Volume 7 Issue 2

June 2024

Looking Expensive: Experts’ Valuation of Paintings Influenced by Context

Qin Li ¹
¹Queensborough Community College, USA
Many factors influence the price of artwork. Few studies have focused on how contextual variables influence price despite their influence on how people experience and evaluate art. I constructed an online experimental study to look at painting setting (i.e., museum, street), painting attributes (i.e., style, reputation), and viewers’ expertise to determine their influences on price. The results of the experiment showed that setting, style, and reputation interact with expertise to affect the pricing of art. Contrary to expectation, the results showed that experts were most influenced by setting manipulation when determining price, despite their reported familiarity with the established paintings. Experts and quasi-experts were also more likely to price paintings according to reputation than novices. There was also a significant three-way interaction between expertise, reputation, and style, whereby those with greater expertise were influenced by reputation and style when pricing art compared to novices.

Optimizing the Use of Machine Learning and Computer Vision in Sport: An Ecological Dynamics Perspective

Cavan Aulton ¹, Ben William Strafford ¹, Keith Davids ¹, and Chuang-Yuan Chiu ¹
¹Sheffield Hallam University, UK
Although machine learning and computer vision is a growing area of research in sports analysis, its implementation in athlete development programs does not guarantee performance improvements. Developers typically design and implement machine learning and computer vision technologies into athlete development programs because of the high level of technical information on performance that can emerge. The value gained from this approach can be limited by siloed working practices in sports organizations and by sporadic approaches to athlete development which can negatively affect skill development. Here, we discuss why the design and integration of machine learning and computer vision in athlete development programs needs to be rationalized by a theoretical framework to guide effective collaborations between sport scientists, technologists, and practitioners. This position paper illustrates how the use (i.e., design and implementation) of machine learning and computer vision technologies in athlete development programs could be underpinned by the structural organization of a Department of Methodology (DoM), and that underpinned by a theoretical framework, such as ecological dynamics. We outline how the integration of machine learning and computer vision technology, underpinned by an ecological theoretical approach, can accomplish the following: (1) support representative learning design, (2) individualize training and assessment of athletes, and (3), enhance, but not replace, the quality of coaching within athlete development programs.

A Comparison of In-person Versus Video-recorded Player Assessment by English Category One Football Academy Scouts

Robin Owen ¹, Seamus Harvey ², Ben Smith ³, and Benjamin Jones ⁴
¹Liverpool Hope University, UK, ²Dundalk Institute of Technology, Ireland, ³Breakaway Data, UK, ⁴The Football Association, UK
Talent identification is undertaken by football academies via scouts who traditionally attend football matches in-person. However, no previous study has compared in-person against video-recorded modalities for scouting. In the present study, a total of 30 scouts working for an English category one football academy observed the same U14 and U15 inter-academy matches either in-person (n = 13) or video-recorded (n = 17). Non-parametric analyses compared modalities (in-person vs video-recorded) in relation to the following: player performance ratings; subjective perception of modality accuracy; observation report submission time; and attentional/affectual factors including scout fatigue, joyality, attentiveness, self-control, and mental effort during observations. Results revealed no significant difference in player performance ratings when comparing in-person and video-recorded observation modalities (p > .05), despite scouts subjectively rating in-person as being the more accurate modality (p < .001). Scouts also reported that the video-recorded modality resulted in significantly quicker observation report submission (p < .001). However, video-recorded observations produced significantly greater fatigue during whole-team observations (p < .05) but not during individual-player observations (p > .05). No significant differences emerged between the modalities for joyality (p > .05), attentiveness (p > .05), self-control (p > .05), nor mental effort (p > .05). The present findings suggest that using video-recordings for the scouting of academy-level football players offers an adequately accurate and possibly more efficient alternative to in-person scouting.

Comparing Apples and Oranges? The Need for Greater Qualitative Clarity in Talent Development Research

Kevin Gavin ^1,2,3, Jamie Taylor ^1,2,4, Áine MacNamara ¹, and Stephen Behan ^1,2
¹Faculty of Science and Health, Dublin City University, Ireland, ²Insight SFI Research Centre for Data Analytics, Dublin City University, Ireland, ³Atlantic Technological University, Ireland, ⁴Grey Matters Performance Ltd., Stratford upon Avon, England
The field of talent development (TD) research and practice has grown significantly over the last two decades, drawing on a range of complex modeling, such as the biopsychosocial and bio-ecological framings, to re-evaluate prescriptive approaches to talent systems. In line with recognition of the social complexity of TD and to promote transferability of findings to practice, we argue the need for researchers to provide greater qualitative clarity in describing the systemic context where research is conducted. By outlining the contextual factors and sport-specific nuances, we suggest that this qualitative clarity will enable researchers to conduct more meaningful cross-study comparisons and promote a more nuanced understanding of TD across diverse sporting contexts. Most importantly, qualitative clarity will enable practitioners to consider the relative transferability of research findings to their own context and to leverage this knowledge to inform evidence-informed practice.