Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)
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Abstract (arXiv)
During football matches, a variety of different parties (e.g., companies) each collect (possibly overlapping) data about the match ranging from basic information (e.g., starting players) to detailed positional data. This data is provided to clubs, federations, and other organizations who are increasingly interested in leveraging this data to inform their decision making. Unfortunately, analyzing such data pose significant barriers because each provider may (1) collect different data, (2) use different specifications even within the same category of data, (3) represent the data differently, and (4) delivers the data in a different manner (e.g., file format, protocol). Consequently, working with these data requires a significant investment of time and money. The goal of this work is to propose a uniform and standardized format for football data called the Common Data Format (CDF). The CDF specifies a minimal schema for five types of match data: match sheet data, video footage, event data, tracking data, and match meta data. It aims to ensure that the provided data is clear, sufficiently contextualized (e.g., its provenance is clear), and complete such that it enables common downstream analysis tasks. Concretely, this paper will detail the technical specifications of the CDF, the representational choices that were made to help ensure the clarity of the provided data, and a concrete approach for delivering data in the CDF. This represents Version 1.0.0 of the CDF.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 3 of its 44 references that are in the map. First posted on arXiv 6 Feb 2025.
Builds on
3 of 44 references.
References · 3
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Citing papers per year: 2025: 1; 2026: 4.
5 papers.
- 2025FOOTPASS: A Multi-Modal Multi-Agent Tactical Context Dataset for Play-by-Play Action Spotting in Soccer Broadcast Videoscited by 5
- 2026VERSA: Verified Event Data Format for Reliable Soccer Analyticscited by 1
- 2026PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectoriescited by 0
- 2026Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Datacited by 0
- 2026Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networkscited by 0
- 2026Automated Detection of Match Phases in Football from Spatio-Temporal Tracking Data Using Graph Neural Networks8 Oct 2026
- 2026Wide Open Gazes: Quantifying Visual Exploratory Behavior in Soccer with Pose Enhanced Positional Data19 Feb 2026
- 2026PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories12 Feb 2026
- 2026VERSA: Verified Event Data Format for Reliable Soccer Analytics29 Jan 2026
- 2025FOOTPASS: A Multi-Modal Multi-Agent Tactical Context Dataset for Play-by-Play Action Spotting in Soccer Broadcast Videos20 Nov 2025