High School Poster: A STARTING POINT FOR PREDICTING TEAM SUCCESS IN VOLLEYBALL

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Abstract

Brian Xu, The Kinkaid School, Student

Abstract

What happens if two perfect volleyball teams face each other? Both teams would hit every serve in, and they would always side out against the other team, causing the set to continue forever as no team could get a two point lead. By thinking about volleyball in this way, it becomes clear that a team must side out more efficiently than their opponent in order to win.

While side out efficiency is the most important metric in volleyball, how good a team is at passing, setting, and hitting and how good the opposing team is at serving, blocking, and digging affects how likely it is for a team to side out. We seek to determine how important each skill is to winning. To do so, we created multivariable regression models using dependent variables that represented each skill and discarded the variables with low coefficient t values until only statistically significant predictors remained. We then picked the model with the lowest RMSE value and highest R-squared value.

We present a regression model that explains 95% of the variation in wins and differs from a team’s actual set win percentage by 2.84 percentage points on average. By performing min-max normalization on the data, we can compare coefficient confidence intervals to determine how important each skill is to winning. These insights enable coaches to develop more focused training sessions that will improve the skills most relevant to winning and to recruit players who possess the skills most relevant to winning.

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