Update dataset card: Eimantas Kulbe credit, coverage plan T1/T2/T3, usage examples
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README.md
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---
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license: cc-by-
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tags:
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- cs2
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- counter-strike
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- esports
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- demos
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size_categories:
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---
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# CounterQuant
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## Usage
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## License
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CC-BY-NC-4.0 — research and non-commercial use.
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---
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license: cc-by-4.0
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task_categories:
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- other
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language:
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- en
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tags:
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- cs2
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- counter-strike
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- esports
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- demos
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- game-theory
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- sports-analytics
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pretty_name: CounterQuant CS2 Demos
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size_categories:
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- 10K<n<100K
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---
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# CounterQuant CS2 Demos
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Raw CS2 (Counter-Strike 2) demo files (.dem) collected from HLTV — the authoritative competitive CS2 demo repository.
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**Curated and maintained by [Eimantas Kulbe](https://github.com/redrum88) as part of the [CounterQuant](https://cs2quant.kedevo.com) esports analytics project.**
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## Dataset Goal
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Provide the CS2 research community with the largest open-access collection of raw professional demo files, covering all competitive tiers from CS:GO's origin through CS2's present day.
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**The philosophy:** squeeze every available demo into one place so anyone can preprocess, parse, or derive features in any way they want — no gatekeeping, no preprocessing lock-in.
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## Coverage
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| Tier | Description | Years | Status |
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|------|-------------|-------|--------|
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| T1 | Major/S-tier events (top 20 world teams) | 2024–present | Active — daily updates |
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| T2 | A-tier events (top 50 teams) | 2024–present | Active — daily updates |
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| T3 | B-tier / regional events | 2024–present | In progress |
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| Historical | CS:GO T1/T2 (pre-2024) | 2012–2023 | Planned |
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## File Structure
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```
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data/
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{year}/
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tier{N}/
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{match_id}/
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{map_name}.dem
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```
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Example:
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```
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data/2024/tier1/2378549/astralis-vs-natus-vincere-mirage.dem
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data/2025/tier2/2401337/team-vitality-vs-g2-inferno.dem
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```
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## Usage
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### Parse with demoparser2 (Python)
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```python
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from demoparser2 import DemoParser
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parser = DemoParser("data/2024/tier1/2378549/astralis-vs-natus-vincere.dem")
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# Player kill events
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kills = parser.parse_event("player_death", player=["name", "team_name"])
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# Round-by-round economy
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rounds = parser.parse_ticks(["total_rounds_played", "cash_spent_t", "cash_spent_ct"])
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```
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### Download via huggingface_hub
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```python
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from huggingface_hub import snapshot_download
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# Download all 2024 T1 demos
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path = snapshot_download(
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repo_id="KEDevO/CounterQuant-CS2-Demos",
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repo_type="dataset",
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allow_patterns="data/2024/tier1/**",
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local_dir="./demos",
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)
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```
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### Stream individual files
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```python
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from huggingface_hub import hf_hub_download
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dem = hf_hub_download(
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repo_id="KEDevO/CounterQuant-CS2-Demos",
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repo_type="dataset",
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filename="data/2024/tier1/2378549/match.dem",
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)
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```
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## What You Can Do With These Demos
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- **Feature engineering**: extract round economy, positioning, utility, player rating
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- **Model training**: win-probability, player rating, team strength models
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- **Research**: CS2 game theory, tactical analysis, team dynamics
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- **Statistics**: compute your own KDA, HLTV Rating 2.0, ADR, Impact
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- **No restrictions**: raw bytes, parse however you like
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## Update Schedule
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New demos are added within 24–48 hours of match completion. The pipeline runs continuously across dedicated VPS infrastructure.
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## Data Source
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Demos sourced from [HLTV.org](https://www.hltv.org) — the official repository for professional CS2 match data. HLTV is the authoritative source for all competitive CS2/CS:GO results and replays.
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## Citation
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If you use this dataset in research, please cite:
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```bibtex
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@dataset{kulbe2025counterquant,
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author = {Eimantas Kulbe},
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title = {CounterQuant CS2 Demos},
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year = {2025},
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publisher = {HuggingFace},
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url = {https://huggingface.co/datasets/KEDevO/CounterQuant-CS2-Demos},
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note = {Continuously updated professional CS2 demo dataset, all tiers, T1/T2/T3}
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}
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```
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## License
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[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — use freely with attribution to Eimantas Kulbe / CounterQuant.
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## Related Resources
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- [CounterQuant Platform](https://cs2quant.kedevo.com) — live CS2 analytics dashboard
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- [CounterQuant Bronze Dataset](https://huggingface.co/datasets/KEDevO/CounterQuant-Bronze) — structured match/player/team data (JSON/Parquet)
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