Login / Signup

The UEFA Heading Study: Heading incidence in children's and youth' football (soccer) in eight European countries.

Florian BeaudouinAsimenia GioftsidouMalte Nejst LarsenKoen LemminkBarry DrustRoberto ModenaJavier Ramos EspinolaMihai MeiuMarc VouillamozTim Meyer
Published in: Scandinavian journal of medicine & science in sports (2020)
To assess the real-life magnitude of the heading incidence in children's and youth' football in eight European countries with different "football cultures," a cross-sectional observational design, in which one match per team in 480 different teams from eight European countries (2017/18-2018/19), was recorded by video. One training session was recorded in 312 teams. Clubs with Under-10, Under-12 (female/male/mixed), and Under-16 female and male teams were eligible to participate. Heading frequencies and types were analyzed. Results are presented as headers per match/training and per team. Incidence rates (IR) per 1000 match/training hours were calculated. Under-10 teams carried out the lowest average number of headers per match (8.8), followed by Under-16 female (17.7), Under-12 (18.4), and Under-16 male (35.5). Total number of headers per match and team varied between countries. 80% of the total number of headers were single intentional headers, 12% heading duels, 3% unintentional headers by getting hit, and 5% others (trends apparent in all age groups). Three head injuries occurred during match play corresponding to an IR of 0.70 (95% CI, 0.23-2.16). The lowest number of headers per training and team was found in Under-10 (21.3), followed by Under-16 females (34.1), Under-12 (35.8), and Under-16 males (45.0). In conclusion, this large-scale study presents novel data about the number and type of headers in youth' football throughout Europe. A more precise understanding of the heading incidence, specifically in young players, is mandatory for the debate of restrictions on heading in youth football.
Keyphrases
  • young adults
  • high school
  • risk factors
  • palliative care
  • mental health
  • physical activity
  • virtual reality
  • quality improvement
  • big data
  • middle aged
  • working memory
  • deep learning
  • artificial intelligence