This study is a systematic review of match analyses of table tennis that can be used for reference in China. Using the China National Knowledge Infrastructure (CNKI) as the search tool and “table tennis” as the keyword, we searched for all papers on table tennis published in Chinese core sports journals between 1980 and 2016. Then, we read through 1209 papers and divided them into different categories according to their publication types and main topics. Among them, 128 papers on match analyses met three inclusion criteria, and hence they were chosen as the reviewed studies. The results show that match analyses of table tennis in China have a sound research foundation, with the “three-phase evaluation method” and research on different topics playing an active role in helping coaches and players understand table tennis matches. Computer-aided match analysis not only enhanced the capacity and speed of data processing, but it also facilitated coaches’ and players’ understanding of tactical features by video feedback and multimedia presentation. Although match analyses of table tennis based on different theories or models are still in a preliminary stage, these studies are indeed pushing the development of this field.

In a basketball play, players who are not in possession of the ball (i.e., off-ball players) can still effectively contribute to the team's offense, such as making a sudden move to create scoring opportunities. Analyzing the movements of off-ball players can thus facilitate the development of effective strategies for coaches. However, common basketball statistics (e.g., points and assists) primarily focus on what happens around the ball and are mostly result-oriented, making it challenging to objectively assess and fully understand the contributions of off-ball movements. To address these challenges, we collaborate closely with domain experts and summarize the multi-level requirements for off-ball movement analysis in basketball. We first establish an assessment model to quantitatively evaluate the offensive contribution of an off-ball movement considering both the position of players and the team cooperation. Based on the model, we design and develop a visual analytics system called OBTracker to support the multifaceted analysis of off-ball movements. OBTracker enables users to identify the frequency and effectiveness of off-ball movement patterns and learn the performance of different off-ball players. A tailored visualization based on the Voronoi diagram is proposed to help users interpret the contribution of off-ball movements from a temporal perspective. We conduct two case studies based on the tracking data from NBA games and demonstrate the effectiveness and usability of OBTracker through expert feedback.

To better understand and explore the development trend of the offensive strategies of the world’s top basketball leagues, this study took NBA shooting data in the regular seasons from 2009/2010 to 2018/2019 as the samples and analysed the relationships between the shooting score ratio and game win probability, and the practical application of offensive strategies in games. The results showed that (1) increasing the number and the percentage of three-point offenses in the game can improve the probability of winning. However, too many two-point shots can affect the team’s winning probability to a certain extent. (2) The strong teams in the NBA focused more on the outside offense, while the weak teams focused more on the inside offense. (3) Statistical data further showed that whether a team’s opponent is strong or weak, taking the offensive strategy that tends toward the outside shot can lead to a higher game win probability than the offensive strategy that tends toward the inside offense.

In recent years, with growing health consciousness, at-home workout has become increasingly popular for its convenience and safety. Most people choose to follow video guidance during exercising. However, our preliminary study revealed that fitness-minded people face challenges when watching exercise videos on handheld devices or fixed monitors, such as limited movement comprehension due to static camera angles and insufficient feedback. To address these issues, we reviewed popular workout videos, identified user requirements, and came up with an augmented reality (AR) solution. Following a user-centered iterative design process, we proposed a design space of AR visual cues for workouts and implemented an AR-based application. Specifically, we captured users’ exercise performance with pose-tracking technology and provided feedback via AR visual cues. Two user experiments showed that incorporating AR visual cues could improve movement comprehension and enable users to adjust their movements based on real-time feedback. Finally, we presented several suggestions to inspire future design and apply AR visual cues to sports training.

The aim of this study is to examine the time period and league-ranking effects within different types of goals in the English Premier League. We investigate these effects through the use of six time periods (taken every 15 minutes), eight types of goals and four categories of teams. Based on the Kruskal-Wallis H test followed by the Bonferroni-adjusted Mann-Whitney U test, the key finding of this study is that different types of goals have varying degrees of time period effects and that there is a significant league-ranking effect both within all six time periods and within six types of goals (excluding throw-in and indirect free-kick). The study also reveals that the final 15 minutes of the game is the most prolific time for scoring. Our findings suggest that the top 5 teams have significant advantages in open play goals (elaborate attack and counter attack), while this overwhelming advantage is weakened in the other four types of goals (direct free-kick, corner kick, penalty, and own goal) and even absent in goals from indirect free-kicks and throw-ins. At the same time, there is no significant difference between the lower-middle 5 teams and the bottom 5 teams.