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Psychological recovery paths of players within a team: a decision tree-assisted longitudinal analysis
Şeval Kayğusuz1, Cengiz Karagözoğlu2
1Department of Sport Management, Faculty of Sport Science, Istanbul Gedik University, Istanbul, Türkiye. seval.kaygusuz@gedik.edu.tr.
Background:
This study aims to monitor the long term psychological recovery of team athletes after competitions. The second aim was to develop a model based on the primary recovery strategies employed by team athletes.
Methods:
The study sample consisted of 38 male football and basketball players aged between 18 and 35 years (sd = 5.51), with an average of 15 years of experience in team sports. Data were collected by using the Sport Psychological Recovery Scale, the Checklist of Individual Strength (CIS) Scale, Emotional State Profile based on Individual Zone of Optimal Functioning (IZOF) methodology and athletes' perceived physical fatigue, perceived mental fatigue, perceived competition success, perceived competition difficulty, and perceived sleep quality were assessed using Virtual Analog Scale (VAS). The collected data were analyzed through line graph visualizations, heat maps, hierarchical cluster analysis, and decision tree modeling.
Results:
Findings from the line graphs revealed substantial individual differences in players' preferences for psychological recovery strategies. Heatmap analyses demonstrated significant correlations between fatigue and recovery-related variables. The dendrogram analysis confirmed the existence of two distinct psychological recovery profiles, characterized as fatigue and recovery patterns. Finally, decision tree modeling identified the flourishing, as the most significant predictor of recovery strategy selection. The model identified a critical flourishing score of 7.19 out of 10. Athletes scoring below this threshold predominantly preferred mental recovery strategies, whereas those scoring above it primarily opted for psychological detachment strategies.
Conclusions:
Flourishing serves as a key determinant role in shaping athletes' psychological recovery strategies. By employing decision tree and cluster-based modeling this study provides novel insights into the individualized dynamics of post-performance recovery. These findings advance the design of personalized psychological support programs for athletes, enhancing both recovery efficacy and long-term well being.
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