diff --git a/experiments/charts/Pipfile b/experiments/charts/Pipfile
new file mode 100644
index 0000000000000000000000000000000000000000..8a0d298d2d7c6c1f549a890c109f2e06f2101c13
--- /dev/null
+++ b/experiments/charts/Pipfile
@@ -0,0 +1,24 @@
+[[source]]
+name = "pypi"
+url = "https://pypi.org/simple"
+verify_ssl = true
+
+[dev-packages]
+
+[packages]
+bokeh = "*"
+jupyter = "*"
+jupyterlab = "<3.0.0"
+jupytext = "*"
+matplotlib = "*"
+nb-black = "*"
+numpy = "*"
+pandas = "*"
+pytility = "*"
+scikit-learn = "*"
+scipy = "*"
+seaborn = "*"
+snaptime = "*"
+
+[requires]
+python_version = "3.8"
diff --git a/experiments/charts/Pipfile.lock b/experiments/charts/Pipfile.lock
new file mode 100644
index 0000000000000000000000000000000000000000..232ec47404e25af80da903f203e364ab59a8b908
--- /dev/null
+++ b/experiments/charts/Pipfile.lock
@@ -0,0 +1,1115 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "270b92c7accd6e97c44c03cb00f0389db1e2afe6aeda7573a011f5b02b457065"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.8"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "appdirs": {
+            "hashes": [
+                "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41",
+                "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"
+            ],
+            "version": "==1.4.4"
+        },
+        "appnope": {
+            "hashes": [
+                "sha256:93aa393e9d6c54c5cd570ccadd8edad61ea0c4b9ea7a01409020c9aa019eb442",
+                "sha256:dd83cd4b5b460958838f6eb3000c660b1f9caf2a5b1de4264e941512f603258a"
+            ],
+            "markers": "sys_platform == 'darwin' and platform_system == 'Darwin'",
+            "version": "==0.1.2"
+        },
+        "argon2-cffi": {
+            "hashes": [
+                "sha256:05a8ac07c7026542377e38389638a8a1e9b78f1cd8439cd7493b39f08dd75fbf",
+                "sha256:0bf066bc049332489bb2d75f69216416329d9dc65deee127152caeb16e5ce7d5",
+                "sha256:18dee20e25e4be86680b178b35ccfc5d495ebd5792cd00781548d50880fee5c5",
+                "sha256:392c3c2ef91d12da510cfb6f9bae52512a4552573a9e27600bdb800e05905d2b",
+                "sha256:57358570592c46c420300ec94f2ff3b32cbccd10d38bdc12dc6979c4a8484fbc",
+                "sha256:6678bb047373f52bcff02db8afab0d2a77d83bde61cfecea7c5c62e2335cb203",
+                "sha256:6ea92c980586931a816d61e4faf6c192b4abce89aa767ff6581e6ddc985ed003",
+                "sha256:77e909cc756ef81d6abb60524d259d959bab384832f0c651ed7dcb6e5ccdbb78",
+                "sha256:7d455c802727710e9dfa69b74ccaab04568386ca17b0ad36350b622cd34606fe",
+                "sha256:8a84934bd818e14a17943de8099d41160da4a336bcc699bb4c394bbb9b94bd32",
+                "sha256:9bee3212ba4f560af397b6d7146848c32a800652301843df06b9e8f68f0f7361",
+                "sha256:9dfd5197852530294ecb5795c97a823839258dfd5eb9420233c7cfedec2058f2",
+                "sha256:b160416adc0f012fb1f12588a5e6954889510f82f698e23ed4f4fa57f12a0647",
+                "sha256:ba7209b608945b889457f949cc04c8e762bed4fe3fec88ae9a6b7765ae82e496",
+                "sha256:cc0e028b209a5483b6846053d5fd7165f460a1f14774d79e632e75e7ae64b82b",
+                "sha256:d8029b2d3e4b4cea770e9e5a0104dd8fa185c1724a0f01528ae4826a6d25f97d",
+                "sha256:da7f0445b71db6d3a72462e04f36544b0de871289b0bc8a7cc87c0f5ec7079fa",
+                "sha256:e2db6e85c057c16d0bd3b4d2b04f270a7467c147381e8fd73cbbe5bc719832be"
+            ],
+            "version": "==20.1.0"
+        },
+        "async-generator": {
+            "hashes": [
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+                "sha256:6ebb3d106c12920aaae42ccb6f787ef5eefdcdd166ea3d628fa8476abe712144"
+            ],
+            "markers": "python_version >= '3.5'",
+            "version": "==1.10"
+        },
+        "attrs": {
+            "hashes": [
+                "sha256:31b2eced602aa8423c2aea9c76a724617ed67cf9513173fd3a4f03e3a929c7e6",
+                "sha256:832aa3cde19744e49938b91fea06d69ecb9e649c93ba974535d08ad92164f700"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3'",
+            "version": "==20.3.0"
+        },
+        "backcall": {
+            "hashes": [
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+            ],
+            "version": "==0.2.0"
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+        "black": {
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+            "markers": "python_version >= '3.6'",
+            "version": "==20.8b1"
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+        "bleach": {
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+            "version": "==3.2.1"
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+        "bokeh": {
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+            ],
+            "version": "==3.7.4.3"
+        },
+        "urllib3": {
+            "hashes": [
+                "sha256:19188f96923873c92ccb987120ec4acaa12f0461fa9ce5d3d0772bc965a39e08",
+                "sha256:d8ff90d979214d7b4f8ce956e80f4028fc6860e4431f731ea4a8c08f23f99473"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4' and python_version < '4'",
+            "version": "==1.26.2"
+        },
+        "wcwidth": {
+            "hashes": [
+                "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784",
+                "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"
+            ],
+            "version": "==0.2.5"
+        },
+        "webencodings": {
+            "hashes": [
+                "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78",
+                "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"
+            ],
+            "version": "==0.5.1"
+        },
+        "widgetsnbextension": {
+            "hashes": [
+                "sha256:079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7",
+                "sha256:bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"
+            ],
+            "version": "==3.5.1"
+        }
+    },
+    "develop": {}
+}
diff --git a/experiments/charts/charts.py b/experiments/charts/charts.py
new file mode 100644
index 0000000000000000000000000000000000000000..19e080a60ac9199876231bc9bf6ee30b00fcdeb5
--- /dev/null
+++ b/experiments/charts/charts.py
@@ -0,0 +1,203 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.7.1
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import json
+from datetime import datetime, timedelta, timezone
+import numpy as np
+import pandas as pd
+import pytz
+from pytility import parse_date
+from snaptime import snap
+
+SEED = 23
+
+# %matplotlib inline
+# %load_ext nb_black
+# %load_ext lab_black
+
+# %%
+games = pd.read_csv(
+    "../../../board-game-data/scraped/bgg_GameItem.csv", index_col="bgg_id"
+)
+games.shape
+
+
+# %%
+def now():
+    return datetime.utcnow().replace(tzinfo=timezone.utc)
+
+
+# %%
+def process_ratings(
+    lines, keys=("item_id", "bgg_id", "bgg_user_name", "bgg_user_rating", "updated_at")
+):
+    for line in lines:
+        item = json.loads(line)
+        if not item or not item.get("bgg_user_rating"):
+            continue
+        yield {
+            k: parse_date(item.get(k)) if k.endswith("_at") else item.get(k)
+            for k in keys
+        }
+
+
+# %%
+try:
+    df = pd.read_feather("charts.feather").set_index("updated_at")
+except Exception:
+    with open(
+        "../../../board-game-scraper/feeds/bgg/RatingItem/2021-01-04T07-24-27-merged.jl"
+    ) as file:
+        df = pd.DataFrame.from_records(process_ratings(file), index="updated_at")
+    df.reset_index().to_feather("charts.feather")
+    df.to_pickle("charts.pickle")
+df.shape
+
+# %%
+df.sample(10, random_state=SEED)
+
+# %%
+weeks = df.resample("W").item_id.count()
+weeks[-52:]
+
+# %%
+weeks.plot()
+
+# %%
+df.bgg_user_rating.hist(bins=10)
+
+
+# %%
+def exp_decay(
+    dates,
+    anchor=None,
+    halflife=60 * 60 * 24 * 30,  # 30 days
+):
+    anchor = pd.Timestamp(anchor) if anchor is not None else pd.Timestamp.utcnow()
+    ages = (anchor - dates).total_seconds()
+    return np.exp2(-ages / halflife)
+
+
+def calculate_charts(
+    ratings,
+    end_date=None,
+    days=30,
+    percentiles=(0.25, 0.75),
+    decay=False,
+):
+    end_date = end_date or pd.Timestamp.utcnow()
+    pct_lower, pct_upper = percentiles
+    ratings = ratings[ratings.index <= end_date]  # don't care past end date
+
+    if decay:
+        halflife = 60 * 60 * 24 * days  # convert to seconds
+        weights = exp_decay(dates=ratings.index, anchor=end_date, halflife=halflife)
+
+        tmp = pd.DataFrame(
+            data={
+                "bgg_id": ratings["bgg_id"],
+                "positive": np.where(
+                    ratings["bgg_user_rating"]
+                    >= ratings["bgg_user_rating"].quantile(pct_upper),
+                    weights,
+                    0,
+                ),
+                "negative": np.where(
+                    ratings["bgg_user_rating"]
+                    <= ratings["bgg_user_rating"].quantile(pct_lower),
+                    weights,
+                    0,
+                ),
+            }
+        )
+
+        grouped = tmp.groupby("bgg_id").sum()
+        raw_scores = grouped["positive"] - grouped["negative"]
+        del grouped, tmp, weights
+
+    else:
+        window = timedelta(days=days)
+        start_date = end_date - window
+        recent_ratings = ratings[ratings.index >= start_date]
+
+        tmp = pd.DataFrame()
+        tmp["positive"] = (
+            recent_ratings[
+                recent_ratings["bgg_user_rating"]
+                >= recent_ratings["bgg_user_rating"].quantile(pct_upper)
+            ]
+            .groupby("bgg_id")["item_id"]
+            .count()
+        )
+        tmp["negative"] = (
+            recent_ratings[
+                recent_ratings["bgg_user_rating"]
+                <= recent_ratings["bgg_user_rating"].quantile(pct_lower)
+            ]
+            .groupby("bgg_id")["item_id"]
+            .count()
+        )
+        tmp.fillna(0, inplace=True)
+        raw_scores = tmp["positive"] - tmp["negative"]
+        del recent_ratings, tmp
+
+    games = ratings.groupby("bgg_id")["bgg_user_rating"].count()
+    scores = raw_scores * games.rank(pct=True, ascending=False)
+    scores.dropna(inplace=True)
+
+    ranking = pd.DataFrame(
+        data={
+            "rank": scores.rank(ascending=False, method="min").astype(int),
+            "score": scores,
+        },
+        index=scores.index,
+    )
+    return ranking.sort_values("rank")
+
+
+# %%
+charts = calculate_charts(df)
+charts["name"] = games["name"]
+charts[:10]
+
+# %% [markdown]
+# # Exponential decay
+#
+# **Idea**: Use exponential decay. For each rating, calculate its weight through its age (e.g., `1` if it's been cast right now, `0.5` if a month ago, `0.25` if two months ago, etc). Then sum weights instead of just counting within window.
+
+# %%
+for year in range(df.index.min().year, df.index.max().year + 1):
+    print(f"Hottest games of the year *{year}*")
+    end_date = datetime(year + 1, 1, 1, tzinfo=pytz.UTC)
+    charts = calculate_charts(ratings=df, end_date=end_date, days=365.25)
+    charts["name"] = games["name"]
+    print(charts[:10])
+    print()
+
+# %%
+chart_date = snap(
+    pd.Timestamp.utcnow(), "@w5@w1"
+)  # release on Friday the charts of the previous week
+charts = calculate_charts(
+    ratings=df,
+    end_date=chart_date,
+    days=7,
+    percentiles=(0.25, 0.75),
+    decay=False,
+)
+charts["name"] = games["name"]
+print(f"Charts for *{chart_date.strftime('%Y-%m-%d')}*")
+charts[:10]