The Relationship of Situational Efficiency Parameters of Volleyball

Game Phases and Their Intrateam Variability with the Set Score

Ivana Klaričić

and Zoran Grgantov

Faculty of Kinesiology, University of Osijek, Croatia

Faculty of Kinesiology, University of Split, Croatia

Keywords: Situational Efficiency, Variability of Game Phases, Volleyball.

Abstract: The purpose of this study is to determine the relationship between situational efficiency parameters of five

phases of the volleyball game and their intrateam variability with the set score. A sample of 40 volleyball sets

played in the European League for Men in 2011 and 2012 were randomly selected. Although, the sample

wasn't recent, the purpose of this methodologically based study was to propose a new performance indicator.

The multiple regression analysis determined a high and positive relationship between the situational efficiency

of five phases of the volleyball game with the set score. It also determined that the intrateam variability

between the phases of the volleyball game had a statistically significant but negative relationship with the set

score. The variability of game phases explained 4.1% of the variance of the score. Conclusion was that a

larger negative deviation in situational efficiency of one phase of the game cannot be compensated only by

the corresponding increase in another phase of the game, as the linear regression model suggests.

1 INTRODUCTION

Performance analysis is a powerful research area,

providing answers to understand the factors that are

critical for participation in elite level sports (Hughes

and Bartlett, 2002). The purpose of performance

analysis is to determine which performance indicators

are the most responsible for a match score.

Each team sport has its specific performance

indicators that are assumed to impact the match score.

These specificities arise from the structure of the

sport itself. The volleyball game consists of six

phases that are sequentially executed: serve,

reception, setting, attack, block and dig (Busca and

Febrer, 2012). Thus, the selection of performance

indicators in volleyball is mostly focused on the

efficiency of game phases. The efficiency of the game

phase can be defined by the efficiency coefficients

(Marcelino et al., 2008) or discrete variables of total

successful or / and unsuccessful performances

(Marcelino, et. al., 2008; Yu et al., 2018). Marcelino

et al. (2008) determined that the relative measures of

the spike were better performance indicators of

success in high level volleyball then the discrete ones.

According to the authors of this study, efficiency

coefficients are more appropriate because they

consider all executions, not only the terminal ones.

In pursuit for predictors with a higher relationship

with the score, researchers consider various relations

between the performance indicators but also various

manners for defining the score. Some of those less

obvious performance indicators were the efficiency

coefficients derived from two or more performance

indicators (Drikos et al., 2009). Drikos et al. (2009)

also determined that performance indicators derived

from the discrete indicators were better predictors

then the discrete ones. Those were the serving

efficiency ratio, defined as the ratio of lost serves to

aces, and the attack efficiency ratio, defined as the

number of kill attacks divided by the sum of attack

errors and kill-blocks. They also defined the team

performance as the ratio of sets won to the total

number of sets. In other study, Drikos et al. (2020)

were determining differences in 12 performance

indicators between volleyball sets classified by two

indipendent factors (gender and type of result). They

tested the effect of those independent factors as well

as the interactions of factors (gender x type of result).

It is very important that the total set of performance

indicators isn't too complex. It has to be simple

enough in order to be logically explained for the

practical purpose.

The performance indicator introduced by the

author in this study is the intrateam variability of the

situational efficiency parameters of volleyball game

Klari

c, I. and Grgantov, Z.

The Relationship of Situational Efﬁciency Parameters of Volleyball Game Phases and Their Intrateam Variability with the Set Score.

DOI: 10.5220/0012164000003587

In Proceedings of the 11th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2023), pages 45-49

ISBN: 978-989-758-673-6; ISSN: 2184-3201

phases. Variability is a measure of the the amount of

dispersion in a dataset. In this study, higher variability

implies greater differences between team's below

average and above average efficiency coefficients of

the game phases in a set. The relationship of the

intrateam variability with the score will give the

answer if a extremely low performance in one game

phase could be compensated by proportionally high

performance in another one.

Given the high sequentiality of game phases in

volleyball, the assumption was that the extremely low

performance in one game phase would consequently

lower the score, that the high performance in another

game phase could not compensate for. The

assumption is that the homogeneity of performance

efficiency of game phases would have the additional

positive impact on the score when teams have the

same cumulative situational efficiency.

The purpose of this study is to determine the

realtionship between situational efficiency

parameters of five phases of the volleyball game and

their intrateam variability with the set score.

2 METHODS

2.1 Set of Entities

The samle of entities were 40 volleyball sets from

matches played in the European Volleyball League

for Men in 2011 and 2012. In order to avoid

dependence of the sample, only the data from one set

of a match and only one team were collected. Both

the set and the team were randomly selected.

2.2 Set of Variables

The predictor variables were the efficiency

coefficients of the five phases of the volleyball game:

serve, reception, spike, block, dig, and their intrateam

variability. The setting was excluded from this study

because of its specific situational efficiency analysis.

The efficiency coefficient of each game phase was

defined as the arithmetic mean of scores of all

performed technical skills within a particular phase in

one set. Each performed skill was evaluated with a

score (1 – 4) according to precisely defined criterion.

The score 1 was an error, 2 was an advantage for the

opponent, 3 was an advantage for the team being

evaluated, and 4 was an ideal performance (reception,

dig) or a point won (serve, spike, block). The

intrateam variability of the game phases was the

calculated standard deviation of the five efficiency

coefficients of each team. The first step was to

standardize the efficiency coefficients of each game

phase. The second step was to calculate the standard

deviation of standardized efficiency coefficients for

each individual team. The criterion variable was the

set score defined as a relative point difference, the

point difference in a set devided by the total number

of points. If the team won the set, the relative point

difference was positive and on the contrary, if the

team lost the set, the relative point difference was

negative. The authors believe that the same point

difference does not represent an equal outcome of the

set in the case when the result is 15 : 13, 25 : 23 or 31

: 29. For this reason, the result in the set was defined

as a relative point difference.

2.3 Data Collection

The data were obtained from the existing videos of

played volleyball matches into prepared forms. It was

done by the first author, who has multiannual playing

experience, an A coaching license and a multiannual

coaching experience in men’s volleyball. The

reliability analysis was conducted with the help of an

expert with multiannual playing, coaching and

notational analysis work experience.

2.4 Statistical Analysis

A reliability analysis was conducted on a sample of 3

randomly selected sets. Spearman’s rank correlation

and Cohen's kappa were calculated to determine the

degree of agreement between the two different

measurements (the first author and the expert) and

two different measurements of the same measurer

(the first author) at intervals of 4 – 6 weeks (test-retest

method).

The descriptive statistics were: arithmetic mean

(Mean), standard deviation (σ), minimum (Min) and

maximum (Max). Normality of distribution was

determined by Shapiro-Wilk test.

Two separate multiple regression analysis were

conducted to determine the relationship between the

efficiency coefficients of the five phases of the

volleyball game and their intrateam variability and

the set score. The first one was conducted without the

variability as a predictor and the second one included

the variability in order to determine its contribution

on the regression model.

The collected data were analysed with the

computer program Statistica for Windows 13.3

(TIBCO Software Inc.).

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

Table 1: Descriptive statistics results.

Mean ± σ Min Max

Relative point difference

-0.01 ± 0.13 -0.32 0.25

Efficiency coefficient-serve 2.14 ± 0.20 1.75 2.50

Efficiency coefficient-reception 2.98 ± 0.26 2.55 3.50

Efficiency coefficient-spike

3.04 ± 0.24 2.44 3.65

Efficiency coefficient-block 2.29 ± 0.39 1.00 3.00

Efficiency coefficient-dig

1.95 ± 0.28 1.22 2.61

Variabilit

ame phases 0.90 ± 0.34 0.39 1.77

Legend: Mean – arithmetic mean, σ – standard deviation, Min – minimal result, Max – maksimal result.

Table 2: The results of two multiple regressions (variability of the game phases included in the second analysis).

part. (%)

R 0.89 Intercept -2.29 -11.31 0.00

80.0% Efficiency coefficient-serve 0.36 0.25 4.37 17.0 0.00

adj

76.5% Efficiency coefficient-reception 0.27 0.14 3.31 11.8 0.00

26.3 Efficiency coefficient-spike 0.48 0.26 5.74 32.6 0.00

p 0.00

Efficiency coefficient-block

0.22 0.08 2.65

5.4

0.01

Efficiency coefficient-dig

0.39 0.19 4.69

12.8

0.00

R 0.91 Intercep

-2.19 -11.20 0.00

82.3% Efficienc

coefficien

-serve 0.38 0.26 4.92 18.2 0.00

79.1% Efficienc

coefficien

-reception 0.26 0.14 3.43 11.5 0.00

25.6 Efficienc

coefficien

-spike 0.47 0.26 5.88 31.6 0.00

0.00 Efficienc

coefficien

loc

0.20 0.07 2.47 4.8 0.02

Efficienc

coefficien

-di

0.37 0.18 4.68 12.2 0.00

Variabilit

ame phases -0.17 -0.07 -2.29 4.1 0.03

Legend: R – coefficient of multiple correlation, R

– coefficient of determination,

adj

– adjusted coefficient of

determination, F – Fisher's test value, β – standardized regression coefficients, b – unstandardized regression coefficients,

part

– partial coefficient of determination,

t – t–test value, p – significance level.

3 RESULTS

Reliability analysis results determined a high

correlation between the two measurements of the

same measurer conducted at two-time points (R =

0.91; κ = 0.92) and the two different measurers (R =

0.92; κ = 0.88).

Two separate multiple regression analysis were

conducted. The first one in order to determine the

extent of the relationship of the efficiency coefficients

of five volleyball game phases and the relative point

difference in the set. The second one was conducted

in order to determine the contribution of the

variability of the game phases witin a team in the

regression model.

The multiple regression analysis showed that all

predictors had a significant relationship with the set

score. The efficiency coefficients of the five phases

of the volleyball game and the variability of the

phases explained a total of 82.3% of the variance of

the relative point difference in the set. All regression

coefficients of game phases were positive, the

increasement of their efficiency coefficients had a

positive impact on the set score. The regression

coefficient of variability was negative, which ment

that the greater the variability of game phases within

the team, the greater negative impact on the set score.

Variability explains 4.1% of the results.

4 DISCUSSION

The purpose of this research was to determine the

extent of the relationship between the efficiency

coefficients of the volleyball game phases and also

their intrateam variability with the set score.

The Relationship of Situational Efﬁciency Parameters of Volleyball Game Phases and Their Intrateam Variability with the Set Score

Descriptive indicators showed that the attack is

the phase of the game that has the highest situational

efficiency coefficient, 3.04 out of 4, the maximal

possible efficiency coefficient. The second one was

the reception with almost equal values, then block and

serve, the dig had the lowest situational efficiency

coefficient, 1.95. Previous research has also shown

that attack have been the most effective phase of the

volleyball game (Eom and Schutz, 1992; Marelić et

al., 1998; Marcelino, et al, 2008; Stutzig et al., 2015).

The block had the highest standard deviation (0.39),

which ment that it was the phase of the game in which

the sample of teams is the least homogeneous. In

contrast, serve had the lowest standard deviation

(0.20). However, a high situational efficiency

coefficients of a game phase does not represent its

impact on the set score.

The correlation coefficient of the arithmetic mean

of the five efficiency coefficient of game phases and

their standard deviation (intrateam variability) was r

= -0.09. This ment that both the teams with high and

the teams with low situational efficiency could have

equally high or low variability. However, it is not

possible to determine the impact of efficiency

coefficients and treir variability on the set score based

on descriptive results. That was why a multiple

regression analysis was conducted to determine the

aforementioned relationship.

The results of multiple regression analysis

showed that the situational efficiency coefficient of

the five phases of the volleyball game have a high

relationship with the set score. All regression

coefficients of game phases were positive,

increasment in their efficiency coefficients had a

positive impact on the set score. The attack was the

phase of the game that explained the most variance of

the results (31.6%), followed by serve (18.2%), dig

(12.2%) and reception (11.5%) and finally block with

only 4.8%. As mentioned, a high efficiency

coefficient does not imply as high impact on the set

score. For example, dig, which had the lowest average

efficiency coefficient of all game phases (1.95), had a

greater relationship (the amount of common variance)

with the set score (12.2%) than both the reception

(11.5%) and the block (4.8%).

The dig and the reception are not the game phases

by which a team is not able to win a point. The team

is able to win a point by the serve, spike and block

(and the opponent error). A high amount of the

common variance of the dig with the score (R

part.

12.2%) in this study was unexpected. According to

Palao et al. (2006), despite the crucial role of the

attack, it is assumed that defensive actions are

fundamental to maintain success in the competition.

Similar to the dig, the reception had an unexpectedly

high relationship with the set score (R

part.

= 11.5%).

But, according to Laios and Moustakidis (2011) the

reception has a high impact on the score. The efficient

reception enables all tactical variants of the team's

attack which makes the attack unpredictable for the

opponent. In contrary to the reception and the dig, the

block as a terminal game phase, had an unexpectedly

low relationship with the set score (R

part.

= 4.8%).

But, in top level volleyball, the block stops only

15¬20 % of the opponent's spikes (Palao et al., 2004).

The reason is that the team's tactics emphasize quick

spikes that make the time deficit to the opponent's

block. That may be the reason the block doesn't win

many points in the set (in this sample 10,4%). On

contrary, The fewest points are won by the serve (in

this sample 4,4%) but the serve has a high

relationship with the score (R

part.

= 18.2%). The

game phases in volleyball are executed in a manner

that makes volleyball a highly sequential sport,

efficiency of every game phase is partially

determined by the previous one.

The intrateam variability of game phases has a

statistically significant relationship with the score, but

the regression coefficient was negative, which shows

that variability had a negative impact on the set score.

The variance of the score explained by the team's

variability was low, 4.1%, but when we consider that

the block explained 4.8%, then the magnitude of the

impact of the variability on the set score can't be

ignored. The consequence of 4.1% common variance

of the variability and the score in practical application

is the number of points in the set. The team with the

lowest variability (0.39) loses approximately 1.4

points and the team with the highest variability (1.77)

loses 6.2 points in a set with a total of 50 points (26 :

24). According to the regression model, the team with

the lowest variability wins 4.8 points more then the

team with the highest variability even the both teams

have the same all situational efficiency coefficients of

the game phases. Theoretically, if more point were

played (31:29) the difference would increase to 5.8

points. It is obvious which team wins the theoretical

match.

For examle, if a team A has higher variability and

lower efficiency coefficient only in block then the

team B, it isn't enough to substitute proportional

backlog in, for example, attack in order to catch up

with the team B, as the linear regression model

without variability suggests. Additional increase has

to be accomplished in order substitute the higher

variability. As already mentioned, the reason to this

icSPORTS 2023 - 11th International Conference on Sport Sciences Research and Technology Support

could be the high sequentiality of the volleyball game.

Although invisible to the observer, in a large number

of sequentialy performed skills during the set,

variability of the game phasese manages to achieve

that 4% negative impact on the set score.

The purpose of the performance analysis is to

determine predictors that impact the score in as many

possible ways. Many less obvious predictors had also

been determined to have an impact on the score.

Various interactions between predictors have been

determined as such less obvious predictors with a

significant impact on the score (Drikos, et al., 2020).

In some research various efficiency coefficients were

derived from predictor variables in order to determine

the relationship with the score (Drikos, et al., 2009).

The intrateam variability between situational

efficiency of game phases showed that the

homogeneity of performance indicators is important

for overall situational efficiency. Given the fact that

in top level sport even the smallest differences can

decide between victory and defeat, the importance of

variability of game phases becomes even more

important. The virtue of this predictor is its

explication simplicity for the scientific and practical

application.

European League for Men is a top level volleyball

competition so the limitation of this study is that it's

results could not refer to other levels of competition

in volleyball. It is difficult to assume would the

intateam variability of the game phases have this type

of impact on the set score if the volleyball sets were

played in a lower level of competition. So the

implication of this study is that the further research

should be conducted with volleyball sets collected

from the lower level of competition.

5 CONCLUSION

The multiple regression results determined a high and

positive relationship between the five phases of the

volleyball game and the relative point difference in

the set. The relationship between the game phases

variability and the relative point difference was also

determined but it was negative. The intrateam

variability between efficiency coefficients of the

game phases has been determined to be another

possible predictor of team's performance. The

practical applicability of the results of this research is

a recommendation for teams to place additional

emphasis in the training process primarily on

increasing the efficiency of game phases with the

lowest efficiency coefficients, and only then on

increasing the efficiency coefficients of game phases

that have the greatest positive impact on the set score.

REFERENCES

Busca, B., & Febrer, J. (2012). Temporal fight between the

middle blocker and the setter in high level volleyball.

International Journal of Medicine and Science of

Physical Activity and Sport, 12(46), 313 – 327.

Drikos, S., Kountouris, P., Laios, A., & Laios, Y. (2009).

Correlates of team performance in volleyball.

International Journal of Performance Analysis in

Sport, 9(2), 149-156.

Drikos, S., Sotiropoulos, K., Barzouka, K., & Angelonidis,

Y. (2020). The contribution of skills in the

interpretation of a volleyball set result with minimum

score difference across genders. International Journal

of Sports Science & Coaching, 15(4), 542-551.

Eom, H.J., & Schutz, R.W. (1992). Transition play in team

performance of volleyball: log-linear analysis.

Research Quarterly for Exercise and Sport, 63(3), 261-

269.

Hughes, M.D, & Bartlett, R.M. (2002). The use of

performance indicators in performance analysis.

Journal of Sports Sciences, 20(10), 739 – 754.

Laios, A. & Moustakidis, A. (2011). The setting pass and

performance indices in Volleyball. International

Journal of Performance Analysis in Sport, 11(1), 3 –

39.

Marcelino, R., Mesquita, I., & 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, 88(2), 1-7.

Marelić, N., Rešetar, T., & Janković, V. (2004).

Discriminant analysis of the sets won and the sets lost

by one team in A1 Italian volleyball league - A case

study. Kinesiology, 36(1), 75-82.

Palao, J.M., Santos, J.A., & Urena, A. (2004). Effect of

team level on skill performance in volleyball.

International Journal of Performance Analysis in

Sport, 4(1), 50 – 60.

Palao, J.M., Santos, J.A., & Urena, A. (2006). Effect of

reception and dig efficacy on spike performance and

manner of execution in volleyball. Journal of Human

Movement Studies, 51(4), 221 – 238.

Stutzig, N., Zimmermann, B., Busch, D., & Siebert, T.

(2015). Analysis of game variables to predict scoring

and performance levels in elite men’s

volleyball. International Journal of Performance

Analysis in Sport, 15(3), 816-829.

Yu, Y., García-De-Alcaraz, A., Wang, L., & Liu, T. (2018).

Analysis of winning determinant performance

indicators according to teams level in Chinese women’s

volleyball. International Journal of Performance

Analysis in Sport, 18(5), 750-763.

The Relationship of Situational Efﬁciency Parameters of Volleyball Game Phases and Their Intrateam Variability with the Set Score