Applying Multi-Resolution Stochastic Modeling to Individual Tennis Points

Individual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this thesis, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how much a player can expect each shot to contribute to a won point, given who struck the shot and where both players are located. This measurement, named ``Expected Shot Win Rate (ESWR), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESWR, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player\u27s in-match strategy

Thinking the GOAT: imitating tennis styles

A tactically aware coach is key to improving tennis players’ games; a coach analyses past matches with two considerations in mind: 1) the style of the player and how that style translates to real-world shot-making, and 2) the intent of a shot, irrespective of the outcome. Modern Hawk-Eye technology deployed in top-tier tournaments has enabled deeper analysis of professional matches than ever before. The aim of this paper is to emulate and augment the qualities of great coaches using data collected by Hawk-Eye; we develop a deep learning approach to imitate tennis players’ responses, to learn individual player styles efficiently, and we demonstrate this using performance metrics and illustrations

Towards Structured Analysis of Broadcast Badminton Videos

Sports video data is recorded for nearly every major tournament but remains archived and inaccessible to large scale data mining and analytics. It can only be viewed sequentially or manually tagged with higher-level labels which is time consuming and prone to errors. In this work, we propose an end-to-end framework for automatic attributes tagging and analysis of sport videos. We use commonly available broadcast videos of matches and, unlike previous approaches, does not rely on special camera setups or additional sensors. Our focus is on Badminton as the sport of interest. We propose a method to analyze a large corpus of badminton broadcast videos by segmenting the points played, tracking and recognizing the players in each point and annotating their respective badminton strokes. We evaluate the performance on 10 Olympic matches with 20 players and achieved 95.44% point segmentation accuracy, 97.38% player detection score ([email protected]), 97.98% player identification accuracy, and stroke segmentation edit scores of 80.48%. We further show that the automatically annotated videos alone could enable the gameplay analysis and inference by computing understandable metrics such as player's reaction time, speed, and footwork around the court, etc.Comment: 9 page

The effect of ball wear on ball aerodynamics: An investigation using hawk-eye data

The Hawk-Eye electronic line-calling system gives players the ability to challenge line-calling decisions. It also creates large datasets of ball and player movements during competitive play. In this paper we used a dataset taken from 5 years of the Davis and Fed Cup tournaments (comprising 71,019 points in total) to examine the effect of ball wear on aerodynamic performance. Balls were categorized as new or used depending on whether they were used in the first two games following a ball change (new) or the last two games before a ball change (used). Data falling into neither category was discarded. The coefficients of drag (Cd) of 9224 first serves from the Davis Cup were calculated by simulating their trajectories. New balls had a significantly lower average Cd of 0.579 compared to used balls’ 0.603 (p < 0.0001)—first serves made with new balls arrive 0.0074 s sooner than first serves made with used balls on average. Large sport datasets can be used to explore subtle effects despite a relative lack of precision

Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories

With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments. In this paper, we propose a deep decision tree architecture for discriminative dictionary learning from adversarial multi-agent trajectories. We first build up a hierarchy for the tree structure by adding each layer and performing feature weight based clustering in the forward pass. We then fine tune the player role weights using back propagation. The hierarchical architecture ensures the interpretability and the integrity of the group representation. The resulting architecture is a decision tree, with leaf-nodes capturing a dictionary of multi-agent group interactions. Due to the ample volume of data available, we focus on soccer tracking data, although our approach can be used in any adversarial multi-agent domain. We present applications of proposed method for simulating soccer games as well as evaluating and quantifying team strategies.Comment: To appear in 4th International Workshop on Computer Vision in Sports (CVsports) at CVPR 201

A Survey of Deep Learning in Sports Applications: Perception, Comprehension, and Decision

Deep learning has the potential to revolutionize sports performance, with applications ranging from perception and comprehension to decision. This paper presents a comprehensive survey of deep learning in sports performance, focusing on three main aspects: algorithms, datasets and virtual environments, and challenges. Firstly, we discuss the hierarchical structure of deep learning algorithms in sports performance which includes perception, comprehension and decision while comparing their strengths and weaknesses. Secondly, we list widely used existing datasets in sports and highlight their characteristics and limitations. Finally, we summarize current challenges and point out future trends of deep learning in sports. Our survey provides valuable reference material for researchers interested in deep learning in sports applications