rss_2.0International Journal of Computer Science in Sport FeedSciendo RSS Feed for International Journal of Computer Science in Sporthttps://sciendo.com/journal/IJCSShttps://www.sciendo.comInternational Journal of Computer Science in Sport 's Coverhttps://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/6005b0e2e797941b18f23dc8/cover-image.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220811T012949Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604800&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20220811%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=005712e9e7357b92e3d3af49ec8cd962d010e1c26a818e2ca271c3efb692ccfd200300Optimizing and dimensioning a data intensive cloud application for soccer player trackinghttps://sciendo.com/article/10.2478/ijcss-2022-0004<abstract> <title style='display:none'>Abstract</title> <p>Cloud-based services revolutionize how applications are designed and provisioned in more and more application domains. Operating a cloud application, however, requires careful choices of configuration settings so that the quality of service is acceptable at all times, while cloud costs remain reasonable. We propose an analytical queuing model for cloud resource provisioning that provides an approximation on end-to-end application latency and on cloud resource usage, and we evaluate its performance. We pick an emerging use case of cloud deployment for validation: sports analytics. We have created a low-cost, cloud-based soccer player tracking system. We present the optimization of the cloud-deployed data processing of this system: we set the parameters with the aim of sacrificing as least as possible on accuracy, i.e., quality of service, while keeping latency and cloud costs low. We demonstrate that the analytical model we propose to estimate the end-to-end latency of a microservice-type cloud native application falls within a close range of what the measurements of the real implementation show. The model is therefore suitable for the planning of the cloud deployment costs for microservice-type applications as well.</p> </abstract>ARTICLE2022-06-15T00:00:00.000+00:00Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithmshttps://sciendo.com/article/10.2478/ijcss-2022-0003<abstract> <title style='display:none'>Abstract</title> <p>This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings include (a) genetic algorithm and particle swarm optimization have been extensively used in the literature, (b) meta-heuristics have been widely applied in the sports of cricket and soccer, (c) the limitations and challenges of using meta-heuristics in sports. Through awareness and discussion on implementation of meta-heuristics, sports analytics research can be rich in the future.</p> </abstract>ARTICLE2022-06-15T00:00:00.000+00:00Wireless inertial sensor system for hammer throwinghttps://sciendo.com/article/10.2478/ijcss-2022-0001<abstract> <title style='display:none'>Abstract</title> <p>The aim of this study is to integrate an inertial sensor inside a hammer to allow a realtime feedback. In the first step we build our own prototype to measure the radial acceleration. In the second step there is a validation with an infrared camera system. It is a comparison between the radial acceleration along the wire axis, that is measured by the sensor against the velocity that is delivered by the infrared camera system. As a result, significant correlation was observed between the measured velocity and the acceleration (r = 0.99, p &lt; 0.001). These suggest that this system can used in the training to improve the technique of the hammer throw.</p> </abstract>ARTICLE2022-03-24T00:00:00.000+00:00An optimization model for the fair distribution of prize money in ATP tournamentshttps://sciendo.com/article/10.2478/ijcss-2022-0002<abstract> <title style='display:none'>Abstract</title> <p>The Association of Tennis Professionals (ATP) distributes a considerable amount of money in prizes each year. Studies have shown that only the top 100 ranked players can self-finance; hence, it is convenient to introduce changes to the prize distribution to promote a more sustainable system. A Linear Programming model to distribute the tournament’s budget under a new concept for the fair distribution of prize money is proposed. Additionally, to distribute the prizes, a function based on the effort of the players is designed. The model was applied to tournaments to demonstrate the impact on improving the player’s prizes distribution.</p> </abstract>ARTICLE2022-03-24T00:00:00.000+00:00A Data Mining Approach to Predict Non-Contact Injuries in Young Soccer Playershttps://sciendo.com/article/10.2478/ijcss-2021-0009<abstract> <title style='display:none'>Abstract</title> <p>Predicting and avoiding an injury is a challenging task. By exploiting data mining techniques, this paper aims to identify existing relationships between modifiable and non-modifiable risk factors, with the final goal of predicting non-contact injuries. Twenty-three young soccer players were monitored during an entire season, with a total of fifty-seven non-contact injuries identified. Anthropometric data were collected, and the maturity offset was calculated for each player. To quantify internal training/match load and recovery status of the players, we daily employed the session-RPE method and the total quality recovery (TQR) scale. Cumulative workloads and the acute: chronic workload ratio (ACWR) were calculated. To explore the relationship between the various risk factors and the onset of non-contact injuries, we performed a classification tree analysis. The classification tree model exhibited an acceptable discrimination (AUC=0.76), after receiver operating characteristic curve (ROC) analysis. A low state of recovery, a rapid increase in the training load, cumulative workload, and maturity offset were recognized by the data mining algorithm as the most important injury risk factors.</p> </abstract>ARTICLE2021-11-28T00:00:00.000+00:00A scoping review using social network analysis techniques to summarise the prevalance of methods used to acquire data for athlete survelliance in sporthttps://sciendo.com/article/10.2478/ijcss-2021-0011<abstract> <title style='display:none'>Abstract</title> <p>To aid the implementation of athlete surveillance systems relative to logistical circumstances, easy-to-access information that summarises the extent to which methods of acquiring data are used in practice to monitor athletes is required. In this scoping review, Social Network Analysis and Mining (SNAM) techniques were used to summarise and identify the most prevalent combinations of methods used to monitor athletes in research studying team, individual, field- and court-based sports (357 articles; SPORTDiscus, MEDLINE, CINHAL, and WebOfScience; 2014-2018 inc.) . The most prevalent combination in team and field-based sports were HR and/or sRPE (internal) and GPS, whereas in individual and court-based sports, internal methods (e.g., HR and sRPE) were most prevalent. In court-based sports, where external methods were occasionally collected in combination with internal methods of acquiring data, the use of accelerometers or inertial measuring units (ACC/IMU) were most prevalent. Whilst individual and court-based sports are less researched, this SNAM-based summary reveals that court-based sports may lead the way in using ACC/IMU to monitor athletes. Questionnaires and self-reported methods of acquiring data are common in all categories of sport. This scoping review provides coaches, sport-scientists and researchers with a data-driven visual resource to aid the selection of methods of acquiring data from athletes in all categories of sport relative to logistical circumstances. A guide on how to practically implement a surveillance system based on the visual summaries provided herein, is also presented.</p> </abstract>ARTICLE2021-11-28T00:00:00.000+00:00Optimizing Player Management Processes in Sports: Translating Lessons from Healthcare Process Improvements to Sportshttps://sciendo.com/article/10.2478/ijcss-2021-0008<abstract> <title style='display:none'>Abstract</title> <p>Typical player management processes focus on managing an athlete’s physical, physiological, psychological, technical and tactical preparation and performance. Current literature illustrates limited attempts to optimize such processes in sports. Therefore, this study aimed to analyze the application of Business Process Management (BPM) in healthcare (a service industry resembling sports) and formulate a model to optimize data driven player management processes in professional sports. A systematic review, adhering to PRISMA framework was conducted on articles extracted from seven databases, focused on using BPM to digitally optimize patient related healthcare processes. Literature reviews by authors was the main mode of healthcare process identification for BPM interventions. Interviews with process owners followed by process modelling were common modes of process discovery. Stakeholder and value-based analysis highlighted potential optimization areas. In most articles, details on process redesign strategies were not explicitly provided. New digital system developments and implementation of Business Process Management Systems were common. Optimized processes were evaluated using usability assessments and pre-post statistical analysis of key process performance indicators. However, the scientific rigor of most experiments designed for such latter evaluations were suboptimal. From the findings, a stepwise approach to optimize data driven player management processes in professional sports has been proposed.</p> </abstract>ARTICLE2021-11-28T00:00:00.000+00:00Offseason Fitness Tests a Collegiate Basketball Strength Coach Should Choose to Predict In-Season Perfomance Based on Sexhttps://sciendo.com/article/10.2478/ijcss-2021-0010<abstract> <title style='display:none'>Abstract</title> <p>Quantification of athletic performance via analysis of scores of off-season fitness tests has become an essential part of the modern strength and conditioning coach (SCC). Player Efficiency Rating (PER) and Efficiency index (EFF) are two of the most used in-season basketball performance metrics in the US. We collected data from male and female basketball players of a National Collegiate Athletic Association (NCAA) program. Based on sex, we examined a) if unadjusted PER (uPER) and EFF reflect different amounts of information and b) which fitness tests predict those two indices more accurately. Our results showed lower means and less variability of the fitness tests scores in women than men. The correlation between uPER and EFF in men was moderate and strong in women. In men, no strong correlation was found between any fitness test and EFF, while full court sprint was strongly correlated with uPER. In women, strong correlations were detected between a) the T-drill and EFF and b) the foul court sprint, the vertical jump, and the T-drill and uPER. The collegiate SCCs should consider that off-season scores of a) the foul court drill may predict uPER more accurately in both men and women and b) the T-drill may predict both EFF and uPER more precisely in women.</p> </abstract>ARTICLE2021-11-28T00:00:00.000+00:00Optimizing Team Sport Training With Multi-Objective Evolutionary Computationhttps://sciendo.com/article/10.2478/ijcss-2021-0006<abstract> <title style='display:none'>Abstract</title> <p>This research introduces a new novel method for mathematically optimizing team sport training models to enhance two measures of athletic performance using an evolutionary computation based approach. A common training load model, consisting of daily training load prescriptions, was optimized using an evolutionary multi-objective algorithm to produce improvements in the mean match-day running intensity across a competitive season. The optimized training model was then compared to real-world observed training and performance data to assess the potential improvements in performance that could be achieved. The results demonstrated that it is possible to increase and maintain a stable level of match-day running performance across a competitive season whilst adhering to model-based and real-world constraints, using an intelligently optimized training design compared a to standard human design, across multiple performance criteria (BF+0 = 5651, BF+0 = 11803). This work demonstrates the value of evolutionary algorithms to design and optimize team sport training models and provides support staff with an effective decision support system to plan and prescribe optimal strategies to enhance in-season athlete performance.</p> </abstract>ARTICLE2021-09-25T00:00:00.000+00:00Comparing bottom-up and top-down ratings for individual soccer playershttps://sciendo.com/article/10.2478/ijcss-2021-0002<abstract> <title style='display:none'>Abstract</title> <p>Correctly assessing the contributions of an individual player in a team sport is challenging. However, an ability to better evaluate each player can translate into improved team performance, through better recruitment or team selection decisions. Two main ideas have emerged for using data to evaluate players: Top-down ratings observe the performance of the team as a whole and then distribute credit for this performance onto the players involved. Bottom-up ratings assign a value to each action performed, and then evaluate a player based on the sum of values for actions performed by that player. This paper compares a variant of plus-minus ratings, which is a top-down rating, and a bottom-up rating based on valuing actions by estimating probabilities. The reliability of ratings is measured by whether similar ratings are produced when using different data sets, while the validity of ratings is evaluated through the quality of match outcome forecasts generated when the ratings are used as predictor variables. The results indicate that the plus-minus ratings perform better than the bottom-up ratings with respect to the reliability and validity measures chosen and that plus-minus ratings have certain advantages that may be difficult to replicate in bottom-up ratings.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Can Elite Australian Football Player’s Game Performance Be Predicted?https://sciendo.com/article/10.2478/ijcss-2021-0004<abstract> <title style='display:none'>Abstract</title> <p>In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.</p> </abstract>ARTICLE2021-08-10T00:00:00.000+00:00Strictness vs. flexibility: Simulation-based recognition of strategies and its success in soccerhttps://sciendo.com/article/10.2478/ijcss-2021-0003<abstract> <title style='display:none'>Abstract</title> <p>Introduction: Recognition and optimization of strategies in sport games is difficult in particular in case of team games, where a number of players are acting “independently” of each other. One way to improve the situation is to cluster the teams into a small number of tactical groups and to analyze the interaction of those groups. The aim of the study is the evaluation of the applicability of SOCCER© simulation in professional soccer by analyzing and simulation of the tactical group interaction.</p> <p>Methods: The players’ positions of tactical groups in soccer can be mapped to formation-patterns and then reflect strategic behaviour and interaction. Based on this information, Monte Carlo-Simulation allows for generating strategies, which – at least from the mathematical point of view – are optimal. In practice, behaviour can be orientated in those optimal strategies but normally is changing depending on the opponent team’s activities. Analyzing the game under the aspect of such simulated strategies revealed how strictly resp. flexible a team follows resp. varies strategic patterns.</p> <p>Approach: A Simulation- and Validation-Study on the basis of 40 position data sets of the 2014/15 German Bundesliga has been conducted to analyze and to optimize such strategic team behaviour in professional soccer.</p> <p>Results: The Validation-Study demonstrated the applicability of our tactical model. The results of the Simulation-Study revealed that offensive player groups need less tactical strictness in order to gain successful ball possession whereas defensive player groups need tactical strictness to do so.</p> <p>Conclusion: The strategic behaviour could be recognized and served as basis for optimization analysis: offensive players should play with a more flexible tactical orientation to stay in possession of the ball, whereas defensive players should play with a more planned orientation in order to be successful. The strategic behaviour of tactical groups can be recognized and optimized using Monte Carlo-based analysis, proposing a new and innovative approach to quantify tactical performance in soccer.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Comparison of the Evaluation of Performance Preconditions in Tennis with the Use of Equal and Expertly Judged Criteria Weightshttps://sciendo.com/article/10.2478/ijcss-2021-0005<abstract> <title style='display:none'>Abstract</title> <p>Tennis performance is influenced by various factors, among which physical performance factors play an important role. The aim of the study was an analysis of possibilities of the use of Saaty’s method for assessing the level of performance prerequisites and comparing the results obtained using equal weights and various weights. The research on Czech female players (U12; n = 211) was based on the results of the TENDIAG1 test battery (9 items) and the results were processed by FuzzME software and relevant statistical methods (correlation coefficient r, Student´s t-test, effect size index d). The results of Saaty’s method show that the most important athletic performance criteria for tennis coaches are the leg reaction time and the running speed, while the least important are endurance and strength. The evaluation using various criteria weights offers a finer scale for assessing athletes’ performance prerequisites despite the proven high degree of association between the results obtained with equal and various weights and the insignificant difference of mean values. The results have shown possibilities for the use of a fuzzy approach in sports practice and motivate further research towards broadening the structure or the number of evaluation criteria.</p> </abstract>ARTICLE2021-08-10T00:00:00.000+00:00Sports Information Systems: A systematic reviewhttps://sciendo.com/article/10.2478/ijcss-2021-0001<abstract> <title style='display:none'>Abstract</title> <p>Many professional sport organizations are currently in the process of finding or already using <italic>sports information systems (SIS)</italic> to integrate data from different information and measurement systems. The problem is that requirements are very heterogeneous. That is why no consistent definition of <italic>SIS</italic> and their categories exist, and it is often not clear which fields and functions <italic>SIS</italic> must cover. This work aims to provide a structured comparison of commercial <italic>SIS</italic> available on the market to provide an overview of the relevant features and characterize categories. Following PRISMA guidelines, a systematic search for relevant <italic>SIS</italic> providers was conducted. A catalog of 164 review items was created to define relevant features of <italic>SIS</italic> and to conduct semi-standardized interviews with product representatives. Overall 36 eligible <italic>SIS</italic> from 11 countries were identified and 21 of them were interviewed. The analysis of the interviews has shown that there are features that are present in all <italic>SIS</italic>, whereas others differ or are generally less represented. As a result, different <italic>SIS</italic> categories have been defined. The study suggests a more differentiated categorization of <italic>SIS</italic> is necessary and terms need to be defined more precisely. This review should be considered when companies designing <italic>SIS</italic> or sport organizations select <italic>SIS</italic>.</p> </abstract>ARTICLE2021-05-08T00:00:00.000+00:00Validation of Velocity Measuring Devices in Velocity Based Strength Traininghttps://sciendo.com/article/10.2478/ijcss-2021-0007<abstract> <title style='display:none'>Abstract</title> <p>To control and monitor strength training with a barbell various systems are on the consumer market. They provide the user with information regarding velocity, acceleration and trajectory of the barbell. Some systems additionally calculate the 1-repetition-maximum (1RM) of exercises and use it to suggest individual intensities for future training. Three systems were tested: GymAware, PUSH Band 2.0 and Vmaxpro. The GymAware system bases on linear position transducers, PUSH Band 2.0 and Vmaxpro base on inertial measurement units. The aim of this paper was to determine the accuracy of the three systems with regard to the determination of the average velocity of each repetition of three barbell strength exercises (squat, barbell rowing, deadlift). The velocity data of the three systems were compared to a Vicon system using linear regression analyses and Bland-Altman-diagrams.</p> <p>In the linear regression analyses the smallest coefficient of determination (R<sup>2</sup>.) in each exercise can be observed for PUSH Band 2.0. In the Bland-Altman diagrams the mean value of the differences in the average velocities is near zero for all systems and all exercises. PUSH Band 2.0 has the largest differences between the Limits of Agreement. For GymAware and Vmaxpro these differences are comparable.</p> </abstract>ARTICLE2021-09-25T00:00:00.000+00:00Intra-seasonal Variability of Ball Speed and Coordination of Two Team-Handball Throwing Techniques in Elite Male Adolescent Players.https://sciendo.com/article/10.1515/ijcss-2016-0001<abstract><title style='display:none'>Abstract</title><p>In sports biomechanics and motor control, a thorough study of coordination variability is important to understanding how the human movement system is organized. From a more applied sport science perspective, knowledge about performance variability is essential regarding the evaluation of true sport specific effects of any intervention. While there are many reports of intervention studies in team-handball, no description of the amount of normal variability is available. This study investigated variability of two important throwing techniques in team-handball within elite junior players over a 4-month period during a competitive season. To evaluate ball speed variability, the intra-individual coefficient of variation was calculated. The 95<sup>th</sup> percentile of ball speed variability over all players was 7%, which can be used as an effect size estimate in future research. For coordination variability, a qualitative description based on the output of neural networks was used. All participants presented multiple coordination patterns, representing multi-stability on a month-to-month timescale and switched between stable states without the manipulation of any control variable. Some limitations in the methodology and applications of neural networks in the present study and in biomechanics and motor control in general are highlighted. When more researchers adopt these methodologies, a more coherent framework for their application can emerge.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Predictive models of the 2015 Rugby World Cup: accuracy and applicationhttps://sciendo.com/article/10.1515/ijcss-2016-0003<abstract><title style='display:none'>Abstract</title><p>The current investigation compared 12 models of outcomes of international rugby union matches and then used the most accurate model to investigate performances within the 2015 Rugby World Cup. The underlying linear regression models were used within a simulation package that introduced random variability about performance evidenced by the residual distribution of the regression analyses. Each model was used within 10,000 simulations of the 2015 Rugby World Cup from which match outcome and team progression statistics were recorded. The most accurate model with respect to the actual 2015 tournament was developed using data from all seven previous tournaments rather than restricting cases to the most recent three tournaments. The model was more accurate when the data used violated the assumptions of linear regression rather than transforming variables to satisfy the assumptions. The model included World ranking points as a predictor variable and was more accurate than corresponding models that represented relative home advantage as well. The most accurate model used separate models for the pool and knockout stage matches although the 9 models that separating these match types were less accurate on average than when the two match types were considered together. This model was used to investigate properties of the 2015 Rugby World Cup. The tournament disadvantaged three teams in the World’s top 5 who were drawn in the same pool. Teams ranked in the World’s top 7 did not perform as well as predicted while teams ranked 16<sup>th</sup> and below performed better than predicted suggesting that the strength in depth in international rugby union is increasing. There was a small effect of having additional recovery days from the previous match compared to the opponents which was worth 4.1 points. The information produced by this research should be considered by those design tournaments such as the Rugby World Cup.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Performance Analysis in Table Tennis - Stochastic Simulation by Numerical Derivationhttps://sciendo.com/article/10.1515/ijcss-2016-0002<abstract><title style='display:none'>Abstract</title><p>The aim of this study was to identify the impact of different tactical behaviors on the winning probability in table tennis. The performance analysis was done by mathematical simulation using a Markov chain model. 259 high-level table tennis games were evaluated by means of a new simulation approach using numerical derivation to remove the necessity to perform a second modeling step in order to determine the difficulty of tactical behaviors. Based on the derivation, several mathematical constructs like directional derivations and the gradient are examined for application in table tennis. Results reveal errors and long rallies as the most influencing game situations, together with the positive effect of risky play on the winning probability of losing players.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Computer Science in Sport – Research and Practice: A book reviewhttps://sciendo.com/article/10.1515/ijcss-2016-0004<abstract><title style='display:none'>Abstract</title><p>Sports informatics and computer science in sport are perhaps the most exciting and fast-moving disciplines across all of sports science. The tremendous parallel growth in digital technology, non-invasive sensor devices, computer vision and machine learning have empowered sports analytics in ways perhaps never seen before. This growth provides great challenges for new entrants and seasoned veterans of sports analytics alike. Keeping pace with new technological innovations requires a thorough and systematic understanding of many diverse topics from computer programming, to database design, machine learning algorithms and sensor technology. Nevertheless, as quickly as the state of the art technology changes, the foundation skills and knowledge about computer science in sport are lasting. Furthermore, resources for students and practitioners across this range of areas are scarce, and the new-release textbook <italic>Computer Science in Sport: Research and Practice</italic> edited by Professor Arnold Baca, provides much of the foundation knowledge required for working in sports informatics. This is certainly a comprehensive text that will be a valuable resource for many readers.</p></abstract>ARTICLE2016-07-27T00:00:00.000+00:00Feature Selection to Win the Point of ATP Tennis Players Using Rally Informationhttps://sciendo.com/article/10.2478/ijcss-2020-0003<abstract><title style='display:none'>Abstract</title><p>In tennis, the accumulation of data has progressed and research on tactical analysis has been conducted. Estimating strategically important factors would have the benefit of providing players with useful advice and helping audience members understand what tennis players are good at. Previous research has been conducted into ways of predicting Association of Tennis Professionals (ATP) tennis match outcomes as well as estimating factors that are important for victories using machine learning models. The challenge of previous research is that the victory factor lacks concreteness. Since we thought the root of the abovementioned problem was that previous researchers used game summary as a feature and did not consider the process of rallies between points, this research focused on calculating the frequency of single shots, two-shot patterns, and specific effective shot patterns from each point rally of ATP singles matches. We then used those data to predict point winners and useful features using L1-regularized logistic regression. The highest accuracy obtained was 66.5%, and the area under the curve (AUC) was 0.689. The most prominent feature we found was the ratio of specific shots by specific players. From these results, our method could reveal more concretely tactical factors than previous studies.</p></abstract>ARTICLE2020-06-29T00:00:00.000+00:00en-us-1