Research on match fluctuation prediction and strategy based on OFS algorithm

Abstract

The aim of this paper is to construct a model for predicting on-field fluctuations in matches by using OFS algorithm, combined with random forest and lattice point search algorithms, and to analyse the key factors in order to improve the coach's decision-making ability in matches. Cross-validation and hyper-parameter tuning techniques were used during the model building process to find the best hyper-parameter combinations through lattice point search to optimise the model performance and generalisation ability. The study provides a detailed description of the model implementation process, including data processing, feature selection, parameter setting, model building, training and evaluation. Meanwhile, a series of targeted recommendations are proposed to address the variability of match fluctuations, including the analysis of past matches, the focus on recent performance, the assessment of psychological quality, the study of opponent's weaknesses, the flexible response to changes in matches and the maintenance of focus, with a view to helping athletes and coaches to formulate a more effective match strategy and to achieve better match results.