You can also upload a file and it will be added to the datasets list.
2. Data filters (optional). Use these to select particular data of interest.
3. Subplot by (optional). One plot will be produced for each unique value of this variable
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.
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.
This app allows the attack patterns in a data set to be explored interactively. Coaches can use this to look for patterns in a team or player's attack choices, including looking for changes under particular circumstances (e.g. when the set is made from particular areas of the court, or late in a close set, or when there is a triple block.)
By default, all attacks with valid position information are shown on the charts. Use the 'Attack outcome' or 'Touched block' filters to remove some attack types, if necessary (e.g. some scouts use zones to indicate the intended attack end location, ignoring any deflection off the block, whereas others use zones to indicate the actual attack end location. If you are following the latter convention, and you want to see a chart of intended attack locations, you might want to filter out 'Touched block', 'Blocked', and/or 'Blocked for reattack' attacks).
If you are plotting by subzone, you can optionally use zone information for attacks that have been scouted without a subzone. Attacks with missing subzone will be spread across all four subzones, with 0.25 weight on each.
The results will be logged to help improve the app. Your DataVolley or VBStats files (if uploaded) will not be kept.
The '2017-18 PlusLiga' example data set (16 matches from that season) was kindly provided by Mark Lebedew.
Version: , using datavolley version 0.15.1