A Data-Driven Analysis of Performance Metrics and Match Outcomes in High School Boys' Volleyball
Year: 2026
Type: preprint
Abstract: Note (September 2026): A corrected and expanded version of this analysis is in preparation and will be posted as a new version of this record. Several conclusions reported here do not survive correction for match length. This study examined the relationship between volleyball performance metrics and match outcomes in high school boys' volleyball. A dataset of 71 matches collected across two competitive seasons was analyzed using descriptive statistics, Pearson correlation analysis, and logistic regression modeling. Correlation analysis identified Side-Out Percentage, Hitting Percentage, and Serve W/L Percentage as the variables most strongly associated with match success. Two logistic regression models were developed to examine the relationship between these performance metrics and match outcomes. A reduced three-variable model consisting of Side-Out Percentage, Hitting Percentage, and Serve W/L Percentage achieved an average five-fold cross-validation accuracy of 88.8%, outperforming an expanded eight-variable model, which achieved an accuracy of 87.3%. Side-Out Percentage demonstrated the strongest association with match outcomes in both models, followed by Serve W/L Percentage and Hitting Percentage. Additional evaluation of the reduced model produced a classification accuracy of 88.9% and an ROC-AUC of 0.977, indicating a strong association between these metrics and match outcomes within this dataset. These findings suggest that a small number of key performance metrics are closely associated with match success and provide practical insight for data-driven volleyball performance evaluation, although validation across additional teams and seasons is needed.
Author: Kang, Alex
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7170799140
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