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Consequently, we utilised a time slot-primarily based dataset from the QSL matches for the last ten seasons and proposed a novel deep mastering (DL) based process to supply a superior model for predicting match outcome. In the present short article, we proposed a DL-primarily based process, SoccerNet, to predict the football match final results in QSL taking into consideration players’ functionality metrics. We showed that DL-primarily based models give a greater result in predicting match results compared to regular feature-primarily based machine understanding models. The proposed model achieved over 82% accuracy in predicting the matching winner for QSL football matches. [newline]The proposed model also suggested the importance of distinct match segments and highlighted the relative significance of the later component of the match to decide outcomes. We also demonstrated that players in distinct field positions have different levels of contribution to match benefits. We also showed that defenders play a important function in winning matches at QSL. Overall, we believe our benefits will help the coaching staff and group management to strengthen the players’ match performance and assistance to focus on the crucial region for their improvement. Apart from the LSTM-based model, we identified handful of other papers that investigated MLP as the model to predict the outcome of football matches.

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