KEDevO/CounterQuant-CS2-GoldLite

CounterQuant CS2 GoldLite

ML-ready feature tables for professional CS2 match outcome modelling — Elo ratings, team form, head-to-head records, and event context. No black boxes, no proprietary signals.

⭐ If you use this dataset in research, a product, or any publication, please cite the author (see Citation below).

GoldLite is the open, reproducible subset of CounterQuant's internal feature pipeline. It provides pre-computed, match-level features derived entirely from public match results and event metadata — no demo parsing required, no market data, no model outputs. Train your own win-prediction model on a decade of professional CS2.

Curated and maintained by Eimantas Kulbe (KEDevO) as part of the CounterQuant esports intelligence platform.

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The CounterQuant Data Stack

GoldLite is the top public-facing tier of a four-layer architecture:

Tier	Dataset	Contents	Status
Raw demos	CounterQuant CS2 Demos	Raw .dem files	Live
Bronze	CounterQuant CS2 Bronze	Tick-level events: kills, damages, flashes, utility	Live, growing
Silver	CounterQuant CS2 Silver	Match results, map scores, player stats, rosters	Staging for export
Gold Lite ← this dataset	—	Elo, form, H2H, event context — ML features	Staging for export

The full internal Gold layer (demo-derived features, market signals, 509-feature ML vectors) is proprietary and powers the CounterQuant prediction API. GoldLite is the public, reproducible subset of that pipeline.

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Dataset at a Glance

Metric	Value
Team Elo records in DB	45,502 (per team, per map pool)
Matches with features	Growing — 2024 first public batch
Year range (planned)	2012 – 2024 (first release)
Feature count (GoldLite)	~50 pre-match features
Full pipeline feature count	509 (internal, not released)
Format	Parquet (Snappy compression)
License	CC BY 4.0

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Current State & Release Schedule

GoldLite is not yet published to HuggingFace as Parquet files. Elo ratings are computed and live in CounterQuant's database. The pre-match feature export is in progress, with 2024 as the first public batch.

Release	Coverage	Target Date	Notes
v1 — 2024	Jan 2024 – Dec 2024	Q3 2026	First public release, alongside Silver
Backfill — 2012–2023	2012 – 2023	Q4 2026	Historical Elo + features
v2 — 2025	2025	2027	2-year delay policy
2026+	2026+	2028+	2-year delay minimum

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What's in GoldLite

GoldLite contains pre-match features computed from historical match results — the information that would be available to a predictor at kick-off. All features are constructed from public sources only.

Feature categories

Category	Features	Source
Elo ratings	Global team Elo, per-map Elo, Elo uncertainty, Elo differential	Computed from Silver match results
Recent form	Win rate last 5/10/20 matches, map differential last 10, days since last match	Silver matches
Head-to-head	All-time H2H record, last 12 months H2H, H2H on specific map	Silver matches
Map pool	Team win rate per map (last 30 games), map familiarity score, most/least played maps	Silver maps
Event context	Prize pool tier, LAN vs online, event stage (groups/playoffs/final), days to event end	Silver events
Roster stability	Avg days together, roster change flag (last 30 days)	Silver rosters
Player aggregate	Avg team rating last 20 maps, avg ADR last 20 maps, avg KAST last 20 maps	Silver player_stats

What GoldLite does NOT include

Excluded	Where it lives
Demo-derived features (eco win rates, KAST, trade kills, utility efficiency)	Internal Gold (CounterQuant API)
Polymarket / betting market odds	Private, never released
XGBoost/LightGBM model output probabilities	Private (CounterQuant predictions)
509-feature full ML vectors	Private (CounterQuant API)
Live round-by-round probability updates	Private (CounterQuant live feed)
Proprietary tactical embeddings	Private, never released

If you need demo-derived features, use Bronze and compute them yourself — that's exactly what CounterQuant does internally.

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Planned File Structure

data/
├── team_elo/
│   ├── team_elo_2024.parquet           # Elo snapshot at each match played in 2024
│   ├── team_elo_2012_2023.parquet      # Historical backfill
│   └── team_elo_current.parquet        # Latest Elo per team per map (updated)
├── match_features/
│   ├── match_features_2024.parquet     # Pre-match feature vector per 2024 match
│   └── match_features_2012_2023.parquet
├── player_rolling_stats/
│   ├── player_rolling_2024.parquet     # Rolling 5/10/20-match averages per player
│   └── ...
└── map_pool/
    ├── map_pool_stats_2024.parquet     # Team win rates per map, as of each match
    └── ...

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Schema Reference

team_elo_YYYY.parquet

Elo rating for each team at the point of each match. One row per team per match.

Column	Type	Description
match_id	int64	Match ID
team_id	int64	Team ID
team_name	string	Team name (denormalized)
map_name	string	all for global Elo; CS2 map name for map-specific Elo
elo_before	float32	Elo rating entering this match
elo_after	float32	Elo rating after this match
elo_change	float32	Delta (positive = won, negative = lost)
uncertainty	float32	Elo uncertainty / confidence interval
matches_played	int32	Total matches used to compute this rating
date	date	Match date

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match_features_YYYY.parquet

Pre-match feature snapshot. One row per match, all features computed from data available before kick-off. Safe for train/test split — no data leakage.

Column	Type	Description
match_id	int64	HLTV match ID
date	date	Match date
tier	int16	Match tier (1/2/3)
is_lan	bool	LAN event
prize_pool_usd	int32	Event prize pool in USD
team1_id	int64	Team 1 ID
team2_id	int64	Team 2 ID
team1_elo	float32	Global Elo entering this match
team2_elo	float32	Global Elo entering this match
elo_diff	float32	team1_elo − team2_elo
team1_form_5	float32	Win rate last 5 matches
team2_form_5	float32	Win rate last 5 matches
team1_form_10	float32	Win rate last 10 matches
team2_form_10	float32	Win rate last 10 matches
team1_map_diff_10	float32	Map differential (maps won − lost) last 10
team2_map_diff_10	float32	Map differential last 10
h2h_team1_wins	int16	All-time H2H wins for team 1
h2h_team2_wins	int16	All-time H2H wins for team 2
h2h_team1_wins_12m	int16	H2H wins last 12 months
h2h_team2_wins_12m	int16	H2H wins last 12 months
team1_days_since_match	int16	Days since team 1's last match
team2_days_since_match	int16	Days since team 2's last match
team1_avg_rating_20	float32	Avg team player rating last 20 maps
team2_avg_rating_20	float32	Avg team player rating last 20 maps
team1_avg_adr_20	float32	Avg team ADR last 20 maps
team2_avg_adr_20	float32	Avg team ADR last 20 maps
roster_change_team1_30d	bool	Team 1 had a roster change in last 30 days
roster_change_team2_30d	bool	Team 2 had a roster change in last 30 days
team1_won	bool	Label — did team 1 win? (null = match not completed)

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player_rolling_YYYY.parquet

Rolling player performance windows, computed at each match. Useful for player-level predictions and ranking models.

Column	Type	Description
match_id	int64	Match ID
player_id	int64	Player ID
player_ign	string	IGN
team_id	int64	Team ID at this match
maps_played_total	int16	Career maps played at this point
rolling_kills_5	float32	Avg kills per map last 5 maps
rolling_kills_10	float32	Avg kills per map last 10 maps
rolling_adr_10	float32	Avg ADR last 10 maps
rolling_adr_20	float32	Avg ADR last 20 maps
rolling_kast_10	float32	Avg KAST last 10 maps
rolling_rating_20	float32	Avg HLTV Rating 2.0 last 20 maps
rating_trend_10	float32	Linear slope of rating over last 10 maps (form direction)
days_since_last_match	int16	Days since player's last match

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map_pool_stats_YYYY.parquet

Team win rates per map, computed at each match date (so you can join to match_features for map-specific models).

Column	Type	Description
as_of_match_id	int64	The match this snapshot was computed for
as_of_date	date	Date of the snapshot
team_id	int64	Team ID
map_name	string	CS2 map name
maps_played	int16	Maps played on this map (last 2 years)
maps_won	int16	Maps won
win_rate	float32	Win rate (0–1)
maps_played_tier1	int16	Maps played at Tier 1 only
win_rate_tier1	float32	Win rate at Tier 1

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Quick Start (Preview)

Once 2024 data is published:

import polars as pl
from huggingface_hub import hf_hub_download

# Download pre-match features for 2024
path = hf_hub_download(
    repo_id="KEDevO/CounterQuant-CS2-GoldLite",
    repo_type="dataset",
    filename="data/match_features/match_features_2024.parquet",
)
df = pl.read_parquet(path)

# Drop rows with incomplete data (ongoing / no-result matches)
df = df.filter(pl.col("team1_won").is_not_null())

X = df.drop(["match_id", "date", "team1_id", "team2_id", "team1_won"])
y = df["team1_won"].cast(pl.Int8)
print(f"Training samples: {len(X)}, Features: {X.width}")

from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
from sklearn.metrics import brier_score_loss, roc_auc_score

X_train, X_test, y_train, y_test = train_test_split(
    X.to_pandas(), y.to_pandas(),
    test_size=0.2, shuffle=False  # time-ordered split
)

model = XGBClassifier(
    n_estimators=300,
    max_depth=5,
    learning_rate=0.05,
    subsample=0.8,
    colsample_bytree=0.8,
    use_label_encoder=False,
    eval_metric="logloss",
)
model.fit(X_train, y_train)

probs = model.predict_proba(X_test)[:, 1]
print(f"Brier score: {brier_score_loss(y_test, probs):.4f}")  # target ≤ 0.21
print(f"AUC-ROC:     {roc_auc_score(y_test, probs):.4f}")    # target ≥ 0.67

# Plot team Elo history
import polars as pl
import matplotlib.pyplot as plt

elo = pl.read_parquet("data/team_elo/team_elo_2024.parquet")
team = elo.filter((pl.col("team_name") == "NAVI") & (pl.col("map_name") == "all"))

plt.plot(team["date"].to_list(), team["elo_before"].to_list())
plt.title("NAVI Global Elo — 2024")
plt.xlabel("Date")
plt.ylabel("Elo")
plt.show()

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Methodology Notes

Elo model

The Elo ratings in GoldLite are computed using a custom variant of the standard Elo formula:

• K-factor: Variable by tier (T1 = 32, T2 = 24, T3 = 20) and match format (bo5 weighted higher)
• Map-specific Elo: Separate rating maintained per CS2 map (de_mirage, de_dust2, etc.)
• Initial Elo: All teams start at 1500. Teams with fewer than 10 matches have higher uncertainty.
• Update frequency: Recomputed from all historical results on demand.

The full GoldLite Elo model is a baseline — it does not incorporate demo-derived features, player-level signals, or market information. It is calibrated to be reproducible and interpretable, not to maximize prediction accuracy.

CounterQuant's production model (internal, not released) additionally incorporates 509 features including economy statistics, KAST, utility efficiency, and market-implied probabilities.

No data leakage guarantee

All features in match_features_YYYY.parquet are constructed from data available strictly before the match date. The team1_won column is the label — it is derived from the match result after the fact. There are no post-match statistics in the feature columns.

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Prediction Targets & Baselines

Model	Target	GoldLite Baseline	Production Target
Match outcome	team1_won (binary)	Brier ≤ 0.22	Brier ≤ 0.21
—	—	AUC-ROC ≥ 0.65	AUC-ROC ≥ 0.67
Map outcome	map winner (binary)	Brier ≤ 0.24	TBD

A random classifier scores Brier = 0.25. An Elo-only model should reach approximately 0.228. The GoldLite feature set adds form, H2H, and event context on top of Elo and should reach around 0.22.

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Related Datasets

Dataset	Contents	Relationship
CounterQuant CS2 Demos	Raw .dem files	Source layer
CounterQuant CS2 Bronze	Tick-level events: kills, damages, flashes, utility	Compute demo-derived features to extend GoldLite
CounterQuant CS2 Silver	Match results, map scores, player stats	GoldLite is derived from Silver — join on match_id
CounterQuant Platform	Live analytics, predictions, API	Production system built on the full 509-feature pipeline
CounterQuant API	REST API access	Access to live predictions and extended features

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Data Release Policy

Layer	Public?	Timeline
GoldLite 2024 (Elo + form + H2H + event)	✅ CC BY 4.0	Q3 2026
GoldLite 2012–2023 (backfill)	✅ CC BY 4.0	Q4 2026
GoldLite 2025	✅ CC BY 4.0	2027 (2-year delay)
Demo-derived features (eco, KAST, utility)	❌ Private	CounterQuant API only
Full 509-feature vectors	❌ Private	CounterQuant API only
Polymarket / market odds	❌ Private	Never released
Model output probabilities	❌ Private	CounterQuant API only

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Citation

If you use this dataset in research, analytics products, or publications, please cite:

BibTeX

@dataset{kulbe2026counterquantgoldlite,
  author       = {Eimantas Kulbe},
  title        = {CounterQuant CS2 GoldLite: ML-Ready Match Features for Professional CS2},
  year         = {2026},
  publisher    = {Hugging Face},
  url          = {https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite},
  note         = {Pre-match Elo ratings, team form, head-to-head records, and event
                  context features for professional CS2 match outcome modelling.
                  Covers 2012--2024 (first release). 2-year delay for recent data.}
}

APA

Kulbe, E. (2026). CounterQuant CS2 GoldLite: ML-Ready Match Features for Professional CS2 [Dataset]. Hugging Face. https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite

Acknowledgement (for papers/articles)

Pre-match features and Elo ratings sourced from CounterQuant CS2 GoldLite (Kulbe, 2026), available at https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-GoldLite under CC BY 4.0.

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License

Creative Commons Attribution 4.0 International (CC BY 4.0)

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, including commercial

Under the condition that you give appropriate credit to Eimantas Kulbe and link back to this dataset.

Full license text: https://creativecommons.org/licenses/by/4.0/

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Dataset maintained by Eimantas Kulbe. For questions, issues, or collaboration: open a discussion on this dataset page or reach out via CounterQuant.