2,946 research outputs found

    Integration of Forecasting, Scheduling, Machine Learning, and Efficiency Improvement Methods into the Sport Management Industry

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    Sport management is a complicated and economically impactful industry and involves many crucial decisions: such as which players to retain or release, how many concession vendors to add, how many fans to expect, what teams to schedule, and many others are made each offseason and changed frequently. The task of making such decisions effectively is difficult, but the process can be made easier using methods of industrial and systems engineering (ISE). Integrating methods such as forecasting, scheduling, machine learning, and efficiency improvement from ISE can be revolutionary in helping sports organizations and franchises be consistently successful. Research shows areas including player evaluation, analytics, fan attendance, stadium design, accurate scheduling, play prediction, player development, prevention of cheating, and others can be improved when ISE methods are used to target inefficient or wasteful areas

    Proceedings of Mathsport international 2017 conference

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    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

    Using Supervised Learning to Predict English Premier League Match Results From Starting Line-up Player Data

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    Soccer is one of the most popular sports around the world. Many people, whether they are a fan of a soccer team, a player of online soccer games or even the professional coach of a soccer team, will attempt to use some relevant data to predict the result of a match. Many of these kinds of prediction models are built based on data from the match itself, such as the overall number of shots, yellow or red cards, fouls committed, etc. of the home and away teams. However, this research attempted to predict soccer game results (win, draw or loss) based on data from players in the starting line-up during the first 12 weeks of the 2018-2019 season of the English Premier League

    Deep-Learning-Based Computer Vision Approach For The Segmentation Of Ball Deliveries And Tracking In Cricket

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    There has been a significant increase in the adoption of technology in cricket recently. This trend has created the problem of duplicate work being done in similar computer vision-based research works. Our research tries to solve one of these problems by segmenting ball deliveries in a cricket broadcast using deep learning models, MobileNet and YOLO, thus enabling researchers to use our work as a dataset for their research. The output from our research can be used by cricket coaches and players to analyze ball deliveries which are played during the match. This paper presents an approach to segment and extract video shots in which only the ball is being delivered. The video shots are a series of continuous frames that make up the whole scene of the video. Object detection models are applied to reach a high level of accuracy in terms of correctly extracting video shots. The proof of concept for building large datasets of video shots for ball deliveries is proposed which paves the way for further processing on those shots for the extraction of semantics. Ball tracking in these video shots is also done using a separate RetinaNet model as a sample of the usefulness of the proposed dataset. The position on the cricket pitch where the ball lands is also extracted by tracking the ball along the y-axis. The video shot is then classified as a full-pitched, good-length or short-pitched delivery

    Soccer Coach Decision Support System

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    The savage essence and nature of sports means those who work on it hunt for the win. The sport enterprise is undergoing a gigantic digital transformation focused on imaging, real time and data analysis employed in the competitions. Conventional process methods in sports management such as fitness and health establishments, training, growth and match or game realisation are all being revolutionized by the sport digitization. In team sports it is well known that is needful an enough and simple digital methodology to organize and construct a feasible strategy. Digitization in sports is perpetually evolving and requires pervasive challenges. The sports and athletics digitization success is based on what is being done with collection of more data. Competitive advantages go to those who produce powerful operations using the data and acting on it in real time. The potential impact of these sport features in sport team operations is powerful. Data does not ride all decisions, but it empowers knowledgeable decisions. In these world circumstances, our vision with this system was born from a dream helping soccer sport management systems embrace and improve its contest success. Our perspective problem is how a decision support system for soccer coaches helps them to take enhancement decisions better. To face this problem we have created a soccer coach decision support system. This system is organised in two joined components; the first simulates the prediction of the soccer match winner through a data driven neural network. This component output activates the second to operate the logic rules learning and provides the stats, analysis, decision making and additionally plans improvements like drills and training procedures. This helps on the preparation towards upcoming matches as well as being aligned with their style and playing concepts. Future scalability and development, will analyse the mental and moral features of the teams by virtue of their athlete’s behavior changes

    Perceptual-Cognitive Assessments in Football

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    Introduction: Assessments with varying levels of perceptual information or action fidelity are commonly used in the detection and identification of talent in football. Common performance assessments can range from either highly sport-specific environments with players being immersed in a realistic environment and interacting with a football (i.e. domain-specific, high ecological validity), to players sitting in front of a computer responding to various shapes and colours with no sport-specific information presented (i.e. domain-generic, low ecological validity). Many testing batteries measure athletes with a multitude of different tests that are placed along various points on ecological validity continuum. However, very few of these assessments are sufficiently validated. For the assessments that attempt to closely replicate the perception-action coupling demands experienced in football game play, there are many conditions that must be met before it can be used in future research and practice. On the other side of the spectrum, it remains contentious whether using assessments that intentionally remove ecological validity from their environments has merit. These non-sport specific assessments attempt to measure the general cognitive abilities of athletes, and many researchers have advocated their usefulness in talent identification programs. Therefore, the collection of aims within this dissertation was three-fold: i) to investigate both the domain-generic and domain-specific perceptual-cognitive abilities of all athletes (i.e. academy to senior players) in order to understand what perceptual-cognitive abilities athletes exhibit, and what factors (i.e. environment and heritable) contributed towards their cognitive profile, ii) to track both domain-specific and domain-generic abilities longitudinally in order to understand their relationships with increased exposure to football training, and iii) to learn from the limitations of the domain-specific skills assessment and incorporate new technologies in order to gain a further insight to investigate how emerging technologies could help to develop more representative assessments. Methods: To understand the between-group differences of domain-generic and domain-specific abilities across the youth developmental period of athletes, a variety of independent studies were undertaken. First, 343 male players (age: 10.34 – 34.72 years; playing experience: 5 – 22 years) from the U12-Senior age groups of a professional German football club were recruited. Age, experience and playing position were recorded to examine which factors contributed more to the development of domain-generic abilities (Chapter 3). Players participated in four generic cognitive tasks aimed at measuring higher-level cognitive functioning: a precued choice response-time task, a stop-signal reaction-time task, a sustained attention task, and a multiple-object tracking task. Second, a new football-specific skills test was used to measure the domain-specific abilities of the athletes throughout adolescence, and the reliability and age-discriminant validity of this new domain-specific skills test was investigated (Chapter 4). Third, 304 players from the same cohort as Chapter 3 had their data analysed longitudinally to track the longitudinal development of both domain-generic (assessments from Chapter 3) and domain-specific (assessment from Chapter 4) abilities across three seasons (Chapter 5). Lastly, the final investigation of the dissertation was divided in two parts to explore how to develop more representative task designs within the football specific skills assessment used in the previous chapters. Accordingly, Chapter 6a) 85 amateur male participants (19.5 ± 5.4 years old; 13.1 ± 6.0 years playing football) completed two sessions in the skills assessment task under two different visual conditions: stroboscopic and full vision Participants were subdivided into skilled (S: top 50%) and less-skilled (LS: bottom 50%) groups using their point score from the full vision condition. Chapter 6b) Exploratory head movements of fourteen U13 and thirteen U23 high-level football players were recorded with a head worn inertial sensor in the skills assessment task, from which the count, frequency and excursion of head movements were extracted before and during ball possession investigate whether visual exploratory action is associated with passing performance. Results: Chapter 3 first demonstrated that a negatively accelerated curve generally best described the relationship between age, experience and domain-generic abilities. Age and experience only explained a very low to moderate proportion of the variance in EFs (marginal explained variance ranged between 2 and 57%). Furthermore, although Chapter 4 revealed that the new domain-specific skills test yielded acceptable test-retest reliability for the correct number of passes to a target (CV = 7.5-11.1; r = 0.48; p<0.001) and the speed at which they completed each trial (CV = 2.6-5.1; r = 0.70; p<0.001), the assessment was not able to differentiate between athletes over the age of 15. This plateau in both the developmental trajectories of domain-generic (Chapter 3) and domain-specific (Chapter 4) abilities was confirmed in the longitudinal study (Chapter 5), revealing that a performance plateau was apparent for domain-specific abilities during adolescence (i.e. 15 years old), whereas domain-generic abilities improved into young adulthood (i.e. 21 years old). Consequently, a further investigation into more representative task design had merit, where Chapter 6a) reported that restricting athletes’ visual feedback in the football skills assessment impacted time of completion per trial to in both S and LS groups equally (S: 0.21s; LS: 0.18s; p=0.543), but S athletes’ accuracy (S: 11.7%; LS: 0.4%; p<0.001) were significantly more affected compared to full vision conditions. Lastly, Chapter 6b) reported that the variables that best explained faster performance were a higher number of head turns before receiving the ball, and a lower number of head turns when in possession of the ball, which older athletes perform better than younger athletes. Discussion/conclusion: Overall, the investigation into domain-generic assessments across Chapter 3 and 5 found that athletes improve their performance during late childhood until reaching adolescent (i.e. average age of 15 years old) and was independent of how many years of experience playing football or which position they played on the field. As the developmental trajectories of high-level football players’ domain-generic abilities reflected those observed in general populations’ despite long-term exposure to football-specific training and gameplay, this questions the relationship between high-level experience’s capacity to improve domain-generic abilities and challenges the validity of including non-sport specific assessments as a measure of football performance potential in high performing athletes. Lastly, despite the best efforts to use highly technical assessments to measure football skills in Chapter 4 and 5, the assessments may have under-represented the perceptual or action components necessary to allow athletes to demonstrate their expertise. Thus, more studies that aim to improve on the task designs of assessment tools has merit, and future studies could build off the foundations from the studies within Chapter 6 [i.e. stroboscopic glasses (6a) and head movement sensors (6b)] as methods to expand on the representativeness of assessment tasks

    Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

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    Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills
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