Explanation and verification of the rules of attack in table tennis tactics

Background The rules of attack in table tennis tactics have been discovered by the coaches and researchers of the Chinese table tennis team (CTTT) through long-term practice. However, they are only empirical judgements and have not been objectively verified. Methods The software "Table Tennis Master" has been used to analyse 200 matches of top players of CTTT against various opponents in recent years to obtain detailed statistics by analysing the effect of attack in the end line (AIEL) and attack out of the end line (AOEL). Results (1) The scoring rate of the players was high after AIEL but very low after AOEL (p < 0.05); (2) the round of service (serve/receive) and level of skills had little influence on the effect of AIEL and AOEL; and (3) the timing of attack had a great influence on the effect of AIEL and AOEL (r > 0.9). Conclusions In the high-level table tennis match, the rules of AIEL and AOEL are scientific. In accordance with the rules, the complex tactics can be simplified to the two concepts, AIEL and AOEL.

June 21–22, 2005—Departure

These two meetings are dramatic and intense as Nell is entering her last hours. On June 21, she feels her energy “drop out of the bottom” and asks for a transfusion. Previously, it helped restore her vitality and made a big difference in her mood. Her physician feels it is not indicated, a very distressing answer for Nell. However, she realizes she is too weak to travel to the clinic and accepts that her body is shutting down. She is actively dying. She discusses objects she wants to give away and occasionally closes her eyes to rest, summoning the last of her energy to reenter our conversation. She is fading as she has predicted, just like Tinker Bell leaving this world for the next. Nevertheless, the life in her eyes ignites when we discuss her beloved tennis and the upcoming Wimbledon tennis match. On June 22, Al summons me to the bedside, where Nell is barely conscious. We both say goodbye as she takes her last breath. Together with her hospice nurse, we say a prayer. Nell has let go, not picking and choosing but rather accepting her ending.

Forecasting serve performance in professional tennis matches

Many research papers on tennis match prediction use a hierarchical Markov Model. To predict match outcomes, this model requires input parameters for each player’s serving ability. While these parameters are often computed directly from each player’s historical percentages of points won on serve and return, doing so fails to address bias due to limited sample size and differences in strength of schedule. In this paper, we explore a handful of novel approaches to forecasting serve performance that specifically address these limitations. By applying an Efron-Morris estimator, we provide a means to robustly forecast outcomes when players have limited match data over the past year. Next, through tracking expected serve and return performance in past matches, we account for strength of schedule across all points in a player’s match history. Finally, we demonstrate a new way to synthesize historical serve data with the predictive power of Elo ratings. When forecasting serve performance across 7,622 ATP tour-level matches from 2014-2016, all three of these proposed methods outperformed Barnett and Clarke’s standard approach.

Random forest model identifies serve strength as a key predictor of tennis match outcome

Tennis is a popular sport worldwide, boasting millions of fans and numerous national and international tournaments. Like many sports, tennis has benefitted from the popularity of rigorous record-keeping of game and player information, as well as the growth of machine learning methods for use in sports analytics. Of particular interest to bettors and betting companies alike is potential use of sports records to predict tennis match outcomes prior to match start. We compiled, cleaned, and used the largest database of tennis match information to date to predict match outcome using fairly simple machine learning methods. Using such methods allows for rapid fit and prediction times to readily incorporate new data and make real-time predictions. We were able to predict match outcomes with upwards of 80%accuracy, much greater than predictions using betting odds alone, and identify serve strength as a key predictor of match outcome. By combining prediction accuracies from three models, we were able to nearly recreate a probability distribution based on average betting odds from betting companies, which indicates that betting companies are using similar information to assign odds to matches. These results demonstrate the capability of relatively simple machine learning models to quite accurately predict tennis match outcomes.

Simulation of Tennis Match Scene Classification Algorithm Based on Adaptive Gaussian Mixture Model Parameter Estimation

This paper presents an in-depth analysis of tennis match scene classification using an adaptive Gaussian mixture model parameter estimation simulation algorithm. We divided the main components of semantic analysis into type of motion, distance of motion, speed of motion, and landing area of the tennis ball. Firstly, for the problem that both people and tennis balls in the video frames of tennis matches from the surveillance viewpoint are very small, we propose an adaptive Gaussian mixture model parameter estimation algorithm, which has good accuracy and speed on small targets. Secondly, in this paper, we design a sports player tracking algorithm based on role division and continuously lock the target player to be tracked and output the player region. At the same time, based on the displacement information of the key points of the player’s body and the system running time, the distance and speed of the player’s movement are obtained. Then, for the problem that tennis balls are small and difficult to capture in high-speed motion, this paper designs a prior knowledge-based algorithm for predicting tennis ball motion and landing area to derive the landing area of tennis balls. Finally, this paper implements a prototype system for semantic analysis of real-time video of tennis matches and tests and analyzes the performance indexes of the system, and the results show that the system has good performance in real-time, accuracy, and stability.

Alterations in mechanical muscle characteristics and postural control induced by tennis match-play in young players

Background Central and peripheric fatigue indicators are among the main reasons for performance decline following competition. Because of the impact of these factors on performance, how these variables are affected by match-play could be of interest, especially in young tennis players. Objective This study aimed to investigate alterations induced by a simulated tennis match on postural control and muscle characteristics in young tennis players. Method Seventeen male junior players took part in pre- and post-competition testing sessions performing postural control (displacement, speed and surface area of center of pressures) and muscle characteristics measurements (tone, stiffness, time to relaxation and elasticity). Between trials, participants played an 80-min simulated tennis match. Results No significant differences were observed in any of the tested variables. Moderate-to-large effect sizes (ES) for decreased stiffness and tone and greater time to relaxation were found between pre- and post-conditions in the right rectus abdominis (−9.8%, −4.4% and 7.8%; ES = 0.8, 0.54 and −0.85). Also, a decrease in tone was found in the right vastus medialis (−7.1%; ES = 0.56), while surface area of the center of pressures with eyes open showed trends towards increasing in post-match measurements (55.9%; ES = 0.56). Conclusion An 80-min simulated tennis match seems insufficient to elicit significant changes in postural control and mechanical muscle characteristics. Results suggest that physiological responses triggered by match-play were closer to those seen after a moderate activity than those present following a strenuous task.

Neocortical Layer 1: An Elegant Solution to Top-Down and Bottom-Up Integration

Many of our daily activities, such as riding a bike to work or reading a book in a noisy cafe, and highly skilled activities, such as a professional playing a tennis match or a violin concerto, depend upon the ability of the brain to quickly make moment-to-moment adjustments to our behavior in response to the results of our actions. Particularly, they depend upon the ability of the neocortex to integrate the information provided by the sensory organs (bottom-up information) with internally generated signals such as expectations or attentional signals (top-down information). This integration occurs in pyramidal cells (PCs) and their long apical dendrite, which branches extensively into a dendritic tuft in layer 1 (L1). The outermost layer of the neocortex, L1 is highly conserved across cortical areas and species. Importantly, L1 is the predominant input layer for top-down information, relayed by a rich, dense mesh of long-range projections that provide signals to the tuft branches of the PCs. Here, we discuss recent progress in our understanding of the composition of L1 and review evidence that L1 processing contributes to functions such as sensory perception, cross-modal integration, controlling states of consciousness, attention, and learning. Expected final online publication date for the Annual Review of Neuroscience, Volume 44 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Modeling and simulating durations of men’s professional tennis matches by resampling match features

In this paper we analyze the factors impacting on the length of a men’s professional tennis match and propose a model to simulate matches’ durations. Two distinctive features of the model are that i) it considers all kinds of events that impact on the duration of a match and ii) it is based only on publicly available data. Once built, the model allows to analyze the impact of different formats or rule changes on matches’ duration. The model is built and validated using a dataset including 19,961 matches played in the period January 2011 – December 2018. The simulated and observed distributions of the durations are compared with an in-depth goodness-of-fit analysis. This points out that the model provides a good description of the real distribution both in the central part and in the tails. We also show that our model improves similar models present in the literature. Finally, several case studies are analyzed: the effect of abolishing the first service or the advantages or both; the new tie-break format at Wimbledon; and the introduction of fifth set tie-break at Roland Garros.