{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:08:18Z","timestamp":1788761298082,"version":"build-2803163510"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Competitive performance in electronic sports (e-sports) depends on rapid decision-making, emotional regulation, and coordinated communication, yet little is known about whether pre-match vocal behavior contains information predictive of competitive outcomes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study applied supervised machine learning to 68 acoustic features extracted at the frame level (50-ms frames, 25-ms step) from 60-second pre-match team communication recordings in 89 professional Counter-Strike: Global Offensive matches; frame-level predictions were aggregated into a match-level score representing the proportion of frames classified as a win. Three predictive conditions were evaluated, acoustic features only, ranking difference only, and a combined model integrating both, using stratified group five-fold cross-validation. Uncertainty was quantified using percentile 95% confidence intervals from 2,000 match-level bootstrap resamples of the pooled out-of-fold predictions, with chance-level discrimination defined as AUC = 0.50.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Across algorithms, models combining voice and ranking achieved the strongest performance, with the Decision Tree classifier reaching a mean bootstrap AUC of 77.3% (95% CI 65.8\u201386.9) and accuracy of 78.6% (95% CI 69.9\u201386.7); all five voice-plus-ranking models had 95% CIs excluding chance. Voice-only models showed more limited evidence of above-chance discrimination: only the Decision Tree (AUC 67.8%, 95% CI 54.8\u201379.2) and Random Forest (AUC 64.0%, 95% CI 50.7\u201376.3) had confidence intervals excluding 0.50, whereas Linear Discriminant Analysis, Logistic Regression, and k-Nearest Neighbors did not. No ranking-only model showed a confidence interval excluding chance. Exploratory LIME-based feature-attribution analyses indicated that ranking difference received the highest within-model attribution in the combined models, while delta spectral flux, chroma standard deviation, and spectral centroid received the highest within-model attribution among acoustic descriptors for tree-based, linear, and distance-based classifiers, respectively; these rankings are descriptive and were not subjected to formal cross-model statistical comparison.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings provide preliminary, dataset-bounded evidence that acoustic patterns in brief pre-match team communication were associated with match outcome and, for some models, contributed predictive information beyond ranking; the retrospective, single-team design does not establish a generalizable behavioral biomarker or a causal link between vocal acoustics and competitive readiness.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyg.2026.1819513","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:55Z","timestamp":1788759235000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting competitive outcomes in professional e-sports from 60-second pre-match voice acoustics using machine learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Gabriel","family":"Kadri","sequence":"first","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raphael I. M.","family":"Santos","sequence":"additional","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Felipe O.","family":"Aguiar","sequence":"additional","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]},{"name":"Artificial Intelligence Research Division, Infinity Doctors Inc., Miami","place":["FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor H. O.","family":"Otani","sequence":"additional","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]},{"name":"Artificial Intelligence Research Division, Infinity Doctors Inc., Miami","place":["FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo R.","family":"Uchida","sequence":"additional","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]},{"name":"Artificial Intelligence Research Division, Infinity Doctors Inc., Miami","place":["FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas M.","family":"Marques","sequence":"additional","affiliation":[{"name":"Mental Health Department, Santa Casa de S\u00e3o Paulo School of Medical Sciences, S\u00e3o Paulo","place":["SP, Brazil"]},{"name":"Artificial Intelligence Research Division, Infinity Doctors Inc., Miami","place":["FL, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.3390\/s25072072","article-title":"EEG-based engagement monitoring in cognitive games","volume":"25","author":"Ahmed","year":"2025","journal-title":"Sensors"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701","article-title":"\u201cOptuna: a next-generation hyperparameter optimization framework,\u201d","author":"Akiba","year":"2019","journal-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-809633-8.20349-X","article-title":"\u201cCross-validation,\u201d","author":"Berrar","year":"2019","journal-title":"Reference Module in Life Sciences Encyclopedia of Bioinformatics and Computational Biology"},{"key":"B4","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.specom.2015.03.004","article-title":"A review of depression and suicide risk assessment using speech analysis","volume":"71","author":"Cummins","year":"2015","journal-title":"Speech Commun."},{"key":"B5","doi-asserted-by":"publisher","first-page":"e0144610","DOI":"10.1371\/journal.pone.0144610","article-title":"pyAudioAnalysis: an open-source python library for audio signal analysis","volume":"10","author":"Giannakopoulos","year":"2015","journal-title":"PloS One"},{"key":"B6","doi-asserted-by":"publisher","first-page":"8904","DOI":"10.1038\/s41598-024-59539-w","article-title":"Changes in concentration performance and alternating attention after short-term virtual reality training in e-athletes: a pilot study","volume":"14","author":"Lachowicz","year":"2024","journal-title":"Scient. Reports"},{"key":"B7","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/lio2.354","article-title":"Automated assessment of psychiatric disorders using speech: a systematic review","volume":"5","author":"Low","year":"2020","journal-title":"Laryng. Investig. Otolaryngol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"660","DOI":"10.3389\/fnhum.2015.00660","article-title":"Virtual reality for enhanced ecological validity and experimental control in the clinical, affective and social neurosciences","volume":"9","author":"Parsons","year":"2015","journal-title":"Front. Hum. Neurosci."},{"key":"B9","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Machine Learn. Res."},{"key":"B10","doi-asserted-by":"publisher","first-page":"875","DOI":"10.2165\/00007256-200131120-00004","article-title":"Psychological factors in sport performance","volume":"31","author":"Raglin","year":"2001","journal-title":"Sports Med."},{"key":"B11","doi-asserted-by":"publisher","author":"Ribeiro","year":"2016","DOI":"10.1145\/2939672.2939778"},{"key":"B12","doi-asserted-by":"publisher","first-page":"10678","DOI":"10.3390\/app151910678","article-title":"Metacognitive modulation of cognitive-emotional dynamics under social-evaluative stress: an integrated behavioural\u2013EEG study","volume":"15","author":"Rovelli","year":"2025","journal-title":"Appl. Sci."},{"key":"B13","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1016\/j.emj.2015.11.006","article-title":"When shared cognition leads to closed minds: temporal mental models, team learning, adaptation and performance","volume":"34","author":"Santos","year":"2016","journal-title":"Euro. Manag. J."},{"key":"B14","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/S0167-6393(02)00084-5","article-title":"Vocal communication of emotion: a review of research paradigms","volume":"40","author":"Scherer","year":"2003","journal-title":"Speech Commun."},{"key":"B15","doi-asserted-by":"crossref","DOI":"10.1002\/9781118706664","volume-title":"Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language Processing","author":"Schuller","year":"2013"},{"key":"B16","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1037\/spy0000337","article-title":"Performance breakdown under pressure among esports competitors","volume":"13","author":"Sharpe","year":"2024","journal-title":"Sport Exerc. Perform. Psychol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1080\/10253890.2019.1584180","article-title":"Stress measurement using speech: Recent advancements, validation issues, and ethical and privacy considerations","volume":"22","author":"Slavich","year":"2019","journal-title":"Stress"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.3389\/fpsyg.2020.01030","article-title":"Physiological and cognitive functions following a discrete session of competitive esports gaming","volume":"11","author":"Sousa","year":"2020","journal-title":"Front. Psychol"},{"key":"B19","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1016\/S0167-8760(00)00175-6","article-title":"Elite pistol shooters: physiological patterning of best vs. worst shots","volume":"41","author":"Tremayne","year":"2001","journal-title":"Int. J. Psychophysiol. Offic. J. Int. Org. Psychophysiol."},{"key":"B20","doi-asserted-by":"publisher","first-page":"e70112","DOI":"10.1002\/smi.70112","article-title":"The fundamental frequency of voice as a potential stress biomarker: a systematic review and meta-analysis","volume":"41","author":"Veiga","year":"2025","journal-title":"Stress Health J. Int. Soc. Investig. Stress"},{"key":"B21","doi-asserted-by":"publisher","first-page":"815678","DOI":"10.3389\/fpsyt.2022.815678","article-title":"Vocal acoustic features as potential biomarkers for identifying\/diagnosing depression: a cross-sectional study","volume":"13","author":"Zhao","year":"2022","journal-title":"Front. Psychiatr."}],"container-title":["Frontiers in Psychology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyg.2026.1819513\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:56Z","timestamp":1788759236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpsyg.2026.1819513\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,7]]},"references-count":21,"alternative-id":["10.3389\/fpsyg.2026.1819513"],"URL":"https:\/\/doi.org\/10.3389\/fpsyg.2026.1819513","relation":{},"ISSN":["1664-1078"],"issn-type":[{"value":"1664-1078","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,7]]},"article-number":"1819513"}}