Samuel Wild

Different talent development (TDE) environments exhibit varying training practices in the rugby league talent identity and development systems (TIDS), which may influence rates of talent development and subsequent productivity of each TDE. This study aimed to compare physical qualities and rates of physical development between different rugby league TDEs within the same TIDS, alongside differences between groups of TDEs based on their level of productivity. A sample of 261 youth rugby league players from six academy teams (i.e., TDEs) within the professional TIDS were tested as part of a league-wide fitness testing battery for measures of anthropometrics, strength, power, speed, and cardiovascular fitness. Linear mixed models revealed medium, significant differences in maximum sprint velocity at the beginning of the season (η 2  = 0.05, p = 0.03) and large, significant differences in the development of prone Yo-Yo IR1 distance over time (η 2  = 0.14–0.18, p < 0.001) between TDEs. No significant differences between groups of TDEs based on their productivity were found. These findings indicate that possible variability in the practices of TDEs mostly leads to small or trivial differences in physical qualities and physical development. Differences in physical qualities and physical development do not appear to relate to the productivity of TDEs, therefore TDEs should focus on holistic development to maximise productivity.

ABSTRACT

The structure of a talent identification and development system (TIDS), in terms of its starting, entry, and exit points is an important consideration for sporting organisations. Early talent identification decisions can be ineffective due to unpredictable and individually variable talent development. Physical qualities are a key contributor to performance in rugby league. Therefore, understanding physical development differences between age groups can inform the structure of the rugby league TIDS by highlighting key phases of development. Between‐player variability in physical development must also be considered to understand the generalisability of age‐group trends. Consequently, this study aimed to compare rates of physical development between annual age groups (i.e., U15, 16, 17, 18) in 261 youth rugby league players from multiple clubs, considering individual differences in development rates. Latent growth curve analysis was used to model rates of physical development for size (i.e., height, mass), strength, power, speed, and cardiovascular fitness in each age group. Results showed that U15s had significantly faster rates of development for body size and strength qualities compared with all older age groups, with large between‐player variability. No differences were apparent between age groups for power, speed, or cardiovascular fitness. These findings suggest that early talent identification and (de)selection decisions may ignore the potential development of body size and strength qualities, which occurs at individually variable rates. Such findings can inform the structure and design of the rugby league TIDS by highlighting expected rates of physical development based on players' age groups.

Player profiling can aid talent identification and development by highlighting strengths and weaknesses, and evaluation training interventions. However, there is currently no consensus in rugby league on the qualities, skills, and characteristics (i.e., factors) which should be profiled, or the methods to use to assess these factors. Consequently, the aims of this two-part study were to 1) establish the most common factors and methods for profiling rugby league players, through a systematic scoping review, and 2) develop consensus on the factors and methods experts believe should be used when profiling rugby league players. In Part 1, a systematic scoping review of studies profiling rugby league players was conducted according to the PRISMA guideline for Scoping Reviews. In Part 2, a panel of 32 experts were invited to participate in a sequential three-round Delphi consensus, used to identify the factors that they believed should be profiled in rugby league players and associated methods of assessment. Part 1 identified 370 studies, which assessed varying numbers of factors from five higher order themes; physical (n=247, 67%), health-related (n=129, 35%), other (n=60, 16%; e.g., playing experience, level of education), technical-tactical (n=58, 16%), and psychological (n=25, 7%). Only 3% of these studies featured female participants (n=11). In Part 2, 120 factors were initially identified, of which 85 reached consensus (≥70% agreement). This included 22 physical, 22 psychological, 20 technical-tactical, 15 health-related, and six player information factors. Collectively, these findings evidence the multidimensional nature of talent in rugby league, highlighting a range of factors across several domains that should be considered when identifying and monitoring talent in the sport. Furthermore, technical-tactical and psychological factors were identified as areas for future research, due to the large number of factors which reached consensus in these areas and the comparatively low amount of research conducted in them.

Purpose: To (a) quantify the internal and external training load (TL) demands of professional ballet dancers; (b) explore the influence of session type, and sex on TL. Methods: Twenty-nine professional ballet dancers (men = 14; women = 15) were monitored over two weeks. Internal TL data was measured using heart rate-derived TRIMP, and external TL using PlayerLoad (PL). TL data from 303 sessions were categorised and analysed according to session type (rehearsal, class, and conditioning), and sex. Results: Dancers averaged ~4.62 per day (~ 26.1 hours/week) comprised of daily (PL: 539 AU; HR TRIMP: 314 arbitrary units (AU)) and weekly (PL: 2608 AU; HR TRIMP: 1503 AU) TL. Rehearsals had the longest duration (197 ± 66 min) but lowest load per minute. Conditioning produced the highest PL (140.1 AU, 95% CI: 120.2-160.0), while class elicited the greatest HR TRIMP (89.4 AU, 95% CI: 72.1-106.7). Session type significantly influenced TL (P = 0.004, η2 = 0.68) with small to moderate differences between rehearsal and conditioning (PL ES = 0.38; TRIMP ES = 0.49). No significant differences were observed by sex (PL, P = 0.57; TRIMP, P = 0.18). Conclusions: Conditioning sessions produced the greatest TL response and should play a key role in professional ballet dancer’s schedules. Although TL did not significantly differ by sex, the study suggests that a uniform training model may not meet individual dancer needs. These findings support the implementation of personalised monitoring and periodised scheduling strategies to optimise both artistic and physical performance in professional ballet.