Dataset Viewer
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split '1222' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Bad split: 1222. Available splits: ['train']
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 219, in compute_first_rows_from_streaming_response
                  iterable_dataset = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1409, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1232, in as_streaming_dataset
                  raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
              ValueError: Bad split: 1222. Available splits: ['train']

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Disclaimer

This work isn’t endorsed by Riot Games and doesn’t reflect the views or opinions of Riot Games or anyone officially involved in producing or managing League of Legends. League of Legends and Riot Games are trademarks or registered trademarks of Riot Games, Inc.

League of Legends Replays Dataset

This dataset contains over 1TB+ (100k+ replays) of League of Legends game replay data for research in gaming analytics, behavioral modeling, and reinforcement learning applications. The dataset is organized by game patch versions (12_22, 12_23, etc.) with parsed packet-level game events. You can find older unorganized replays here.

Data Format

Each match JSONL file contains multiple maches of chronologically ordered list of packets representing all game events:

{
  "events": [
    {
      "WaypointGroup": {
        "time": 1.2,
        "waypoints": {
          "1001": [{"x": 100.5, "z": 200.3}]
        }
      }
    },
    {
      "CastSpellAns": {
        "time": 10.23234,
        "champion_caster_id": 1073741859,
        "spell_name": "AkaliE",
        "level": 1,
        "source_position": {
          "x": 14045.15,
          "z": 13559.334
        },
        "target_ids": [],
        "windup_time": 0.25,
        "cooldown": 14.5,
        "mana_cost": 30.0,
        "slot": 2
      }
    },
    {
      "BasicAttackPos": {
        "time": 122.12,
        "source_id": 1073741859,
        "target_id": 1073741858,
        "source_position": {
          "x": 9222.389,
          "z": 2501.3594
        },
        "target_position": {
          "x": 9266.0,
          "z": 2522.0
        },
      }
    },
    {
      "ReplicationData": {
        "time": 721.11426,
        "1073741859": {
          [
            {
              "name": "health",
              "data": 1516.6107
            },
          ]
        }
      }
    },
  ]
}

Usage

Loading the Dataset

There's two ways to use this dataset:

Option 1: Using the Gym Environment (Recommended)

pip install league-of-legends-decoded-replay-packets-gym

import league_of_legends_decoded_replay_packets_gym as lol_gym

dataset = lol_gym.ReplayDataset([
    "12_22/batch_001.jsonl.gz",
], repo_id="maknee/league-of-legends-decoded-replay-packets")

dataset.load(max_games=1)
print(f"Match has {len(dataset[0])} packets")

Option 2: Manual Download and Processing

from huggingface_hub import hf_hub_download
import json
import gzip

# Download and process directly
local_file = hf_hub_download(
    repo_id="maknee/league-of-legends-decoded-replay-packets",
    filename="12_22/batch_001.jsonl.gz",
    repo_type="dataset"
)

# Process compressed file directly
with gzip.open(local_file, 'rt', encoding='utf-8') as f:
    for line_num, line in enumerate(f):
          match_data = json.loads(line)
          packets = match_data["events"]
          print(f"Match {line_num+1} has {len(packets)} packets")

Packet Schema

The dataset contains 20 packet types capturing all game events:

Packet Type Description
CreateHero Champion spawn and initialization
HeroDie Champion death events
WaypointGroup Movement commands and pathfinding
WaypointGroupWithSpeed Movement commands with speed data
EnterFog Entity entering fog of war
LeaveFog Entity leaving fog of war
UnitApplyDamage Damage dealt between units
DoSetCooldown Ability cooldown updates
BasicAttackPos Basic attack with positional data
CastSpellAns Spell/ability casting events
BarrackSpawnUnit Minion spawning from barracks
SpawnMinion General minion spawn events
CreateNeutral Neutral monster creation
CreateTurret Turret/tower initialization
NPCDieMapView NPC death (map view)
NPCDieMapViewBroadcast NPC death broadcast
BuyItem Item purchase events
RemoveItem Item removal/selling
SwapItem Item slot swapping
UseItem Item activation/usage
Replication Game state synchronization

For complete packet definitions and Python dataclasses, see packets.py.

Applications

This dataset enables research in several domains:

  • Reinforcement Learning
  • Game Analytics
  • Behavioral Research

Examples from gym

🎯 Prediction Results:
==============================
Action: Use W Ability
Confidence: 0.354
State Value: -0.681
Target Position: (7266, 3750) world coords
Coordinate Confidence: X=0.158, Y=0.080
Unit Target: 0
Unit Confidence: 1.000
✅ Prediction completed successfully!

Citation

If you use this dataset in your research, please cite:

@dataset{league_of_legends_decoded_replay_packets_2025,
  title={League of Legends Decoded Replay Packets Dataset},
  author={maknee},
  year={2025},
  url={https://huggingface.co/datasets/maknee/league-of-legends-decoded-replay-packets}
}

License

This dataset is released under the Apache 2.0 License.

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