SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners
Jassim AlMulla1, Mohammad Tariqul Islam2, Hamada R H Al-Absi1
1College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Plos One
|August 1, 2023
Summary
Predicting football match outcomes is crucial. A deep learning model, SoccerNet, achieved over 80% accuracy in forecasting Qatar Stars League winners by analyzing player performance data.
Area of Science:
- Sports Analytics
- Machine Learning in Football
Background:
- Winning football matches is the primary objective for clubs worldwide.
- Predicting match outcomes is vital for strategic planning and performance improvement.
- Existing studies often focus on player performance metrics to forecast winners.
Purpose of the Study:
- To develop and evaluate a deep learning model for predicting football match winners.
- To analyze player performance and its impact on match results in the Qatar Stars League (QSL).
- To identify key match segments and player roles influencing game outcomes.
Main Methods:
- Utilized a decade of Qatar Stars League (QSL) match data (2011-2022).
- Developed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning model.
- Incorporated 15-minute time-slotted match and player data from the STATS platform.
Main Results:
- SoccerNet achieved over 80% accuracy in predicting match winners.
- Defenders demonstrated a more dominant role in QSL matches compared to midfielders and forwards.
- The final 15-30 minutes of matches significantly impacted the outcome.
Conclusions:
- The proposed deep learning model offers a novel approach to football match prediction in the MENA region.
- Findings can assist QSL coaching staff and management in strategic decision-making.
- Analysis highlights the critical influence of specific player positions and late-game performance on match results.
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