Introduction
Performance analysis and statistics are very important in volleyball at high level. Nishijima et al. (1987) supports that volleyball consist of six basic technical skills (pass, attack, block, dig, serve and set) and two complexes (complex1, attack after serve pass & complex 2, attack after defense). The performance of a team as far as these skills are concerned is very important for the achievement of its goals. Laios and Kountouris (2005) studied the differences between the Olympic Games of Sidney 2000 and Athens 2004 in the effectiveness of technical skills in men’s volleyball, concluding that attack is the most important skill in volleyball. We tested the hypothesis, that the amount of points the team has gathered by the end of a regular season, can be predicted from the effectiveness of the technical skills. We also tested the hypothesis that attack after serve pass is more important than attack after defense for a team’s success.
Methods
All the matches of the Greek professional men’s League are recorded and analyzed through the software Data Volley (Data Project s.r.l., Bologna, Italy) by one scout men registering every touch of the ball during the game. We analyzed the data in relation to all games (132) of the entire A1men’s regular season 2005-06 for each team (N=12). We considered the effectiveness of the following eleven parameters: aces(S#), lost services(S=), attack points after serve pass(AP#), attack errors after serve pass(AP=), attack stuffed after serve pass (AP/), attack points after defense(AD#), attack errors after defense(AD=), attacks stuffed after defense(AD/), direct blocks(B#), perfect passes(P#) and passes errors(P=). A Pearson r correlation coefficient was computed between these variables and team points in the final ranking of the regular season (PFR). A multivariate regression analysis has been carried out to detect the relationship among parameters and their decisive role in the success of a volleyball team in the entire championship.
Results
The most correlated parameter with PFR is AP# (r=0,866, p=0,001), AP/ (r=-0,810, p=0,001) while AP= is medium correlated (r=-0,670, p=0,017). Attack in complex 2 didn’t correlate with PFR. Thus AD# (r=0,289, p=0,362) and AD/ (r=-0,546, p=0,066). A negative correlation appears for AD=(r=-0,657, p=0,020). A highly correlated parameter is S# (r=0,665, p= 0,018) but not S= (r=0,096, p=0,768). So we create a new parameter for service (SR) which consists of the ratio of the S# to S=. SR is very strongly correlated to PFR (r=-0,816, p= 0,001). There isn’t any correlation between PFR and B# (r=0,183, p=0,569). The same thing is true for P= (r=0,183, p= 0,569). But P#(r=0, 787, p=0,002) are more correlated to PFR. Thus, to create a multivariate regression analysis model with dependent variable the amount of points a team has gathered by the end of a regular season, only two dependent variables can enter: Attack points after pass (AP#) and the ratio of S# to S=(SR). The coefficient of multivariate regression analysis is R= 0,917 and R²=0,841.
Discussion
The parameter with the highest correlation to the final’s ranking points is the attack points after pass. So attack during complex 1 may be considered as the most important skill in men’s volleyball. Also it is very important to keep the ratio of lost serves to aces near to number 2. The strong correlation of attack in complex 1 and serve make teams work more on these technical skills and in complex 1 of the game (attack after pass).
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Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
