Robust Pipeline for 'VALD' 'ForceDecks' Data Extraction and Analysis

Provides a robust and reproducible pipeline for extracting, cleaning, and analyzing athlete performance data generated by 'VALD' 'ForceDecks' systems. The package supports batch-oriented data processing for large datasets, standardized data transformation workflows, and visualization utilities for sports science research and performance monitoring. It is designed to facilitate reproducible analysis across multiple sports with comprehensive documentation and error handling.

vald.extractor

Robust Pipeline for VALD ForceDecks Data Extraction and Analysis

vald.extractor extends the valdr package by providing a production-ready, fault-tolerant pipeline for extracting, cleaning, and visualizing VALD ForceDecks data across multiple sports. Designed for CRAN submission with comprehensive documentation and enterprise-grade error handling.

The Problem

Organizations using VALD ForceDecks face three critical challenges:

1. API Stability: Manual exports or large data pulls frequently timeout, causing incomplete datasets
2. Data Cleaning: Team/sport names are inconsistent ("Football" vs "Soccer" vs "FSI"), requiring hours of manual categorization
3. Code Duplication: Analyzing multiple test types (CMJ, DJ, ISO) requires duplicate code for each metric suffix

The Solution

vald.extractor solves these problems through:

• Chunked Batch Processing: Extracts data in manageable chunks (100 tests at a time) with fault-tolerant error handling
• Automated Sports Taxonomy: Regex-based pattern matching standardizes inconsistent naming conventions
• Generic Programming: Strips test-type suffixes to enable writing analysis code once that works for all tests

Installation

# Install from CRAN (when available)
install.packages("vald.extractor")

# Or install development version from GitHub
# install.packages("devtools")
devtools::install_github("praveenmaths89/vald.extractor")

Quick Start

library(vald.extractor)

# 1. Set VALD credentials
valdr::set_credentials(
  client_id     = "your_client_id",
  client_secret = "your_client_secret",
  tenant_id     = "your_tenant_id",
  region        = "aue"
)

# 2. Fetch test and trial data in chunks (prevents timeout)
vald_data <- fetch_vald_batch(
  start_date = "2020-01-01T00:00:00Z",
  chunk_size = 100
)

# 3. Fetch and standardize athlete metadata
metadata <- fetch_vald_metadata(
  client_id     = "your_client_id",
  client_secret = "your_client_secret",
  tenant_id     = "your_tenant_id"
)

athlete_metadata <- standardize_vald_metadata(
  profiles = metadata$profiles,
  groups   = metadata$groups
)

# 4. Apply automated sports classification
athlete_metadata <- classify_sports(athlete_metadata)
table(athlete_metadata$sports_clean)

# 5. Transform to wide format and join with metadata
# ... (see vignette for complete pipeline)

# 6. Split by test type with suffix removal
test_datasets <- split_by_test(final_analysis_data)

cmj_data <- test_datasets$CMJ  # Column names: "PEAK_FORCE_Both", not "PEAK_FORCE_Both_CMJ"
dj_data  <- test_datasets$DJ   # Same column names enable generic analysis

# 7. Generate summary statistics
summary_vald_metrics(cmj_data, group_vars = c("sex", "sports"))

# 8. Visualize trends and comparisons
plot_vald_trends(cmj_data, metric_col = "PEAK_FORCE_Both", group_col = "profileId")
plot_vald_compare(cmj_data, metric_col = "JUMP_HEIGHT_Both", group_col = "sports", fill_col = "sex")

Key Features

1. Fault-Tolerant Batch Extraction

# Processes 5000 tests without timeout errors
vald_data <- fetch_vald_batch(
  start_date = "2020-01-01T00:00:00Z",
  chunk_size = 100,  # Adjust based on API performance
  verbose = TRUE
)

# If chunk 23 fails, chunks 1-22 and 24+ still succeed
# Error messages indicate which rows failed for debugging

Why it matters: Organizations with large historical datasets (5000+ tests) cannot extract data in a single API call. The chunked approach with tryCatch error handling ensures partial extraction succeeds even if some chunks fail.

2. Automated Sports Taxonomy

metadata <- classify_sports(metadata, group_col = "all_group_names")

# Before:
# "Team A - Football", "Soccer U18", "FSI Elite", "Basketball", "BBall"

# After:
# "Football", "Football", "Football", "Basketball", "Basketball"

table(metadata$sports_clean)
#> Football    Basketball    Cricket    Swimming    Track & Field
#>      523           198        145          87              234

The Value Add: Multi-sport organizations waste hours manually categorizing athletes. This regex-based system handles 15+ sports out-of-the-box and is easily extensible.

3. Generic Test-Type Analysis

# Write analysis code ONCE that works for ALL test types
analyze_bilateral_asymmetry <- function(test_data) {
  test_data %>%
    mutate(
      asymmetry = (PEAK_FORCE_Left - PEAK_FORCE_Right) /
                  ((PEAK_FORCE_Left + PEAK_FORCE_Right) / 2) * 100
    )
}

# Apply to CMJ, DJ, ISO without code changes
test_datasets <- split_by_test(final_data)
cmj_with_asymmetry <- analyze_bilateral_asymmetry(test_datasets$CMJ)
dj_with_asymmetry  <- analyze_bilateral_asymmetry(test_datasets$DJ)
iso_with_asymmetry <- analyze_bilateral_asymmetry(test_datasets$ISO)

DRY Principle: Without suffix removal, you'd need separate code for PEAK_FORCE_Left_CMJ, PEAK_FORCE_Left_DJ, etc. This package enables true generic programming.

4. Metadata Patching

# Fix missing/incorrect demographics from external Excel file
cmj_data <- patch_metadata(
  data = cmj_data,
  patch_file = "corrections.xlsx",
  fields_to_patch = c("sex", "dateOfBirth")
)

# Unknown values are replaced with corrections
table(cmj_data$sex)
#> Before: Male: 450, Female: 380, Unknown: 45
#> After:  Male: 470, Female: 405, Unknown: 0

5. Publication-Ready Visualizations

# Longitudinal trends
plot_vald_trends(
  data = cmj_data,
  metric_col = "JUMP_HEIGHT_Both",
  group_col = "profileId",
  facet_col = "sports"
)

# Cross-sectional comparisons
plot_vald_compare(
  data = cmj_data,
  metric_col = "PEAK_FORCE_Both",
  group_col = "sports",
  fill_col = "sex"
)

Documentation

• Vignette: End-to-End Pipeline: From API to Multi-Sport Analysis
• Function Reference: ?fetch_vald_batch, ?standardize_vald_metadata, ?split_by_test, etc.
• GitHub Repository: https://github.com/praveenmaths89/vald.extractor

Production Use Cases

vald.extractor is designed for:

• Multi-Sport Organizations: National sport institutes, university athletic departments, professional academies
• Longitudinal Research: Track athlete development over months/years with automated weekly updates
• Cross-Sectional Studies: Compare performance across sports, sexes, age groups
• Clinical Settings: Monitor return-to-sport progression, ACL rehab, injury risk

Comparison to Manual Workflow

Roadmap for R Journal Submission

The R Journal article will focus on:

1. Technical Innovation: Chunked extraction architecture with fault tolerance
2. Domain Contribution: Automated sports taxonomy as a time-saving tool for practitioners
3. Software Engineering: Modular design, comprehensive testing, CRAN-compliant documentation
4. Reproducible Research: Complete workflow from raw API to publication figures

Key Message: "Automating domain-specific data taxonomy for multi-organizational sports science"

Citation

If you use vald.extractor in published research, please cite:

Chougale PD, Ananthakumar U (2026). vald.extractor: Robust Pipeline for VALD
ForceDecks Data Extraction and Analysis. R package version 0.1.0.
https://github.com/praveenmaths89/vald.extractor

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-sport-taxonomy)
3. Add tests for new functionality
4. Submit a pull request

Common contributions:

• Add patterns for new sports in classify_sports()
• Improve error messages
• Add new visualization themes
• Extend to other VALD devices (NordBord, DynaMo, etc.)

License

MIT License - see LICENSE file for details.

Acknowledgments

• VALD Performance: For providing the ForceDecks API and valdr package
• tidyverse team: For creating the tools that make this package possible
• Sports scientists: Who provided real-world use cases and taxonomy requirements

Support

• Issues: GitHub Issues
• Email: [email protected]
• Documentation: Run vignette("end-to-end-pipeline", package = "vald.extractor")

Status: Ready for CRAN submission pending:

• [ ] Final testing on multiple VALD tenants
• [ ] CRAN comment responses
• [ ] Logo design (hex sticker)
• [ ] pkgdown website deployment

Maintainer: Praveen D Chougale ([email protected])