Understanding DataVolley File Structures

DataVolley has become the industry standard for volleyball match scouting and analysis, but its proprietary .dvw file format can be intimidating for analysts looking to build custom workflows. This post breaks down the file structure and provides Python code for parsing match data.

File Format Overview

DataVolley files are essentially SQLite databases with a specific schema that stores:

  • Match information (teams, date, venue)
  • Player rosters and substitutions
  • Every rally with detailed action codes
  • Statistical aggregations

Python Parsing Approach

import sqlite3
import pandas as pd

def parse_dvw_file(file_path):
    """Parse DataVolley .dvw file and return structured data"""
    conn = sqlite3.connect(file_path)
    
    # Extract match info
    match_info = pd.read_sql_query("SELECT * FROM MatchInfo", conn)
    
    # Extract all actions
    actions = pd.read_sql_query("SELECT * FROM Actions ORDER BY RallyNumber, ActionTime", conn)
    
    # Parse action codes
    actions['Skill'] = actions['ActionCode'].str[0]
    actions['Evaluation'] = actions['ActionCode'].str[1]
    
    conn.close()
    return match_info, actions

# Usage
match, actions = parse_dvw_file('match.dvw')
print(f"Loaded {len(actions)} actions from {len(actions['RallyNumber'].unique())} rallies")

Key Learnings

After processing 500+ match files, here are the critical insights:

  1. Action codes are consistent - The first character always indicates the skill (S=Serve, R=Receive, A=Attack, etc.)
  2. Timing matters - ActionTime field is crucial for proper rally sequencing
  3. Player substitutions require special handling to maintain correct player mappings

Performance Considerations

Large files (>10,000 actions) can benefit from chunked processing:

def process_large_dvw(file_path, chunk_size=1000):
    conn = sqlite3.connect(file_path)
    
    # Get total action count
    total_actions = pd.read_sql_query("SELECT COUNT(*) as count FROM Actions", conn)['count'][0]
    
    for offset in range(0, total_actions, chunk_size):
        chunk = pd.read_sql_query(
            f"SELECT * FROM Actions ORDER BY RallyNumber, ActionTime LIMIT {chunk_size} OFFSET {offset}",
            conn
        )
        yield process_chunk(chunk)
    
    conn.close()

This approach has enabled me to build automated pipelines that process entire seasons of matches while maintaining detailed rally-level analysis capabilities.

Next Steps

Future work includes:

  • Building a dedicated Python package for DVW parsing
  • Creating visualization tools for rally-by-rally analysis
  • Integrating with live scoring APIs for real-time analytics

The key is understanding that once you unlock the data from these proprietary formats, you can build virtually any analytics workflow your program needs.