The effect of different resistance and assistance loads on 30-m sprint kinematics.

Resisted sprint and assisted sprint are the two main types of training methods used by athletes in sprint training, so optimizing resisted sprint training and assisted sprint training process is beneficial for improving athletes' sprint performance. Kinematics is the most intuitive parameter that reflects the quality of training during running process, and it is particularly important to analyze the gait of athletes during resisted and assisted sprint process. Therefore, this paper investigates the effects of resisted and assisted sprint on the sprint kinematics of sprinters in the first 30 meters to demonstrate the targeted effects of resisted and assisted sprint training. The experimental results show that compared to the unloaded running, male collegiate sprinters increase their total step count, decrease their step length, increase their step time, increase their contact time, whereas have almost no change in the flight time when performing the 30-m resisted sprint. Male collegiate sprinters decrease their total step count, increase their step length, increase their step time, decrease their contact time and increase their flight time, when performing the 30-m assisted sprint. In addition, it is found that resisted sprint training is beneficial for improving the athletes' power and explosiveness during the acceleration phase, thereby improving acceleration ability. However, prolonged and frequent resisted sprint training may reduce the step length and step frequency of athletes. Assisted sprint training is beneficial for shortening the contact time of athletes, improving their step length and flight time, and enabling them to overspeed, thereby increasing their maximum speed ability.

Characterizing player's playing styles based on player vectors for each playing position in the Chinese Football Super League.

Characterizing playing style is important for football clubs on scouting, monitoring and match preparation. Previous studies considered a player's style as a combination of technical performances, failing to consider the spatial information. Therefore, this study aimed to characterize the playing styles of each playing position in the Chinese Football Super League (CSL) matches, integrating a recently adopted Player Vectors framework. Data of 960 matches from 2016-2019 CSL were used. Match ratings, and 10 types of match events with the corresponding coordinates for all the line-up players whose on-pitch time exceeded 45 minutes were extracted. Players were first clustered into eight positions. A player vector was constructed for each player in each match based on the Player Vectors using Nonnegative Matrix Factorization (NMF). Another NMF process was run on the player vectors to extract different types of playing styles. The resulting player vectors discovered 18 different playing styles in the CSL. Six performance indicators of each style were investigated to observe their contributions. In general, the playing styles of forwards and midfielders are in line with football performance evolution trends, while the styles of defenders should be reconsidered. Multifunctional playing styles were also found in high-rated CSL players.

Quantifying the Effectiveness of Defensive Playing Styles in the Chinese Football Super League.

Establishing and illustrating a predictive and prescriptive model of playing styles that football teams adopt during matches is a key step toward describing and measuring the effectiveness of styles of play. The current study aimed to identify and measure the effectiveness of different defensive playing styles for professional football teams considering the opponent's expected goal. Event data of all 1,120 matches played in the Chinese Football Super League (CSL) from the 2016 to 2020 seasons were collected, with fifteen defense-related performance variables being extracted. The PCA model (KMO = 0.76) output eight factors that represented 7 different styles of play (factor 6 and 8 represent one style of play) and explained 85.17% of the total variance. An expected goal (xG) model was built using data related to 27,852 shots. Finally, the xG of the opponent was calculated in the multivariate regression model, outputting five factors that (p < 0.05) explained 41.6% of the total variance in the xG of the opponent and receiving a dangerous situation (factor 7) was the most apparent style (31.3%). Finally, the predicted model with defensive styles correlated with actual xG of the opponent at r = 0.62 using the 2020 season as testing data which showed that the predicted xG was correlated moderately with the actual. The result indicated that if the team strengthened the defense closed to the own goal, high intensity confrontation, and defense of goalkeeper, meanwhile making less errors and receiving less dangerous situations, the xG of the opponent would be greatly reduced.

Functional movement screen dataset collected with two Azure Kinect depth sensors.

This paper presents a dataset for vision-based autonomous Functional Movement Screen (FMS) collected from 45 human subjects of different ages (18-59 years old) executing the following movements: deep squat, hurdle step, in-line lunge, shoulder mobility, active straight raise, trunk stability push-up and rotary stability. Specifically, shoulder mobility was performed only once by different subjects, while the other movements were repeated for three episodes each. Each episode was saved as one record and was annotated from 0 to 3 by three FMS experts. The main strength of our database is twofold. One is the multimodal data provided, including color images, depth images, quaternions, 3D human skeleton joints and 2D pixel trajectories of 32 joints. The other is the multiview data collected from the two synchronized Azure Kinect sensors in front of and on the side of the subjects. Finally, our dataset contains a total of 1812 recordings, with 3624 episodes. The size of the dataset is 190 GB. This dataset provides the opportunity for automatic action quality evaluation of FMS.