Login / Signup

Decision-Making Skills in Youth Basketball Players: Diagnostic and External Validation of a Video-Based Assessment.

David RöschFlorian SchultzOliver Höner
Published in: International journal of environmental research and public health (2021)
Decision-making is a central skill of basketball players intending to excel individually and contribute to their teams' success. The assessment of such skills is particularly challenging in complex team sports. To address this challenge, this study aimed to conceptualize a reliable and valid video-based decision-making assessment in youth basketball. The study sample comprised youth basketball players of the German U16 national team (n = 17; MAge = 16.01 ± 0.25 years) and students of a sports class (n = 17; MAge = 15.73 ± 0.35 years). Diagnostic validity was tested by determination of the performance levels according to response accuracy as well as response time in the assessment. External validity was examined by investigation of the correlation between the diagnostic results of the elite athletes and their real game performance data associated with passing skills. Logistic regression analysis revealed that the diagnostic results discriminate between performance levels (χ2(2) = 20.39, p < 0.001, Nagelkerke's R2 = 0.60). Multiple regression analysis demonstrated a positive relationship between the diagnostic results and assists (F(2,10) = 4.82, p < 0.05; R2 = 0.49) as well as turnovers per game (F(2,10) = 5.23, p < 0.05; R2 = 0.51). However, no relationship was detected regarding the assist-turnover ratio. Further, response time discriminated within the elite athletes' performance data but not between performance levels while for response accuracy the opposite is the case. The results confirm the diagnostic and external validity of the assessment and indicate its applicability to investigate decision-making skills in youth basketball.
Keyphrases
  • decision making
  • mental health
  • high school
  • young adults
  • quality improvement
  • palliative care
  • medical students
  • electronic health record
  • machine learning
  • single cell
  • high resolution