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.

Research into the perceptions, practices and engagement of coaches with performance analysis is fundamental to informing the continued development of the discipline and its practitioners. The aim of this study was to survey a large sample of coaches of varying profiles, regarding their use and integration of performance analysis, to explore its perceived value and barriers to its use. Survey data were analysed from 538 Irish coaches qualified at Level 1 and above (82% male), from 37 sports. A spectrum of coaches’ engagement with performance analysis, ranging from no formal performance analysis (49%), to coaches doing their own analysis (coach-as-analyst, 38%) and coaches with analyst-support (13%) was observed. The Level 2 coaching qualification was a key distinguisher between coaches using and not using performance analysis. Significant differences exist between the practices of coaches with and without analyst-support. Coaches with analyst-support are more likely to have regular access to video, spend more time analysing, regularly use performance analysis to inform training and use computerised analysis software. Barriers to the use of performance analysis varied along the spectrum of performance analysis engagement. This research can underpin future developments in coach and analyst education that will optimise the systematic use of PA across all levels of sports performance.

Over 2000 Gaelic Football clubs compete annually for the honour of playing in the All-Ireland club finals in Croke Park in front of up to 30,000 people. There are no published performance data for club level Gaelic football, despite evidence of considerable performance analysis activity. This study aims to establish benchmark profiles for Senior, Intermediate and Junior grade club Gaelic football and investigate which variables are most closely associated with winning. Data from all tiers of the Ulster club football championship of 2015 and 2016 (n = 48) were analysed using a range of validated operational definitions measuring 17 variables. Differences between winning and losing performance were tested using a Mann-Whitney U test. Across all grades, six variables proved significant (p < 0.05), three were directly related to scoring (points, number of scores and total score), the others related to the effective use of possession (possession: scores ratio; turnover rate and productivity (scores per possession)). Several others are specific to certain grades, and are directly linked to successful performance at that level. Our findings can be used by club coaches and analysts as comparable profiles for measuring team performance and targeting improvements associated with successful performance.

The lack of sport science research conducted on female sport, makes it challenging for coaches and sport science practitioners to develop an evidence-based approach. Ladies Gaelic football (LGF) is the most popular female sport in Ireland with over 200,000 members, however there has been no research to benchmark performance. The current study investigates the game characteristics of LGF by developing analysis system which could provide benchmark profiles of successful performance at elite level and evaluate the transferability of the game intelligence available within LGF’s brother sport, Gaelic football (GF). Thirty-one games (62 performances) were analysed from the 2019 and 2020 TG4 All-Ireland Senior Ladies Football Championship using NacSport Scout+. Statistical differences between winning and losing performances were identified using paired sample t-tests (p ≤ 0.05) and Wilcoxon signed ranked tests (p ≤ 0.05). Winning teams were found to be significantly superior to losing teams, demonstrating superior ability to gain and use possession. LGF and men’s GF teams adopt different strategic approaches to the game. The current study provides a first insight into the nature of LGF with benchmark data, facilitating a better understanding of the game demands of LGF and the key points of difference between male and LGF.

Mangan, S, Malone, S, Ryan, M, Mc Gahan, J, Warne, J, Martin, D, O'Neill, C, Burns, C, and Collins, K. Influence of team rating on running performance in elite Gaelic football. J Strength Cond Res 32(9): 2584–2591, 2017—It is currently unknown how team rating influences running performance in Gaelic football. Global positioning system technologies were used to quantify match-running performance within 5 elite Gaelic football teams over a period of 5 years (2012–2016). In total 780 player data sets were collected over 95 matches. Running performance variables included total distance, high-speed distance (≥17 km·h⁻¹), and the percentage of high-speed distance. Team ratings were determined objectively using the Elo rating system for Gaelic football. Reference team rating had trivial effects on total distance (p = 0.011, partial η² = 0.008) and high-speed distance (p = 0.011, partial η² = 0.008). Opposition team rating had small effects on total distance (p = 0.005, partial η² = 0.016) and high-speed distance (p = 0.001, partial η² = 0.020). Top-tier teams cover greater total distances and high-speed distance than lower tier teams. Players cover considerably less total distance and high-speed distance against tier-3 and tier-4 teams. Tier-1 players ran a significantly higher percentage of distance at high speed than players who played for tier-2 teams (p = 0.020). The competitive advantage of top-tier Gaelic football teams is closely linked with their ability to demonstrate a higher physical intensity than lower tier teams.