Running Performance of Soccer Players During Matches in the 2018 FIFA World Cup: Differences Among Confederations

Abstract

With the purpose of quantifying the differences in the running performance of soccer players during matches from different continental confederations, data of 1508 match observations generated from 559 players in 59 matches at the 2018 FIFA World Cup held in Russia were analyzed. Generalized mixed linear modeling was carried out to estimate the effect of confederations on each of the selected thirteen match running performance related variables (total distance covered, top speed achieved, number of sprints, distance covered and time spent in walking, jogging, low-speed running, moderate-speed running, and high-speed running), controlling the effects of match result, competition phase, and team and opponent strength. Results showed that the differences in the match running performance of UEFA and CONMEBOL players were trivial (ES between 0.04 and 0.14); players from AFC, CAF, and CONCACAF covered less total distance (ES between 0.26 and 0.54), spent less playing time, and covered less distance in jogging and low-speed running (ES between 0.20 and 0.53), whereas they spent more time walking (ES between 0.27 and 0.41) as compared with players from UEFA and CONMEBOL; top speed achieved, number of sprints made, and time spent and distance covered in the moderate- and high-speed running intensity zones by players from all confederations were similar (ES between 0.01 and 0.15), with an exception that high-speed-running distance covered by CONCACAF players was less than that by CAF players (2.0 ± 1.5 m/min vs. 2.3 ± 1.7 m/min, ES = 0.23, ±90% CL: ±0.21).

Analysis of Running Performance in the Offensive and Defensive Phases of the Game: Is It Associated with the Team Achievement in the UEFA Champions League?

“…Specifically, studies have reported that less successful teams achieve greater RP (e.g., total distance covered and high-intensity distance covered) [20,21], while more recent studies have revealed similar RP in both successful and unsuccessful teams [22,23]. It is possible that these inconsistencies might be characterised by differences in the geographical, cultural, historical and social aspects of soccer teams from different countries [24][25][26].…”

Predicting Wins, Losses and Attributes’ Sensitivities in the Soccer World Cup 2018 Using Neural Network Analysis

“…World Cup matches are available to analyze and explain player's and team's performance. The increased accessibility of detailed match data is largely due to the progression made in semi-and fully-automated tracking [1][2][3][4]. Noticeably, the technical and tactical match performance of professional soccer players, has been shown to be affected by several different contextual, situational and positional attributes [5][6][7][8][9][10].…”

“…In the last decade, there has been a plethora of research describing and analyzing the match performance data from soccer World Cups using several approaches such as multivariate analyses and machine learning [1][2][3][4][11][12][13][14][15], passing networks based on space, time and the multilayer nature of the game [16] or based on spatial and temporal entropy related to football teams and their players by means of a pass-based interaction [17] and social network analyzes to study the interaction between a player and their teammates (for example a ball passing network) through graph theory to assess the structural and topographical characteristics of personal interactions between team members [18]. This type of descriptive research provides important information that can be used to improve training and adapt tactics, however analyses, such as machine learning, can identify performance indicators, whether physical or technical that may predict what will occur during the match [19][20][21][22].…”

Load Measures in Training/Match Monitoring in Soccer: A Systematic Review

“…Subsequently, we recommend, as defined by Giménez [ 75 ], that a speed zone <2.0 m/s should be considered as a “rest” in the assessment of ratios related to the distance covered. If an elite European soccer player covers 107 ± 12 m/min [ 124 ], approximately 1.78 ± 0.2 m/s, the definition of a speed zone >2.0 m/s as “rest” includes a speed usually higher than the average running speed presented in competition by national teams and, therefore, can hide the part of the “work” developed. Furthermore, with regard to accelerations and decelerations, we recommend the definition of three zones used by Curtis [ 68 ]: “low intensity”, 0.0 to 2.0 m/s 2 ; “moderate intensity”, 2.0 to 4.0 m/s 2 ; and “high intensity”, greater than 4.0 m/s 2 .…”