Construct Validation of a New Instrument to Measure Declarative Tactical Knowledge in Basketball

Development and Validation of an Observational Game Analysis Tool with Artificial Intelligence for Handball: Handball.ai

Performance analysis based on artificial intelligence together with game-related statistical models aims to provide relevant information before, during and after a competition. Due to the evaluation of handball performance focusing mainly on the result and not on the analysis of the dynamics of the game pace through artificial intelligence, the aim of this study was to design and validate a specific handball instrument based on real-time observational methodology capable of identifying, quantifying, classifying and relating individual and collective tactical behaviours during the game. First, an instrument validation by an expert panel was performed. Ten experts answered a questionnaire regarding the relevance and appropriateness of each variable presented. Subsequently, data were validated by two observers (1.5 and 2 years of handball observational analysis experience) recruited to analyse a Champions League match. Instrument validity showed a high accordance degree among experts (Cohen's kappa index (k) = 0.889). For both automatic and manual variables, a very good intra- ((automatic: Cronbach's alpha (α) = 0.984; intra-class correlation coefficient (ICC) = 0.970; k = 0.917) (manual: α = 0.959; ICC = 0.923; k = 0.858)) and inter-observer ((automatic: α = 0.976; ICC = 0.961; k = 0.874) (manual: α = 0.959; ICC = 0.923; k = 0.831) consistency and reliability was found. These results show a high degree of instrument validity, reliability and accuracy providing handball coaches, analysts, and researchers a novel tool to improve handball performance.

RECOVERY OF BASKETBALL PLAYERS AFTER OFFENSIVE TECHNIQUE TRAINING

ABSTRACT Introduction: The shot accuracy and standardization in basketball games are closely related to the quality of the team’s scoring. The percentage of throws is fundamental to know if the basketing movement can be correctly detected and judged. In traditional basketball, players’ skills are composed of specific movements. With the gradual development of these technical movements, coaches should pay more attention to each athlete so that their technical problems can be identified and worked on early. Objective: This article discusses how basketball players move, catch the ball on the court, and execute the shot to the basket. Methods: We randomly and voluntarily selected 90 basketball players by sampling. The independent variables, biomarkers, and kinetic patterns were properly cataloged and processed. The main test items included body, lower limb, and groin circumference. Finally, mathematical statistics were used to analyze the correlation of each index. The relationship between basketball players’ movement posture and basic offensive techniques was analyzed. Results: In basketball, basic attack techniques in multifactorial training remarkably influences their score. The role of score recovery in basketball is significant. Conclusion: The quality of basketing action is directly related to the technical level of offensive basketball. A variety of ball training can improve the skill level of the basketball player. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Construct Validity Evidence in the Motor Coordination Test with Ball - MCTB

Our purpose with this study was to analyze construct validity evidence for the Motor Coordination Test with Ball (MCTB). Our participant sample included 1620 students (741 girls; 879 boys) aged between 10-12 years. Evidence for construct-validity of the four tasks using exploratory factor analysis (EFA) and adequacy indices confirmed a good fit of a final two-dimensional model (BTS = .000; KMO = .83; loading .53 to .91; communalities ranging from .31 to .86; explained variance of 63.5%; goodness-of-fit index (GFI) = .99; adjusted GFI (AGFI) = .97; RMSR = .03), and confirmatory factor analysis (CFA) (X²/df = 4.9; GFI = .97; comparative fit index (CFI) = .92; TLI = .90; Root-Mean-Square Error of Aproximation (RMSEA) = .03). We concluded that, in a Brazilian sample, the MCTB has good construct validity for motor coordination tests with balls; this helps qualify this test for use in schools and in the sports context.