Sports Match Prediction Analysis Applying Machine Learning Model with Rule-Based Approach

EasyChair Preprint 15982

Abstract

This research introduces an innovative approach to match prediction, combining machine learning with a rule-based approach, focusing on PBA (Philippine Basketball Association), and PVL (Premier Volleyball League). Objectives include creating a hybrid model, optimizing datasets, and deploying the model in a web application. The research shows accuracy for basketball (82.86%) and volleyball (90.20%), with random forest model and feature importance analysis highlighting key predictors. The conclusion emphasizes the model's value in sports prediction and recommends further exploration of the proposed approach.

Keyphrases: Random Forest Machine Learning in prediction, Rule based approach, Sports Analytics

@booklet{EasyChair:15982,
  author    = {Jay Abaleta and Ma. Christina Navarro and Jerome Alvez and Vinard James Damasco and Yvan Sinues and John Miguel Jusayan and Harvey Delos Santos},
  title     = {Sports Match Prediction Analysis Applying Machine Learning Model with Rule-Based Approach},
  howpublished = {EasyChair Preprint 15982},
  year      = {EasyChair, 2025}}