Datasetpublic

wolframko/pipes-lie-embargo-dominus

Betty Dota 2 Pro Matches Dataset 9,388 professional Dota 2 matches parsed from replay files with per-second game state snapshots and combat log events. Dataset Structure matches.parquet (9,388 rows) One row per match. Contains metadata, STRATZ player statistics, and draft data. Column Type Description match_id int64 Dota 2 match ID league_name string Tournament name league_tier string PROFESSIONAL, MAJOR, etc. duration_sec int64… See the full description on the dataset page: https://huggingface.co/datasets/wolframko/pipes-lie-embargo-dominus.

sourceHugging Facemitupdated 6mo agoView on Hugging Face

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Dataset Card

Betty Dota 2 Pro Matches Dataset

9,388 professional Dota 2 matches parsed from replay files with per-second game state snapshots and combat log events.

Dataset Structure

matches.parquet (9,388 rows)

One row per match. Contains metadata, STRATZ player statistics, and draft data.

Column	Type	Description
match_id	int64	Dota 2 match ID
league_name	string	Tournament name
league_tier	string	PROFESSIONAL, MAJOR, etc.
duration_sec	int64	Match duration in seconds
radiant_win	bool	True if Radiant won
radiantteamid / direteamid	int64	Team IDs
p0..p9heroid	int64	Hero ID per player (STRATZ)
p0..p9_kills/deaths/assists	int64	Final KDA per player
p0..p9_gpm/xpm/networth	int64	Economy stats per player
p0..p9gpmper_min	list[int]	Gold per minute time-series
p0..p9networthper_min	list[int]	Net worth per minute
p0..p9herodamagepermin	list[int]	Hero damage per minute
stratzpbhero_id	list[int]	STRATZ pick/ban hero IDs
replaypbhero_id	list[int]	Replay-parsed pick/ban hero IDs

ticks/ (189,120,930 rows)

One row per game-tick per hero. ~1,500 ticks per match x 10 heroes. Per-second snapshots of full game state.

Column	Type	Description
match_id	int64	Match ID
tick	int32	Raw tick number
game_time	float	Game time in seconds
slot	int8	Player slot (0-9)
hero	string	Hero internal name
x, y	float	Map position coordinates
hp, max_hp	int32	Current and max health
mana, max_mana	float	Current and max mana
gold	int32	Current gold
net_worth	int32	Total net worth
xp	int32	Total experience
kills, deaths, assists	int16	KDA at this moment
last_hits, denies	int16	CS at this moment
level	int8	Hero level
str, agi, int_	float	Attribute values
armor	float	Armor value
move_speed	int16	Movement speed
item0..item5	string	Items in 6 inventory slots

events/ (295,810,843 rows)

One row per combat log event. Damage, kills, gold gains, XP, healing, purchases.

Column	Type	Description
match_id	int64	Match ID
tick	int32	Raw tick number
game_time	float	Game time in seconds
timestamp	float	Exact event timestamp
event_type	string	DOTACOMBATLOGDAMAGE, DEATH, GOLD, HEAL, PURCHASE, _XP
source	string	Source entity (e.g. npcdotahero_invoker)
target	string	Target entity
value	int32	Damage/gold/xp amount
item	string	Item name (for PURCHASE events)

Statistics

—Matches: 9,388 professional games from 2025
—Ticks: 189,120,930 per-second hero state snapshots
—Events: 295,810,843 combat log entries
—Size: 5.97 GB compressed Parquet (from 103 GB JSON)
—Source: Valve replay files (.dem) parsed with manta + STRATZ API metadata

Usage

python

from datasets import load_dataset

# Load matches metadata
matches = load_dataset("wolframko/pipes-lie-embargo-dominus", data_files="matches.parquet", split="train")

# Load ticks (large - use streaming)
ticks = load_dataset("wolframko/pipes-lie-embargo-dominus", data_files="ticks/*.parquet", split="train", streaming=True)

# Load events
events = load_dataset("wolframko/pipes-lie-embargo-dominus", data_files="events/*.parquet", split="train", streaming=True)

python

import pandas as pd

# Quick analysis with pandas
matches_df = pd.read_parquet("hf://datasets/wolframko/pipes-lie-embargo-dominus/matches.parquet")
print(f"Matches: {len(matches_df)}, Radiant winrate: {matches_df.radiant_win.mean():.1%}")

Data Pipeline

1.Fetch: Match IDs from STRATZ GraphQL API (pro leagues 2025)
2.Download: Replay files (.dem.bz2) from Valve CDN
3.Parse: Per-second hero states + combat log via manta (Go)
4.Enrich: Player/team stats from STRATZ API
5.Convert: JSON to Parquet with full validation

License

MIT