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Introduction: This study examined the relationship between ball release points and pitching performance among professional baseball pitchers, with a focus on variability., Methods: We used open-source data to compare ball release point variability between Major League Baseball (MLB) and Minor League Baseball (MiLB) players. The relationship between pitching performance and variability was analyzed using multiple regression analysis., Results: MLB players exhibited smaller ball release point variability compared to MiLB players. The analysis showed that pitching performance was strongly related to ball release point variability, especially in the horizontal direction on the coronal plane. Horizontal ball release point variability was most strongly related to strikeout ability among pitching performances., Discussion: These results suggest that reducing horizontal ball release point variability may improve pitching performance, particularly by increasing strikeouts and reducing home runs allowed. This study provides a data-driven approach to understanding the mechanics of pitching and can be applied to the development of advanced training methods and technical solutions aimed at improving pitching performance in baseball players., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Wakamiya, Nagamoto, Yamaguchi, Okunuki, Maemichi, Liu, Ogawa, Kobayashi and Kumai.)

Since gaining popularity via the Moneyball story near the turn of the century, data analytics has become a common feature of high-level baseball. In recent years, optical tracking technologies have allowed data to move beyond basic box-score measures of performance (e.g., strikeouts and batting averages) to high-detail measures of ball movement following a pitch (e.g., spin rates, release speeds, and ending locations) or hit (e.g., exit velocity and launch angle). Such information has provided analysts with new ways to measure the game and communicate complex results via helpful visualizations. The data described in this article were recorded by an optical tracking unit stationed on a collegiate baseball field over a series of 21 exhibition matches. The technology was monitored by a trained individual who confirmed, cleaned, and compiled the data after every game. Each observation ( n = 3,344) corresponds to a pitch that was thrown during a game, and the associated columns provide a variety of descriptive information related to the pitch and its eventual outcome. Data of this detail at the collegiate level of play can be difficult to acquire, and the high level of granularity allows for a variety of analyses and visualizations to be conducted for baseball and non-baseball purposes., (© 2024 The Author(s).)

Biodiversity is critical for maintaining ecosystem function but is threatened by increasing anthropogenic pressures. In the Southern Ocean, a highly biologically productive region containing many endemic species, proactive management is urgently needed to mitigate increasing pressures from fishing, climate change, and tourism. Site-based conservation is one important tool for managing the negative impacts of human activities on ecosystems. The Key Biodiversity Area (KBA) Standard is a standardized framework used to define sites vital for the persistence of global biodiversity based on criteria and quantitative thresholds. We used tracking data from 14 species of Antarctic and subantarctic seabirds and pinnipeds from the publicly available Retrospective Analysis of Antarctic Tracking Data (RAATD) data set to define KBAs for a diverse suite of marine predators. We used track2kba, an R package that supports identification of KBAs from telemetry data through identification of highly used habitat areas and estimates of local abundance within sites. We compared abundance estimates at each site with thresholds for KBA criteria A1, B1, and D1 (related to globally threatened species, individual geographically restricted species, and demographic aggregations, respectively). We identified 30 potential KBAs for 13 species distributed throughout the Southern Ocean that were vital for each individual species, population, and life-history stage for which they were determined. These areas were identified as highly used by these populations based on observational data and complement the ongoing habitat modeling and bioregionalization work that has been used to prioritize conservation areas in this region. Although further work is needed to identify potential KBAs based on additional current and future data sets, we highlight the benefits of utilizing KBAs as part of a holistic approach to marine conservation, given their significant value as a global conservation tool., (© 2024 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

Ongoing technological advances have led to a rapid increase in the number, type and scope of animal-tracking studies. In response, many software tools have been developed to analyse animal movement data. These tools generally focus on movement modelling, but the steps required to clean raw data files from different tracking devices have been largely ignored. Such pre-processing steps are often time-consuming and involve a steep learning curve but are crucial for the creation of high-quality, standardised and shareable data. Moreover, decisions made at this early stage can substantially influence subsequent analyses, and in the current age of reproducibility crisis, the transparency of this process is vital. Here we present an open-access, reproducible toolkit written in the programming language R for processing raw data files into a single cleaned data set for analyses and upload to online tracking databases (found here: https://github.com/ExMove/ExMove). The toolkit comprises well-documented and flexible code to facilitate data processing and user understanding, both of which can increase user confidence and improve the uptake of sharing open and reproducible code. Additionally, we provide an overview website (found here: https://exmove.github.io/) and a Shiny app to help users visualise tracking data and assist with parameter determination during data cleaning. The toolkit is generalisable to different data formats and device types, uses modern 'tidy coding' practices, and relies on a few well-maintained packages. Among these, we perform spatial manipulations using the package sf. Overall, by collating all required steps from data collection to archiving on open access databases into a single, robust pipeline, our toolkit provides a valuable resource for anyone conducting animal movement analyses and represents an important step towards increased standardisation and reproducibility in animal movement ecology., (© 2024 The Author(s). Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.)

The conservation of long-distance migratory birds requires coordination between the multiple countries connected by the movements of these species. The recent expansion of tracking studies is shedding new light on these movements, but much of this information is fragmented and inaccessible to conservation practitioners and policy makers. We synthesized current knowledge on the connectivity established between countries by landbirds and raptors migrating along the African-Eurasian flyway. We reviewed tracking studies to compile migration records for 1229 individual birds, from which we derived 544 migratory links, each link corresponding to a species' connection between a breeding country in Europe and a nonbreeding country in sub-Saharan Africa. We used these migratory links to analyze trends in knowledge over time and spatial patterns of connectivity per country (across species), per species (across countries), and at the flyway scale (across all countries and all species). The number of tracking studies available increased steadily since 2010 (particularly for landbirds), but the coverage of existing tracking data was highly incomplete. An average of 7.5% of migratory landbird species and 14.6% of raptor species were tracked per country. More data existed from central and western European countries, and it was biased toward larger bodied species. We provide species- and country-level syntheses of the migratory links we identified from the reviewed studies, involving 123 populations of 43 species, migrating between 28 European and 43 African countries. Several countries (e.g., Spain, Poland, Ethiopia, Democratic Republic of Congo) are strategic priorities for future tracking studies to complement existing data, particularly on landbirds. Despite the limitations in existing tracking data, our data and results can inform discussions under 2 key policy instruments at the flyway scale: the African-Eurasian Migratory Landbirds Action Plan and the Memorandum of Understanding on the Conservation of Migratory Birds of Prey in Africa and Eurasia., (© 2022 The Authors. Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)

With the growing availability of position data in sports, spatiotemporal analysis in soccer is a topic of rising interest. The aim of this study is to validate a performance indicator, namely D-Def, measuring passing effectiveness. D-Def calculates the change of the teams' centroid, centroids of formation lines (e.g., defensive line), teams' surface area, and teams' spread in the following three seconds after a pass and therefore results in a measure of disruption of the opponents' defense following a pass. While this measure was introduced earlier, in this study we aim to prove the usefulness to evaluate attacking sequences. In this study, 258 games of Dutch Eredivisie season 2018/19 were included, resulting in 13,094 attacks. D-Def, pass length, pass velocity, and pass angle of the last four passes of each attack were calculated and compared between successful and unsuccessful attacks. D-Def showed higher values for passes of successful compared to unsuccessful attacks (0.001 < p ≤ 0.029, 0.06 ≤ d ≤ 0.23). This difference showed the highest effects sizes in the penultimate pass (d = 0.23) and the maximal D-Def value of an attack (d = 0.23). Passing length (0.001 < p ≤ 0.236, 0.08 ≤ d ≤ 0.17) and passing velocity (0.001 < p ≤ 0.690, -0.09 ≤ d ≤ 0.12) showed inconsistent results in discriminating between successful and unsuccessful attacks. The results indicate that D-Def is a useful indicator for the measurement of pass effectiveness in attacking sequences, highlighting that successful attacks are connected to disruptive passing. Within successful attacks, at least one high disruptive action (pass with D-Def > 28) needs to be present. In addition, the penultimate pass ("hockey assist") of an attack seems crucial in characterizing successful attacks.

We study the automatic annotation of situations in soccer games. At first sight, this translates nicely into a standard supervised learning problem. However, in a fully supervised setting, predictive accuracies are supposed to correlate positively with the amount of labeled situations: more labeled training data simply promise better performance. Unfortunately, non-trivially annotated situations in soccer games are scarce, expensive and almost always require human experts; a fully supervised approach appears infeasible. Hence, we split the problem into two parts and learn (i) a meaningful feature representation using variational autoencoders on unlabeled data at large scales and (ii) a large-margin classifier acting in this feature space but utilize only a few (manually) annotated examples of the situation of interest. We propose four different architectures of the variational autoencoder and empirically study the detection of corner kicks, crosses and counterattacks. We observe high predictive accuracies above 90% AUC irrespectively of the task., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Fassmeyer, Anzer, Bauer and Brefeld.)

The estimation of player positions is key for performance analysis in sport. In this paper, we focus on image-based, single-angle, player position estimation in padel. Unlike tennis, the primary camera view in professional padel videos follows a de facto standard, consisting of a high-angle shot at about 7.6 m above the court floor. This camera angle reduces the occlusion impact of the mesh that stands over the glass walls, and offers a convenient view for judging the depth of the ball and the player positions and poses. We evaluate and compare the accuracy of state-of-the-art computer vision methods on a large set of images from both amateur videos and publicly available videos from the major international padel circuit. The methods we analyze include object detection, image segmentation and pose estimation techniques, all of them based on deep convolutional neural networks. We report accuracy and average precision with respect to manually-annotated video frames. The best results are obtained by top-down pose estimation methods, which offer a detection rate of 99.8% and a RMSE below 5 and 12 cm for horizontal/vertical court-space coordinates (deviations from predicted and ground-truth player positions). These results demonstrate the suitability of pose estimation methods based on deep convolutional neural networks for estimating player positions from single-angle padel videos. Immediate applications of this work include the player and team analysis of the large collection of publicly available videos from international circuits, as well as an inexpensive method to get player positional data in amateur padel clubs.

The advent of miniaturized biologging devices has provided ecologists with unprecedented opportunities to record animal movement across scales, and led to the collection of ever-increasing quantities of tracking data. In parallel, sophisticated tools have been developed to process, visualize and analyse tracking data; however, many of these tools have proliferated in isolation, making it challenging for users to select the most appropriate method for the question in hand. Indeed, within the r software alone, we listed 58 packages created to deal with tracking data or 'tracking packages'. Here, we reviewed and described each tracking package based on a workflow centred around tracking data (i.e. spatio-temporal locations (x, y, t)), broken down into three stages: pre-processing, post-processing and analysis, the latter consisting of data visualization, track description, path reconstruction, behavioural pattern identification, space use characterization, trajectory simulation and others. Supporting documentation is key to render a package accessible for users. Based on a user survey, we reviewed the quality of packages' documentation and identified 11 packages with good or excellent documentation. Links between packages were assessed through a network graph analysis. Although a large group of packages showed some degree of connectivity (either depending on functions or suggesting the use of another tracking package), one third of the packages worked in isolation, reflecting a fragmentation in the r movement-ecology programming community. Finally, we provide recommendations for users when choosing packages, and for developers to maximize the usefulness of their contribution and strengthen the links within the programming community., (© 2019 The Authors. Journal of Animal Ecology © 2019 British Ecological Society.)

Background: Bar code- or radio frequency identification (RFID)-based medical instrument management systems have gradually been introduced in the field of surgical medicine for the individual management and identification of instruments. We hypothesized that individual management of instruments using RFID tags can provide previously unavailable information, particularly the precise service life of an instrument. Such information can be used to prevent medical accidents caused by surgical instrument failure. This study aimed to predict the precise service life of instruments by analyzing the data available in instrument management systems., Methods: We evaluated the repair history of instruments and the usage count until failure and then analyzed the data by the following three methods: the distribution of the instrument usage count was determined, an instrument failure probability model was generated through logistic regression analysis, and survival analysis was performed to predict instrument failure., Results: The usage count followed a normal distribution. Analysis showed that instruments were not used uniformly during surgery. In addition, the Kaplan-Meier curves plotted for five types of instruments showed significant differences in the cumulative survival rate of different instruments., Conclusions: The usage history of instruments obtained with RFID tags or bar codes can be used to predict the probability of instrument failure. This prediction is significant for determining the service life of an instrument. Implementation of the developed model in instrument management systems can help prevent accidents due to instrument failure. Knowledge of the instrument service life will also help in developing a purchase plan for instruments to minimize wastage.

Telemetry is a key, widely used tool to understand marine megafauna distribution, habitat use, behavior, and physiology; however, a critical question remains: "How many animals should be tracked to acquire meaningful data sets?" This question has wide-ranging implications including considerations of statistical power, animal ethics, logistics, and cost. While power analyses can inform sample sizes needed for statistical significance, they require some initial data inputs that are often unavailable. To inform the planning of telemetry and biologging studies of marine megafauna where few or no data are available or where resources are limited, we reviewed the types of information that have been obtained in previously published studies using different sample sizes. We considered sample sizes from one to >100 individuals and synthesized empirical findings, detailing the information that can be gathered with increasing sample sizes. We complement this review with simulations, using real data, to show the impact of sample size when trying to address various research questions in movement ecology of marine megafauna. We also highlight the value of collaborative, synthetic studies to enhance sample sizes and broaden the range, scale, and scope of questions that can be answered., (© 2019 by the Ecological Society of America.)

In professional soccer, nowadays almost every team employs tracking technology to monitor performance during trainings and matches. Over the recent years, there has been a rapid increase in both the quality and quantity of data collected in soccer resulting in large amounts of data collected by teams every single day. The sheer amount of available data provides opportunities as well as challenges to both science and practice. Traditional experimental and statistical methods used in sport science do not seem fully capable to exploit the possibilities of the large amounts of data in modern soccer. As a result, tracking data are mainly used to monitor player loading and physical performance. However, an interesting opportunity exists at the intersection of data science and sport science. By means of tracking data, we could gain valuable insights in the how and why of tactical performance during a soccer match. One of the most interesting and most frequently occurring elements of tactical performance is the pass. Every team has around 500 passing interactions during a single game. Yet, we mainly judge the quality and effectiveness of a pass by means of observational analysis, and whether the pass reaches a teammate. In this article, we present a new approach to quantify pass effectiveness by means of tracking data. We introduce two new measures that quantify the effectiveness of a pass by means of how well a pass disrupts the opposing defense. We demonstrate that our measures are sensitive and valid in the differentiation between effective and less effective passes, as well as between the effective and less effective players. Furthermore, we use this method to study the characteristics of the most effective passes in our data set. The presented approach is the first quantitative model to measure pass effectiveness based on tracking data that are not linked directly to goal-scoring opportunities. As a result, this is the first model that does not overvalue forward passes. Therefore, our model can be used to study the complex dynamics of build-up and space creation in soccer.

Aim: To provide a method of analyzing penguin tracking data to identify priority at-sea areas for seabird conservation (marine IBAs), based on pre-existing approaches for flying seabirds but revised according to the specific ecology of Pygoscelis penguin species., Location: Waters around the Antarctic Peninsula, South Shetland, and South Orkney archipelagos (FAO Subareas 48.1 and 48.2)., Methods: We made key improvements to the pre-existing protocol for identifying marine IBAs that include refining the track interpolation method and revision of parameters for the kernel analysis (smoothing factor and utilization distribution) using sensitivity tests. We applied the revised method to 24 datasets of tracking data on penguins (three species, seven colonies, and three different breeding stages-incubation, brood, and crèche)., Results: We identified five new marine IBAs for seabirds in the study area, estimated to hold ca. 600,000 adult penguins., Main Conclusions: The results demonstrate the efficacy of a new method for the designation of a network of marine IBAs in Antarctic waters for penguins based on tracking data, which can contribute to an evidence-based, precautionary, management framework for krill fisheries.

Major hurdles for Microsoft's HoloLens as a tool in medicine have been accessing tracking data, as well as a relatively high-localisation error of the displayed information; cumulatively resulting in its limited use and minimal quantification. The following work investigates the augmentation of HoloLens with the proprietary image processing SDK Vuforia, allowing integration of data from its front-facing RGB camera to provide more spatially stable holograms for neuronavigational use. Continuous camera tracking was able to maintain hologram registration with a mean perceived drift of 1.41 mm, as well as a mean sub 2-mm surface point localisation accuracy of 53%, all while allowing the researcher to walk about a test area. This represents a 68% improvement for the later and a 34% improvement for the former compared with a typical HoloLens deployment used as a control. Both represent a significant improvement on hologram stability given the current state-of-the-art, and to the best of the authors knowledge are the first example of quantified measurements when augmenting hologram stability using data from the RGB sensor.

Shots are an essential part of the language of tennis yet little is known about the distinct types of shots in the professional game. In this study, we build a taxonomy of shots for elite tennis players using tracking data from multiple years of men's and women's matches at the Australian Open. Our taxonomy is constructed using model-based multi-stage functional data clustering, an unsupervised machine learning approach. Among 270,023 men's and 178,136 women's shots, we found 13 distinct types of serves to both the Ad and Deuce court for male players and 17 and 15 types to the Ad and Deuce for female players. More variety was found among serve returns and rally shots compared to the serve; with less variety on the backhand than forehand. There was also more overlap in the physical characteristics of groundstroke shots between male and female players than on serve. Shot type was strongly associated with winning points and shots in the highest speed and lowest net clearance categories tended to be the most effective. This data-driven dictionary of shots provides a framework for analysis of elite player performance, characterizing playing style, and designing more representative practice.

The growing field of movement ecology uses high resolution movement data to analyze animal behavior across multiple scales: from individual foraging decisions to population-level space-use patterns. These analyses contribute to various subfields of ecology- inter alia behavioral, disease, landscape, resource, and wildlife-and facilitate facilitate novel exploration in fields ranging from conservation planning to public health. Despite the growing availability and general accessibility of animal movement data, much potential remains for the analytical methods of movement ecology to be incorporated in all types of geographic analyses. This review provides for the Geographical Information Sciences (GIS) community an overview of the most common movement metrics and methods of analysis employed by animal ecologists. Through illustrative applications, we emphasize the potential for movement analyses to promote transdisciplinary GIS/wildlife-ecology research., Competing Interests: Declaration of Interest Statement The authors declare no potential conflicts of interest.