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The analyses of the data obtained followed a phenomenological procedure (Aanstoos, 1983) trying to grasp and identifying meaning units, specifying central themes, and then articulating their psychological sense or meaning in order to examine the nature of the meeting in a successfully coach-athlete relationship.

Results: Results from interviews with both the coach and the athlete in the team sport coach-athlete relationship indicated that factors such as social support, commitment, humor, use of time, attention, and carefulness are all important facets for the dimension building a successfully relationship. In the dimension communication vital facets appear to be thoroughly, the frequency, the consistency between verbal and non verbal communication, and the art of the informal communication during everyday practice. For the dimensions leadership and performance and instruction it seems to be respectively consistency for the coaching process, and focus on the impact of intrinsic motivation (Jowett, 2008) and evaluation of own development as most important.

Discussion: In general both the coach and the athlete underlined the significance of consistency and commitment for a successfully relationship in order to perform in sport. The study also revealed that communication and democratic leadership is vital factors for the meeting in the relationship for both the coach and athlete to grow and develop which is in line with former research.

References Aanstoos CM (1983). J Pheno Psych, 14, 243-266.

Jowett S (2008). Scand J Med Sci, 18, 664-673.




Introduction: The purpose of the study was to investigate in a sample of Scandinavian elite coaches, the relationship between perceptions of their own leadership behavior and their perception of the coach-athlete relationship.

Method 149 elite coaches (e.g. national top and/or international level) from Denmark (n=50), Norway (n=50) and Sweden (n=49) participated (134 male, mean age 38.3±9.8yrs; 15 female, mean age 39.1±8.4). 58 percent of the coaches were coaches of individual sports and 42 percent were coaches of team sports. 59 percent of the participants had at some point, undertaken academic study related to sport at university level. A paper-copy questionnaires were distributed by post to all coaches listed in each country’s athletic federation index.

Leadership behaviour: Coaches self reported their perceptions of their own leadership behaviors using the Leadership Scale for Sport (Chelladurai & Saleh, 1980). The Cronbach’s alpha for self reported leadership behavior was:.79 training and instruction,.59 positive feedback,.66 social support,.78 democratic behavior, and.46 autocratic.

Coach-athlete relationship: The nature of the coach-athlete relationship was evaluated using the 13-item Nordic Coach–Athlete Relationship Questionnaire (NOR-CART-Q; Jowett & Ntoumanis, 2003). The Cronbach’s alpha for closeness, commitment, and complementarity was.83,.72 and.67 respectively.

Results: Positive feedback, training and instruction, and democratic behavior were the most frequent self-reported behavior subscales amongst the coaches. Moreover, it is a positive relationship between commitment and training and instruction (r=.25, p 0.01) positive feedback (r= 22, p 0.01) and social support behavior(r=.22 p 0.01). Complementarity was positively related to training and instruction behavior (r=.17, p 0.01). Multilevel logistic regression analyses indicated a significant difference between coaches in Denmark and Sweden on commitment (5.39 vs. 5.95) and complementarity (5.82 vs. 6.26) and coaches who are educated in sport use more positive feedback (4.25 vs. 4.41). Moreover, coaches with more than 10 years experiences in coaching us significantly more training and instruction (3.65 vs. 3.82) and social support (3.02 vs. 3.23) than coaches with less experiences. Furthermore, coaches in individual sport reported more democratic behavior (3.80 vs. 3.23) and less autocratic behavior (2.56 vs. 2.78) then coaches in team sport.

–  –  –

Discussion: In general coaches with more experience and more sport education used more positive feedback and social support. The study also revealed differences in leadership behavior between individual and team sport which is in line with former research.

References Chelladurai P, Saleh SD (1980). Int J Sport Psych, 2, 34-45.

Jowett S, Ntoumanis (2003). Scand J Med Sci, 14, 245-257.




INTRODUCTION: The penalty is important in a football game, because of the percentage of achieved goals, and since through them the tie situations can be resolved. It is known by “the lottery’.

We reject, however, these statements, because in previous studies we’ve found that, considering 8 possible directions (1 - initial position, 2 - up, 3 other – right: high, medium low and, 3 more identical to the left) for the goalkeeper to displace in order to defend the ball, in more than 40% of the situations, they’ve chosen the correct direction (even arriving late), which would not have been possible if the decision was purely random.

The question we ask, to diagnose and prescribe training situations with greater intentionality, is: which variables settle the success in a penalty?

METHODS: We’ve analyzed 250 penalties, through image digitization of video, football games of the FIFA and UEFA top competitions over the last 10 years.

Knowing the space (e) that the ball should travel to the goal (11m), the goal dimensions (2.44mx7,32m), that one second of film contains 25 frames (0,04 seconds), we measured the time the goalkeepers took to move up to intercept the ball, the time that the ball took to reach the goal or be intercepted, the moment when the goalkeeper started his displacement in relation with the kick, and the ball average speed We’ve also defined, in the situations that haven’t ended in goal, what corrections should be made into the striker to be successful.

We’ve made the same analysis for the goalkeepers in situations where they could not avoid the goal.

In this sense, we’ve defined what corrections should be made and when these could result in success for both the striker and the goalkeeper.

After making the sequence we’ve functionally defined what was happening in each penalty.

RESULTS: From the 250 penalties analyzed, we’ve seen that:

- 157 (62,8%) were scored;

- The other 93 (37,2%) weren’t scored. From theses, 44 were intercepted by the goalkeeper (47,3%) and in the other 49 the ball was not kicked in the goal direction (52,7%).

- We’ve also get that:

• the goalkeepers reaction time was at least 0,7 seconds (complex reaction time)

• the ball time to reach the goal even varied between 0,4 and 1,2 seconds was only higher that 0,6 seconds when the goalkeeper was already out of balance when the ball was kicked.

• the goalkeepers started their displacement, in average, 0,4 seconds before the ball was kicked.

• in 47% of the situations the goalkeepers moved in the ball direction.

DISCUSSION: Although the penalty is not a purely random situation, despite the great variability of situations and values for the different variables involved, we’ve set a time where key decisions are taken either to the goalkeeper and the striker.

This is the time for the kinetic energy accumulation of the movement of different segments used in the kick. At this stage there is an inverse relationship between the acceleration time and the speed that the ball can reach.




Despite the abundance of scientific literature, databases and electronic papers concerning sport sciences (Lippi et al., 2008), there is a general perception that coaches lack operative data to the decision making process (Midgley et al., 2007), that is the major task of the sport training process (Abraham et al. 2006). In this sense, we came to a paradox: the amount of available data may not correspond to the enhancement of sport performance during training process. This paradox may be related to a data overflow problem (Liautaud & Hammond, 2000).

Under the scope of contributing to minimise that ‘‘gap’’ between research and coaching practice, this work aims (1) to evaluate the usefulness of research data, concerning the needs of the coaches and (2) to propose some major solutions to minimise this problem.

This study used a survey to evaluate the perceptions of 47elite coaches in Portugal regarding both the usefulness of data in scientific literature and data overflow problem. Results show that elite coaches lack operative data, or because they perceived is not available for ecologic training process, or it is available but not in an “adequate”, understandable language.

Among this investigation, we reach several major conclusions: (1) data overflow may be a problem in sport sciences; (2) to do a proper use of available data, it is necessary the implementation of mechanisms in data’s search, selection and treatment, according to Information Management Theory (Eppler & Mengis, 2004; Savolainen, 2007) and (3) research results in sport sciences must be oriented to the training process, and transmitted more efficiently to the coaches (similar results were pointed out by Williams & Kendall, 2007).

ABRAHAM, A., Collins, D., Martindale, R. (2006). The coaching schematic: validation through expert coach consensus. J Sports Sci., 24(6), 549-564.

EPPLER, M. Mengis J. (2004). The Concept of Information Overload: A Review of Literature from Organization Science, Accounting, Marketing, MIS, and Related Disciplines. The Information Society, 20(5), 325-344.

LIAUTAUD, B., Hammond, M. (2000) e-Business Intelligence: Turning Information into Knowledge into Profit. Ed. McGraw-Hill Professional.

LIPPI, G., Guidi, G. C., Nevill, A., Boreham C. (2008). The growing trend of scientific interest in sports science research. J. Sports Sci. 26(1), 1ANNUAL CONGRESS OF THE EUROPEAN COLLEGE OF SPORT SCIENCE TH Wednesday, June 24th, 2009 MIDGLEY, A. W., McNaughton, L. R., Jones, A. M. (2007). Training to Enhance the Physiological Determinants of Long-Distance Running Performance: Can Valid Recommendations be Given to Runners and Coaches Based on Current Scientific Knowledge? Sports Med.

37(10), 857-880.

SAVOLAINEN R. (2007). Filtering and withdrawing: strategies for coping with information overload in everyday contexts. Journal of Information Science, 33(5), 611.

WILLIAMS, S. J., Kendall, L. (2007). Perceptions of elite coaches and sports scientists of the research needs for elite coaching practice. J.

Sports Sci., 25(14), 1577 – 1586.




INTRODUCTION: In football training to be able to identify the variables involved in a feint makes it possible to do specific corrections in the action of a player, even if it is a higher level one (but not only), because it allows diagnosing the cause of the error and what can be most useful to train, prescribing exercises that require more emphasis on the improvement of relevant aspects and not the generality of the situation, and even to control the progress that is being achieved through the measurement of the evolution that is happening in the factors trained.

So, it’s increased the profits yield because the training is directed to the important aspects in that situation for that player.

METHODS: We’ve analyzed 276 feint situations through image digitization of videos from high competitive level football matches of clubs and national teams.

In the analysis were considered the time of each frame (0.04 seconds) and the relationship established between the stimuli that were provided by the player who made the feint and the response of the opponent.

We’ve considered also as a point of reference of minimum reaction time 0.4 seconds.

We’ve found in 134 of analyzed situations, those that were not successful, what corrections should be made to avoid the error.

In all feints we’ve analyzed the relation between the two players and the corrections that each could make to improve his performance.

We’ve also analyzed a number as high as possible of the same situations from the same players, and also in these sets of situations obtain successful as well as missed feints.

RESULTS: From the 276 analyzed situations we’ve obtained the following distribution:

- Total of players analysed 35

- From this 35 players, 26 of them have a sample of 10 feints with an average of 6 succeeded and 4 missed.

- From the other 9 players we have a percentage of 12,8 feints from each one being 30 of them missed.

We’ve seen that the successful feints were not the ones with shorter execution time, on the contrary, in 76% of them, they were significantly slower that the identical feints from the same player than the feints that were missed.

When considering what corrections should be made, was considered that the action of the opponent who made the cut would be the same and found that in 91% of cases if the player action had been slower (taking into account that the opponent’s position would be the same) that he would had naturally succeeded.

DISCUSSION: The success of a feint is not dependent of the speed with which it is made but in the effect it is possible to obtain in the opponent and the ability of the player who will perform the feint has to wait for the signal that sends to the opponent will make effect before changing his action to what he must to do.

The feint, as we set, ’is played’ in times of decision making and reaction times of both players.

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