task_categories:table-question-answering license:afl-3.0 size_categories:1K<n<10K format:csv modality:tabular modality:text library:datasets library:dask library:mlcroissant library:polars region:us
---
license: afl-3.0
task_categories:
- table-question-answering
---

# 🏟️ Football Match Prediction Dataset

This dataset contains historical football match data designed to train and evaluate machine learning models for predicting various match outcomes, including:

- **Odd/Even** goals prediction
- **Yes/No** for both teams scoring
- **Over/Under** total goals scored

The dataset supports multiple prediction tasks such as classification, regression, and probabilistic forecasting of football outcomes.

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## 📁 Dataset Overview

| Column Name         | Description                                        | Example            |
|---------------------|----------------------------------------------------|--------------------|
| `date`              | Date when the match was played                     | 2024-02-20         |
| `league`            | League or competition name                         | Premier League     |
| `home_team`         | Name of the home team                               | Manchester United  |
| `away_team`         | Name of the away team                               | Liverpool          |
| `home_score`        | Goals scored by the home team                       | 2                  |
| `away_score`        | Goals scored by the away team                       | 1                  |
| `full_time_result`  | Match result (H=Home Win, D=Draw, A=Away Win)       | H                  |
| `over_2_5`          | 1 if total goals > 2.5, else 0                      | 1                  |
| `both_teams_score`  | 1 if both teams scored, else 0                      | 1                  |
| `odd_even_total`    | 1 if total goals are odd, 0 if even                 | 1                  |
| `home_odds`         | Decimal odds for home team win                      | 1.80               |
| `draw_odds`         | Decimal odds for a draw                             | 3.40               |
| `away_odds`         | Decimal odds for away team win                      | 4.20               |

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## 🔍 Usage

This dataset can be used for:
- Training machine learning models for football match outcome prediction.
- Statistical analysis of team performance based on historical trends.
- Building betting strategy simulations using predictive analytics.

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## 🏗️ How to Load the Dataset

You can load this dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("username/your-dataset-repo-name")
print(dataset)

✨ Dataset Splits
The dataset is divided as follows:

Training Set: 80%
Validation Set: 10%
Test Set: 10%
⚡ Preprocessing Suggestions
Handle missing values (NaN) through imputation or removal.
Convert categorical columns (league, home_team, away_team) using one-hot encoding or embeddings.
Normalize numerical features (home_odds, away_odds) for model training.
Engineer features such as form metrics, head-to-head records, and average goals.
📜 Source
The dataset is compiled from publicly available football match data sources and is intended for research and educational purposes only.

⚠️ License
CC BY 4.0 — You are free to use this dataset with attribution.

💬 Citation
bibtex
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@dataset{username_football_prediction_2025,
  author       = {Your Name or Username},
  title        = {Football Match Prediction Dataset},
  year         = 2025,
  url          = {https://huggingface.co/datasets/username/your-dataset-repo-name},
}
🙌 Acknowledgements
Special thanks to the football analytics community for insights on sports data modeling and predictive analysis.

yaml
Copy code

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### ✨ **What This README Covers:**
- Clear **description** of the dataset and its predictive applications.
- Detailed **column explanations** for easy understanding.
- **Usage instructions** for loading the dataset using Hugging Face's `datasets` library.
- **Preprocessing suggestions** tailored for your prediction tasks (odd/even, over/under, etc.).
- **Proper licensing**, **citation**, and **acknowledgement** sections.

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### 🎯 **Next Steps for You:**
1. Update:
   - Replace `username/your-dataset-repo-name` with your actual Hugging Face username and repo name.
   - Adjust the **column descriptions** if your dataset contains additional features.
2. Upload this `README.md` directly to your Hugging Face dataset repo under the **"Files and versions"** tab.

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Would you like to add specific data sources or expand on the feature engineering part for advanced users? 🚀