Basketball Data Analysis Using Spark Framework and K-Means Algorithm

Optimizing wheelchair basketball lineups: A statistical approach to coaching strategies

This study was designed to support the tactical decisions of wheelchair basketball (WB) coaches in identifying the best players to form winning lineups. Data related to a complete regular season of a top-level WB Championship were examined. By analyzing game-related statistics from the first round, two clusters were identified that accounted for approximately 35% of the total variance. Cluster 1 was composed of low-performing athletes, while Cluster 2 was composed of high-performing athletes. Based on data related to the second round of the Championship, we conducted a two-fold evaluation of the clusters identified in the first round with the team's net performance as the outcome variable. The results showed that teams where players belonging to Cluster 2 had played more time during the second round of the championship were also those with the better team performance (R-squared = 0.48, p = 0.035), while increasing the playing time for players from Classes III and IV does not necessarily improve team performance (r2 = -0.14, p = 0.59). These results of the present study suggest that a collaborative approach between coaches and data scientists would significantly advance this Paralympic sport.

Retracted: Basketball Data Analysis Using Spark Framework and K-Means Algorithm

[This retracts the article DOI: 10.1155/2021/6393560.].

Big Data and Learning Analytics Model for Promoting Physical Literacy in College Students in China

Physical literacy has gained much popularity in educational circles who are working on the improvement of curriculum and overall standard of education. It involves a holistic lifelong comprehensive learning approach that includes movements and physical activities. Overall, it has positive effects on physical, psychological, social, and cognitive health of individuals, so physical literacy exemplifies the dedication to raise a healthier, more active generation. Numerous factors interacting between humanities and social sciences affect the promotion of physical literacy, so such a study will be interdisciplinary which will consider across all social and individual factors. The current research proposes a system dynamic "SD" model to promote students' physical literacy by building a complete causal loop diagram of the model to illustrate the general system. Based on the casual loop diagram, the system is then presented as four subsystems. The model is simulated by allocating 14 different changes of indexes in the physical literacy promotion system to find better allocations for optimal effectiveness in promoting physical literacy. Simulations are carried out by using the Apache Spark architecture utilizing "Big Data" tools for effective, speedy, and reliable analysis and results. The study proposes that different physical literacy indexes in different grades require attention; the optimal promotion of physical literacy can be achieved by increasing the physical knowledge of lower-grade students and increasing the physical attitude of higher-grade students. The model can be used to make decisions about efficient physical literacy management and physical literacy promotion planning.