A comparison of physical characteristics in starters and non-starters in a Japanese university rugby football players

Abstract

The purpose of this study was to compare the characteristics of starters and nonstarters with respect to the anthropometric and physical fitness in a Japanese university rugby team. Our sample pool consisted of 54 players of the K University rugby team, who were registered for the official games (all 7 games/year) in 2018 and 2019. The starter group (26 players) comprised of players with more than 3 games/year while the non-starter group (28 players) comprised of players that registered for official games but played less than 3 games. Anthropometric parameters, such as height, body mass, skinfold (8 items), girth (5 items), body composition, muscular strength, intermittent endurance, and sprint time, were assessed. We found that the starter group for forwards had lower skinfold (Biceps, Iliac crest, Supraspinale, Abdominal, Front thigh, and Medial calf) and percentage body fat than in the non-starter group. The starter group for backs had muscle strength of narrow chinning is higher than in the non-starter group. Finally, our study helped to clarify the physical factors that influence the difference in athletic performance between starters and non-starters in university rugby team. The forward starters had lower percentage body fat and better body composition and backs starters had better narrow chinning. The findings provide information to university rugby players who want to become starters, as well as help the coaching staff understand the factors that need to be strengthened in the players.

Sports Injury Surveillance Systems and Guidelines in Japan: A Systematic Review

“…Forty-four articles did not describe any sports ISSs or ISGs, one did not report sports injury data, three had retrospective study designs, 11 described surveillance systems that had ceased operation or were not ongoing, and seven presented duplicated information. The search process identified 28 studies, including six English articles 23 24 25 26 27 28 and 22 Japanese articles 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 , that met the inclusion criteria. A flow diagram of the screening and selection process of the studies is shown in Fig 1 .…”

“…Consensus statement on injury definitions and data collection procedures for studies of injuries in rugby union 38 39 40 42 43…”

“…Only one of the 23 studies recorded data on professional athletes 35 , whereas the remaining studies focus on non-professional athletes (university athletes: n=17, high school athletes: n=1, junior high school athletes: n=1, elementary school athletes: n=2, and athletes ranging from junior high school to university level: n=1) 23 24 25 26 27 28 31 32 33 34 36 37 38 39 40 42 43 46 47 48 49 50 . Seven articles included data for both male and female athletes 24 26 28 31 33 49 50 , 10 described data regarding only male athletes 23 35 38 39 40 42 43 46 47 48 , and five included data regarding only female athletes 27 32 34 36 37 .…”

“…Three of the 23 articles targeted study populations within multiple sports settings 28 31 50 , five within the rugby union 38 39 40 42 43 , four within football 25 35 36 37 , three within American football 23 46 47 , three within basketball 24 34 49 , one within lacrosse 32 , one within badminton 26 , one within baseball 48 , one within handball 33 , and one within swimming 27 . Only one of the 23 studies recorded data on professional athletes 35 , whereas the remaining studies focus on non-professional athletes (university athletes: n=17, high school athletes: n=1, junior high school athletes: n=1, elementary school athletes: n=2, and athletes ranging from junior high school to university level: n=1) 23 24 25 26 27 28 31 32 33 34 36 37 38 39 40 42 43 46 47 48 49 50 . Seven articles included data for both male and female athletes 24 26 28 31 33 49 50 , 10 described data regarding only male athletes 23 35 38 39 40 42 43 46 47…”

“…Of the included studies, 14 (60.9%) used ISSs or ISGs developed overseas as a resource for the development of other data collection tools or methods 23 24 25 27 32 33 38 39 40 42 46 47 48 49 , whereas five (21.7%) used them for data analysis 26 35 36 37 43 and four (17.4%) used them for data collection 28 31 34 50 .…”