<abstract xmlns="http://www.w3.org/1999/xhtml"><p>In volleyball, due to the sequential structure of the game, each outcome results from events that follow consistent consecutive patterns: pass–set–attack–outcome, serve–outcome and block–dig–set–counter attack–outcome. There are three possible outcomes: point won, point lost, and rally continuation. With the aim of quantifying the importance of volleyball skills, data of world champions of the male International Volleyball Federation tournaments for three age categories (Youth, Juniors and Men) were used to construct a transition matrix between subsequent moves and skills within the game. A Dirichlet-Multinomial Bayesian model was used to estimate the transition probabilities between the subsequent moves along with the marginal probability of success of each skill in the complex. The prior distribution of each transition probabilities between moves/skills was elicited to incorporate experts' opinion. For the final evaluation of the skills a simple Monte Carlo scheme was applied to obtain a random sample from the posterior distribution. The findings of the study indicate that the relative importance of volleyball skills is robust across world champions of different age categories. Slight variations are observed on specific skills. A new index (Quantile Mid-range Ratio) is proposed for highlighting skills that are valuable for team’s gameplay.</p></abstract>

In volleyball, due to the sequential structure of the game, each outcome results from events that follow consistent consecutive patterns: pass–set–attack–outcome, serve–outcome and block–dig–set–counter attack–outcome. There are three possible outcomes: point won, point lost, and rally continuation. With the aim of quantifying the importance of volleyball skills, data of world champions of the male International Volleyball Federation tournaments for three age categories (Youth, Juniors and Men) were used to construct a transition matrix between subsequent moves and skills within the game. A Dirichlet-Multinomial Bayesian model was used to estimate the transition probabilities between the subsequent moves along with the marginal probability of success of each skill in the complex. The prior distribution of each transition probabilities between moves/skills was elicited to incorporate experts' opinion. For the final evaluation of the skills a simple Monte Carlo scheme was applied to obtain a random sample from the posterior distribution. The findings of the study indicate that the relative importance of volleyball skills is robust across world champions of different age categories. Slight variations are observed on specific skills. A new index (Quantile Mid-range Ratio) is proposed for highlighting skills that are valuable for team’s gameplay.

Bayesian Analysis of Skills Importance in World Champions Men’s Volleyball across Ages

National Kapodistrian University of Athens

Athens University of Economics & Business

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

In volleyball, due to the sequential structure of the game, each outcome results from events that follow consistent consecutive patterns:...

Bayesian Analysis of Skills Importance in World Champions Men’s Volleyball across Ages

Published Online: Aug 21, 2019

Page range: 24 - 44

DOI: https://doi.org/10.2478/ijcss-2019-0002

Keywords
performance analysis, Markov chain, transition matrix, multinomial ditsribution, juniors

© 2019 Sotirios Drikos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Bayesian Analysis of Skills Importance in World Champions Men’s Volleyball across Ages

Published Online: Aug 21, 2019

Page range: 24 - 44

DOI: https://doi.org/10.2478/ijcss-2019-0002

Keywordsperformance analysis, Markov chain, transition matrix, multinomial ditsribution, juniors

© 2019 Sotirios Drikos et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
performance analysis, Markov chain, transition matrix, multinomial ditsribution, juniors