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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Article
Author(s)
Riccardo Izzo1, Ciro Hosseini Varde’i1 and Vito Azzone2
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DOI:10.17265/2332-7839/2019.02.001
Affiliation(s)
1.Department of Biomolecular Sciences, School of Health and Sport Science, University of Urbino Carlo Bo, Urbino, PU 61029, Italy
2.Strength and Conditioning Coach Italian National Team U-21, Club Italia, FIGC
ABSTRACT
The aim of this research was to analyze the PPM of Armenian Premier League using 25 GPS 10 Hz (K-Sport,
Montelabbate, Italy), in order to compare data from literature from major
European championship. In total 25 matches were analyzed from one team militant
in Armenian Premier League (10 wins, 10 loses and 5 draws),
33 different players (age 24.3 ± 4.2, height 1.76 ± 4.2 and weight 74 ± 3.5)
and in total 270 performances (10.8
for matches). Matches were divided in First Half (T1), Second Half (T2), Second
Half Substitution (T2
Sub), Full Match, Full Match Substitution (Full Match Sub) and Total Match
(Total) that include all the players detected during a games. Furthermore, all matches were divided even by
quarters 0-15, 15-30, 30-45, 45-60, 60-75 and 75-90, only players that played all match were
included in this analyses, in order to check the performance decreasing during
the time. All data were added in an Excel Spreadsheet in order to
build a database, to organize and better analyze data, cataloging events for
data, results, and role of players. Average data from Armenian Premier League
in comparison with literature data from European and Italian Championship show
that in every parameter, the Armenian
Premier League is under the average.
This is obviously related to the non-high level of the Armenian football that
is in fact placed at the 106° place of the FIFA ranking (update 7 June 2019).
Physical data represent an indicator of
performance but also of the qualitative level of the league and of individual
players. For this reason, the use of
the match analysis can be decisive to verify the PPM, in order to better
evaluate the championships, teams, and
players, in order to build an easier search and discovery of talents.
KEYWORDS
Match analysis, performance model, GPS, comparison, European championship.
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