A team’s first minute of conversation before an esports match may contain information associated with its eventual result, according to a 2026 study of 89 Counter-Strike matches.
The strongest results came from models that combined the team’s pre-match voice recording with the gap between the teams’ rankings. A decision-tree model achieved an area under the curve, or AUC, of 77.3%, with a 95% confidence interval of 65.8% to 86.9%. Its accuracy was 78.6%, with a confidence interval of 69.9% to 86.7%.
Those figures indicate that the model performed better than a 50% chance-level benchmark in this dataset. They do not show that voice communication caused a team to win, or that the model would perform as well with other teams.
What the researchers analyzed
The study examined the first 60 seconds of pre-match communication recorded through players’ headsets during warm-up. The matches were played by one professional five-player team.
Because the players shared one audio channel, the analysis captured the team’s collective sound rather than identifying the contribution of individual players. Researchers divided the recordings into 50-millisecond slices and described each slice using 68 acoustic features, including measures related to energy, spectral patterns, Mel-frequency cepstral coefficients and chroma.
The researchers then used five model types to assess whether the audio and ranking information could distinguish wins from losses. The models were evaluated on unseen matches using five splits, with every match tested once and audio slices from the same match kept out of both the training and test sets.
Ranking remained the strongest input
All five models that combined voice with ranking had confidence intervals above the 50% chance level. The other four combined models produced AUCs ranging from 66.0% to 70.1%.
However, ranking was the top-weighted input in all five combined models. The researchers described those feature-importance findings as exploratory, and they did not conduct a formal statistical test comparing the combined models with ranking-only models.
That means the study does not directly establish how much predictive information voice added beyond team rankings. No ranking-only model clearly beat chance; the best ranking-only AUC was 57.5%.
Voice alone produced mixed results
Only two of the five voice-only models clearly performed above chance. The decision tree had an AUC of 67.8%, with a 95% confidence interval of 54.8% to 79.2%. The random forest had an AUC of 64.0%, with a confidence interval of 50.7% to 76.3%.
Results for the other three voice-only models were less conclusive. Linear discriminant analysis, k-nearest neighbors and logistic regression produced AUCs of 61.5%, 60.1% and 55.8%, respectively, but their confidence intervals dipped below 50%.
What the audio cannot explain
The study did not measure stress, arousal or anxiety. Although the researchers discussed a possible pathway in which these states might influence voice patterns and match outcomes, they presented it as a conceptual framework rather than a demonstrated mechanism.
The observed patterns could instead reflect team habits, tactical communication or differences in microphones and recording conditions. Headsets and recording conditions varied across sessions.
The researchers also used group audio, which prevented the analysis from separating individual players’ voices. The models used NearMiss undersampling, and the study did not compare that approach with alternative data-handling methods.
Why replication matters
All 89 matches came from the same five players. The models were not tested on another team, game or competitive level, and the researchers said that the accuracy estimates may be optimistic until the approach is validated with outside data and replicated.
If similar findings are confirmed, the researchers proposed that voice analysis could become a voluntary practice tool. For example, a team might compare a 60-second warm-up sample with its usual baseline.
They also said such a system should not be used to select lineups, discipline players or suggest a mental-health diagnosis. Any real-world use, in their view, would require player consent, restricted access and clear reporting of uncertainty.
For now, the study offers an early signal that collective pre-match communication may contain information linked with performance in one team’s matches. It does not establish a reliable universal predictor of esports results, and it leaves the independent contribution of voice unresolved.
AI tools were used to assist with the preparation of this article under our editorial standards.
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