Volleyball reporting

See the 'About' tab for more information about this app. Contact: .

Dataset selection

Other settings


See also

DataVolley file validator

More detailed blocking analysis

Dataset summary

Analysis summary

A summary of some common team statistics. Select a team of interest on the left, or leave it as 'ALL TEAMS' to get statistics across the whole data set/league. (Individual player selection is not used here.)


Key

Teams comparison

A subset of the 'Analysis summary' statistics, giving a convenient comparison between teams in the data set. (Team and player selections are not used here.)


Key

Serving

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more serves).


Plots


This follows the serve aggressiveness method presented here . Hover over each point to identify the corresponding player. Colours indicate teams. The blue lines show the average reference curves from the 2015/16 Polish PlusLiga competition.


Key

Reception

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more receptions).


Plots


Key

Setting

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more sets).


Plots

Scatter plot

Key

Attacking

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more attacks).


Plots


Key

Middle blocking report


Key

Influence on play

Middle blockers can influence the game in subtle ways, for example by causing the opposition to change their attack patterns. These influences may not be reflected in simple statistics such as block kills or touches. This analysis attempts to quantify some of these influences.

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more opposition attacks).


Key

Libero reception

Note that the data used in these analyses only include points when a libero was on court.

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more receptions).


Key

Libero defence

For an explanation of the ATT/D statistic, see Mark Lebedew's post.

1. Group by which variables? The data will be grouped by these variables in the table and plot.

2. Click a column heading to sort the table by that column. Use the boxes at the top of each column to filter the table (e.g. to show only rows corresponding to 10 or more defensive opportunities).


Key

Your uploaded files are processed and combined into a working data set. The output from the processing script and summaries of the data set are shown in this section. It is worth checking this log to make sure the processing went OK.

In particular, you will want to ensure that the team_id and player_id values are consistent across different DataVolley files. The team_id and player_id values are used to uniquely identify each team and player, and so if a player has different identifiers in different files, the app will think that these are different players.


Data processing log

The output from the processing script is shown below. It is worth checking this log to make sure the processing went OK.

Teams summary

The list of team names and team_id values. If a team appears more than once, it likely has different team_id values in different files.

Matches summary

A summary of the matches in this data set. This might help find duplicate or missing matches.

Players summary

The list of player names and player_id values. If a player appears more than once, they likely have different player_id values in different files.

About this app

This app allows users to generate reports and conduct analyses on volleyball data. Analyses can easily be run on different teams to compare them, or multiple teams at once to obtain league-wide insights. The app provides analytical and graphical plotting capabilities that are difficult or impossible to achieve with other software packages.

It works with data files from the DataVolley scouting software. Users have full control over their data sets, and reports can be updated as new match files are added (e.g. as the season progresses).

A club's files can be uploaded and maintained by one or more people (say, coaches or scouts), and access provided to coaches and players with chosen constraints — for example, players might only be able to view their own performance reports.

When completed, this app will be available to use with your own data on a subscription basis. In the meantime, the app is open for testing on your own data.

Use at your own risk! This app is being made available as a pre-release version. It is still in a relatively early stage of development. Expect things to change without warning, or not to work properly in the first place. Science Untangled and this application have no affiliation with or endorsement from DataVolley.


Contributors

Mark Lebedew , head coach of the Australian national men's team.


General usage notes

  • Subscribed users can upload their own data sets to work with, and update those data sets as new matches are played
  • The 'Analysis summary' tab provides an overall summary of common performance statistics. Other tabs provide more detailed reporting on different aspects of the game
  • [To add: notes about the filtering, grouping operations that allow a wide range of different analyses to be conducted]

Future functionality

(To be implemented in a later release.)

  • Reports can be saved and re-run on updated data
  • [To add]

Correction for small sample sizes

[To add: an explanation of the empirical Bayes method used for small sample size correction]


Example data set

The 'MEVZA Men 2013' example data set was obtained from the Middle European Volleyball Zonal Association website.

Note that these files were scouted with no start or end zone information on any play. Random values for the start and end zone of serves and receptions have been added here, purely for the purpose of demonstrating the functionality of this app. But they're random, so they don't actually mean anything.


Version: , using datavolley version 0.6.3