A Study on Robust Feature Selection Methods Using LASSO, LASSO Variants and Ridge Regression in Sports
subject: Group Lasso; Lasso; Regularization; Relaxed Lasso; Ridge regression
description: Regularization tools like Lasso have made a substantial progresses in regression modelling, particularly to high-dimensional data and multicollinear data. Whereas Ridge regression uses L2 regularization to address the problem of multicollinearity, Lasso uses L1 regularization to conduct regression and feature selection together. The weaknesses of Lasso under correlated predictors have inspired the creation of a number of improved variants. The present paper will do a comparative analysis of simple Lasso, Ridge and Lasso extensions like Elastic Net, Adaptive Lasso, Group Lasso and Relaxed Lasso on real-world sports data, with special focus on a new implementation of the improved Relaxed Lasso that involves three optimization strategies: systematic grid search, extended lambda sequences, and nested cross-validation structures. The comparison has been done in terms of feature selection, resistance to outliers, and prediction accuracy on Ice Hockey, Cricket, and NBA data. Various measures of errors are used to analyze models: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Coefficient of Determination (R2 ). The results have shown that the Enhanced Relaxed Lasso performs best regarding improvements in performance especially in cricket data and still serves as a competitive data in different sporting scenarios. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
creator: Piravin, B.; Kalaivani, S.; Prasad, Smrity
source: Communications in Computer and Information Science;Volume;2853 CCIS;pp.298-312
publisher: Springer Science and Business Media Deutschland GmbH
date: 01-01-2026
rights: Restricted Access; Hardcopy may be available in the library
relation: ISSN: 18650929; ISBN: 978-981957291-5;
language: English
type: Conference paper
coverage: Piravin B., Department of Data Science and Statistics, CHRIST University, Bengaluru, India; Kalaivani S., Department of Data Science and Statistics, CHRIST University, Bengaluru, India; Prasad S., Department of Data Science and Statistics, CHRIST University, Bengaluru, India