Relationship Between Attacking Parameters and Competitive Success in the SEC During the 2023–2024 Regular Season
Main Article Content
Abstract
The influence of attacking performance on achieving favorable outcomes in modern volleyball is one of the most decisive factors in overall team performance. Its analysis requires a systematic approach that enables coaches to optimize their teams’ efficiency and effectiveness through the examination of statistical evidence. Focused on the Southeastern Conference (SEC), this study examines the relationship between attacking parameters—effectiveness and efficiency—and competitive success across different player roles during the 2023 and 2024 regular seasons. The findings provide a deeper understanding of the impact of offensive performance on match outcomes and offer practical tools for designing technical-tactical strategies in high-performance collegiate settings.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This magazine is available in open access under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (http://creativecommons.org/licenses/by-nc-sa/4.0/).
References
Allan, M. L. (2009). Measuring skill importance in women’s soccer and volleyball (Master’s thesis, Brigham Young University). BYU ScholarsArchive. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2669&context=etd
Borges, T. O., Moreira, A., Bacchi, R., Finotti, R. L., Ramos, M., Lopes, C. R. & Aoki, M. S. (2017). Validation of the VERT wearable jump monitor device in elite youth Volleyball players. Biology of sport, 34(3), 239-242. https://doi.org/10.5114/biolsport.2017.66000 DOI: https://doi.org/10.5114/biolsport.2017.66000
Cieminski, K. (2017). The efficiency of executing technical actions by female Volleyball players depending on their positions on the court. Baltic Journal of Health and Physical Activity, 9(3), 44-52. https://doi.org/10.29359/BJHPA.09.3.04 DOI: https://doi.org/10.29359/BJHPA.09.3.04
Cojocaru, A. M., Cojocaru, M. & Grapă, F. (2019). Study the Efficiency of the Attack in Volleyball at the Female Teams in Division A1. Gymnasium - Scientific Journal of Education, Sports, and Health, 20(2), 12-22. https://doi.org/10.29081/gsjesh.2019.20.2.02 DOI: https://doi.org/10.29081/gsjesh.2019.20.2.02
Fotia, J. y Grianta, S. (2019). El voleibol a estudio: Método, técnicas e instrumentos para el análisis. Actas 13 Congreso Argentino de Educación Física y Ciencias, 30 de septiembre al 4 octubre de 2019, Universidad Nacional de La Plata, Facultad de Humanidades y Ciencias de la Educación, Departamento de Educación Física. https://www.memoria.fahce.unlp.edu.ar/trab_eventos/ev.12874/ev.12874.pdf
Gold Medal Squared (2024). Coaching clinic manual. Versión 2024. GMS Publications. https://www.goldmedalsquared.com/
Grianta, S. (2013). Voleibol: Un modelo de juego basado en la estadística [en línea]. 10 Congreso Argentino de Educación Física y Ciencias, 9 al 13 de septiembre de 2013, La Plata. Memoria Académica. https://www.memoria.fahce.unlp.edu.ar/trab_eventos/ev.3082/ev.3082.pdf
Grianta, S. (2015). Utilización de vídeos para el logro de resultados deportivos. 11 Congreso Argentino de Educación Física y Ciencias, 28 de septiembre al 10 octubre de 2015, Ensenada, Argentina. Memoria Académica. Disponible en http://www.memoria.fahce.unlp.edu.ar/trab_eventos/ev.7394/ev.7394.pdf
Harabagiu, N. (2019). Specialists' opinion is concerned with the utility of the data volley software in performance Volleyball. University Arena - Journal of Physical Education, Sport, and Health, (3)2. DOI: https://doi.org/10.62229/UaIII_2_19-8
Hudl (s. f.). Volleymetrics: Advanced analytics for college and pro volleyball teams. https://www.hudl.com/products/volleymetrics
Mukaka, M. M. (2012). Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi medical journal: the journal of Medical Association of Malawi, 24(3), 69-71.
Palao, J. M. (2018). Side-out success and ways that points are obtained in Women’s college Volleyball. Journal of Sports Analytics. 4(4), 243-250. https://doi.org/10.3233/jsa-180153 DOI: https://doi.org/10.3233/JSA-180153
Powers, S., Stancil, L. & Consiglio, N. (2024). Estimating individual contributions to team success in women’s college volleyball. [preprint arXiv:2402.01083]. https://arxiv.org/abs/2402.01083
Rotta, K. & Kranak, M. P. (2020). Spikes, Tips, and Points: Matching in College Volleyball Attacks? Behavior Analysis: Research and Practice. 21(1), 42-50. https://doi.org/10.1037/bar0000197 DOI: https://doi.org/10.1037/bar0000197
Sanghvi, D., Sinha, R. & Saxena, A. (2021). Analyzing and predicting NCAA volleyball match outcome using machine learning techniques. Proceedings of the International Conference on Artificial Intelligence and Machine Learning (ICAIW), pp. 1-8. CEUR Workshop Proceedings. https://ceur-ws.org/Vol-2992/icaiw_wdea_2.pdf
Szabo, D. A. (2016). Modalities of Using the Information Provided by the Statistical Program Click and Scout for Improving the Outside Hitters’ Service Efficiency in Volleyball Game. The European Proceedings of Social and Behavioural Sciences. https://doi.org/10.15405/epsbs.2016.06.47 DOI: https://doi.org/10.15405/epsbs.2016.06.47
Trinsey, J. (2022). Statistical Benchmarks. These benchmarks are for Women’s International Volleyball. Gold Medal Squared. Elite Volleyball Training.
Valdemoros, I. (2024). Influence of Attack Performance on the OVC Volleyball Regular Seasons 2022 & 2023. [Master’s thesis, Eastern Illinois University, USA]. https://thekeep.eiu.edu/theses/5048
VolleyMetrics (2024). Statistical Benchmarks. Hudl. Elite Volleyball Training. [recurso en línea]. https://www.hudl.com/products/volleymetrics