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README.md

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 ---
-license: mit
+pretty_name: TacSIm
+task_categories:
+- reinforcement-learning
+tags:
+- multi-agent
+- imitation-learning
+- football
+- soccer
+- trajectory
+- tactical-imitation
+- sequential-data
+- google-research-football
+- benchmark
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: Train/**/*.csv
+  - split: validation
+    path: Validation/**/*.csv
+  - split: test
+    path: Test/**/*.csv
 ---
+
+# TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation
+
+## Dataset Description
+
+**TacSIm** is a multi-agent football trajectory dataset designed for research on tactical style imitation, multi-agent imitation learning, trajectory generation, and long-horizon football simulation.
+
+The dataset is organized into three predefined splits:
+
+- **Train**
+- **Validation**
+- **Test**
+
+These splits should be preserved when training and evaluating models so that reported results remain comparable.
+
+> **Paper:** *TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation*  
+> **Venue:** CVPR 2026  
+---
+
+## Dataset Structure
+
+### Recommended Repository Layout
+
+```text
+TacSIm/
+├── README.md
+├── Train/
+│   ├── 00001.csv
+│   ├── 00002.csv
+│   └── ...
+├── Validation/
+│   ├── 00001.csv
+│   ├── 00002.csv
+│   └── ...
+└── Test/
+    ├── 00001.csv
+    ├── 00002.csv
+    └── ...
+```
+
+Each CSV file represents one football trajectory segment.
+
+### Trajectory Length
+
+In the released format:
+
+- each file contains **100 time steps**;
+- the sampling interval is **0.1 seconds**;
+- each file therefore represents a **10-second trajectory**;
+- rows are ordered chronologically.
+
+A model may be evaluated at shorter horizons by using the first:
+
+| Evaluation horizon | Number of rows |
+|---|---:|
+| 3 seconds | 30 |
+| 5 seconds | 50 |
+| 10 seconds | 100 |
+
+---
+
+
+### Player Positions
+
+For each controlled player \(i \in \{1,\ldots,11\}\):
+
+```text
+player_i_x
+player_i_y
+```
+
+These columns contain the player's normalized two-dimensional position at each time step.
+
+`player_1` is treated as the goalkeeper in the TacSIm team-level evaluation protocol. Players `player_2` through `player_11` are treated as outfield players.
+
+
+### Complete Column List
+
+```text
+player_1_x, player_1_y,
+player_2_x, player_2_y,
+player_3_x, player_3_y,
+player_4_x, player_4_y,
+player_5_x, player_5_y,
+player_6_x, player_6_y,
+player_7_x, player_7_y,
+player_8_x, player_8_y,
+player_9_x, player_9_y,
+player_10_x, player_10_y,
+player_11_x, player_11_y,
+ball_x, ball_y,
+player_1_action,
+player_2_action,
+player_3_action,
+player_4_action,
+player_5_action,
+player_6_action,
+player_7_action,
+player_8_action,
+player_9_action,
+player_10_action,
+player_11_action
+```
+
+---
+
+## Coordinate Convention
+
+The coordinates follow the normalized Google Research Football field representation.
+
+The nominal coordinate ranges are approximately:
+
+```text
+x: [-1.00, 1.00]
+y: [-0.42, 0.42]
+```
+
+Small values outside these nominal limits may occur because of simulator dynamics, boundary behavior, or preprocessing. Users should avoid silently clipping coordinates unless clipping is explicitly part of their experimental protocol.
+
+The released files contain only the two-dimensional coordinates used by the TacSIm benchmark. Ball height is not included in this CSV format.
+
+---
+
+## Loading the Dataset
+
+### Load with Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("YOUR_USERNAME/TacSIm")
+
+print(dataset)
+print(dataset["train"][0])
+```
+
+The YAML configuration at the top of this card maps the repository folders to the following Hugging Face splits:
+
+```text
+Train      -> train
+Validation -> validation
+Test       -> test
+```
+
+### Important Note About Trajectory Boundaries
+
+When multiple CSV files are loaded through `datasets.load_dataset`, Hugging Face may concatenate their rows within each split. The original source filename is not necessarily preserved as a feature.
+
+For methods that require each 100-frame trajectory to remain a separate episode, download the repository while preserving its directory structure:
+
+```python
+from pathlib import Path
+
+import pandas as pd
+from huggingface_hub import snapshot_download
+
+dataset_root = Path(
+    snapshot_download(
+        repo_id="YOUR_USERNAME/TacSIm",
+        repo_type="dataset",
+    )
+)
+
+train_files = sorted((dataset_root / "Train").glob("*.csv"))
+validation_files = sorted(
+    (dataset_root / "Validation").glob("*.csv")
+)
+test_files = sorted((dataset_root / "Test").glob("*.csv"))
+
+trajectory = pd.read_csv(train_files[0])
+
+print(train_files[0].name)
+print(trajectory.shape)
+```
+
+### Load One Trajectory
+
+```python
+import pandas as pd
+
+trajectory = pd.read_csv("Train/02304.csv")
+
+player_positions = trajectory[
+    [
+        column
+        for column in trajectory.columns
+        if column.startswith("player_")
+        and (
+            column.endswith("_x")
+            or column.endswith("_y")
+        )
+    ]
+]
+
+ball_positions = trajectory[["ball_x", "ball_y"]]
+
+player_actions = trajectory[
+    [
+        column
+        for column in trajectory.columns
+        if column.endswith("_action")
+    ]
+]
+```
+
+### Convert One File to Arrays
+
+```python
+import numpy as np
+import pandas as pd
+
+trajectory = pd.read_csv("Train/02304.csv")
+
+player_columns = []
+
+for player_id in range(1, 12):
+    player_columns.extend(
+        [
+            f"player_{player_id}_x",
+            f"player_{player_id}_y",
+        ]
+    )
+
+action_columns = [
+    f"player_{player_id}_action"
+    for player_id in range(1, 12)
+]
+
+players = trajectory[player_columns].to_numpy(
+    dtype=np.float32
+).reshape(-1, 11, 2)
+
+ball = trajectory[["ball_x", "ball_y"]].to_numpy(
+    dtype=np.float32
+)
+
+actions = trajectory[action_columns].to_numpy(
+    dtype=np.int64
+)
+
+print("Players:", players.shape)  # (100, 11, 2)
+print("Ball:", ball.shape)        # (100, 2)
+print("Actions:", actions.shape)  # (100, 11)
+```
+
+---
+
+## Dataset Splits
+
+The dataset contains fixed training, validation, and test partitions.
+
+| Split | Purpose | Number of trajectory files |
+|---|---|---:|
+| Train | Model training | 10756 |
+| Validation | Hyperparameter selection and model development | 2304 |
+| Test | Final benchmark evaluation | 2304 |
+
+The test set should not be used for model selection or hyperparameter tuning.
+
+To count the files locally:
+
+```python
+from pathlib import Path
+
+for split in ["Train", "Validation", "Test"]:
+    count = len(list(Path(split).glob("*.csv")))
+    print(split, count)
+```
+
+---
+
+## Benchmark Tasks
+
+TacSIm supports research on:
+
+1. **Multi-agent behavior cloning**
+2. **Multi-agent imitation learning**
+3. **Football trajectory prediction**
+4. **Tactical style imitation**
+5. **Long-horizon multi-agent simulation**
+6. **World-model learning**
+7. **Offline reinforcement learning**
+8. **Multi-agent action prediction**
+
+Depending on the experimental setting, a model may use historical player positions, ball positions, and player actions to predict future trajectories or future joint actions.
+
+---
+
+## Benchmark Evaluation
+
+The TacSIm benchmark evaluates generated trajectories at:
+
+```text
+3 seconds
+5 seconds
+10 seconds
+```
+
+Ball-trajectory similarity is evaluated under multiple spatial grid resolutions:
+
+```text
+10 × 6
+15 × 10
+20 × 12
+30 × 20
+105 × 68
+```
+
+The benchmark includes the following trajectory-level measurements:
+
+- **Spatial Occupancy Similarity**
+- **Movement Vector Similarity**
+
+Extended team-level evaluation may additionally report:
+
+- **Team Formation Compactness Similarity**
+- **Team Speed Similarity**
+
+For reproducible comparisons, all methods should use the same:
+
+- train/validation/test split;
+- temporal horizons;
+- coordinate convention;
+- grid resolutions;
+- goalkeeper handling;
+- aggregation procedure.
+
+---
+
+## Intended Uses
+
+The dataset is intended for academic research in:
+
+- multi-agent learning;
+- sports analytics;
+- football simulation;
+- imitation learning;
+- trajectory modeling;
+- tactical behavior generation;
+- world models;
+- sequential decision making.
+
+The dataset may also be used for teaching and reproducibility studies related to multi-agent football environments.
+
+---
+
+
+## Data Quality and Preprocessing
+
+Each trajectory should be checked for:
+
+- exactly 100 ordered time steps;
+- all 35 required columns;
+- finite coordinate values;
+- action IDs within 0–18;
+- consistent coordinate orientation;
+- consistent player indexing.
+
+A basic validation example is:
+
+```python
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+required_columns = [
+    item
+    for player_id in range(1, 12)
+    for item in (
+        f"player_{player_id}_x",
+        f"player_{player_id}_y",
+    )
+] + [
+    "ball_x",
+    "ball_y",
+] + [
+    f"player_{player_id}_action"
+    for player_id in range(1, 12)
+]
+
+for csv_file in Path("Train").glob("*.csv"):
+    data = pd.read_csv(csv_file)
+
+    assert len(data) == 100
+    assert all(
+        column in data.columns
+        for column in required_columns
+    )
+    assert np.isfinite(
+        data[required_columns].to_numpy()
+    ).all()
+
+    actions = data[
+        [
+            f"player_{player_id}_action"
+            for player_id in range(1, 12)
+        ]
+    ].to_numpy()
+
+    assert actions.min() >= 0
+    assert actions.max() <= 18
+```
+
+---
+
+## Ethical Considerations
+
+TacSIm contains simulated trajectories and does not contain personal information, biometric identifiers, or recordings of real individuals.
+
+Researchers should clearly distinguish simulated findings from conclusions about real athletes or teams.
+
+---
+
+## Citation
+
+Please cite the TacSIm paper when using this dataset:
+
+```bibtex
+@inproceedings{wen2026tacsim,
+  title={TacSIm: A Dataset and Benchmark for Football Tactical Style Imitation},
+  author={Wen, Peng and Wang, Yuting and Wang, Qiurui},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={20014--20023},
+  year={2026}
+}
+```
+
+---
+
+## Contact
+
+For questions, issues, or benchmark submissions, please contact:
+
+```text
+Name:Peng Wen
+Email: wenpengsc@gmail.com
+
+```