Printer Friendly

Tactical determinants of setting zone in elite men's volleyball.


Elite-level sport is a highly demanding activity, requiring a long-term investment in an arduous process in order to optimize training and competition processes (Jager and Schollhorn, 2007). At this point, performance analysis emerges as a powerful research field, providing answers to scientific questions and practical training issues (Hughes and Bartlett, 2002). Within this scope, match or notational analysis allows a comprehensive framework for analyzing the game, as long as it considers the dynamics concerning the interactions between game events (Hale, 2001).

The interactions between the two opposing teams lead to the emergence of unique game patterns. This specificity is strongly related to the momentary conditions and critical events due to their inherent variability from condition to condition. Therefore, extrapolating the findings to other matches is not only difficult, but may also be misleading (Lames and McGarry, 2007). Nonetheless, there might be sequential patterns that are common to a diversified set of matches, levels and competitions, and research has been following this trend (Afonso and Mesquita, 2011; Eom and Schutz, 1992; Jager and Schollhorn, 2007; Newton and Aslam, 2009).

As deterministic approaches are seldom applicable to sports, the concept of situational probabilities must be addressed in team sports (Ranyard and Charlton, 2006; Ward and Williams, 2003). Each game scenario presents distinct possibilities of evolution, but their probabilities of occurrence are not evenly distributed. Therefore, an accurate knowledge of situational probabilities allows performers to act based upon what the possibilities of action with the highest likelihood of happening are (Walter et al., 2007), economizing attentional resources (Eckstein et al., 2006; Williams, 2009). To unfold situational probabilities, or probable chains of actions, the uni- or bivariate statistical analysis should be surpassed, giving place to multivariate statistics (Lames and McGarry, 2007), since they are capable of grasping interactions between several variables. Indeed, notational analysis research tends to follow Thelen's (2005) recommendations, avoiding the establishment of simple cause-and-effect relationships and accepting the possibility of numerous interactions being non-linear (Hale, 2001). Among such statistics, match analysis has increasingly recurred to t-patterns (Magnusson, 2000), log-linear analysis (Eom and Schutz 1992), Markov chains (Blanco et al., 2003; Bukiet et al., 1997; Lames and McGarry 2007; Newton and Aslam 2009), sequential lag analysis (Afonso et al., 2010), and multinomial logistic regression (Afonso and Mesquita, 2011).

In volleyball, stable game patterns have been detected in several studies (Afonso and Mesquita, 2011; Afonso et al., 2011; Eom and Schutz, 1992; Marcelino et al. 2008) with a relatively deterministic structure due to its non-invasion character (Mesquita, 2005). This feature might augment the probability of association of certain variables, thus allowing research to detect nuclear determinants of the game derived from variables related to the sport's internal dynamics.

Since team sports encompass dynamic processes of cooperation and opposition, characterized by the pursuit of the point and by the avoidance of the same goal being achieved by the opposite team (Lames and McGarry, 2007), the attack efficacy, namely in volleyball, emerges as the strongest predictor of the final result (Castro and Mesquita, 2008; Laios and Kountouris, 2004; Marcelino et al., 2008; Palao et al., 2005). At this ambit, it is of foremost importance to understand which game patterns afford the attaining of higher attack efficacies. Indeed, according to the literature, a great percentage of the attack efficacy relies on the quality of the setting action (Bergeles et al., 2009), which, in high-level volleyball, is performed by a specialist player, the setter. It is known that the quality of attack is mainly dependent on the zone where the setter performs the set (Afonso et al., 2010). For instance, quick and multiple attacks are more likely to be performed when the setter contacts the ball within the excellent zone (i.e., an area of 1-2 meters away from the net, and 2-3 meters from the right sideline) providing ideal conditions for the establishment of a good relationship between the attackers and the setter (Coleman, 2002).

In turn, the setter's action is constrained by a number of factors that should be taken into account in a thorough analysis (Mesquita and Graga, 2002). Studies have shown that preceding actions, namely the features of the opponent serve and the first contact in reception and defence, could predetermine the setter's actions and, consequently, the attacker's efficacy (Barzouka et al., 2006; Papadimitriou et al., 2004). For instance, the tennis jump serve is known to impair performance in reception, thus conditioning the subsequent actions (Katsikadelli, 1998b; Stromsik et al., 2002) and being prevalent in high-level volleyball (Lirola, 2006). However, these studies have used bivariate statistics, which appear to be limited, oversimplifying the complex nature of team sports. Through the interplay analysis of the factors that might interfere with the action of setting, it may be possible to better comprehend the nature of the game, thus contributing with valuable information both for the practice and research (Afonso et al., 2010).

In team sports, the analysis of interactions between game actions should be examined considering the game phase where they emerged, since its nature and configuration is determined for it. Particularly, in high-level men's volleyball, the complex I, or side-out, is considered a decisive phase of the game (Barzouka et al., 2006; Palao et al., 2007). This game complex comprises the actions of serve-reception, set, and attack, always following a serve from the opponent (Selinger and Ackermann-Blount, 1986), and encompassing a specific logic of attack organization, distinct from that of other game complexes, such as complex II or transition. A hallmark of complex I is the strong relationship established between the opponent's serve and the quality of the reception influencing the space where the set is performed. Since the setting zone is highly determinant of the attack efficacy, and being limited by the nature and quality of the serve-reception and opponent serve it becomes relevant to analyze possible game actions related to these factors (Afonso et al., 2005; Mesquita et al., 2007; Palao et al., 2005).

Therefore, the purpose of this study was to examine probabilistic relationships that might predict the setting zone, in the complex I in elite-level men's volleyball. This will allow perceiving what the precedent game actions that emerge as determinants of the setting zone are, offering new insights to volleyball match analysis as well as to the practice field.



Thirty-one volleyball matches comprising of 114 sets, and 5117 rallies, were analyzed encompassing all teams that participated in the 2007 FIVB World Cup. Twelve of the best world teams play for this important title, after which the best-ranked teams gain direct access to the Olympic Games. The observed teams were: Argentina, Australia, Brazil, Bulgaria, Egypt, Japan, Korea, Puerto Rico, Spain, Tunisia and United States. Only sequences in which the serve allowed serve-reception with sufficient quality to warrant the setting action to take place were analyzed. Furthermore, sequences culminating with attack by the setter were removed.

Variables and instrument

The dependent variable was the setting zone, and the independent variables were the server player, serve type, serve direction, serve depth, reception zone, receiver player and reception type. These variables were chosen due to their direct influence upon the first contact, which will, in turn, constrain the setting action.

The server player corresponds to the player who makes the serve action (setter, zone 4 attacker, middle-attacker or opposite), following the division proposed by Selinger and Ackermann-Blount (1986).

For serve type, three categories were considered (Laios and Kountouris, 2004; Lirola, 2006; Palao et al., 2004 and 2005): float serve (serve with unpredictable changes in the path of the ball due to a firm and quick hit with no follow-through of the arm); jump float serve (performed with jump, preceded or not by displacement; the trajectory of the ball is not uniform along its route); and tennis jump serve (strong, attack-like movement).

Serve direction concerns the trajectory of the ball in the serve action. Three categories were considered (Marcelino et al., 2011): crosscourt to right (right diagonal), crosscourt to left (left diagonal) and down the line (parallel).

Regarding serve depth, three categories were also established, after adaptation of the proposal of Marcelino et al. (2011): short (between the net and the three-meter line), intermediate (between the three-meter line and two meters away from the endline), and long (the last two meters of the court).

Concerning the receiver player, six categories were defined: libero (a player specialized in serve-reception and defensive tasks), best zone 4 attacker (the one closest to the setter in the rotation) when in defensive position (zones 5, 6 or 1), best zone 4 attacker when in offensive position (zones 4, 3 or 2), second zone 4 attacker when in defensive position, second zone 4 attacker when in offensive position, and others (other players who may occasionally perform this action).

Reception type comprised two categories (Castro and Mesquita, 2010): high reception (reception performed with an overhand pass) and low reception (using the forearm pass).

Two strategies were used to develop the categories of the reception zone and setting zone, while fulfilling the requirements for content and construct validity. These models were created in agreement with the changing trends of high-level male volleyball; they were firstly based on extended literature analysis (Castro and Mesquita, 2010; Esteves and Mesquita, 2007; Selinger and Ackermann-Blount, 1986), and subsequently submitted to a validation process conceived in two phases. In the first phase, a pilot study was implemented to analyze if the considered zones allowed distinctive reception and setting conditions. Subsequently, expert validation process was achieved (a doctorate in volleyball and a coach who was twice a World Champion). The experts were given a list of variables, categories, and a sample of volleyball matches and were asked to observe and categorize the rallies. The inter-rater agreement exhibited Cohen's Kappa values above 0.80.

The reception zone refers to the location where players receive the opponent's serve. In assessing this variable, four categories were defined (Esteves and Mesquita, 2007): reception zone 1 (a zone 7 meters wide and 3.5m deep, starting 2m away from the net and 1m of each sideline), reception zone 2 (a zone 3.5m wide and 2.5m deep, located 1m from the left sideline and 1m from the endline), reception zone 3 (a zone 3.5m wide and 2.5m deep, located 1m from the right sideline and 1m from the endline), and reception zone 4 (corresponding to an area with 1 meter of the both sidelines, center line and endline) (Figure 1).


The topographical model for the setting was composed of three well defined zones (Castro and Mesquita, 2010): excellent setting zone (ESZ), an area of 8[m.sup.2,] 2m deep from the net and 4m wide, at a distance of 2m from the right sideline and 3m from the left sideline; acceptable setting zone (ASZ), an area of 6[m.sup.2], 2m deep from zone 1 and 3m wide, at a distance of 2m from the right sideline and 4m from the left sideline; and not acceptable setting zone (PSZ), roughly equivalent to the playable area, excluding the two previously mentioned zones (Figure 2).

Data gathering and analysis

Data was collected with a digital video camera used by the Research Office of the Portuguese Volleyball Federation, and directly recorded into a computer. The camera was mounted at the bottom of the court, affording a longitudinal view. An observation form for recording the information (Excel) was used, including general information about the game, data regarding each set, complementary information, and data pertinent for this study. Multinomial logistic regression was applied, in order to obtain the estimated likelihood of occurrence of the dependent variable, based on the values of the independent variables (p [less than or equal to] 0.05). First, each variable was tested individually; as all proved to be related to setting zone, a final adjusted model was tested, encompassing all variables in study. The adjusted model excluded the serve direction. In this non-linear model of regression, the estimated regression coefficients represent the estimated change in the log-odds, corresponding to a unit change in the corresponding explanatory variable conditional on the other explanatory variables remaining constant (Landau & Everitt, 2004).

Concerning observation reliability, 116 sequences were analyzed, totaling 17.85% of the sample. The sequences were retrieved from four distinct matches and teams (Russia, Italy, Azerbaijan, and China). Intraobserver reliability presented Kappa values ranging from 0.965 to 1.000, while inter-observer reliability ranged from 0.936 to 1.000, in all cases fulfilling the minimum of 0.75 appointed by the literature (Fleiss, 2003).



The adjusted model for the Setting Zone showed to be statistically significant ([[ALEPH].sup.2] = 214.708, p = 0.001), with the reference category for the dependent variable being excellent setting zone. Tables 1 and 2 present the results concerning the adjusted model. While Table 1 presents results related to the serve, Table 2 presents results related to the serve-reception. As they concern the same model, this division intends merely to facilitate the reading of the data.

As shown in Table 1, only serve direction was excluded from the adjusted model, as it did not reveal statistical meaning for predicting the setting zone. Regarding the serve type, the jump float serve induced a lower likelihood of the set being performed in the not acceptable zone (Adj. OR: 0.610), compared with the excellent zone. Also, the jump float serve reduced the likelihood of the setting action occurring in the acceptable setting zone (Adj. OR: 0.738), compared to the tennis jump serve. Therefore, the jump float serve proved less effective than the tennis jump serve.

With respect to the server player, the opposite player (Adj. OR: 0.568), the zone 4 attacker (Adj. OR: 0.608), and the setter (Adj. OR: 0.628), made setting in the not acceptable zone less likely to occur, when contrasted to the serve performed by the middle-attacker. With respect to the acceptable zone, no statistically significant results were found.

Relatively to serve depth, it was noted that serves to the intermediate zone diminished the likelihood of setting in the not acceptable zone to occur (Adj. OR: 0.557), comparatively to the long serve. No statistically significant differences were found between the likelihoods of setting in the acceptable zone or in the excellent zone, regardless of serve depth.

Reception performed in reception zones 1 (Adj. OR: 0.277), 2 (Adj. OR: 0.517) and 3 (Adj. OR: 0.711) resulted in diminished likelihood of setting in the not acceptable zone, compared to reception in zone 4, as they promoted more frequent setting in the excellent zone. Additionally, reception in zone 2 reduced the likelihood of setting occurring in the acceptable setting zone (Adj. OR: 0.738), compared to reception in zone 4.

As for the receiver player, reception by the 1st (Adj. OR: 1.370) and 2nd zone 4 attackers (Adj. OR: 1.672) when in defensive position presented a greater likelihood of the set being carried out in the not acceptable setting zone as compared to the excellent zone, relative to the reception made by the libero. The model showed no statistically significant differences between the likelihoods of setting in the excellent and acceptable zones, with respect to the receiver player.

Finally, the analysis of the reception type revealed that the low reception variable induced a greater likelihood of the setter performing the set in the not acceptable setting zone (Adj. OR: 2.427) in relation to the excellent zone, in comparison with high reception. No differences were found for setting in the excellent or acceptable zones according to reception type.


The main purpose of this study was to examine probabilistic relationships that could predict the setting zone in complex I, in elite-level men's volleyball. Indeed, this study provided valuable insights concerning the game actions, related with the opponent team and the own team, that determine the setting zone in elite level men's volleyball.

Only the serve direction showed not to be predictive of the setting zone, suggesting that the receiving angle of the ball did not affect its quality, similar to what was apparent in the study of Afonso et al. (2010), in elite level women's volleyball. At this level, technical influences are usually balanced between the teams, and tactical aspects become determinant in differentiating the performance. As the serve is a very predictable action, constituting the most closed skill in volleyball (Mesquita, 2005), it may be classified as a predominantly technical action. Therefore, it is acceptable that serve direction does not predict the setting zone.

Concerning other serve variables, the tennis jump serve showed to be the most effective way of unbalancing the quality of serve-reception, as had been pointed out in previous studies (Katsikadelli, 1998b; Stromsik et al., 2002); hence, the prevalent choice of high-level male players to perform this type of serve (Lirola, 2006). Since the type of serve significantly influenced the setting zone, it is possible to understand that the practice of the serve-reception in high-level men's volleyball should preferably cover the tennis jump serve.

Furthermore, the middle-attacker revealed a more effective serve, which may be related to its technical-tactical versatility (Afonso and Mesquita, 2011; Afonso et al., 2010) and can show a new trend in the high-level men's volleyball. Since this player is usually free from defensive actions, as well as from serve-reception, he may invest more time practicing his serve, attack, and block. These results also suggest that the opposite's serve, usually powerful, no longer creates the same amount of damage to the opponent's reception, or that the reception has adapted to more strong and powerful serves. It is possible that, after a phase in which powerful serves have proved very effective, teams are now adjusting to it, hence more tactical serves may again arise to unbalance serve-reception. An alternative explanation resides in an overuse of the opposite player in the attack action, possibly promoting fatigue that impairs the serve action. Alternatively, it may be that high-level teams need to diversify their tactical solutions according to both situational demands and to strategic planning, as hinted in the study of Marcelino et al. (2011). Further studies are warranted in order to explore these lines of thought.

The depth of the opponents' serve was shown to determine the setting zone as the longer serve more often forced the receivers to make more pronounced movements towards the ball, thus impairing serve-reception quality and, possibly, slowing the attack velocity concerning players with the double function of both receiving and attacking, such as the zone 4 attackers. Serve reception is essential in determining the quality of the setting action (Afonso et al., 2010). Namely, serve depth merits analysis, considering that the distance to the setter's zone may influence the quality of the serve reception, as had already been noted in the study of Katsikadelli (1998a). This explanation, however, is only tentative, and further studies should approach this issue in more detail.

The game actions related with serve-reception also determined the setting zone. Indeed, the space where receivers touch the ball showed to determine the setting zone as zone 4 attackers, when in defensive zone, presented some difficulties in the serve-reception action, receiving more often to the not acceptable setting zone. This may be due to the fact that they receive in conjunction with the libero, and therefore receive more times, as the opponents' server players tend not to serve to the libero, who is a specialist serve-receive and defensive player that has proved to enhance the quality of serve reception (Joao et al., 2006). For that reason, results suggest the need to expand the zone of intervention of the libero in order to release the zone 4 attackers from that responsibility as much as possible and deliver them to the attack.

With reference to the reception zone, zone 4 shows to be less effective than the other zones, which is an expected result, since this area is the most extended of the court (both in the sidelines and endline). Even though the serve to this zone is more effective, it also poses a greater risk, a risk that servers may not be willing to take. Marcelino et al. (2011) have demonstrated that, in high-level men's volleyball, risk management is a common strategy used in critical moments of the matches. In a study in soccer, Bar-Eli et al. (2009) described results hinting that same strategy. Although the most difficult penalty kicks to stop were those that reached the upper third part of the goal, only around 13% of the penalty kicks were actually directed at this zone. As such, the higher serving efficacy of the middle hitter could be related to the targeted zone. Future research is needed to develop a deeper understanding of this issue.

Analyzing the reception type, it was found that the low reception proved to be less effective than the overhead reception. This is an interesting result, since it seems that overhand passing improves the reception. Probably, it is not the case that overhead reception is a better technique per se, but it can only be applied in the less powerful serves with high trajectories; therefore, this should advise servers to avoid low-power serves with such trajectories, since they will improve the quality of serve-reception (Palao et al., 2004).

Applied statistics afford insights into the interaction dynamics of the game, allowing discriminating differentiated effects of distinct variables and establishing the power of certain predictors of performance. By focusing on the variables with the most predictive power, and on the nature of their interactions, performers may better allocate their attention towards the most pertinent cues at each moment (Eckstein et al., 2006; Williams, 2009). As volleyball presents a more deterministic structure than most team sports (Mesquita, 2005), knowledge of these interactional models provides valuable insights into the dynamics of the action sequences, therefore affording coaches important information and guidance concerning the training process and team management during competition.

Future studies should analyze these variables according to match status and quality of the opposition, since those aspects may induce variations in the teams' behaviors (Marcelino et al., 2011). Match status should consider the importance of the match outcome, as well as temporary variations in the result, factors that are expected to interfere with the teams' performance and strategies. Quality of the opposition might be based on teams' rankings and/or recent relevant results; it is likely that teams will change their game patterns depending on the difficulties presented by the opponents. The interaction of quality of opposition and match status as its inter ference in performance indicators should be examined, providing a deeper understanding of game performance and new insights for practice, competition, and research.


This study highlighted that the prediction of the setting zone was assessed by a wide range of game actions, including serve type, server player, reception zone, serve depth, receiver player, and reception type, therefore affording valuable data for coaches to better contextualize performance and guide the preparation process. The tennis jump serve, commonly also a deeper serve, has posed substantial difficulties to the reception. Furthermore, although the libero is fulfilling its function, it seems advisable to improve the reception skills of the zone 4 attackers, since the opponents have been attempting to exploit them in this game action or if possible to expand the zone of the libero. It has also been shown that the middle-attackers exhibit a more effective serve, reflected upon the difficulties posed to the serve-reception. This facet may be widely explored in practice, especially since these players aren't usually involved in defensive actions.

Key points

* A set of key variables interact and allow predicting the setting zone, an important variable in determining attack efficacy in high-level men's volleyball.

* The tennis jump serve, deep serves, receptions near the endline or sidelines, serves from the middle-players, receptions by the zone 4 attackers when in defensive zone, and low reception enhance the utilization of non-ideal setting zones.

* By focusing on the variables with the most predictive power, performers may better allocate their attention towards the most pertinent cues at each moment.

* Knowledge of these interactive models provides valuable insights into the dynamics of the action sequences, affording coaches important information and guidance.


Afonso, J. and Mesquita, I. (2011) Determinants of block cohesiveness and attack efficacy, in high-level women's volleyball. European Journal of Sport Science 11(1), 69-75.

Afonso, J., Mesquita, I., Marcelino, J. and Silva, J. (2010) Analysis of the setter's tactical action in high-performance women's Volleyball. Kinesiology 42(1), 82-89.

Afonso, J., Mesquita, I. and Palao, J. (2005) Relationship between the use of commit-block and the number of blockers and block effectiveness. International Journal of Performance Analysis in Sport 5(2), 36-45.

Bar-Eli, M., Azar, O. and Lurie, Y. (2009) (Ir)rationality in action: do soccer players and goalkeepers fail to learn how to best perform during a penalty kick? Progress in Brain Research 174, 97-108.

Barzouka, K., Nikolaidou, M., Malousaris, G. and Bergeles, N. (2006) Performance excellence of male setters and attackers in Complex I and II on Volleyball teams in the 2004 Olympic Games. International Journal of Volleyball Research 9(1), 19-24.

Bergeles, N., Barzouka, K. and Nikolaidou, M. (2009) Performance of male and female setters and attackers on Olympic-level volleyball teams. International Journal of Performance Analysis in Sport 9(1), 141-148.

Blanco, A., Losada, J. and Anguera, M.T. (2003) Data analysis techniques in observational designs applied to the environmentbehaviour relation. Medio Ambiente y Comportamiento Humano 4, 111-126.

Bukiet, B., Harold E. and Palacios, J. (1997) A Markov Chain approach to baseball, Operations Research 45, 14-23.

Castro, J. and Mesquita, I. (2008) Study of the implications of offensive space in the characteristics of the attack, in high-level men's volleyball. Portuguese Journal of Sport Sciences 8(1), 114-125.

Castro, J. and Mesquita, I. (2010) Analysis of the attack tempo determinants in volleyball's complex II - a study on elite male teams. International Journal of Performance Analysis in Sport 10(3), 197-206.

Coleman, J. (2002) Scouting opponents and evaluating team performance. In: The volleyball coaching bible. Eds: Shondell, D. and Reynaud, C. Champaign, Illinois: Human Kinetics Publishers. 321-346.

Eckstein, M., Drescher, B. and Shimozaki, S. (2006) Attentional cues in real scenes, saccadic targeting, and Bayesian priors. Psychological Science 17(11), 973-980.

Eom, H.J. and Schutz, R.W. (1992) Statistical analyses of volleyball team performance. Research Quarterly for Exercise and Sport 63(1), 11-18.

Esteves, F. and Mesquita, I. (2007) Study of the setting zone in highlevel men's volleyball in function of the setter and type of set. Portuguese Journal of Sport Sciences 7(1), 36.

Fleiss, J.L. (2003) Statistical methods for rates and proportions. 3rd Edition. Wiley-Interscience.

Hale, T. (2001) Do human movement scientists obey the basic tenets of scientific inquiry? Quest 53(2), 202-215.

Hughes, M. and Bartlett, R. (2002) The use of performance indicators in performance analysis. Journal of Sports Sciences 20(10), 739754.

Jager, J. and Schollhorn, W. (2007) Situation-oriented recognition of tactical patterns in volleyball, Journal of Sports Sciences 25, 1345-1353.

Joao, P., Mesquita, I., Sampaio, J. and Moutinho, C. (2006) Comparative analysis between the libero player and the core receivers for the attack organization, in Volleyball. Portuguese Journal of Sport Sciences 6(3), 318-328.

Katsikadelli, A. (1998a) Reception and the attack serve of the world's leading volleyball teams. Journal of Human Movement Studies 34(5), 223-232.

Katsikadelli, A. (1998b) The evolution of serve tactics of the world's leading volleyball teams. Coaching and Sport Science Journal 3(1), 21-24.

Laios, K. and Kountouris, P. (2004) Evolution in men's volleyball skills and tactics as evidenced in the Athens 2004 Olympic Games. International Journal of Performance Analysis in Sport 5(2), 18.

Lames, M. and McGarry, T. (2007) On the search for reliable performance indicators in game sports, International Journal of Performance Analysis in Sport 7, 62-79.

Landau, S. and Everitt, B. (2004) A handbook of statistical analysis using SPSS. London: Chapman & Hall/CRC Press.

Lirola, D. (2006) Research and analysis of the serve in the current high performance Men's Volleyball. International Journal of Sport Science 2(5), 12-28.

Magnusson, M. (2000) Discovering hidden time patterns in behavior: tpatterns and their detection, Behavior Research Methods, Instruments and Computers 32, 93-110.

Marcelino, R., Mesquita, I. and Afonso, J. (2008) The weight of terminal actions in volleyball. Contributions of the spike, serve and block for the teams' rankings in the World League 2005. International Journal of Performance Analysis in Sport 8(2), 1-7.

Marcelino, R., Mesquita, I. and Sampaio, J. (2011) Effects of quality of opposition and match status on technical and tactical performances in elite volleyball. Journal of Sports Sciences 29(7), 733741.

Mesquita, I. (2005) Contextualizing training in volleyball: The contribute of constructivism. In: The context of decision-making. Tactical action in sport. Ed: Araujo, D. Lisbon: Vision and Contexts. 355-378.

Mesquita, I. and Graca, A. (2002) Probing the strategic knowledge of an elite volleyball setter: a case study. International Journal of Volleyball Research 5(1), 13-17.

Mesquita, I., Manso, F. and Palao, J. M. (2007) Defensive participation and efficacy of the libero in volleyball. Journal of Human Movement Studies 52(2), 95-107.

Newton, P. and Aslam, K. (2009) Monte Carlo tennis: a stochastic Markov chain model. Journal of Quantitative Analysis in Sports 5(3), 1-42.

Palao, J. M., Santos, J. and Urena, A. (2004) Effect of team level on skill performance in volleyball. International Journal of Performance Analysis in Sport 4(2), 50-60.

Palao, J. M., Santos, J. and Urena, A. (2005) Effect of the Setter's Position on the Block in Volleyball. Journal of Human Movement Studies 48, 25-40.

Palao, J. M., Santos, J. and Urena, A. (2007) Effect of the manner of spike execution on spike performance in Volleyball. International Journal of Performance Analysis in Sport 7(2), 126-138.

Papadimitriou, K., Pashali, E., Sermaki, I., Mellas, S. and Papas, M. (2004) The effect of the opponents' serve on the offensive actions of Greek setters in volleyball games. International Journal of Performance Analysis in Sport 4(1), 23-33.

Ranyard, R. and Charlton, J. (2006) Cognitive processes underlying lottery and sports gambling decisions: the role of stated probabilities and background knowledge. European Journal of Cognitive Psychology 18(2), 234-254.

Selinger, A. and Ackermann-Blount, J. (1986) Arie Selinger's Power Volleyball. St.Martin's Press, New York.

Stromsik, P., Lehnert, M. and Hanik, Z. (2002) Characteristics of the spike serves of the best players at the European Senior Men's Volleyball Championships 2001. Physical Education in Sport 46, 441-442.

Thelen, E. (2005) Dynamic systems theory and the complexity of change. Psychoanalytic Dialogues 15(2), 255-283.

Walter, F., Lames, M. and McGarry, T. (2007) Analysis of sports performance as a dynamical system by means of the relative phase. International Journal of Computer Science in Sport 6(2), 35-41.

Ward, P. and Williams, A. M. (2003) Perceptual and cognitive skill development in soccer: the multidimensional nature of expert performance. Journal of Sport and Exercise Psychology 25(1), 93-111.

Williams, A.M. (2009) Perceiving the intentions of others: how do skilled performers make anticipation judgments? Progress in Brain Research 174, 73-83.




Faculty of Sport, Oporto University, Portugal



Research interests

Decision-making, notational analysis, training methodology.


Francisca ESTEVES


Faculty of Sport, Oporto University, Portugal



Research interests

Notational analysis.




Faculty of Sport, Oporto University, Portugal



Research interests

Notational analysis, instructional approaches, teaching and coaching team sports.


Luke Oliver THOMAS


Top Flight Volley Ltd, England.



Research interests

Coaching and coach education.




Professor of Sport Pedagogy, Volleyball, Faculty of Sport, Oporto Universit y, Portugal



Research interests

Coach education, instructional approaches, teaching and coaching team sports, game analysis.


Jose Alfonso Francisca Esteves (1) [mail], Rui Araujo (1), Luke Thomas (2), Isabel Mesquita (1)

(1) University of Porto, Faculty of Sport, Portugal, (2) Top Flight Volley Ltd., England

Received: 08 June 2011 / Accepted: 02 November 2011 / Published (online): 01 March 2012

[mail] Jose Afonso

Rua Dr. Placido Costa, 91 - 4200.450 Porto, Portugal
Table 1. Prediction model for setting zone. Variables related to the

 Excellent Not OR Crude OR Adjusted
 * (%) acceptable
Serve type

Jump float serve 82.7 6.9 .40 .61
 (.31-.50) (.46-.81)
Tennis jump 71.7 15.2
 serve ([dagger])

Server player

Setter 76.4 12.3 .56 .63
 (.41-.77) (.45-.87)
Zone 4 attacker 74.3 12.1 .57 .61
 (.43-.74) (.46-.81)
Opposite 79.8 9.0 .39 .57
 (.29-.52) (.41-.78)
Middle-attacker 68.6 19.7

Serve depth

Short 73.9 14.2 .56 1.45
 (.33-.93) (.77-2.73)
Intermediate 79.3 9.3 .34 .56
 (.28-.42) (.43-.73)
Long ([dagger]) 63.6 22.0

 P Acceptable OR Crude OR Adjusted

Serve type

Jump float serve .001 10.4 .68 .74
 (.55-.85) (.57-.95)
Tennis jump 13.2
 serve ([dagger])

Server player

Setter .005 11.3 .87 .94
 (.61-1.2) (.66-1.35)
Zone 4 attacker .001 13.6 1.07 1.15
 (.79-1.46) (.84-1.57)
Opposite .000 11.2 .83 1.01
 (.60-1.13) (.72-1.42)
Middle-attacker 12.1

Serve depth

Short .252 11.9 .72 .89
 (.41-1.26) (.46-1.76)
Intermediate .000 11.4 .64 .78
 (.51-.80) (.59-1.04)
Long ([dagger]) 14.4


Serve type

Jump float serve .020

Tennis jump
 serve ([dagger])

Server player

Setter .750

Zone 4 attacker .392

Opposite .949


Serve depth

Short .745

Intermediate .087

Long ([dagger])

* reference category for the dependent variable ([dagger]) reference
category for the independent variable

Table 2. Prediction model for setting zone. Variables related to the

 Excellent Not OR Crude
 * (%) acceptable (%)

 Reception zone

 Zone 1 83.8 5.4 .21 (.12-.39)
 Zone 2 80.7 8,6 .35 (.28-.44)
 Zone 3 76.4 11.2 .48 (.37-.62)
Zone 4 ([dagger]) 65.7 20.1

 Receiver player

 1st Z4 def. pos. 75.9 13.3 1.21 (.92-1.59)
 1st Z4 off. pos. 75.0 11.6 1.06 (.78-1.45)
 2nd Z4 def. pos. 74.3 13.6 1.26 (.95-1.67)
 2nd Z4 off. pos. 75.8 11.5 1.04 (.76-1.43)
Libero ([dagger]) 77.8 10.4

 Reception type

 Low reception 86.1 3,8 3.96 (2.25-6.97)
 High reception 74.8 13.0

 OR Adjusted P Acceptable

 Reception zone

 Zone 1 .28 (.14-.56) .000 10.8
 Zone 2 .52 (.39-.69) .000 10.7
 Zone 3 .71 (.52-.98) .037 12.4
Zone 4 ([dagger]) 14.2

 Receiver player

 1st Z4 def. pos. 1.37 (1.03-1.82) .031 10.8
 1st Z4 off. pos. 1.25 (.90-1.75) .190 13.5
 2nd Z4 def. pos. 1.67 (1.24-2.26) .001 12.1
 2nd Z4 off. pos. 1.08 (.76-1.55) .662 12.7
Libero ([dagger]) 11.8

 Reception type

 Low reception 2.43 (1.34-4.39) .003 10.1
 High reception 12.3

 OR Crude OR Adjusted P

 Reception zone

 Zone 1 .60 (.38-.95) .78 (.45-1.34) .366
 Zone 2 .61 (.48-.78) .74 (.56-.98) .034
 Zone 3 .75 (.58-.97) .91 (.66-1.26) .576
Zone 4 ([dagger])

 Receiver player

 1st Z4 def. pos. .91 (.69-1.21) .95 (.71-1.27) .733
 1st Z4 off. pos. 1.15 (.86-1.55) 1.27 (.93-1.72) .137
 2nd Z4 def. pos. 1.05 (.79-1.40) 1.15 (.85-1.55) .359
 2nd Z4 off. pos. 1.08 (.79-1.46) 1.06 (.76-1.49) .737
Libero ([dagger])

 Reception type

 Low reception 1.39 (.97-2.00) 1.10 (.74-1.64) .629
 High reception

* reference category for the dependent variable ([dagger]) reference
category for the independent variable
COPYRIGHT 2012 Journal of Sports Science and Medicine
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2012 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research article
Author:Afonso, Jose; Esteves, Francisca; Araujo, Rui; Thomas, Luke; Mesquita, Isabel
Publication:Journal of Sports Science and Medicine
Article Type:Report
Date:Mar 1, 2012
Previous Article:Validation of the MyWellness key in walking and running speeds.
Next Article:Retired matches among male professional tennis players.

Terms of use | Privacy policy | Copyright © 2020 Farlex, Inc. | Feedback | For webmasters