ArticleFrontiers in public health2026
Dual-target modeling of team performance using regularized regression with expanding-window rolling validation and engineered feature ablation: methodological insights for small-sample digital public health analytics.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Accurate estimation of health outcomes from small-sample time-series data remains a persistent challenge in digital public health, particularly when data are limited and tailored analytical frameworks are scarce. This study addresses this gap by proposing a systematic machine learning pipeline that, while demonstrated on Women's National Basketball Association (WNBA) team performance data, may offer a useful methodological reference for small-sample public health estimation tasks. We collect team-level statistics from 20 WNBA seasons (2006-2025) and construct nine engineered indicators capturing quarter-level scoring dynamics, positional synergy, and defensive efficiency. Two target variables are modeled separately: Points Per Game (PPG) as a process indicator and Win Percentage (WIN%) as an outcome indicator. Eight regression algorithms are evaluated under an expanding-window rolling validation framework that respects temporal ordering. Results demonstrate that regularized linear models consistently outperform ensemble tree-based and kernel methods for both targets: Lasso Regression achieves
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