"
+nest-asyncio = ">=1.4"
+packaging = ">=22"
+psutil = ">=5.7"
+pyzmq = ">=25"
+tornado = ">=6.2"
 traitlets = ">=5.4.0"
 
 [package.extras]
-cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+cov = ["coverage[toml]", "matplotlib", "pytest-cov", "trio"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
 pyqt5 = ["pyqt5"]
 pyside6 = ["pyside6"]
-test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0,<9)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "9.1.0"
+version = "9.4.0"
 description = "IPython: Productive Interactive Computing"
 optional = false
 python-versions = ">=3.11"
 files = [
-    {file = "ipython-9.1.0-py3-none-any.whl", hash = "sha256:2df07257ec2f84a6b346b8d83100bcf8fa501c6e01ab75cd3799b0bb253b3d2a"},
-    {file = "ipython-9.1.0.tar.gz", hash = "sha256:a47e13a5e05e02f3b8e1e7a0f9db372199fe8c3763532fe7a1e0379e4e135f16"},
+    {file = "ipython-9.4.0-py3-none-any.whl", hash = "sha256:25850f025a446d9b359e8d296ba175a36aedd32e83ca9b5060430fe16801f066"},
+    {file = "ipython-9.4.0.tar.gz", hash = "sha256:c033c6d4e7914c3d9768aabe76bbe87ba1dc66a92a05db6bfa1125d81f2ee270"},
 ]
 
 [package.dependencies]
@@ -1065,17 +1122,17 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "itemadapter"
-version = "0.11.0"
+version = "0.12.0"
 description = "Common interface for data container classes"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "itemadapter-0.11.0-py3-none-any.whl", hash = "sha256:07bc1a26a51f124ec155b80ee3d170eda06ffccd7ceba99c08bea68ad4de5fcd"},
-    {file = "itemadapter-0.11.0.tar.gz", hash = "sha256:3b0f27f4c5e2e8ae415d83e3d60d33adb7ba09b98c30638bc606fb1dff2ecdd2"},
+    {file = "itemadapter-0.12.0-py3-none-any.whl", hash = "sha256:3a8302b0a6358d5e180f9c1b415055b32dd42eba0c22e3adf6b2c1ab824d63b0"},
+    {file = "itemadapter-0.12.0.tar.gz", hash = "sha256:a5088243e88efe3098f1720855e707176e7355ad87d1d20e2b03297f5d15d1be"},
 ]
 
 [package.extras]
-attrs = ["attrs (>=18.1.0)"]
+attrs = ["attrs (>=20.1.0)"]
 pydantic = ["pydantic (>=1.8)"]
 scrapy = ["scrapy (>=2.2)"]
 
@@ -1144,13 +1201,13 @@ files = [
 
 [[package]]
 name = "joblib"
-version = "1.4.2"
+version = "1.5.1"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "joblib-1.4.2-py3-none-any.whl", hash = "sha256:06d478d5674cbc267e7496a410ee875abd68e4340feff4490bcb7afb88060ae6"},
-    {file = "joblib-1.4.2.tar.gz", hash = "sha256:2382c5816b2636fbd20a09e0f4e9dad4736765fdfb7dca582943b9c1366b3f0e"},
+    {file = "joblib-1.5.1-py3-none-any.whl", hash = "sha256:4719a31f054c7d766948dcd83e9613686b27114f190f717cec7eaa2084f8a74a"},
+    {file = "joblib-1.5.1.tar.gz", hash = "sha256:f4f86e351f39fe3d0d32a9f2c3d8af1ee4cec285aafcb27003dda5205576b444"},
 ]
 
 [[package]]
@@ -1180,13 +1237,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.23.0"
+version = "4.25.0"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jsonschema-4.23.0-py3-none-any.whl", hash = "sha256:fbadb6f8b144a8f8cf9f0b89ba94501d143e50411a1278633f56a7acf7fd5566"},
-    {file = "jsonschema-4.23.0.tar.gz", hash = "sha256:d71497fef26351a33265337fa77ffeb82423f3ea21283cd9467bb03999266bc4"},
+    {file = "jsonschema-4.25.0-py3-none-any.whl", hash = "sha256:24c2e8da302de79c8b9382fee3e76b355e44d2a4364bb207159ce10b517bd716"},
+    {file = "jsonschema-4.25.0.tar.gz", hash = "sha256:e63acf5c11762c0e6672ffb61482bdf57f0876684d8d249c0fe2d730d48bc55f"},
 ]
 
 [package.dependencies]
@@ -1199,23 +1256,24 @@ jsonschema-specifications = ">=2023.03.6"
 referencing = ">=0.28.4"
 rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3987-syntax = {version = ">=1.1.0", optional = true, markers = "extra == \"format-nongpl\""}
 rpds-py = ">=0.7.1"
 uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
 webcolors = {version = ">=24.6.0", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
 format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
-format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=24.6.0)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "rfc3987-syntax (>=1.1.0)", "uri-template", "webcolors (>=24.6.0)"]
 
 [[package]]
 name = "jsonschema-specifications"
-version = "2024.10.1"
+version = "2025.4.1"
 description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "jsonschema_specifications-2024.10.1-py3-none-any.whl", hash = "sha256:a09a0680616357d9a0ecf05c12ad234479f549239d0f5b55f3deea67475da9bf"},
-    {file = "jsonschema_specifications-2024.10.1.tar.gz", hash = "sha256:0f38b83639958ce1152d02a7f062902c41c8fd20d558b0c34344292d417ae272"},
+    {file = "jsonschema_specifications-2025.4.1-py3-none-any.whl", hash = "sha256:4653bffbd6584f7de83a67e0d620ef16900b390ddc7939d56684d6c81e33f1af"},
+    {file = "jsonschema_specifications-2025.4.1.tar.gz", hash = "sha256:630159c9f4dbea161a6a2205c3011cc4f18ff381b189fff48bb39b9bf26ae608"},
 ]
 
 [package.dependencies]
@@ -1263,13 +1321,13 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt
 
 [[package]]
 name = "jupyter-core"
-version = "5.7.2"
+version = "5.8.1"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
-    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
+    {file = "jupyter_core-5.8.1-py3-none-any.whl", hash = "sha256:c28d268fc90fb53f1338ded2eb410704c5449a358406e8a948b75706e24863d0"},
+    {file = "jupyter_core-5.8.1.tar.gz", hash = "sha256:0a5f9706f70e64786b75acba995988915ebd4601c8a52e534a40b51c95f59941"},
 ]
 
 [package.dependencies]
@@ -1278,8 +1336,8 @@ pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_
 traitlets = ">=5.3"
 
 [package.extras]
-docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
-test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+docs = ["intersphinx-registry", "myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<9)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-events"
@@ -1309,27 +1367,27 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p
 
 [[package]]
 name = "jupyter-lsp"
-version = "2.2.5"
+version = "2.2.6"
 description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "jupyter-lsp-2.2.5.tar.gz", hash = "sha256:793147a05ad446f809fd53ef1cd19a9f5256fd0a2d6b7ce943a982cb4f545001"},
-    {file = "jupyter_lsp-2.2.5-py3-none-any.whl", hash = "sha256:45fbddbd505f3fbfb0b6cb2f1bc5e15e83ab7c79cd6e89416b248cb3c00c11da"},
+    {file = "jupyter_lsp-2.2.6-py3-none-any.whl", hash = "sha256:283783752bf0b459ee7fa88effa72104d87dd343b82d5c06cf113ef755b15b6d"},
+    {file = "jupyter_lsp-2.2.6.tar.gz", hash = "sha256:0566bd9bb04fd9e6774a937ed01522b555ba78be37bebef787c8ab22de4c0361"},
 ]
 
 [package.dependencies]
-jupyter-server = ">=1.1.2"
+jupyter_server = ">=1.1.2"
 
 [[package]]
 name = "jupyter-server"
-version = "2.15.0"
+version = "2.16.0"
 description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "jupyter_server-2.15.0-py3-none-any.whl", hash = "sha256:872d989becf83517012ee669f09604aa4a28097c0bd90b2f424310156c2cdae3"},
-    {file = "jupyter_server-2.15.0.tar.gz", hash = "sha256:9d446b8697b4f7337a1b7cdcac40778babdd93ba614b6d68ab1c0c918f1c4084"},
+    {file = "jupyter_server-2.16.0-py3-none-any.whl", hash = "sha256:3d8db5be3bc64403b1c65b400a1d7f4647a5ce743f3b20dbdefe8ddb7b55af9e"},
+    {file = "jupyter_server-2.16.0.tar.gz", hash = "sha256:65d4b44fdf2dcbbdfe0aa1ace4a842d4aaf746a2b7b168134d5aaed35621b7f6"},
 ]
 
 [package.dependencies]
@@ -1378,13 +1436,13 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (>
 
 [[package]]
 name = "jupyterlab"
-version = "4.4.0"
+version = "4.4.5"
 description = "JupyterLab computational environment"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "jupyterlab-4.4.0-py3-none-any.whl", hash = "sha256:61d33991fbb352cc7caac08bd0c34577fea86d8d5d9772600d9d5a6bcbc882c0"},
-    {file = "jupyterlab-4.4.0.tar.gz", hash = "sha256:f1767d5f0104e40f3b4a63bf6892bbef8e4704dcabf0c78408a3bdc411792f04"},
+    {file = "jupyterlab-4.4.5-py3-none-any.whl", hash = "sha256:e76244cceb2d1fb4a99341f3edc866f2a13a9e14c50368d730d75d8017be0863"},
+    {file = "jupyterlab-4.4.5.tar.gz", hash = "sha256:0bd6c18e6a3c3d91388af6540afa3d0bb0b2e76287a7b88ddf20ab41b336e595"},
 ]
 
 [package.dependencies]
@@ -1403,7 +1461,7 @@ tornado = ">=6.2.0"
 traitlets = "*"
 
 [package.extras]
-dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.9.9)"]
+dev = ["build", "bump2version", "coverage", "hatch", "pre-commit", "pytest-cov", "ruff (==0.11.4)"]
 docs = ["jsx-lexer", "myst-parser", "pydata-sphinx-theme (>=0.13.0)", "pytest", "pytest-check-links", "pytest-jupyter", "sphinx (>=1.8,<8.2.0)", "sphinx-copybutton"]
 docs-screenshots = ["altair (==5.5.0)", "ipython (==8.16.1)", "ipywidgets (==8.1.5)", "jupyterlab-geojson (==3.4.0)", "jupyterlab-language-pack-zh-cn (==4.3.post1)", "matplotlib (==3.10.0)", "nbconvert (>=7.0.0)", "pandas (==2.2.3)", "scipy (==1.15.1)", "vega-datasets (==0.9.0)"]
 test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-console-scripts", "pytest-cov", "pytest-jupyter (>=0.5.3)", "pytest-timeout", "pytest-tornasync", "requests", "requests-cache", "virtualenv"]
@@ -1447,13 +1505,13 @@ test = ["hatch", "ipykernel", "openapi-core (>=0.18.0,<0.19.0)", "openapi-spec-v
 
 [[package]]
 name = "jupytext"
-version = "1.17.0"
+version = "1.17.2"
 description = "Jupyter notebooks as Markdown documents, Julia, Python or R scripts"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "jupytext-1.17.0-py3-none-any.whl", hash = "sha256:d75b7cd198b3640a12f9cdf4d610bb80c9f27a8c3318b00372f90d21466d40e1"},
-    {file = "jupytext-1.17.0.tar.gz", hash = "sha256:c74adf6d205a778f481189e986b6066af79bdeb69099f4138d933cc15c398fb6"},
+    {file = "jupytext-1.17.2-py3-none-any.whl", hash = "sha256:4f85dc43bb6a24b75491c5c434001ad5ef563932f68f15dd3e1c8ce12a4a426b"},
+    {file = "jupytext-1.17.2.tar.gz", hash = "sha256:772d92898ac1f2ded69106f897b34af48ce4a85c985fa043a378ff5a65455f02"},
 ]
 
 [package.dependencies]
@@ -1562,151 +1620,124 @@ files = [
     {file = "kiwisolver-1.4.8.tar.gz", hash = "sha256:23d5f023bdc8c7e54eb65f03ca5d5bb25b601eac4d7f1a042888a1f45237987e"},
 ]
 
+[[package]]
+name = "lark"
+version = "1.2.2"
+description = "a modern parsing library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "lark-1.2.2-py3-none-any.whl", hash = "sha256:c2276486b02f0f1b90be155f2c8ba4a8e194d42775786db622faccd652d8e80c"},
+    {file = "lark-1.2.2.tar.gz", hash = "sha256:ca807d0162cd16cef15a8feecb862d7319e7a09bdb13aef927968e45040fed80"},
+]
+
+[package.extras]
+atomic-cache = ["atomicwrites"]
+interegular = ["interegular (>=0.3.1,<0.4.0)"]
+nearley = ["js2py"]
+regex = ["regex"]
+
 [[package]]
 name = "lxml"
-version = "5.3.2"
+version = "6.0.0"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 optional = false
-python-versions = ">=3.6"
+python-versions = ">=3.8"
 files = [
-    {file = "lxml-5.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:c4b84d6b580a9625dfa47269bf1fd7fbba7ad69e08b16366a46acb005959c395"},
-    {file = "lxml-5.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:b4c08ecb26e4270a62f81f81899dfff91623d349e433b126931c9c4577169666"},
-    {file = "lxml-5.3.2-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ef926e9f11e307b5a7c97b17c5c609a93fb59ffa8337afac8f89e6fe54eb0b37"},
-    {file = "lxml-5.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:017ceeabe739100379fe6ed38b033cd244ce2da4e7f6f07903421f57da3a19a2"},
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 html-clean = ["lxml_html_clean"]
 html5 = ["html5lib"]
 htmlsoup = ["BeautifulSoup4"]
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 [[package]]
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 [package.dependencies]
@@ -2348,112 +2469,137 @@ ptyprocess = ">=0.5"
 
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 fpx = ["olefile"]
 mic = ["olefile"]
 test-arrow = ["pyarrow"]
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 typing = ["typing-extensions"]
 xmp = ["defusedxml"]
 
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 name = "platformdirs"
-version = "4.3.7"
+version = "4.3.8"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
 optional = false
 python-versions = ">=3.9"
 files = [
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 [package.extras]
@@ -2463,18 +2609,18 @@ type = ["mypy (>=1.14.1)"]
 
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+version = "1.32.0"
 description = "Blazingly fast DataFrame library"
 optional = false
 python-versions = ">=3.9"
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 ]
 
 [package.dependencies]
@@ -2490,7 +2636,7 @@ calamine = ["fastexcel (>=0.9)"]
 cloudpickle = ["cloudpickle"]
 connectorx = ["connectorx (>=0.3.2)"]
 database = ["polars[adbc,connectorx,sqlalchemy]"]
-deltalake = ["deltalake (>=0.19.0)"]
+deltalake = ["deltalake (>=1.0.0)"]
 excel = ["polars[calamine,openpyxl,xlsx2csv,xlsxwriter]"]
 fsspec = ["fsspec"]
 gpu = ["cudf-polars-cu12"]
@@ -2511,13 +2657,13 @@ xlsxwriter = ["xlsxwriter"]
 
 [[package]]
 name = "prometheus-client"
-version = "0.21.1"
+version = "0.22.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
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 ]
 
 [package.extras]
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 [[package]]
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+version = "0.5.0"
 description = "Pure-Python robots.txt parser with support for modern conventions"
 optional = false
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 [[package]]
@@ -2598,53 +2744,54 @@ tests = ["pytest"]
 
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+version = "21.0.0"
 description = "Python library for Apache Arrow"
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 optional = false
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+zope-interface = ">=5.1.0"
+
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+optional = false
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+
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 [[package]]
 name = "tabulate"
@@ -3639,13 +3862,13 @@ test = ["pytest", "ruff"]
 
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@@ -3660,33 +3883,34 @@ testing = ["mypy", "pytest", "pytest-gitignore", "pytest-mock", "responses", "ru
 
 [[package]]
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 [[package]]
@@ -3727,13 +3951,13 @@ test = ["argcomplete (>=3.0.3)", "mypy (>=1.7.0)", "pre-commit", "pytest (>=7.0,
 
 [[package]]
 name = "twisted"
-version = "24.11.0"
+version = "25.5.0"
 description = "An asynchronous networking framework written in Python"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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 [package.dependencies]
@@ -3746,40 +3970,41 @@ typing-extensions = ">=4.2.0"
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+mypy = ["appdirs (>=1.4.0)", "bcrypt (>=3.1.3)", "coverage (>=7.5,<8.0)", "cryptography (>=3.3)", "cython-test-exception-raiser (>=1.0.2,<2)", "h2 (>=3.2,<5.0)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "idna (>=2.4)", "mypy (==1.10.1)", "mypy-zope (==1.0.6)", "priority (>=1.1.0,<2.0)", "pydoctor (>=24.11.1,<24.12.0)", "pyflakes (>=2.2,<3.0)", "pyhamcrest (>=2)", "pyopenssl (>=21.0.0)", "pyserial (>=3.0)", "python-subunit (>=1.4,<2.0)", "pywin32 (!=226)", "service-identity (>=18.1.0)", "sphinx (>=6,<7)", "sphinx-rtd-theme (>=1.3,<2.0)", "towncrier (>=23.6,<24.0)", "twistedchecker (>=0.7,<1.0)", "types-pyopenssl", "types-setuptools", "wsproto"]
+osx-platform = ["appdirs (>=1.4.0)", "appdirs (>=1.4.0)", "bcrypt (>=3.1.3)", "bcrypt (>=3.1.3)", "cryptography (>=3.3)", "cryptography (>=3.3)", "cython-test-exception-raiser (>=1.0.2,<2)", "cython-test-exception-raiser (>=1.0.2,<2)", "h2 (>=3.2,<5.0)", "h2 (>=3.2,<5.0)", "httpx[http2] (>=0.27)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "hypothesis (>=6.56)", "idna (>=2.4)", "idna (>=2.4)", "priority (>=1.1.0,<2.0)", "priority (>=1.1.0,<2.0)", "pyhamcrest (>=2)", "pyhamcrest (>=2)", "pyobjc-core", "pyobjc-core", "pyobjc-core (<11)", "pyobjc-core (<11)", "pyobjc-framework-cfnetwork", "pyobjc-framework-cfnetwork", "pyobjc-framework-cfnetwork (<11)", "pyobjc-framework-cfnetwork (<11)", "pyobjc-framework-cocoa", "pyobjc-framework-cocoa", "pyobjc-framework-cocoa (<11)", "pyobjc-framework-cocoa (<11)", "pyopenssl (>=21.0.0)", "pyopenssl (>=21.0.0)", "pyserial (>=3.0)", "pyserial (>=3.0)", "pywin32 (!=226)", "pywin32 (!=226)", "service-identity (>=18.1.0)", "service-identity (>=18.1.0)", "wsproto", "wsproto"]
 serial = ["pyserial (>=3.0)", "pywin32 (!=226)"]
 test = ["cython-test-exception-raiser (>=1.0.2,<2)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "pyhamcrest (>=2)"]
 tls = ["idna (>=2.4)", "pyopenssl (>=21.0.0)", "service-identity (>=18.1.0)"]
-windows-platform = ["appdirs (>=1.4.0)", "appdirs (>=1.4.0)", "bcrypt (>=3.1.3)", "bcrypt (>=3.1.3)", "cryptography (>=3.3)", "cryptography (>=3.3)", "cython-test-exception-raiser (>=1.0.2,<2)", "cython-test-exception-raiser (>=1.0.2,<2)", "h2 (>=3.2,<5.0)", "h2 (>=3.2,<5.0)", "httpx[http2] (>=0.27)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "hypothesis (>=6.56)", "idna (>=2.4)", "idna (>=2.4)", "priority (>=1.1.0,<2.0)", "priority (>=1.1.0,<2.0)", "pyhamcrest (>=2)", "pyhamcrest (>=2)", "pyopenssl (>=21.0.0)", "pyopenssl (>=21.0.0)", "pyserial (>=3.0)", "pyserial (>=3.0)", "pywin32 (!=226)", "pywin32 (!=226)", "pywin32 (!=226)", "pywin32 (!=226)", "service-identity (>=18.1.0)", "service-identity (>=18.1.0)", "twisted-iocpsupport (>=1.0.2)", "twisted-iocpsupport (>=1.0.2)"]
+websocket = ["wsproto"]
+windows-platform = ["appdirs (>=1.4.0)", "appdirs (>=1.4.0)", "bcrypt (>=3.1.3)", "bcrypt (>=3.1.3)", "cryptography (>=3.3)", "cryptography (>=3.3)", "cython-test-exception-raiser (>=1.0.2,<2)", "cython-test-exception-raiser (>=1.0.2,<2)", "h2 (>=3.2,<5.0)", "h2 (>=3.2,<5.0)", "httpx[http2] (>=0.27)", "httpx[http2] (>=0.27)", "hypothesis (>=6.56)", "hypothesis (>=6.56)", "idna (>=2.4)", "idna (>=2.4)", "priority (>=1.1.0,<2.0)", "priority (>=1.1.0,<2.0)", "pyhamcrest (>=2)", "pyhamcrest (>=2)", "pyopenssl (>=21.0.0)", "pyopenssl (>=21.0.0)", "pyserial (>=3.0)", "pyserial (>=3.0)", "pywin32 (!=226)", "pywin32 (!=226)", "pywin32 (!=226)", "pywin32 (!=226)", "service-identity (>=18.1.0)", "service-identity (>=18.1.0)", "twisted-iocpsupport (>=1.0.2)", "twisted-iocpsupport (>=1.0.2)", "wsproto", "wsproto"]
 
 [[package]]
 name = "types-python-dateutil"
-version = "2.9.0.20241206"
+version = "2.9.0.20250708"
 description = "Typing stubs for python-dateutil"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "types_python_dateutil-2.9.0.20241206-py3-none-any.whl", hash = "sha256:e248a4bc70a486d3e3ec84d0dc30eec3a5f979d6e7ee4123ae043eedbb987f53"},
-    {file = "types_python_dateutil-2.9.0.20241206.tar.gz", hash = "sha256:18f493414c26ffba692a72369fea7a154c502646301ebfe3d56a04b3767284cb"},
+    {file = "types_python_dateutil-2.9.0.20250708-py3-none-any.whl", hash = "sha256:4d6d0cc1cc4d24a2dc3816024e502564094497b713f7befda4d5bc7a8e3fd21f"},
+    {file = "types_python_dateutil-2.9.0.20250708.tar.gz", hash = "sha256:ccdbd75dab2d6c9696c350579f34cffe2c281e4c5f27a585b2a2438dd1d5c8ab"},
 ]
 
 [[package]]
 name = "types-requests"
-version = "2.32.0.20250328"
+version = "2.32.4.20250611"
 description = "Typing stubs for requests"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "types_requests-2.32.0.20250328-py3-none-any.whl", hash = "sha256:72ff80f84b15eb3aa7a8e2625fffb6a93f2ad5a0c20215fc1dcfa61117bcb2a2"},
-    {file = "types_requests-2.32.0.20250328.tar.gz", hash = "sha256:c9e67228ea103bd811c96984fac36ed2ae8da87a36a633964a21f199d60baf32"},
+    {file = "types_requests-2.32.4.20250611-py3-none-any.whl", hash = "sha256:ad2fe5d3b0cb3c2c902c8815a70e7fb2302c4b8c1f77bdcd738192cdb3878072"},
+    {file = "types_requests-2.32.4.20250611.tar.gz", hash = "sha256:741c8777ed6425830bf51e54d6abe245f79b4dcb9019f1622b773463946bf826"},
 ]
 
 [package.dependencies]
@@ -3787,13 +4012,13 @@ urllib3 = ">=2"
 
 [[package]]
 name = "types-tqdm"
-version = "4.67.0.20250417"
+version = "4.67.0.20250516"
 description = "Typing stubs for tqdm"
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "types_tqdm-4.67.0.20250417-py3-none-any.whl", hash = "sha256:d43fc9a295be1f94083c744a09099c033c4dea293ff9a07bab9f34bfbffaaf80"},
-    {file = "types_tqdm-4.67.0.20250417.tar.gz", hash = "sha256:bfcc4099d8d48df54e53f3ea64708cbcc1d1c4039ca7619594189da8c03c7be2"},
+    {file = "types_tqdm-4.67.0.20250516-py3-none-any.whl", hash = "sha256:1dd9b2c65273f2342f37e5179bc6982df86b6669b3376efc12aef0a29e35d36d"},
+    {file = "types_tqdm-4.67.0.20250516.tar.gz", hash = "sha256:230ccab8a332d34f193fc007eb132a6ef54b4512452e718bf21ae0a7caeb5a6b"},
 ]
 
 [package.dependencies]
@@ -3801,13 +4026,13 @@ types-requests = "*"
 
 [[package]]
 name = "typing-extensions"
-version = "4.13.2"
-description = "Backported and Experimental Type Hints for Python 3.8+"
+version = "4.14.1"
+description = "Backported and Experimental Type Hints for Python 3.9+"
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
-    {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
+    {file = "typing_extensions-4.14.1-py3-none-any.whl", hash = "sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76"},
+    {file = "typing_extensions-4.14.1.tar.gz", hash = "sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36"},
 ]
 
 [[package]]
@@ -3837,13 +4062,13 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake
 
 [[package]]
 name = "urllib3"
-version = "2.4.0"
+version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
 files = [
-    {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"},
-    {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"},
+    {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
+    {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
 ]
 
 [package.extras]
@@ -3969,4 +4194,4 @@ testing = ["coverage[toml]", "zope.event", "zope.testing"]
 [metadata]
 lock-version = "2.0"
 python-versions = "3.12.*"
-content-hash = "eb5f23ad9daa943cda942af2b29423ab2e4f54de11ec4d54ba4a9241b9af8a33"
+content-hash = "5cc0393b2dbe033b45cce40403e71191318873911010be2c40630a113b2c5c48"
diff --git a/experiments/elo/pyproject.toml b/experiments/elo/pyproject.toml
index 899cd6a0b8ff133183d252f82fe16450683ab06f..31c3e9c3768bf376578df852196f9174c2949872 100644
--- a/experiments/elo/pyproject.toml
+++ b/experiments/elo/pyproject.toml
@@ -8,13 +8,18 @@ authors = ["Markus Shepherd <markus@recommend.games>"]
 python = "3.12.*"
 
 board-game-merger = "*"
+funcy = "*"
+jmespath = "*"
 matplotlib = "*"
+networkx = "*"
+nevergrad = "*"
 numpy = "*"
 polars = {version = "*", extras = ["numpy", "pandas", "pyarrow"]}
 requests = "*"
 scikit-learn = "*"
 scipy = "!= 1.14.1"
 scrapy = "*"
+scrapy-extensions = ">=1.0.1"
 seaborn = "*"
 statsmodels = "*"
 tabulate = "*"