Purpose: To quantify how many of the top 50 under-18 (U18) sprinters in the world managed to become top 50 ranked as adult competitors. The authors also described the career trajectory of athletes ranked in the top 50 during either U18 or senior category. Methods: A total of 4924 male and female athletes competing in sprint races and ranked in the International Association of Athletics Federations (now World Athletics) lists in any of the seasons between the 2000 and 2018 were included in the study. The athletes ranked in the top 50 positions of all-time lists during U18, senior, or both categories were analyzed. Results: Only 17% of the male and 21% of the female top 50 ranked U18 managed to become top 50 ranked senior athletes. The top 50 ranked senior athletes consistently produced yearly larger improvements during late adolescence and early adulthood compared with those who ranked in the top 50 at U18. Furthermore, top 50 ranked senior athletes reached their peak performance later compared with the top 50 ranked only in U18. Conclusions: This study confirms that early success in track and field is not a good predictor of success at senior level in sprinting events. The yearly performance improvements and their tracking provide the most suitable approach to identify athletes more likely to succeed as elite performers in adulthood. The authors hope that the results of this study can provide useful comparative data and reference criteria for talent-identification and -development programs.
Gennaro Boccia, Marco Cardinale, and Paolo Riccardo Brustio
Alexandru Nicolae Ungureanu, Paolo Riccardo Brustio, Gennaro Boccia, Alberto Rainoldi, and Corrado Lupo
Purpose: To evaluate if the internal training load (ITL; Edwards heart rate [HR]-based and session-rating of perceived exertion [RPE] methods) is affected by the presession well-being perception, age, and position in elite (ie, Serie A2) female volleyball training. Methods: Twelve female elite volleyball players (age: 22  y, height: 1.80 [0.06] m, body mass: 74.1 [4.3] kg) were monitored using an HR monitor during 32 team training sessions (duration: 1:36:12 [0:22:24], in h:min:s). Linear mixed-effects models were applied to evaluate if well-being perception (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) may affect ITL depending on age and tactical position. Results: Presession perceived fatigue influenced ITL according to the session-RPE (P = .032) but not according to the Edwards method. Age was inversely correlated to the Edwards method (P < .001) and directly correlated to the session-RPE (P = .027). Finally, central blockers experienced a higher training load than hitters (P < .001) and liberos (P < .001) for the Edwards method, as well as higher than hitters (P < .001), liberos (P = .003), and setters (P = .008) for session-RPE. Conclusions: Findings indicated that female volleyball players’ perceived ITL is influenced by presession well-being status, age, and position. Therefore, coaches can benefit from this information to specifically predict players’ ITL in relation to their individual characteristics.
Corrado Lupo, Alexandru Nicolae Ungureanu, Gennaro Boccia, Andrea Licciardi, Alberto Rainoldi, and Paolo Riccardo Brustio
Purpose: The present study aimed to verify if practicing tackles during rugby union training sessions would affect the players’ internal training load and acute strength loss. Method: A total of 9 male Italian Serie A rugby union players (age: 21  y) were monitored by means of an integrated approach across 17 sessions, 6 with tackles (WT) and 11 with no tackles (NT). Edwards training load was quantified using heart-rate monitoring. Global positioning system devices were used to quantify the total distance and time at >20 W. Work-to-rest ratio was quantified by means of a video analysis. Before (PRE) and after (POST) the session, the players’ well-being and rating of perceived exertion were measured, respectively. The countermovement jump and plyometric push-up jump tests were performed on a force plate to record the players’ PRE–POST concentric peak force. Linear mixed models were applied to quantify the differences between WT and NT in terms of training load and PRE–POST force deltas, even controlling for other training factors. Results: The Edwards training load (estimated mean [EM]; standard error [SE]; WT: EM = 214, SE = 11.8; NT: EM = 194, SE = 11.1; P = .01) and session rating of perceived exertion (WT: EM = 379, SE = 21.9; NT: EM = 277, SE = 16.4; P < .001) were higher in WT than in NT. Conversely, no difference between the sessions emerged in the countermovement jump and plyometric push-up concentric peak force deltas. Conclusions: Although elite rugby union players’ external and internal training load can be influenced by practicing tackles, upper- and lower-limb strength seem to not be affected.