license: apache-2.0 language: - en datasets: - AmjadKha/FootballPredictionDataset metrics: - accuracy: 0.49 tags: - football - prediction - classification

Model Card for Football Outcome Prediction

This model predicts the outcome of football matches, using historical data such as team performance, player stats, and match conditions to determine the winner.

Model Details

Model Description

This model uses machine learning techniques to predict the outcome of football matches. It considers various parameters such as team stats, previous match outcomes, and player performance. The model is trained on a dataset of historical football match results, and it predicts whether a given match will result in a win for Team A, Team B, or a draw.

Developed by: Amjad Khaliliah
Finetuned from model: Not applicable (this is a custom model)
Model type: Classification
Language(s): English
License: Apache-2.0
Framework: TensorFlow / XGBoost (depending on the model used)
Data source: AmjadKha/FootballPredictionDataset (Kaggle or Custom)

Model Sources

Repository: [GitHub or Hugging Face Repository Link]
Demo: [Link to a hosted demo, if available]

Uses

Direct Use

This model is directly applicable for predicting the outcomes of football matches based on input features such as team statistics, player performance, and match history. It is useful for sports analysts, bettors, and anyone interested in football match predictions.

Downstream Use

The model can be integrated into larger applications, such as sports prediction platforms, betting systems, or sports analysis tools.

Out-of-Scope Use

This model should not be used in applications requiring absolute precision or in high-stakes scenarios, as football matches are influenced by unpredictable factors that can't be captured entirely by historical data (e.g., weather, injuries).

Bias, Risks, and Limitations

Bias: The model may be biased if the training data does not represent all teams fairly, such as when certain leagues or teams are overrepresented.
Risks: There are risks of overfitting, especially if the data is not properly preprocessed or balanced. The model's predictions may not always be accurate due to the inherent randomness in football matches.
Limitations: The model may struggle with predicting outcomes in scenarios involving major changes, like player transfers or unexpected injuries, as these factors are often not represented in historical data.

Recommendations

Users should carefully evaluate predictions made by the model, especially when using it in high-risk scenarios like betting. It is recommended to continuously update the model with new data to improve its performance.