We employed the Brazilian Mood Scale (BMS) through Google Forms to assess the mood state of the athletes, which was sent to them at 8 pm the day before the first match. We considered only responses received until 10 pm (GMT-3). In addition to the BMS, we included two extra questions: "last night's sleep hours" and "how do you feel now?". The athletes were required to choose one of the following five items: very bad, bad, regular, good, or excellent. We collected statistical data of the matches, including points scored, points conceded, set , set defeats, matches won, and match defeats. To verify the correlation and differences between variables, we employed Bayesian analysis. The Bayesian ANOVA model, which included sleep quality on fatigue, was 12.465 times more likely than the null model. Post-hoc analyses revealed that bad sleepers were 5.545 times more likely to experience more fatigue than good sleepers. Our findings suggest that worse sleep quality is associated with a higher likelihood of experiencing increased fatigue. We also found anecdotal evidence, using Bayesian analysis, for a potential interference of sleep quality in confusion, vigor, and tension. However, when testing the interference of sleep quality in depression or anger using frequentist analysis, we did not detect any significant differences. Finally, our data did not support any hypotheses of interference of sleep quality in match statistics. Key words: Bayesian analysis, fatigue, depression, confusion, sport, performance.