Procedural knowledge, decision-making and game ...

International Journal of Performance Analysis in Sport 2011, 11, 1-13.

Procedural knowledge, decision-making and game performance analysis in Female Volleyball’s attack according to the player’s experience and competitive success Araújo, Rui, Afonso, José and Mesquita, Isabel Centre of Research, Education, Innovation and Intervention in Sport, Faculty of Sport, University of Porto, Portugal.

Abstract The purpose of this study was to analyse procedural knowledge, decisionmaking and game performance of the zone 4 attackers in Volleyball, according to the players’ experience and competitive performance. A verbal interview protocol was implemented immediately after randomly chosen live game actions to analyze procedural knowledge. Decisionmaking and players’ performance were analyzed by video images. Oneway Anova, Independent Samples t-Test, and Pearson Chi-Square were applied to data analysis. The results showed that more experienced and more successful players play more under goal concepts than condition concepts and concern more often with the opponent. Specifically, more experienced players mentioned more goal concepts and less sophisticated conditions, as more successful players mentioned more action concepts. More experienced players also presented fewer condition concepts than the less experienced ones. Successful players tended to make more appropriate decisions. However, as player’s experience and competitive success are multidimensional variables they might not be fully well characterized by the number of years of practice and competitive results. Therefore, future research is required, and it should include other criteria to characterize both variables. Moreover, qualitative analysis is needed, since it will allow a deeper understanding of the tactical development of the players according to the specific nature of the training process, and also considering the competitive success and the player’s experience as multidimensional variables. Keywords: game performance, competitive performance

1. Introduction Performance in high strategy motor tasks is a function of the individual‟s competence in skill execution, but also of its physical and perceptual attributes (e.g.: size, speed, acuity), physical conditioning, knowledge, psychological characteristics and intuition (Abernethy, Thomas, & Thomas, 1993). Game performance can be divided into cognitive and skill components, and both variables contribute to the development of expertise (Thomas, 1994). Decision-making and knowledge can be included in the cognitive component, whereas the skill component refers to motor execution. 1

Understanding the sport-specific knowledge is essential to the study of skilled sport behaviour (Thomas, French and Humphries, 1986). These authors define sport knowledge as “a complex product of cognitive knowledge about current situation and past events combined with a player’s ability to produce the sport skill(s) required” (Thomas, French and Humphries, 1986, p.259). In literature concerning the problematic of knowledge in sport (McPherson & Thomas, 1989; Thomas, 1994; Thomas & Thomas, 1994) three different types of knowledge are usually distinguished: declarative knowledge (“what to do”), representing factual information, like rules or history, a knowledge that can be memorized and then recalled exactly as it was memorized (Thomas & Thomas, 1994); procedural knowledge (“how”), involving how to do something, being further divided into procedural motor (e.g., the process of executing the movement) and response selection (e.g. what movement to execute in the situation) (Thomas, 1994); and strategic knowledge (“general rules”), a special type of procedural knowledge involving goal-directed procedures that may be used at very different times, before, during, or after a task performance (Alexander & Judy, 1988). Previous studies have analyzed the relationship between the three types of knowledge, decision-making, skill, and game performance (Thomas, 1994, McPherson & French, 1991; Iglesias, Moreno, Santos-Rosa, Cervelló, & Del Villar, 2005). Others have focused on the characteristics that differentiate the knowledge of the players according to their level of expertise in sport (Al-Abood, Bennett, Moreno Hernandez, Ashford, & Davids, 2002; McPherson, 1999a, 1999b, 2000; Moreno, Moreno, Ureña, García, & Del Villar, 2008a). However, these previous investigations have focused mainly on age, practice and competition experience, and players‟ ranking (such as Tennis). As a summary of the main research, results indicate that the knowledge of experienced players in relation to novices is larger and more complete, prepared, structured, organized and sophisticated. Experts know how and when to use that knowledge, identifying, remembering and efficiently manipulating the relevant information at any time, allowing a faster and more successful decision-making (Del Villar, García, Iglesias, Moreno, & Cervelló, 2007; Del Villar, Iglesias, Moreno, Fuentes, & Cervelló, 2004; Dodds, Griffin, & Placek, 2001; Iglesias et al., 2005; McPherson, 1999b; Rink, French, & Tjeerdsma, 1996). Specifically, players with a greater degree of experience have a higher level of declarative and procedural knowledge than players with lower experience (Thomas, French, Thomas, & Gallagher, 1988). Nevertheless, these studies have only considered extreme groups (experts versus novices), having discarded intermediate groups, which could explain the greatly distinct procedural knowledge‟s patterns found between the two groups. Moreover, the most commonly applied indicator for game performance evaluation was the players‟ ranking, namely in tennis (McPherson, 1999a, 1999b, 2000). However, teams‟ ranking might not be the best indicator, since a player‟s quality is not linearly associated with it. Thus, the number of national titles conquered by a player in his career could be a more pertinent indicator to individual success characterization in team sports, since it might reflect his performance on a number of different teams. 2

Since volleyball is considered a tactical and strategic sport, the study of procedural knowledge, decision-making and game performance assume particular importance for understanding the variables that affect the players‟ performance. Currently, several authors (Castro & Mesquita, 2008; César & Mesquita, 2006; Marcelino, Mesquita, & Afonso, 2008; Palao, Santos, & Ureña, 2004a, 2004b, 2005; Yiannis, Panagiotis, Ioannis, & Alkinoi, 2004) refer to the attack as a nuclear feature in the teams' performance, both in men and women‟s volleyball. Furthermore, zone 4 is described as being the most requested zone in the offensive manoeuvre of the teams at the highest level of play, both in side-out and in transition (Afonso & Mesquita, 2005). This requires a very tactical, technical and powerful zone 4 attacker that is denominated “security player”, since he has to respond effectively in hampered attack situations (Marcelino, Mesquita, & Afonso, 2008; Mesquita & César, 2007). The purpose of this study was to analyse the procedural knowledge, decision-making and game performance of the zone 4 attackers, in Volleyball, according to the players‟ experience and competitive performance.

2. Methods 2.1. Participants The sample comprised twenty zone 4 Volleyball attackers from four different teams: one under-16 (n=6), one under-18 (n=6), and two adult teams (n=8). Despite these adult teams participating in different competitive divisions, the interviewed players presented similar game level. 2.2. Variables and Instruments Independent variables In this study, three groups with distinct experience as players were considered. Players with more than 10 years of practice (more experienced players) were distinguished from the others (Ericsson, Krampe, & Tesch-Romer, 1993). However, for a more detailed and equitable analysis, the remaining groups were distinguished in two sets: less experienced players (0-5 years of practice) and intermediately experienced players (5-10 years of practice). Less experienced players (n=8, 40%) presented a mean of 2.88±0.99, with minimum of 1 and maximum of 4 years of practice. Intermediately experienced players (n=3, 15%) exhibited a mean of 7.33±0.58, a minimum of 7 and maximum of 8 years of practice. Finally, more experienced players (n=9, 45%) presented a mean of 16.99±6.01. The minimum value found for this group was 10 and the maximum 25 years of practice. This group presented superior values relatively to age (28.78±8.07) and number of practices per week (5.22±1.56) when compared to the other two groups. The inferior values corresponded to the less experienced players (15.38±0.74 to age and 3.63±0.52 to number of practice per week). Intermediately experienced players presented similar values to less experienced players (15.67±2.31 to age and 3.67±0.58). Regarding competitive success, players were divided into two different groups according to the number of national titles conquered: less successful players (players 3

without national titles conquered) (n=13, 65%); and more successful players (players that have conquered national titles in their carriers) (n=7, 35%). Less successful players presented inferior values according to age (16.00±1.91) and number of practice per week (3.85±1.07) when compared to more successful players (37.57±6.68 and 5.29±1.38, respectively). Dependent variables Procedural Knowledge in action For the evaluation of the procedural knowledge of the zone 4 attackers during the competition, a verbal interview protocol was implemented immediately after randomly chosen live game actions, more specifically after attack actions. This protocol was adapted from McPherson and Thomas (1989) and McPherson (2000), who had previously applied it in Tennis. This type of protocol was used in sports in order to analyze the knowledge representation of the players, as drafted by McPherson & Thomas (1989), and was implemented in research within different sports: Tennis (Garcia, Moreno, Iglesias, Moreno, & Del Villar, 2006; McPherson, 1999a, 1999b; McPherson & Kernodle, 2007; McPherson & Thomas, 1989), in Baseball (McPherson, 1993), and Volleyball (Moreno et al., 2008a; Moreno, Moreno, Ureña, Iglesias, & Del Villar, 2008b). In order to deepen the knowledge base in sports, these authors have developed a verbal interview to be implemented during the decision-making in simulated game situations or in competition. Therefore, this instrument intended to know "which" information the players attended to and "how" they used it (McPherson & Kernodle, 2003). The use of oral interviews proved to be suitable for accessing the representation of issues in sport (McPherson, 1994). The interview consisted in one broad question – “What were you thinking about while playing that point?” The zone 4 attackers had to verbally respond to this question immediately following the execution of an attack, in 6 vs. 6 situations in practice. The 6 vs. 6 game situations were not interrupted, since a substitute player immediately entered the field, and stayed there during the period the other player was out being interviewed. Decision-making To evaluate the decision-making, 123 game actions were observed. Three independent observers (expert volleyball coaches) have evaluated the players‟ decision-making, through a classification of each action as appropriate or inappropriate. For the purposes of this evaluation, the players‟ verbal reports were not considered; only their actions were taken into account. The action was deemed appropriate if two out of three expert coaches classified it in that way. These expert coaches showed 77% agreements. Analyzed players exhibited 67.7% of appropriate decisions and 33.3% of inappropriate decisions. Game performance Concerning the evaluation of the players‟ performance, the efficacy of 479 attack actions was analyzed following Afonso & Mesquita‟s (in press) classification: Kill attack (KILL) - conquering the point; Error (ERR) - direct error or blocked attack; Neutral attack (NEU) – attack easily defended by the opponent or recovered by the own 4

team after block coverage; ATP - attack posing difficulties on the opponent‟s defence, although not automatically resulting in a point. 2.3. Data collection The attacker‟s responses were recorded on audiotape using an .mp3 recorder, which were later transcribed and coded. The verbal reports of each player were literally transcribed and later quantitatively analyzed by means of a protocol model structured for Tennis (McPherson, 1999a, 1999b, 2000) and adapted to the requirements of Volleyball (Moreno et al., 2008a). The units of information were classified according to five main conceptual categories: goal concepts, condition concepts, action concepts, regulatory concepts, and do concepts (McPherson, 1999b). Due both to the reduced number of regulatory and do concepts, and to their similarity to action concepts, these three types were grouped into one functional unit, keeping the name of action concepts: (a) Goal concepts: reflected the means by which the game was won or the purpose of a chosen action or the specification of a condition referring to the game‟s goal structure (e.g., “getting the ball in”, “exploit the block”); b) Condition concepts: which specify when or under what conditions to apply the action or patterns of action to achieve the goal (e.g., the opponent position on court, opponent‟s weakness); c) Action concepts: which refer to the action selected or patterns of action that may produce goal-related changes in the context of a sport situation (e.g., “move to the net”). These concepts also specify whether the intended action was carried out or not (e.g., a statement that indicates the participant failed to execute a serve), and how to perform the action (statement that includes mechanical labels about how to execute the action). Goal, condition and action concepts were also examined for concept sophistication, corresponding to the subcategories. In the research by McPherson (1999a, 1999b, 2000) in tennis, three-goal hierarchy levels were differentiated. In a study conducted in Volleyball, (Moreno et al., 2008a) considered the existence of team members and took it into account, thus having included a fourth hierarchical level referring to the subconcept goals about their team members. In the present study, hierarchical levels were analyzed for conditions: (i) level 0: player‟s subconcept goals or conditions about themselves; (ii) level 1: player‟s subconcept goals or conditions about team members; (iii) level 2: player‟s subconcept goals or conditions about their opponent; (iv) level 3: subconcept goals or conditions of other nature (reduced „n‟). Each identified condition or action concept was classified according to one of the following levels of sophistication: (i) quality level 0: inappropriate or weak; (ii) quality level 1: appropriate without any details or features; (iii) quality level 2: appropriate with one detail or features; (iv) quality level 3: appropriate with two or more features. Decision-making and players’ performance were analyzed by video images in an Asus F3JC-APO2H laptop, using the Playing Media Files Software from Microsoft Windows Media Player (version 11). 2.4. Data analysis Each player provided data regarding: name, age, team’s age, number of years of practice, number of years in specific function within the game, number of training sessions per week, national titles conquered (6 vs. 6), number of participations in 5

national finals (6 vs. 6), and their usual condition as starting player or substitute. In addition, twenty zone 4 attackers were interviewed, in a total of 123 reports. Data was analyzed trough descriptive (mean, standard deviation, minimum and maximum values) and inferential statistics. The procedural knowledge according to the players‟ experience was analyzed trough content analysis. Registration units from this analysis were compared trough a One-way Anova test. Post-hoc trough the Tukey test was applied to verify which groups enclosed those differences. Regarding competitive success, the procedural knowledge was analyzed trough Independent Samples T Test. Game performance, decision-making, players’ experience and competitive success were analyzed trough chi-square ( 2) test, with analysis of the adjusted residual, to test the association between these variables. Statistical significance was set at 5% and all analyses were performed in SPSS 17.0. 2.5. Reliability of the observation Intra-observer (the two observations occurred with an interval of more than 15 days) and inter-observer (observation for an observer with experience in this role and knowledgeable in volleyball) reliability were measured. Fourteen interviews (11%) were analyzed, more than the minimum acceptable value of 10% stipulated by the literature (Tabachnick & Fidell, 2007). To measure the degree of reliability, the formula of Bellack et al. (1966, cited by Van der Mars (1989)) was used. Results for intra-observer reliability showed percentages of agreement above the minimum indicated, namely 80% (Tabachnick & Fidell, 2007). The minimum value found was 85.7% for game performance, action concepts and condition quality level and the maximum value of 100% in the variables goal concepts, goal hierarchical levels, condition hierarchical level (0), condition hierarchical level (1) and action quality level. Relatively to the inter-observer reliability, results also showed percentage of agreements above the threshold value of 80%. The minimum value found was 87.5% for game performance, condition concepts, action concepts, condition hierarchical level (2) and condition quality level. The maximum value of 100% was found in goal concepts, goal hierarchical level (1), goal hierarchical level (3) and condition hierarchical level (3).

3. Results The results showed significant differences for goal concepts, in which more experienced players have presented higher values than less and intermediately experienced ones (Table 1). Significant differences were also found according to condition concepts; in this case, however, more experienced players have presented lower values. No significant differences were found for action concepts (F=2.748, p=0.088). There were significant differences for goal hierarchical level (0) and goal hierarchical level (2) between less experienced and more experienced players. In both cases, more experienced players have presented higher values. This group also distinguished from 6

the other two groups on condition hierarchical level (1) and condition hierarchical level (2), having shown lower values. The comparison of condition quality level showed significant differences, in which more experienced players have presented the lowest values. Nonetheless, no differences were found according to actions quality level (F=2.025, p=0.145). Also, no significant differences were found between the three groups for game performance ( 2=8.336, p=0.501) and decision-making ( 2=4.482, p=0.219). Table 1 – Players‟ procedural knowledge according to their experience as player POST-HOC MEAN Comparisons Sig. Group M 1-2 0.659 1 0.15 Goal concepts 13.363 0.000 1-3 0.000 2 0.28 2-3 0.017 3 0.69 1-2 0.047 1 2.69 Condition concepts 28.634 0.000 1-3 0.000 2 3.39 2-3 0.000 3 1.49 1-2 0.464 1 0.08 Goal hierarchical level 4.898 0.009 1-3 0.006 2 0.22 (0) 2-3 0.534 3 0.35 1-2 0.941 1 0.06 Goal hierarchical level 4.457 0.014 1-3 0.013 2 0.11 (2) 2-3 0.191 3 0.36 1-2 0.855 1 1.02 Condition hierarchical 14.970 0.000 1-3 0.000 2 1.11 level (1) 2-3 0.000 3 0.44 1-2 0.187 1 1.48 Condition hierarchical 8.720 0.000 1-3 0.015 2 1.94 level (2) 2-3 0.001 3 0.95 1-2 0.187 1 2.48 Condition quality level 12.190 0.000 1-3 0.001 2 2.89 2-3 0.000 3 1.85 Legend: Group 1 – less experienced players ([0-5[ years of practice); Group 2 – intermediately experienced players ([5-10[ years of practice; Group 3 – more experienced players (more than 10 years of practice)

ANOVA Sig.

Procedural knowledge

F

Relatively to the comparison of procedural knowledge according to the competitive success, significant differences were found (table 2). Players with more competitive success showed higher values of goal concepts than players with less competitive success. These players also presented goals more related to themselves, to the opponent, and to goals of other nature than players without competitive success. On the other hand, these players showed fewer values of conditions related to themselves and less sophisticated actions.

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Table 2 – Players‟ procedural knowledge according to their competitive success MEAN Group M 1 0.24 Goal concepts 8.952 0.003 2 0.73 1 0.15 Goal hierarchical level (0) 19.936 0.000 2 0.38 1 0.13 Goal hierarchical level (2) 15.280 0.000 2 0.33 1 0.00 Goal hierarchical level (3) 15.702 0.000 2 0.04 1 0.21 Condition hierarchical level (0) 4.726 0.032 2 0.11 1 2.30 Action quality level 26.911 0.000 2 2.06 Legend: Group 1 – Players with competitive success (without national titles conquered; Group 2 – Players with competitive success (with national titles conquered)

T- Test Sig.

Procedural Knowledge

F

Results also showed an association between competitive success and decision-making ( 2=3.942, p=0.047). Players with competitive success presented higher values than expected concerned to appropriate decisions. On the contrary, players with less competitive success presented higher values than expected on the inappropriate decisions (Table 2). Finally, results showed no significant relationship between competitive performance and game performance ( 2=2.984, p=0.406). Table 3 – Players‟ decision-making according to their competitive success

Appropriate decision Inappropriate decision

Decision-making

Competitive Success

Total

Less successful players

More successful players

47(52)

35(30)

82(82)

% Decision-making

57.3

42.7

100

% of Total

38.2

28.5

66.7

Adjusted Residual

-2.0

2.0

31(26)

10(15)

41(41)

%Decision-making

75.6

24.4

100

% of Total

39.7

22.2

33.3

Adjusted Residual

2.0

-2.0

78(78)

45(45)

123(123)

63.4

36.6

100

Count (expected count)

Count (expected count)

Count (expected count) % of Total

8

4. Discussion The purpose of this study was to analyse the procedural knowledge, decision-making and game performance of the zone 4 attackers, in Volleyball, according to the players‟ experience and their competitive success. The findings obtained regarding the procedural knowledge were in line with the competitive success and the player‟s experience, as the results have only differed concerning the conditions hierarchical levels. More experienced and more successful players have presented higher goals, which were more oriented to themselves and to the opponents, comparatively to less and intermediately experienced players, and to less successful players, respectively. The more experienced players‟ prevalence of goal concepts and their orientation to the opponent confirms previous research with Volleyball setters (Moreno et al., 2008a; Mesquita and Graça, 2002). Notwithstanding, the findings of the present study do not corroborate previous studies in Tennis (McPherson, 1999a, 1999b), in which no significant differences were found between the groups regarding the frequency of goal concepts. These apparent contradictory results can be explained by the fact that Tennis is a sport with different features from those of team sports. The open and changing nature of the actions typical of team sports (Thomas, 1994), like Volleyball, provides a broader range of indicators to make decisions. Therefore, differences found in the present study in respect to goal concepts could be explained for the higher number of indicators present in the game. Moreover, the results of the present study suggest that more experienced and more successful players play more under goals than conditions concepts and concern more often with the opponent. These findings do not corroborate previous studies in other sports (Tennis) (Garcia et al., 2008; McPherson, 1999a, 2000) and in Volleyball (Moreno et al., 2008a), in which more experienced setters have exhibited more conditions. Concerning the concepts quality level, results showed that more experienced and more successful players presented conditions and action concepts less sophisticated, respectively, again not corroborating previous findings (McPherson, 1999b, 2000; Moreno et al., 2008a; García et al., 2008). Nevertheless, in the study of Moreno et al. (2008a) all players played in the national team (hence, in a high competitive level) despite having distinct experience. In the same line, in condition hierarchical level more experienced players showed fewer conditions related to teammates, as more successful players related more to the opponent. These findings can be explained by the higher orientation to the goals both by more experienced and more successful players. Notwithstanding, although the conditions were not mentioned, they could be implied in the goal concepts reports (e.g., when the player‟s goal is to exploit the opponent block, this player has to know how it is formed, despite not referring to this condition). With respect to decision-making analysis according to competitive success, results of the present study suggest that players with competitive success tend to present more appropriate decisions, corroborating previous findings (Vaeyens, Williams, Mazyn, & Philippaerts, 2007). These authors have studied differences in responses accuracy between soccer players and non-players, having found that the players made more accurate decisions. Concerning game performance, no differences were found according 9

to players‟ experience and competitive success, again not corroborating previous investigations (McPherson & Thomas, 1989), where experts have showed better game performance than novice players. The somewhat unexpected results of this study may be explained based on different factors. For instance, the heterogeneity of the years of experience within each group may have promoted a smaller differentiation between the groups. On the other hand, the criterion used to classify players‟ experience might not have been the most appropriate. As Ericsson, Krampe, & Tesch-Romer (1993) claim, expertise depends on several factors and a higher amount of experience does not, by itself, lead to expertise. Furthermore, in the present study, the more experienced players with high competitive success are not world-class experts, but merely “arbitrary experts” (Thomas, 1994). In addition, previous studies have considered only two distinct groups – experts and novices - whilst in this study a continuum of experience was considered, with three groups having been formed, from the less experienced to more experienced players. All of these reasons could explain, in part, the unexpected findings of this study, considering previous research. Furthermore, as player‟s experience and competitive success are multidimensional variables, they are not well characterized by years of practice and competitive results only. Therefore, future research is required, and it should include other criteria to characterize both variables. Moreover, qualitative analysis is needed, since it will allow a deeper understanding of the tactical development of the players according to the specific nature of the training process considering competitive success and player‟s experience. 5. Conclusions Findings from this study highlight that player‟s experience and competitive performance differentiate the procedural knowledge of the players under analysis. The results suggest that more experienced and more successful players play more under goals than conditions and concern more often with the opponent. Relatively to game performance, no differences were found according to players‟ experience and competitive success. According to decision-making accuracy, the results have showed differences according to competitive success, in which more successful players tended to make more appropriate decisions. Relevant implications can be withdrawn for the process of training. The procedural knowledge development should be taken into account by coaches in the training process. Players should have the opportunity to practice decision-making processes and be provided with proper instruction regarding these aspects. Competitive experiences may also facilitate the building of more sophisticated knowledge structures. For future studies, it would be important to access the training of decision-making throughout the players‟ long-term development, considering the situated nature of the learning contexts, in order to better understanding about the players‟ tactical development.

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