Volleyball Statistics Comparison of Bergen Reily vs Jordan Poulter

#This is based on data collected by the programs stats. Poulter’s individual games were not easily found so standard deviations and errors were not calculated. My husband wanted me to find those data, and well, I am just not that crazy.

Assists Statistics Summary

## # A tibble: 2 × 5
##   Player Avg_AssistPerSet Min_AssistPerSet Max_AssistPerSet Count_Seasons
##   <chr>             <dbl>            <dbl>            <dbl>         <int>
## 1 Bergen             10.6             10.3             11.1             3
## 2 Jordan             10.8             10.5             11.5             4
## [1] "--- Visualization ---"

Digs per Set by Setter

## [1] "--- Summary Statistics (DigsperSet) ---"
## # A tibble: 2 × 4
##   Player Avg_DigsPerSet Min_DigsPerSet Max_DigsPerSet
##   <chr>           <dbl>          <dbl>          <dbl>
## 1 Bergen           2.70           2.6            2.85
## 2 Jordan           2.13           1.48           2.51

##Visualize Digs per Set by Setter across Seasons

## [1] "--- Visualization ---"

Visualize Blocks per Set by Setter across seasons

##Visualize Kills per Set by Setter across Seasons

##Visualization of Errors per set by each setter

##Visualization of Service Errors per set by each setter

##Visualization of Service Aces per set by each setter-does not capture overpass to kills by setters’ teams: ^_^

Visualization of Kills per set by each setter

Team Hitting PCT Statistics Summary

## # A tibble: 2 × 4
##   Player Avg_TeamShtgPct Min_TeamShtgPct Max_TeamShtgPct
##   <chr>            <dbl>           <dbl>           <dbl>
## 1 Bergen           0.302           0.273           0.348
## 2 Jordan           0.261           0.231           0.281

Team Hitting PCT Visualization

## [1] "--- Visualization ---"

Team Hitting PCT Visualization with Coach given season

Final Four Appearance in NCAA D1 Tournament

## [1] "--- Summary of F4 Appearances by Player ---"
## # A tibble: 2 × 2
##   Player F4_Count
##   <chr>     <int>
## 1 Bergen        2
## 2 Jordan        1

#This analysis was conducted to test the null, in that there is no difference between the setters. Given the limited data, one can run a Wilcox test, if the alpha is less than .01(given chance in sports the alpha needs to be smaller than 0.05), you can reject the null.

## 
##  Wilcoxon rank sum exact test
## 
## data:  Assistperset by Player
## W = 4, p-value = 0.6286
## alternative hypothesis: true location shift is not equal to 0
## 
##  Wilcoxon rank sum exact test
## 
## data:  Digsperset by Player
## W = 12, p-value = 0.05714
## alternative hypothesis: true location shift is not equal to 0
## 
##  Wilcoxon rank sum exact test
## 
## data:  KperS by Player
## W = 4, p-value = 0.6286
## alternative hypothesis: true location shift is not equal to 0
## 
##  Wilcoxon rank sum exact test
## 
## data:  blocksperset by Player
## W = 0, p-value = 0.05714
## alternative hypothesis: true location shift is not equal to 0
## 
##  Wilcoxon rank sum test with continuity correction
## 
## data:  teamshtgpct by Player
## W = 10, p-value = 0.2118
## alternative hypothesis: true location shift is not equal to 0

The alpha was greater than 0.05, we do not have evidence to reject the null for all comparisons. Ergo, there is not the evidence to claim based on this comparison that Bergen is not “as good” as Jordan. Bergen’s stats show growth, something any athlete would want to see.

##Coaching can improve player performance (see entrance of new coaches). I want to know what Tamas did in 2018.

##Do players have physical limits? Yes. #But I hypothesize that the teams focus on different skills in their setters. Jordan was a risk taker. Bergen is less error prone. Could Bergen work on her initial velocity to get higher for one more block a game? Sure. But she is very good at assisting and digging and, well, setting based on those hitting stats.