footballdocile

Footballdocile is a term used in football data management to describe a framework and set of practices aimed at standardizing the documentation, provenance, and reproducibility of football-related data and analytics pipelines. It applies to a range of data types, including match event logs, player tracking data, and tactical annotations, with an emphasis on clarity and accessibility for researchers, journalists, and developers.

Origin and etymology

The label emerged in online football analytics communities in the late 2010s as practitioners sought a concise way to reference datasets and workflows that are fully documented and easily verifiable. The term blends references to football with a notion of docility in the sense of well-organized and traceable information that can be followed by others.

Core principles

Footballdocile centers on four pillars: data provenance and lineage, standardized event coding and metadata, explicit methodological notes and data dictionaries, and transparent licensing with versioned, reproducible processing pipelines. It encourages open documentation of data collection methods, assumptions, and quality checks, as well as accessible code and processing scripts.

Scope and components

A footballdocile-compliant project typically includes: event logs with a defined dictionary; metadata about competitions and seasons; documentation of data sources and collection procedures; versioned data and code repositories; and clear licensing terms to enable reuse.

Impact and adoption

The concept has influenced several open-data initiatives and collaborative research efforts by promoting comparability and reproducibility. Adopters aim to improve the reliability of analyses, facilitate cross-study replication, and enhance communication between researchers, practitioners, and the public.

Limitations

Adoption is not universal, and maintaining comprehensive documentation requires ongoing effort. Variations in primary data sources and evolving analytics methods can challenge standardization, potentially limiting cross-project comparability without careful alignment.