Physical performance profile of sub-elite juvenile Gaelic Games players and the prevalence of a Relative Age Effect (RAE)

Gaelic Games are the indigenous sports played in Ireland, the most popular being Gaelic football and hurling. The games are contact sports and the physical demands are thought to be similar to those of Australian Rules football, rugby union, rugby league, field hockey, and lacrosse (Delahunt et al., 2011). The difference in chronological age between children in a single age group is known as relative age and its consequences as the RAE, whereby younger players are disadvantaged (Del Campo et al., 2010). The purpose of this study was to describe the physical and performance profile of sub-elite juvenile Gaelic Games players and to establish if a RAE is present in this cohort and any influence physiological moderator variables may have on this. Following receipt of ethical approval (EHSREC11-45), six sub-elite county development squads (Under-14/15/16 age groups, male, n=115) volunteered to partake in the study. Anthropometric data including skin folds and girths were collected. A number of field tests of physical performance including 5 and 20m speed, vertical and broad jump distance, and an estimate of VO2max were carried out. Descriptive data are presented as Mean SD. Juvenile sub-elite Gaelic Games players aged 14.53 0.82 y were 172.87 7.63 cm tall, had a mass of 64.74 11.06 kg, a BMI of 21.57 2.82 kg.m-2 and 9.22 4.78 % body fat. Flexibility, measured by sit and reach was 33.62 6.86 cm and lower limb power measured by vertical and broad jump were 42.19 5.73 and 191.16 25.26 cm, respectively. Participant time to complete 5m, 20m and an agility test (T-Test) was 1.12 0.07, 3.31 0.30 and 9.31 0.55 s respectively. Participant’s estimated VO2max was 48.23 5.05 ml.kg.min-1. Chi-Square analysis of birth month by quartile (Q1 = January-March) revealed that a RAE was present in this cohort, whereby an over-representation of players born in Q1 compared with Q2, Q3 and Q4 was evident (2 = 14.078, df = 3, p = 0.003). Kruskal-Wallis analysis of the data revealed no significant difference in any of the performance parameters based on quartile of birth (Alpha level = 0.05).This study provides a physical performance profile of juvenile sub-elite Gaelic Games players, comparable with those of other sports such as soccer and rugby. This novel data can inform us of the physical requirements of the sport. The evidence of a RAE is similar to that observed in other contact sports such as soccer and rugby league (Carling et al, 2009; Till et al, 2010). Although a RAE exists in this cohort, this cannot be explained by any physical/physiological moderator variables. Carling C et al. (2009). Scandinavian Journal of Medicine and Science in Sport 19, 3-9. Delahunt E et al. (2011). Journal of Athletic Training 46, 241-5. Del Campo DG et al. (2010). Journal of Sport Science and Medicine 9, 190-198. Delorme N et al. (2010). European Journal of Sport Science 10, 91-96. Till K et al. (2010). Scandinavian Journal of Medicine and Science in Sports 20, 320-329.

Recognising team activities from noisy data

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Representing team behaviours from noisy data using player role

Due to their unobtrusive nature, vision-based approaches to tracking sports players have been preferred over wearable sensors as they do not require the players to be instrumented for each match. Unfortunately however, due to the heavy occlusion between players, variation in resolution and pose, in addition to fluctuating illumination conditions, tracking players continuously is still an unsolved vision problem. For tasks like clustering and retrieval, having noisy data (i.e. missing and false player detections) is problematic as it generates discontinuities in the input data stream. One method of circumventing this issue is to use an occupancy map, where the field is discretised into a series of zones and a count of player detections in each zone is obtained. A series of frames can then be concatenated to represent a set-play or example of team behaviour. A problem with this approach though is that the compressibility is low (i.e. the variability in the feature space is incredibly high). In this paper, we propose the use of a bilinear spatiotemporal basis model using a role representation to clean-up the noisy detections which operates in a low-dimensional space. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labeled data.

Creative industries as a globally contestable policy field

The development of the creative industries “proposition” has caused a great deal of controversy. Even as it has been examined and adopted in several, quite diverse, jurisdictions as a policy language seeking to respond to both creative production and consumption in new economic conditions, it is subject to at times withering critique from within academic media, cultural and communication studies. It is held to promote a simplistic narrative of the merging of culture and economics and represents incoherent policy; the data sources are suspect and underdeveloped; there is a utopianization of “creative” labor; and a benign globalist narrative of the adoption of the idea. This article looks at some of these critiques of creative industries idea and argues against them.

Increasing the rigour and trustworthiness of participatory evaluations : learnings from the field

Participatory evaluation and participatory action research (PAR) are increasingly used in community-based programs and initiatives and there is a growing acknowledgement of their value. These methodologies focus more on knowledge generated and constructed through lived experience than through social science (Vanderplaat 1995). The scientific ideal of objectivity is usually rejected in favour of a holistic approach that acknowledges and takes into account the diverse perspectives, values and interpretations of participants and evaluation professionals. However, evaluation rigour need not be lost in this approach. Increasing the rigour and trustworthiness of participatory evaluations and PAR increases the likelihood that results are seen as credible and are used to continually improve programs and policies.----- Drawing on learnings and critical reflections about the use of feminist and participatory forms of evaluation and PAR over a 10-year period, significant sources of rigour identified include:----- • participation and communication methods that develop relations of mutual trust and open communication----- • using multiple theories and methodologies, multiple sources of data, and multiple methods of data collection----- • ongoing meta-evaluation and critical reflection----- • critically assessing the intended and unintended impacts of evaluations, using relevant theoretical models----- • using rigorous data analysis and reporting processes----- • participant reviews of evaluation case studies, impact assessments and reports.