Abstract
This work provides a new dataset method intended to build a biomechanical training model for the free-throws shots in basketball. Eight youth players from Jordanian secondary public school were video recorded from the sagittal plane executing free throw shots in basketball. Collectively (480) video clips were recorded and analyzed using image processing techniques to identify the ball track. Video processing involves extracting (11) different parameters that may affect the free throw in basketball game after detecting the ball trajectory. Creation of this dataset and its subsequent use for extracting free-throws information yielded several insights. First, a set of most important features were identified as those affecting the free-throws score in basketball. Second, our data set can be trained and tested using machine learning classifiers for building a new biomechanical training model based on set of rules that can be useful for both trainers and trainee to rehearse on successful free-throws in basketball. The dataset is being made publicly available at www.ju.edu.jo.
Highlights
Basketball is one of the most important sports in the world
The use of kinematic analysis methods for these different skills is based on interpreting and dividing skills and comparing them with the ideal performance to correct mistakes, achieve high and optimal motor skill and avoid injuries during practice. (Kilani & Odtallah, 2008) Kinematic analysis is an essential knowledge of effective basketball skills training. (Kilani, Slim, & Al-Kilani, 2009). It adds to the coaches a correct background to help them to display the skill and movement correctly and know the technical points that must be focused in the training of basketball skills. (Eslim, AlKilani, & Kilani, 2010) Studies in modeling and simulation were designed to determine the optimum in many motor skills, including the skill of free throw in basketball. (Okazaki, Rodacki, & Satern, 2015; Kilani, & Abu Eisheh, 2010; Al-Kilani, & Kilani, 1993) and the effect of shooting distance on energy flow in basketball jump shot was conducted by Nakano, Fukashiro, & Yoshioka, (2018)
This study aims to implement artificial intelligence techniques and machine learning algorithms, such as the REPTree algorithm and the Random Tree algorithm to create a new dataset that can be used to build a biomechanical model that facilitates the task of trainers and trainees by training on a set of rules that lead to successful shoots with a high accuracy level
Summary
Basketball is one of the most important sports in the world. Coaches face many problems related to access to the best technical performance of motor skills in athletes' sports. (Kilani, Slim, & Al-Kilani, 2009) It adds to the coaches a correct background to help them to display the skill and movement correctly and know the technical points that must be focused in the training of basketball skills. In (Liu et al, 2011) they proposed a shot identification technique in basketball video based on ball tracking. They used mean-shift algorithm for ball tracking and Kalman filter and the hoop was identified using SURF (Speeded Up Robust Features). (Fu & et al, 2011) provided throwing location estimation using the ball trajectory in a basketball game They extracted 2-D trajectories based on physics-based algorithm to track the ball and ; they developed the 3-D trajectories based on their previous work
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