A new approach to comparing the demands of small-sided games and soccer matches

• Main Authors: Mauro Mandorino (Author), Antonio Tessitore (Author), Sebastien Coustou (Author), Andrea Riboli (Author), Mathieu Lacome (Author)
• Format: Book
• Published: Termedia Publishing House, 2023-12-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 0860-021X; 2083-1862; 10.5114/biolsport.2024.132989
• Keywords: euclidean distance; performance; external load; overload; similarity; Sports medicine; RC1200-1245; Biology (General); QH301-705.5
• Source: Biology of Sport, Vol 41, Iss 3, Pp 15-28 (2023)

To improve soccer performance, coaches should be able to replicate the match's physical efforts during the training sessions. For this goal, small-sided games (SSGs) are widely used. The main purpose of the current study was to develop similarity and overload scores to quantify the degree of similarity and the extent to which the SSG was able to replicate match intensity. GPSs were employed to collect external load and were grouped in three vectors (kinematic, metabolic, and mechanical). Euclidean distance was used to calculate the distance between training and match vectors, which was subsequently converted into a similarity score. The average of the pairwise difference between vectors was used to develop the overload scores. Three similarity (Sim kin , Sim met , Sim mec ) and three overload scores (OVER kin , OVER met , OVER mec ) were defined for kinematic, metabolic, and mechanical vectors. Sim met and OVER met were excluded from further analysis, showing a very large correlation ( r > 0.7, p < 0.01) with Sim kin and OVER kin . The scores were subsequently analysed considering teams' level (First team vs. U19 team) and SSGs' characteristics in the various playing roles. The independentsample t -test showed ( p < 0.01) that the First team presented greater Sim kin ( d = 0.91), OVER kin ( d = 0.47), and OVER mec ( d = 0.35) scores. Moreover, a generalized linear mixed model (GLMM) was employed to evaluate differences according to SSG characteristics. The results suggest that a specific SSG format could lead to different similarity and overload scores according to the playing position. This process could simplify data interpretation and categorize SSGs based on their scores.