Analysis of team and individual blocking performance from DataVolley files. See the 'About' tab for details on methods. Contact: .
You can also upload a file and it will be added to the datasets list.
- Adjustments to cope with some scouting conventions
- Middle blocker detail tab added
- App has been rewritten with improved interface, rotation options, and support for VBStats files. The old app can still be accessed here if necessary
- Example data set added, other minor updates
- Additional detail added to the blocking report
Note: values for TEAM are based on all opposition attacks. Values for individual players are based on attacks against this blocker (i.e. when this blocker participated in the block).
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 data 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.
The output from the processing script is shown below. It is worth checking this log to make sure the processing went OK.
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.
A summary of the matches in this data set. This might help find duplicate or missing matches.
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.
The subscription fee is $12 AUD per month (approximately $9 USD or €8 EUR at current exchange rates).
The results will be logged to help improve the app. Your DataVolley or VBStats files (if uploaded) will not be kept.
The app calculations can be broken down into three broad steps:
For this app to be useful, you either need files in which the number of blockers has been recorded on all attacks (these can be VBStats or DataVolley files), OR you need files that use the standard attack codes (X5, V5, etc).
The options that you choose will be determined by the source of your data files, the scouting conventions that you have used.
Inferring player positions (#1 above)
Figuring out blocker participation (#2 above)
There are two components to this. The first is the "Blocking strategy" selection, for which there is currently only one option - "Standard" strategy. This means that we are assuming that outside hitters block in position 4, middles in 3, and opposites/setters in 2. The exception to this is when the blocking team is receiving with the setter in position 1, in which case the opposite blocks in 4 and the outside in 2.
The second component is the "Number of blockers from" selection.
Generating performance indicators (#3 above) Based on this information, the number and types of block attempts are calculated for each blocking player, and player statistics (block kills, opposition attack percentage and efficiency, etc) are calculated.
This approach can be more informative than traditional blocking stats (simple counts of block kills or touches) because it gives a more accurate reflection of the participation of an individual blocker, including in double- or triple-blocks. It can reveal blockers that have an influence on the opposition's attack, even if that influence does not show itself as direct block kills or touches.
Variations to these assumptions are possible if your team does not follow the above patterns. For example, the front-row blocking position of each player can be inferred from the data, rather than assuming the blocking positions described above. For this or other functionality (e.g. recognizing your custom attack combination codes, or bulk processing of multiple files), contact .
The '2017-18 PlusLiga' example data set (16 matches from that season) was kindly provided by Mark Lebedew.
Version: , using datavolley version 0.13.5