Advanced Football Match Winning Probability Prediction: A CNN-BiLSTM_Att Model with Player Compatibility and Dynamic Lineup Analysis

In recent years, with the continuous expansion of the football market, the prediction of football match-winning probabilities has become increasingly important, attracting numerous professionals and institutions to engage in the field of football big data analysis. Pre-match data analysis is crucial for predicting match outcomes and formulating tactical strategies, and all top-level football events rely on professional data analysis teams to help teams gain an advantage. To improve the accuracy of football match winning probability predictions, this study has taken a series of measures: using the Word2Vec model to construct feature vectors to parse the compatibility between players; developing a winning probability prediction model based on LSTM to capture the dynamic changes in team lineups; designing an improved BILSTM_Att winning probability prediction model, which distinguishes the different impacts of players on match outcomes through an attention mechanism; and proposing a CNN-BILSTM_Att winning probability prediction model that combines the local feature extraction capability of CNN with the time series analysis of BILSTM. These research efforts provide more refined data support for football coaching teams and analysts. For the general audience, these in-depth analyses can help them understand the tactical layouts and match developments on the field more deeply, thereby enhancing their viewing experience and understanding of the matches.

@article{Quan2025,
  title     = {Advanced Football Match Winning Probability Prediction: A CNN-BiLSTM_Att Model with Player Compatibility and Dynamic Lineup Analysis},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Tao Quan and Yingling Luo},
  doi       = {10.14569/IJACSA.2025.0160238},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160238}
}