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

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

Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Micro-Level Analysis and Visualization of Tennis Shot Patterns with Fractal Tables

Sports data analysis and visualization can be a useful tool for gaining insights into the games. In this paper, we present a new technique to analyze and visualize shot patterns in tennis matches. Tennis is a complicated game that involves a rich set of tactics and strategies. The current tennis data analyses are usually conducted at a high level that often fail to show useful patterns and nuances embedded in low level data. Based on a very detailed database of professional tennis matches, we have developed a system to analyze the serve and shot patterns so that a user can explore questions such as What are the favorite patterns of this player? What are the most effective patterns for this player? This can help tennis experts, players, and fans gain deeper insight into the sport

Visual Analytics Of Sports Data

In this dissertation, we discuss analysis and visualization of performance anxiety in tennis matches along with confidence and momentum. We also discuss the micro-level analysis and visualization of tennis shot patterns with fractal tables and tactical rings, followed by discussion about mapping a tennis player\u27s style of play with a visual analysis technique called tennis fingerprinting. According to sports psychology, anxiety, confidence and momentum has a big impact on an athlete\u27s performance in a sport event. Although much work has been done in sports data analysis and visualization, analysis of anxiety, confidence and momentum has rarely been included in recent literature. We propose a method to analyze a tennis player\u27s anxiety level, confidence and momentum levels during a tennis match. This method is based on the psychological theories of anxiety and a database of over 4,000 professional tennis matches. Since sports data analysis and visualization can be a useful tool for gaining insights into the games, we present new techniques to analyze and visualize the shot patterns in tennis matches via our Fractal Tables and Tactical Rings. Tennis is a complicated game that involves a rich set of tactics and strategies. The current tennis analysis are usually conducted at a high level, which often fail to show the useful patterns and nuances embedded in low level data. However, based on a very detailed database of professional tennis matches, we have developed a system to analyze the serve and shot patterns so that an user can explore questions such as What are the favorite patterns of this player? What are the most effective patterns for this player? This can help tennis experts and fans gain a deeper insight and appreciation of the sport that are not usually obvious just by watching the match. Further, we present a new visual analytics technique called Tennis Fingerprinting to analyze tennis players\u27 tactical patterns and styles of play. In tennis, style is a complicated and often abstract concept that cannot be easily described or analyzed. The proposed visualization method is an attempt to provide a concrete and visual representation of a tennis player\u27s style

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

Adversarial content manipulation for analyzing and improving model robustness

The recent rapid progress in machine learning systems has opened up many real-world applications --- from recommendation engines on web platforms to safety critical systems like autonomous vehicles. A model deployed in the real-world will often encounter inputs far from its training distribution. For example, a self-driving car might come across a black stop sign in the wild. To ensure safe operation, it is vital to quantify the robustness of machine learning models to such out-of-distribution data before releasing them into the real-world. However, the standard paradigm of benchmarking machine learning models with fixed size test sets drawn from the same distribution as the training data is insufficient to identify these corner cases efficiently. In principle, if we could generate all valid variations of an input and measure the model response, we could quantify and guarantee model robustness locally. Yet, doing this with real world data is not scalable. In this thesis, we propose an alternative, using generative models to create synthetic data variations at scale and test robustness of target models to these variations. We explore methods to generate semantic data variations in a controlled fashion across visual and text modalities. We build generative models capable of performing controlled manipulation of data like changing visual context, editing appearance of an object in images or changing writing style of text. Leveraging these generative models we propose tools to study robustness of computer vision systems to input variations and systematically identify failure modes. In the text domain, we deploy these generative models to improve diversity of image captioning systems and perform writing style manipulation to obfuscate private attributes of the user. Our studies quantifying model robustness explore two kinds of input manipulations, model-agnostic and model-targeted. The model-agnostic manipulations leverage human knowledge to choose the kinds of changes without considering the target model being tested. This includes automatically editing images to remove objects not directly relevant to the task and create variations in visual context. Alternatively, in the model-targeted approach the input variations performed are directly adversarially guided by the target model. For example, we adversarially manipulate the appearance of an object in the image to fool an object detector, guided by the gradients of the detector. Using these methods, we measure and improve the robustness of various computer vision systems -- specifically image classification, segmentation, object detection and visual question answering systems -- to semantic input variations.Der schnelle Fortschritt von Methoden des maschinellen Lernens hat viele neue Anwendungen ermöglicht – von Recommender-Systemen bis hin zu sicherheitskritischen Systemen wie autonomen Fahrzeugen. In der realen Welt werden diese Systeme oft mit Eingaben außerhalb der Verteilung der Trainingsdaten konfrontiert. Zum Beispiel könnte ein autonomes Fahrzeug einem schwarzen Stoppschild begegnen. Um sicheren Betrieb zu gewährleisten, ist es entscheidend, die Robustheit dieser Systeme zu quantifizieren, bevor sie in der Praxis eingesetzt werden. Aktuell werden diese Modelle auf festen Eingaben von derselben Verteilung wie die Trainingsdaten evaluiert. Allerdings ist diese Strategie unzureichend, um solche Ausnahmefälle zu identifizieren. Prinzipiell könnte die Robustheit “lokal” bestimmt werden, indem wir alle zulässigen Variationen einer Eingabe generieren und die Ausgabe des Systems überprüfen. Jedoch skaliert dieser Ansatz schlecht zu echten Daten. In dieser Arbeit benutzen wir generative Modelle, um synthetische Variationen von Eingaben zu erstellen und so die Robustheit eines Modells zu überprüfen. Wir erforschen Methoden, die es uns erlauben, kontrolliert semantische Änderungen an Bild- und Textdaten vorzunehmen. Wir lernen generative Modelle, die kontrollierte Manipulation von Daten ermöglichen, zum Beispiel den visuellen Kontext zu ändern, die Erscheinung eines Objekts zu bearbeiten oder den Schreibstil von Text zu ändern. Basierend auf diesen Modellen entwickeln wir neue Methoden, um die Robustheit von Bilderkennungssystemen bezüglich Variationen in den Eingaben zu untersuchen und Fehlverhalten zu identifizieren. Im Gebiet von Textdaten verwenden wir diese Modelle, um die Diversität von sogenannten Automatische Bildbeschriftung-Modellen zu verbessern und Schreibtstil-Manipulation zu erlauben, um private Attribute des Benutzers zu verschleiern. Um die Robustheit von Modellen zu quantifizieren, werden zwei Arten von Eingabemanipulationen untersucht: Modell-agnostische und Modell-spezifische Manipulationen. Modell-agnostische Manipulationen basieren auf menschlichem Wissen, um bestimmte Änderungen auszuwählen, ohne das entsprechende Modell miteinzubeziehen. Dies beinhaltet das Entfernen von für die Aufgabe irrelevanten Objekten aus Bildern oder Variationen des visuellen Kontextes. In dem alternativen Modell-spezifischen Ansatz werden Änderungen vorgenommen, die für das Modell möglichst ungünstig sind. Zum Beispiel ändern wir die Erscheinung eines Objekts um ein Modell der Objekterkennung täuschen. Dies ist durch den Gradienten des Modells möglich. Mithilfe dieser Werkzeuge können wir die Robustheit von Systemen zur Bildklassifizierung oder -segmentierung, Objekterkennung und Visuelle Fragenbeantwortung quantifizieren und verbessern

What Role for Empirics in International Trade?

In the field of international trade, data analysis has traditionally had quite modest influence relative to that of pure theory. At one time, this might have been rationalized by the paucity of empirics in the field or its weak theoretical foundations. In recent years empirical research has begun to provide an increasingly detailed view of the determinants of trade relations. Yet the field as a whole has been slow to incorporate these findings in its fundamental worldview. In this paper, we outline and extend what we view as key robust findings from the empirical literature that should be part of every international economists working knowledge.