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José M. Pratas, Anna Volossovitch and Ana I. Carita

Introduction In soccer, it has been demonstrated that performance of teams can be influenced by the scoreline ( Lago-Peñas, 2012 ; Gómez et al., 2013). Soccer players perform significantly less high-intensity activity when winning than when losing or when the score is tied ( Lago et al., 2010 ). It was also shown that teams had longer periods of possession in matches when they were losing than when they were winning (Lago-Peñas and Dellal, 2010; Lago-Peñas and Gomez-Lopez, 2014), teams played more in the attack and defensive zones when the score was level

Open access

Parag Shah

Abstract

Introduction. In cricket, the evaluation of individual player performance has been based on measures such as batting and bowling averages. These statistics are used to quantify the batting and bowling performance of cricketers, but there are no statistics for measuring the performance of fielders. This paper introduces a measure that can be used to assess the fielding performance of cricketers. Method. Various factors that are considered important in fielding are quantified to scores based on the ball-by-ball information of a match for each cricketer. The fielding points of each ball are then combined to calculate the total fielding points of a cricketer in a given match. All the fielding points are then added in order to obtain total fielding points of a cricketer up to a given match. Average fielding points are obtained by dividing the total fielding score by the number of matches played. Data. To demonstrate these measures, the first ODI match of India against Zimbabwe played on 11th June, 2016, is examined. Conclusion. The recommended measures can be used to quantify the fielding performances of cricketers for a series of matches, whether it is ODI or Twenty20 cricket. They make it possible to assess the average fielding performance of each player. Individual fielding performance scores can then be aggregated to measure the overall fielding performance of a team.

Open access

Bessem Mkaouer, Monèm Jemni, Helmi Chaabene, Samiha Amara, Ahmad Njah and Mokhtar Chtara

). Six international judges, including two judges for the difficulty scores (D) and four judges for the execution scores (E) evaluated the gymnasts’ technical performance according to the FIG’s code of points (FIG, 2009). Each gymnast was required to perform exactly the same routines in both competitions. Statistical Analysis Data are reported as means ± standard deviations and confidence intervals at the 95% level (95% CI). Effect size ( d ) was calculated using GPOWER software “Bonn FRG, Bonn University, Department of Psychology” ( Erdfelder et al., 1996 ). The

Open access

Olyvia Donti, Gregory C. Bogdanis, Maria Kritikou, Anastasia Donti and Kalliopi Theodorakou

Introduction The relationship between the competition score and physical fitness variables in rhythmic gymnastics has been examined in a number of previous studies ( Bobo-Arce and Mendez-Rial, 2013 ; Hume et al., 1993 ; Rutkauskaite and Skarbalius, 2009 ). Anthropometric variables such as body composition, the arm span and mid-thigh circumference, have been suggested as significant determinants of the rhythmic gymnastics competition score ( Douda et al., 2008 ; Purenovic-Ivanovic and Popovic, 2014 ). Physical fitness variables such as flexibility

Open access

Csaba Gábriš, Martin Kojnok, Marián Vanderka and Milan Kabát

internatinal tournaments. British Journal of Sports Medicine . suppl. 1, pp. 3-7. ISSN 1473-0480. 7. KIESEL, K. et al., 2011. Functional movement test scores improve following a standardized off-season intervention program in profesional football players. Scandinavian Journal of Medicine and Science in Sports . Vol. 21., pp. 287-292. ISSN 1600-0838. 8. MINTHORN, L. et al., 2014. An individualized training program may improve functional movement patterns among adults. Journal of Sport Rehabilitation, vol. [Epub ahead of print]. ISSN 1543-3072. 9. SMITH

Open access

Paweł Linek, Edward Saulicz, Andrzej Myśliwiec, Monika Wójtowicz and Tomasz Wolny

evaluating motor control through functional movement and dynamic coordination analysis ( Cook et al., 2006a , b ). The FMS has also been used as an injury prediction tool in competitive athletes. Recent studies have shown that athletes with an FMS score under 14 were more likely to sustain an injury than their peers who received more than 14 points ( Kiesel et al., 2007 , 2011 ; Chorba et al., 2010 ). Another study has shown that, regardless of the score obtained in the FMS test, asymmetry in movements pointed to a more than 2-fold higher risk of injury ( Kiesel et al

Open access

Carlos Lago-Peñas and Alexandre Dellal

References Bate R. Football chance. Tactics and strategy. In: Science and Football V. Eds: Reilly T., Less A, Davies K. and Murphy W. 1988. London: E and FN Spon. 293-301. Bloomfield JR, Polman RCJ, O'Donoghue PG. Effects of score-line on team strategies in FA Premier League Soccer. J Sports Sci, 2005. 23: 192-193. Bloomfield JR, Polman, RCJ, O'Donoghue PG. Effects of score-line on intensity of play in midfield and forward players in the FA Premier League. J Sports Sci, 2005. 23

Open access

Konstantinos Papanikolaou, Athanasios Chatzinikolaou, Theodoros Pontidis, Alexandra Avloniti, Chariklia K. Deli, Diamanda Leontsini, Dimitrios Draganidis, Panagiotis D. Tsimeas, Lefteris Rafailakis, Athanasios Z. Jamurtas, Peter Krustrup, Magni Mohr and Ioannis G. Fatouros

al., 2015 ). The speed of the test progresses gradually, and the 2x20-m shuttle runs are followed by an active 5-s recovery phase. It has been stated recently that the Yo-Yo IE2 test scores have a positive correlation with running performance and HR responses in competitive elite male and female soccer players ( Bradley et al., 2011 , 2014) and that the training-induced alterations in soccer-specific endurance capacity of elite male and female players during a competitive season affect performance during the test ( Bradley et al., 2014 ; Heisterberg et al., 2013

Open access

Mariusz Nowak and Ryszard Panfil

Abstract

Purpose. Many studies analyzing the game of tennis overlook assessing such variables as the pure ability to play the game or the skill needed to take an opponent by surprise. This can be quantified in terms of a player’s shot flexibility, variability, velocity, or by the conscious or intuitive adaptability one can possess towards anticipating return shots, how best to hit the ball in order to keep it in play, as well as the buildup of delivering a shot or in scoring a point. The aim of the study was to identify the ability to score points in tennis based on an original set of assessment criteria that were used to measure the different effective plays against an opponent. This included measuring the variability, spatial flexibility and variability of shots taken, as well as the willingness to make risky plays. Methods. The study analyzed the match play of two elite tennis players, Roger Federer and Novak Djokovic, who competed against one another in the final of the 2007 US Open and the semi-final of 2008 Australian Open. Video recording of the two games was used to score and measure the proposed criteria. Results. The study found numerous quantitative and qualitative aspects that could assess the performance of the players. This included measuring the variety, spatial flexibility and variability of shots taken, as well as the willingness to make risky plays. Shot variety, flexibility and variability, as well as the amount of risk taken during game play, were quantifiable in nature. Taking into account the high sporting level of the players, the obtained results are undoubtedly of considerable educational value. Conclusion. The results allow for the conclusion that the teaching process in tennis demands the introduction of significant modifications aimed at the rationalization of technique and the introduction of criteria that can measure player effectiveness.

Open access

Julen Castellano, Pedro Silva, Oidui Usabiaga and Daniel Barreira

player numbers involved in the task by measuring their length per width ratio. Other corroborative examples of a constraints-led approach applied to soccer SSGs can be found in studies that investigated manipulations of numerical relationships ( Silva et al., 2014c ; Travassos et al., 2014 ) as well as the type and number of scoring targets ( Travassos et al., 2014 ). With respect to this latter study, manipulations of the number of scoring targets from 2 to 6 in soccer SSGs elicited an increase in the distance between the teams’ centroids, promoting different spatial