Man &amp; machine: Adaptive tools for the contemporary performance analyst

Robertson, Sam

doi:10.1080/02640414.2020.1774143

Cited by 36 publications

(49 citation statements)

References 44 publications

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“…This system focuses strongly on the technical skills required to carry out the role of an applied performance analyst. Robertson's (2020a) recent article considering how PAs are the human interface with technology and data, emphasises the importance of a broader professional skill set, highlighting open-mindedness, versatility, evidence-based decisionmaking, and thorough evaluation of practice as key traits for analysts of the future. Thus, in addition to the development of strong technical and PA skills, the identification and cultivation of other elements of practice, such as relationship building and professional behaviours, is also a key area of an analyst's development.…”

Section: Introductionmentioning

confidence: 99%

Developing a framework for professional practice in applied performance analysis

Martin

Donoghue

Bradley

et al. 2021

International Journal of Performance Analysis in Sport

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Applied performance analysts are increasingly seen as sports science professionals; however, there is no accepted framework for professional practice. The purpose of this study is to develop and validate a framework for professional practice in applied performance analysis (PA) which identifies the components of practice and the expertise underpinning it.A six-step framework analysis was conducted: (1) An initial conceptual framework was devised based on well-accepted components of applied practice; (2) A systematic review identified 90 papers relating to applied PA practice; (3) Papers were coded to the initial framework and additional themes recorded; (4) Themes were analysed and synthesised to construct a draft framework; (5) This draft was validated by surveying 24 experienced applied performance analysts and academic experts; (6) A revised framework is reported based on stakeholder engagement feedback.Nine components of practice were identified; establishing relationships and defining roles, needs analysis and service planning, system design, data collection and reliability checking, data management, analysis, reporting to key stakeholders, facilitation of feedback to athletes and service review and evaluation. Our evidence suggests that applied PA practice is underpinned by five areas of expertise: contextual awareness, building relationships, performance analysis and sporting expertise, technical expertise and professional behaviours.

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Section: Introductionmentioning

confidence: 99%

Developing a framework for professional practice in applied performance analysis

Martin

Donoghue

Bradley

et al. 2021

International Journal of Performance Analysis in Sport

View full text Add to dashboard Cite

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“…Following these arguments, the main finding of our study is that our machine learning models could reliably identify and predict the physical performance of a player after 15 min in the match. The early prediction of physical performance can support a decision support system as advocated by Robertson [12] and further illustrates the opportunities provided by machine learning in player monitoring during the match.…”

Section: Discussionmentioning

confidence: 93%

“…As highlighted in a survey paper by Nosek, Brownlee, Drust and Andrew (2020) [11], staff and IT solutions struggle with giving helpful feedback to the coach after training sessions due to the amount and complexity of the data and their often inconclusive communication [11]. In order to enable helpful, timely feedback, Robertson advocated using machine learning approaches as decision support for the coach [12]. Decision support provides superior efficacy when the volume of the data is large, and the data are complex [13].…”

Section: Introductionmentioning

confidence: 99%

Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

Dijkhuis

Kempe

Lemmink

2021

Entropy

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Substitution is an essential tool for a coach to influence the match. Factors like the injury of a player, required tactical changes, or underperformance of a player initiates substitutions. This study aims to predict the physical performance of individual players in an early phase of the match to provide additional information to the coach for his decision on substitutions. Tracking data of individual players, except for goalkeepers, from 302 elite soccer matches of the Dutch ‘Eredivisie’ 2018–2019 season were used to enable the prediction of the individual physical performance. The players’ physical performance is expressed in the variables distance covered, distance in speed category, and energy expenditure in power category. The individualized normalized variables were used to build machine learning models that predict whether players will achieve 100%, 95%, or 90% of their average physical performance in a match. The tree-based algorithms Random Forest and Decision Tree were applied to build the models. A simple Naïve Bayes algorithm was used as the baseline model to support the superiority of the tree-based algorithms. The machine learning technique Random Forest combined with the variable energy expenditure in the power category was the most precise. The combination of Random Forest and energy expenditure in the power category resulted in precision in predicting performance and underperformance after 15 min in a match, and the values were 0.91, 0.88, and 0.92 for the thresholds 100%, 95%, and 90%, respectively. To conclude, it is possible to predict the physical performance of individual players in an early phase of the match. These findings offer opportunities to support coaches in making more informed decisions on player substitutions in elite soccer.

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“…A further consideration is that the resources required for large teams of specialized support staff and technology (Malone et al, 2019), may not be available post COVID-19. This presents an opportunity to streamline operations by merging roles and enhancing organizational cohesion by moving to a more generalist model whereby general sports science support is provided across these functions by an appropriately skilled but reduced number of personnel (Robertson, 2020).…”

Section: Talent Scoutingmentioning

confidence: 99%

With Crisis Comes Opportunity: Redesigning Performance Departments of Elite Sports Clubs for Life After a Global Pandemic

et al. 2021

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The suspension of major sporting competitions due to the global COVID-19 pandemic had a substantial negative impact on the sporting industry. As such, a successful and sustainable return to sport will require extensive modifications to the current operations of sporting organizations. In this article we argue that methods from the realm of sociotechnical systems (STS) theory are highly suited for this purpose. The aim of the study was to use such methods to develop a model of an Australian Football League (AFL) club’s football department. The intention was to identify potential modifications to the club’s operations to support a return to competition following the COVID-19 crisis. Subject Matter Experts from an AFL club participated in three online workshops to develop Work Domain Analysis and Social Organization and Cooperation Analysis models. The results demonstrated the inherent complexity of an AFL football department via numerous interacting values, functions and processes influencing the goals of the system. Conflicts within the system were captured via the modeling and included pursing goals that may not fully reflect the state of the system, a lack of formal assessment of core values, overlapping functions and objects, and an overemphasis on specialized roles. The current analysis has highlighted potential areas for modification in the football department, and sports performance departments in general.

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Man & machine: Adaptive tools for the contemporary performance analyst

Cited by 36 publications

References 44 publications

Developing a framework for professional practice in applied performance analysis

Developing a framework for professional practice in applied performance analysis

Early Prediction of Physical Performance in Elite Soccer Matches—A Machine Learning Approach to Support Substitutions

With Crisis Comes Opportunity: Redesigning Performance Departments of Elite Sports Clubs for Life After a Global Pandemic

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