Biology of Sport
eISSN: 2083-1862
ISSN: 0860-021X
Biology of Sport
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vol. 41
Original paper

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

Mauro Mandorino
1, 2
Antonio Tessitore
Sebastien Coustou
Andrea Riboli
3, 4
Mathieu Lacome
2, 5

  1. Performance and Analytics Department, Parma Calcio 1913, 43121 Parma, Italy
  2. Department of Movement, Human and Health Sciences, University of Rome “Foro Italico”, Piazza L. de Bosis 6, 00135 Rome, Italy
  3. MilanLab Research Department, AC Milan S.p.a., Milan, Italy
  4. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy
  5. French Institute of Sport (INSEP), Research Department, Laboratory Sport, Expertise and 11 Performance (EA 7370), Paris, France
Biol Sport. 2024; 41(3): 15–28
Online publish date: 2023/12/20
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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 (Simkin, Simmet, Simmec) and three overload scores (OVERkin, OVERmet, OVERmec) were defined for kinematic, metabolic, and mechanical vectors. Simmet and OVERmet were excluded from further analysis, showing a very large correlation (r > 0.7, p < 0.01) with Simkin and OVERkin. 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 Simkin (d = 0.91), OVERkin (d = 0.47), and OVERmec (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.

Euclidean distance, Performance, External load, Overload, Similarity

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