Sports analytics: Designing a volleyball game analysis decision-support tool using big data

From 2006-2012, George Mason University´s (GMU) division I men´s and women´s volleyball teams were outplayed by their top competitors within their associated conference. Analysis of historic data showed that the GMU´s men´s and women´s volleyball teams have a lower probability of scoring points on average of 0.21 and 0.05 respectively. The win/loss outcome is a function of the combinations of sequences of events caused by team´s actions and coach´s tactics. The data is so complex that no human can comprehensively conduct the analysis. A Computer-Aided Analysis Tool (CAAT) is needed to analyze the underlying trends contributing to the wins and losses as well as provide a meaningful recommendation to improve the overall team performance in a volleyball game. The CAAT determines the probability of each transition that can occur in a volleyball game, uses an Absorbing Markov Chain to evaluate how events influence the point scoring probability, and runs a Monte Carlo Simulation to analyze how random variations in transition probabilities, caused by extreme conditional scenarios can affect the team performance and end result of a game. Four design alternatives were identified through analysis of historic data and evaluated for improving team performance through specific skill improvement training: 1) Increasing aces; 2) Increasing kills; 3) Increasing blocks; 4) Decreasing errors. A utility analysis was conducted to determine the most effective design alternative to achieve the target level of performance. Based on the utility analysis, the GMU´s women´s and men´s teams must focus on increasing their blocks. Out of 10 blocks, at least 9 should lead to a point for the men and 3 should lead to a point for the women in order to achieve the target level of performance.