Psychotype and Thinking Style as Predictors of Success in Esports

Objectives. This study aimed to identify the cluster structure of esports athletes’ psychological profiles based on psychotype, thinking style, age, and gaming experience, with the objective of applying the findings to training programs and psychological support. Materials and methods. The study involved 53 esports players aged 18 to 27. Data were collected using psychodiagnostic tools: an adapted MBTI model, the Eysenck Personality Inventory (EPI), subjective scales for assessing emotional stability, and a thinking style questionnaire. Pearson’s chi-square (χ²) test for independent samples was used to compare observed and expected frequencies. Cluster analysis was conducted using the Statistica “Data Mining” module (k-means method, Euclidean distance, initial centers with maximum distance, and 10-fold cross-validation). ANOVA was applied to assess statistically significant differences in means within and between clusters. Bonferroni’s post hoc test was used to determine which cluster pairs differed substantially. Hypotheses were tested at a significance level of α = 0.05. Results. Three clusters were identified based on psychotype: “Socially Adaptive” (32.1%), “Introverted Analysts” (45.3%), and “Active Extraverts” (22.6%). Participants in the “Active Extraverts” cluster were older and had significantly more gaming experience. Two clusters emerged based on thinking style: “Structured Thinkers” (60.4%) and “Adaptive Thinkers” (39.6%), which differed considerably in their preference for clear instructions and planning strategies. The majority of players (over 85%) demonstrated a logical or analytic-intuitive thinking style. A marked relationship was found between thinking style cluster affiliation and the overall score on the thinking style scale (rpb = 0.672; p < 0.0001), confirming the validity of the clustering. The findings align with international data on cognitive flexibility, adaptability, and the role of mental coaching in esports. Conclusions. The study proves that cluster analysis enables the typologization of esports athletes based on cognitive-behavioral profiles, thereby providing a foundation for the development of individualized training and psychological coaching programs.