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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.

IntroductionThis study examined the relationship between ball release points and pitching performance among professional baseball pitchers, with a focus on variability.MethodsWe 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.ResultsMLB 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.DiscussionThese 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.

Abstract Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.

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.

Abstract 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.

Sex differences in diet and foraging behaviour are common in sexually dimorphic species, often driven by differences in the cost of locomotion or ability to exploit different ecological niches. However, sex-specific foraging strategies also occur in monomorphic or slightly dimorphic species where the drivers are poorly understood. Here, we study sex differences in foraging of northern gannets (Morus bassanus), where females are only slightly heavier than males. Using concurrently tracked gannets (298 full foraging trips from 81 individuals) and fishing vessels across 5 years, we quantify individual-based vessel-associated putative foraging, and relate this to discard consumption. We found a significant positive relationship between time spent in vessel-associated foraging and discard consumption for both sexes. However, while females showed greater proportions of vessel-associated foraging than males, discarded fish contributed less to the diet of females in all years. These results contrast with previous suggestions that female gannets interact with vessels less often than males, and are consistent with competitive exclusion of females from trawler-associated discards. Our findings give insight into sexual differences in foraging behaviour in the absence of dimorphism that are necessary to predict their response to environmental and anthropogenic changes.

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.

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.

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.

This paper reports on the 1st Workshop on Movement Pattern Analysis, held as a pre-GIScience 2010 workshop in September 2010 in Zurich, Switzerland. The report outlines the scientific motivation for the event, summarizes its main contributions and outcomes, discusses the implications of the gathering, and indicates directions for the road ahead.