Skill Evaluation in Women's Volleyball

Abstract

BYU ScholarsArchive CitationFlorence, Lindsay W.; Fellingham, Gilbert W.; Vehrs, Pat R.; and Mortensen, Nina P., "Skill Evaluation in Women's Volleyball" (2008). AbstractThe Brigham Young University Women's Volleyball Team recorded and rated all skills (pass, set, attack, etc.) and recorded rally outcomes (point for BYU, rally continues, point for opponent) for the entire 2006 home volleyball season. Only sequences of events occurring on BYU's side of the net were considered. Events followed one of these general patterns: serve-outcome, pass-setattack-outcome, or block-dig-set-attack-outcome. These sequences of events were assumed to be first-order Markov chains where the quality of each contact depended only on the quality of the previous contact but not explicitly on contacts further removed in the sequence. We represented these sequences in an extensive matrix of transition probabilities where the elements of the matrix were the probabilities of moving from one state to another. Each row of the count matrix, consisting of the number of times play moved from one transition state to another during the season, was assumed to have a multinomial distribution. A Dirichlet prior was formulated for each row, so posterior estimates of the transition probabilities were then available using Gibbs sampling. The different paths in the transition probability matrix were followed through the possible sequences of events at each step of the MCMC process to compute the posterior probability density that a perfect pass results in a point, a perfect set results in a point, etc. These posterior probability densities are used to address questions about skill performance in BYU Women's Volleyball.

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

“…Finally, for the skill of defense there are two states: free ball and dig, either after serve or after the attack. Following the approach of Florence et al (2008) dig related skills were merged in a single state in order to avoid having to deal with a sparse matrix of transition frequencies. All the matches were video recorded and the observer was a volleyball coach, expert in evaluation and recording of the volleyball performance data and excellent user of the software.…”

“…Conversely, research concerning the importance of skills and their execution level on the final outcome of a single rally is rather limited. Only the studies of Florence, Fellingham, Vehrs, and Mortensen (2008) and Miskin et al (2010) have attempted to account for the interaction of volleyball skills within a single rally using data from a women's team. Research so far has investigated, almost exclusively, the top age category of each gender (Alfonso, Esteves, Araujo, Thomas, & Mesquita, 2012;Kountouris, Drikos, Aggelonidis, Laios, & Kyprianou, 2015;Zetou, Tsigilis, Moustakidis, & Komninakidou, 2006) with the exception of the articles of Costa, Caetano, Ferreira, Alfonso, and Costa (2011) and Garcia de Alcaraz, Ortega, and Palao (2015) which examined possible determinants of the attack effectiveness for youth male volleyball teams and across ages, respectively.…”

Determining The Game Model In Atac, In Junior Girls Volley (U18)

“…This kind of analysis, that can only be performed if the starting zones of the attacks or attack combinations have been scouted, allows you to view in detail, the game distribution of the setter, point by point. If you choose to analyses an attack after reception, an effect value will be displayed for the reception that preceded the attack (Florence, Fellingham, Vehrs, & Mortensen, 2008) The main advantages of using a model for the description are: abstraction, summary, low price and rapidity.…”

Exploring the potential of the plus/minus in NCAA women’s volleyball via the recovery of court presence information

“…Examples of work focusing on single skills include how setters respond to blocking formations (Araújo et al, 2010), the importance of the speed of sets (Fellingham et al, 2013), what affects the type and quality of serves (Quiroga et al, 2012), and how to optimize service error rates (Burton & Powers, 2015). Other work has taken a more general approach examining the relative importance of in-game actions by linking skills to the probability of scoring (Florence et al, 2008, Miskin et al, 2010 or to winning (Claver Rabaz et al, 2013, Eom & Schutz, 1992. The literature is devoid, however, of attempts to quantify player contribution in terms that are comparable across position or in ways that account for actions that do not involve touching the ball like the plus/minus.…”