20,404 research outputs found

    Analysis of physical-activity profiles when running with the ball in a professional soccer team

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    This study characterised physical demands when running with the ball in a professional soccer team and (1) determined activity profiles during match play; (2) examined effects of fatigue and (3) investigated differences according to playing position. Thirty French League 1 matches from two competitive seasons (2007-2008, 2008-2009) were analysed using multi-camera computerised tracking. Players (n=27) ran a mean total distance of 191.0±38.0 m with the ball of which 34.3% was covered at speeds >19.1 km/h, 25.6% between 14.1-19.0 km/h, 12.5% between 11.1-14.0 km/h and 27.6% at <11.0 km/h. Mean distance covered per possession was 4.2±0.7 m, speed at ball reception was 10.3±0.9 km/h while mean and peak speeds during runs were 12.9±1.0 km/h and 24.9±2.4 km/h. Mean time in possession, duration and touches per possession were 53.4±8.1 s, 1.1±0.1 s and 2.0±0.2. There were differences across playing positions for all variables (P at least 0.017 and effect size at least 0.5). Total distance run did not differ between halves but varied over the course of matches (p<0.001) decreasing just before half-time. These findings provide valuable information about the physical and technical requirements of running with the ball that could be useful in the prescription of general and individualised training programmes

    Interpreting physical performance in professional soccer match-play: Should we be more pragmatic in our approach?

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    Academic and practitioner interest in the physical performance of male professional soccer players in the competition setting determined via time-motion analyses has grown substantially over the last four decades leading to a substantial body of published research and aiding development of a more systematic evidence-based framework for physical conditioning. Findings have forcibly shaped contemporary opinions in the sport with researchers and practitioners frequently emphasising the important role that physical performance plays in match outcomes. Time-motion analyses have also influenced practice as player conditioning programmes can be tailored according to the different physical demands identified across individual playing positions. Yet despite a more systematic approach to physical conditioning, data indicate that even at the very highest standards of competition, the contemporary player is still susceptible to transient and end-game fatigue. Over the course of this article, the author suggests that a more pragmatic approach to interpreting the current body of time-motion analysis data and its application in the practical setting is nevertheless required. Examples of this are addressed using findings in the literature to examine: a) the association between competitive physical performance and ‘success’ in professional soccer, b) current approaches to interpreting differences in time-motion analysis data across playing positions and, c) whether data can realistically be used to demonstrate the occurrence of fatigue in match-play. Gaps in the current literature and directions for future research are also identified

    Influence of opposition team formation on physical and skill-related performance in a professional soccer team

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    This study examined the influence of opposition team formation on physical and skill-related performance in a professional soccer team. Performance in forty-five French League 1 matches played over three competitive seasons (2007-08, 2008-09, and 2009-10) was analysed using multi-camera computerised tracking. Players (n=21) in the reference team (using a 4-3-3/4-5-1 formation) were analysed in matches against three opposition team formations: 4-4-2 (11 games), 4-3-3/4-5-1 (16 games) and 4-2-3-1 (18 games). Performance was compared for defending and midfield units as a whole and individually across four positions: fullbacks, central-defenders and central- and wide-midfielders. Collectively, players covered a greater total distance (p<0.05) and distance in low/moderate-intensity running (0-14.3km/h) (p<0.05) in matches against a 4-2-3-1 compared to a 4-4-2 formation. Distance covered in high-intensity (14.4-19.7km/h) and very high-intensity running (≥19.8km/h) was not affected by opposition formation. In contrast, players covered more distance in total high-intensity performance (≥14.4km/h) when the reference team was in possession against a 4-4-2 compared to a 4-2-3-1 formation (p<0.05) while more distance was run at these speeds when the reference team was out of possession against a 4-2-3-1 (p<0.01) and a 4-3-3 (p<0.05) compared to a 4-4-2 formation. Players ran less distance at low/moderate intensities in the second- versus first-half of matches against all three formations (p<0.01 to p<0.05) whereas total distance and high-intensity performance was unaffected. None of the measures of physical performance across the individual playing positions were affected by opposition team formation. Skill-related performance varied according to opposition formation: players as a whole performed more passes versus a 4-4-2 than a 4-2-3-1 (p<0.01), ground and aerial duels versus a 4-2-3-1 compared to a 4-4-2 (both p<0.01); 1-touch passes versus a 4-2-3-1 compared to a 4-4-2 (p<0.01) and a 4-3-3/4-5-1 (p<0.05). The mean number of touches per possession was highest versus a 4-4-2 compared to a 4-3-3/4-5-1 (p<0.01) and a 4-2-3-1 (p<0.01). While skill-related performance across the four individual playing positions was generally unaffected by opposition team formation, mean pass length was greater in central-midfielders against a 4-4-2 compared to 4-3-3/4-5-1 (p<0.05) and 4-2-3-1 (p<0.01) formations. In general, these findings suggest that physical performance in the reference team was not greatly affected by opposition team formation. In contrast, skill-related demands varied substantially according to opponent formation and may have consequences for tactical and technical preparation and team selection policies

    The role of motion analysis in elite soccer

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    The optimal physical preparation of elite soccer (association football) players has become an indispensable part of the professional game especially due to the increased physical demands of match-play. The monitoring of players’ work-rate profiles during competition is now feasible through computer-aided motion analysis. Traditional methods of motion analysis were extremely labour intensive and were largely restricted to university- based research projects. Recent technological developments have meant that sophisticated systems, capable of quickly recording and processing the data of all players’ physical contributions throughout an entire match, are now being used in elite club environments. In recognition of the important role motion analysis now plays as a tool for measuring the physical performance of soccer players, this review critically appraises various motion analysis methods currently employed in elite soccer and explores research conducted using these methods. This review therefore aims to increase the awareness of both practitioners and researchers of the various motion analysis systems available, identify practical implications of the established body of knowledge, while highlighting areas that require further exploration

    PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

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    The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this paper, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players' evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by {\sf PlayeRank} and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank -- i.e. searching players and player versatility --- showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics

    Visual exploratory activity in youth soccer players

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    In-season internal and external training load quantification of an elite European soccer team

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    Elite soccer teams that participate in European competitions need to have players in the best physical and psychological status possible to play matches. As a consequence of congestive schedule, controlling the training load (TL) and thus the level of effort and fatigue of players to reach higher performances during the matches is therefore critical. Therefore, the aim of the current study was to provide the first report of seasonal internal and external training load that included Hooper Index (HI) scores in elite soccer players during an in-season period. Nineteen elite soccer players were sampled, using global position system to collect total distance, high-speed distance (HSD) and average speed (AvS). It was also collected session rating of perceived exertion (s-RPE) and HI scores during the daily training sessions throughout the 2015-2016 in-season period. Data were analysed across ten mesocycles (M: 1 to 10) and collected according to the number of days prior to a one-match week. Total daily distance covered was higher at the start (M1 and M3) compared to the final mesocycle (M10) of the season. M1 (5589m) reached a greater distance than M5 (4473m) (ES = 9.33 [12.70, 5.95]) and M10 (4545m) (ES = 9.84 [13.39, 6.29]). M3 (5691m) reached a greater distance than M5 (ES = 9.07 [12.36, 5.78]), M7 (ES = 6.13 [8.48, 3.79]) and M10 (ES = 9.37 [12.76, 5.98]). High-speed running distance was greater in M1 (227m), than M5 (92m) (ES = 27.95 [37.68, 18.22]) and M10 (138m) (ES = 8.46 [11.55, 5.37]). Interestingly, the s-RPE response was higher in M1 (331au) in comparison to the last mesocycle (M10, 239au). HI showed minor variations across mesocycles and in days prior to the match. Every day prior to a match, all internal and external TL variables expressed significant lower values to other days prior to a match (p<0.01). In general, there were no differences between player positions. Conclusions: Our results reveal that despite the existence of some significant differences between mesocycles, there were minor changes across the in-season period for the internal and external TL variables used. Furthermore, it was observed that MD-1 presented a reduction of external TL (regardless of mesocycle) while internal TL variables did not have the same record during in-season match-day-minus.: The authors state that there were no salaries’ fund from a tobacco company. Also, the authors are not aware of any competing interests. This project was supported by the National Funds through FCT—Portuguese Foundation for Science and Technology (UID/DTP/04045/2013)—and the European Fund for Regional Development (FEDER) allocated by European Union through the COMPETE 2020 Programme (POCI-01-0145- FEDER-006969)—competitiveness and internationalization (POCI). All funding received for this work from any of the following organizations: National Institutes of Health (NIH); Welcome Trust; Howard Hughes Medical Institute (HHMI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.info:eu-repo/semantics/publishedVersio

    Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis

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    Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from –4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group × time ANOVA revealed that experts had less EQ before backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from –1.5 to 1 s (rs = –.48 - –.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = –.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills

    The effect of ball-handling on lower extremity mechanics in soccer

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    HRS Honors Research Thesis ScholarshipNearly 240,000 soccer injuries are estimated to have occurred in the United States in 2014 with a high number of them non-contact in nature and involving the lower extremities. These injuries result in time-loss from training or match play, potential psychological consequences, and financial burdens. Epidemiological research suggests that these non-contact injuries may occur more frequently while ball-handling compared to defending in soccer. However, no prior studies have investigated the biomechanical implications of controlling a soccer ball with the feet while running and cutting that may help explain this finding. The purpose of this study was to compare knee and ankle joint moments and angles implicated in non-contact soccer injury mechanisms demonstrated during run-to-cut maneuvers performed with and without dribbling a soccer ball. Our hypothesis was that the cutting maneuvers performed while dribbling a ball would have a detrimental effect on biomechanical parameters associated with non-contact ankle and knee injuries. Seventeen healthy male collegiate soccer players participated in the study. Subjects performed ball-handling and running maneuvers while running straight ahead and also at a 45° cutting angle. All data were collected using three-dimensional motion capture with force plates embedded in the floor. Ball-handling had a significant effect on peak ankle internal rotation angle (p=0.010) and knee abduction angle (p=0.024). Changes in other parameters of interest, including peak ankle inversion moment and peak knee abduction moment, did not reach significance (p>0.05). In conclusion, ball-handling in soccer can detrimentally alter lower extremity joint mechanics of dynamic movements. The results of this study support the need for coaches to consider the implications of an athlete’s sport-specific movements when creating training programs for teams and individuals.A three-year embargo was granted for this item.Academic Major: Biomedical Scienc
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