Volleyball reporting

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Dataset selection


News and updates

August 2018

- New: jump load estimation. Thanks to Francesco Oleni for the idea and help in putting it together

July 2018

- Serve, reception, and attack plots by court coordinates


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


One plot will be produced for each group defined above — i.e. one plot for each row in the above table. Empty plots indicate that the chosen start/end information is missing from the data.

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


One plot will be produced for each group defined above — i.e. one plot for each row in the above table. Empty plots indicate that the chosen start/end information is missing from the data.

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


One plot will be produced for each group defined above — i.e. one plot for each row in the above table. Empty plots indicate that the chosen start/end information is missing from the data.

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).


Plots


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

Jumping

Monitoring an athlete's jumping load can be an important part of load management and injury prevention. While wearable devices are available for such monitoring, we can also estimate competition jumping load from scouted match data, based on the number of serves, attacks, blocks, and jump sets an athlete performs. See the explanation at the bottom of the page for more details, and also this post on jumps in the 2018 Men's Volleyball Nations League tournament.

Weightings: (see 'Details' below for an explanation of what these are used for)

Jump serve: 
Jump-float serve: 
Jump set 1 (#+): 
Jump set 2 (!): 
Wing/backrow attack: 
Middle attack 1 (#+): 
Middle attack 2 (!): 
Middle block: 
Wing block: 
Wing pipe block: 
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 jumps).


Plots


Details

Jumps in different situations are given different weightings, specified by the values at the top of this page. These weightings can be considered as either 'jump effort' (e.g. an athlete jump-float serving is jumping on every serve, but probably not jumping with the same intensity as a jump server), or as 'jump participation' (e.g. the blocker in position 2 or 4 does not participate in every block attempt against a backrow pipe attack).

Note that the analysis uses player role (middle/outside/etc) and attack start zone information. The results may be incorrect if this information has not been entered correctly in the scouted files.

Jumps are counted by the following rules:

  • each serve: 1 jump, weighted by 'Jump serve' (for serves scouted as jump serves) or 'Jump-float serve' (all other serves)
  • attacks by opposite/outside players (front or back row): 1 jump, weighted by 'Wing/backrow attack'
  • each reception/pass/freeball rated as 'perfect' (#) or 'good' (+): 1 jump for the front-row middle attacker, weighted by 'Middle attack 1'
  • each reception/pass/freeball rated as 'OK' (!): 1 jump for the front-row middle attacker, weighted by 'Middle attack 2'
  • each opposition attack: 1 jump for the front-row middle blocker, weighted by 'Middle block'. The middle blocker is assumed to attempt to block every attack
  • each opposition attack from the left or right side (front or back row): 1 jump for the associated wing blocker (i.e. the player blocking in position 2 for left-side attacks, or 4 for right-side attacks), weighted by 'Wing block'. This requires that the attack start_zone information has been populated in the data
  • each opposition pipe attack: 1 jump for each of the wing blockers, weighted by 'Wing pipe block'. This requires that the attack start_zone information has been populated in the data
  • each set on a 'perfect' (#) or 'good' (+) reception/pass/freeball: 1 jump for the setter, weighted by 'Jump set 1'
  • each set on an 'OK' (!) reception/pass/freeball: 1 jump for the setter, weighted by 'Jump set 2'.

Jumps for a blocking player are only counted when the player is front row. Outside hitters are assumed to block in position 4, except in P1/reception when they are assumed to block in position 2. Opposites are assumed to block in 2, except in P1/reception. Setters are assumed to block in position 2.

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.

here.

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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, players, and technical staff.

Privacy and disclaimer: The results will be logged to help improve the app. Your DataVolley files remain your property and we do not use them for any other purpose, nor share them anywhere.

Use at your own risk. While every effort has been made to make the reports accurate, no warranty is given. 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.

Michael Mattes , co-trainer and scout, GCDW Herrsching Volleyball Bundesliga.

Francesco Oleni, Chinese Senior Men’s National Team.


Example data sets

The '2017-18 PlusLiga' example data set (16 matches from that season) was kindly provided by Mark Lebedew.

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

Note that these MEVZA files were scouted with no start or end zone information on any play. Random start and end zone values have been added to some plays, 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 0.6.6