Intelligent Strategic Planning Method based Algorithm (ISPMA) for Estimation of Soccer Sports Match Outcome - Madan, Karun; Taneja, Kavita; Taneja, Harmunish (International journal of mathematical, engineering and management sciences, 2025)
Estimating the soccer match outcome with adequate accuracy is still one of the biggest challenges in the sports domain. In this work, the proposed novel Intelligent Strategic Planning Method based Algorithm (ISPMA) carries out dynamic estimation of soccer team performance in terms of the match outcome and noticeably outperforms the existing state of the art methods due to its unique features. In this work, the output of the four feature selection machine learning techniques i.e. Pearson correlation, forward selection, Extra tree classifier, and CHI-square is firstly unified before feeding these selected features as an input to the seven classifiers i.e. SVM (Support Vector Machine), Naïve Bayes, KNN (K-Nearest Neighbor.), Decision Tree, Random Forest, Logistic Regression, and AdaBoost. The dataset comprises eleven seasons of the English premier league and 3762 matches have been used to train the model and 418 matches to test the same. Such a reasonable size of soccer dataset is not common in previous studies. Another unique feature of this work is the time of estimation as estimation can be done during the progression of the game based on match statistics associated with the first half of the match. The proposed method uses a novel approach by computing the average values of the selected set of features for the victory of the team to estimate match results. By using these computed average values, ISPMA generates strategic planning based suggestions for the second half of the match. The strategic planning generated by the proposed method facilitates estimating the team performance and shifting the momentum from one team to another and can assist the coach, managers, and the team in carrying out effective decision-making for better match outcome.