Performance analysis of beach volleyball match outcomes A validated efficiency-based prediction model from 10,095 AVP/FIVB matches
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Abstract
Predicting the outcomes of elite beach volleyball matches involves the integration of technical and demographic performance measures. Using data from 10,095 elite professional beach volleyball matches (2002-2019), a logistic regression model was developed and validated to predict match outcome with six different predictor variables: kill efficiency differential, aces differential, error rate differential, dig efficiency differential, age difference, and height difference. After assessing for multicollinearity, these variables were used in the model. Results show that the model is well-calibrated (Brier Score = 0.208; Hosmer-Lemeshow test = 0.388), and it discriminates well between the two possible outcomes (area under curve = 0.658; 95% confidence interval: 0.638 – 0.678). In addition to being well calibrated, results also indicate that the model is internally valid, with little evidence of over fitting (shrinkage = 0.995), and temporally valid, as its ability to predict match outcomes has remained relatively consistent across the 18-year period studied. Additionally, results from gender stratified analysis indicated no differences in predictive accuracy for males and females. Overall, the validated model can be viewed as a robust support tool to assist coaches and teams with performance evaluations, strategic planning and competitive placements.
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