As a up and coming sport, powerlifting is gathering more and more attetion. Powerlifters vary in their strength levels and performances at different ages as well as differing in height and weight. Hence the questions arises on how to establish the relationship between age and weight. It is difficult to judge the performance of athletes by artificial expertise, as subjective factors affecting the performance of powerlifters often fail to achieve the desired results. In recent years, artificial intelligence has made groundbreaking strides. Therefore, using artificial intelligence to predict the performance of athletes is among one of many interesting topics in sports competitions. Based on the artificial intelligence algorithm, this research proposes an analysis model of powerlifters’ performance. The results show that the method proposed in this paper can predict the best performance of powerlifters. Coefficient of determination-R2=0.86 and root-mean-square error of prediction-RMSEP=20.98 demonstrate the effectiveness of our method.

Powerlifting is a strength sport that is quite popular in the world. Powerlifters have their power levels varied at different ages and body weights, and their power levels are closely related to their performance. Therefore, studying the impact of age and weight on the performance of powerlifters is an important work. The traditional method relies mainly on artificial experience to judge the performance, and often does not get the desired results. In recent years, machine learning has developed rapidly, and applying machine learning in sports is a very interesting topic. This study is based on a new machine learning algorithm to construct a prediction model for the best performance of powerlifters. We propose a doublelayer extreme learning machine based on affine transformation and two-layer extreme learning machine theory (AF-DELM). Then use a dynamic weight-gravitational search algorithm to improve the AF-DELM networks. The results show that the algorithm can better predict the performance and provide an effective predictive aid for the powerlifting competition.INDEX TERMS Gravitational search algorithm, gravitational-double layer extreme learning machine, powerlifting performance, prediction model.

Background: Football is a team sport; players often have fierce ball disputes to limit the opponent's ability, resulting in a lot of physical consumption. Objective: To evaluate the professional development of physical fitness for U10 five-a-side football teams, this study introduces several exercises to test and improve their physical strength. Methods: We introduce 5 tests, including long jump on the spot, 15-meter sprint, 5x30m sprints, 5-minute running, and 1-minute rope skipping. 6 months of training was divided into 3 phases with four 90-minute training sessions/week. Phase 1 was aimed for the players to adapt to normal training, phase 2 was to develop the maximum focus speed, and phase 3 was to develop professional physical strength. Results: The results show that after 6 months of training, the participants’ physical achievements in all tests have witnessed growth. The growth was statistically significant because tcalculated > tstandard at the possibility P < 0.05, especially in the test of rope skipping in one minute with the highest growth rate W = 9.47%. Conclusion: The results of this research can be used as a reference and scientific basis of general physical fitness development in football for kids to build training programs and improve their effectiveness.

Aim: Assessment of male students’ basic badminton technique development at high schools Objective: Physical education is an integral part of high school curriculums, contributing to students’ comprehensive development, and badminton is a sport of special interest at this level. Methods: However, due to different starting points, academic pressure and lack of frequent practice, students’ badminton training and performance are affected more or less. From this situation, we recognize the need to assess student’s basic techniques in high school badminton teams. Results: The study results demonstrate that 3 out of 7 tests to assess male students’ techniques in high school badminton teams show obvious progress and statistical significance. Conclusion: In particular, the moving and making straight drop shots has the highest growth rate with W = 13.5%, tcalculated=3.53.

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