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REVIEW 3 major objections 5 minor 2 cited by

Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)

T0 review · 3 major / 5 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read The paper proposes the Common Data Format (CDF), a versioned minimal schema for five types of football match data — match sheets, video, events, tracking, and meta data — designed so that different vendors' data can be ingested and…

desk verdict A serious, stakeholder-backed football data format proposal with fixable internal errors and a real gap between the 'unambiguous' claim and the event semantics — deserves peer review and revision. read the letter →

arxiv 2505.15820 v4 pith:JO3DQYH7 submitted 2025-02-06 cs.DB cs.AI

Gabriel Anzer , Kilian Arnsmeyer , Pascal Bauer , Joris Bekkers , Ulf Brefeld , Jesse Davis , Nicolas Evans , Matthias Kempe
show 3 more authors
Samuel J Robertson Joshua Wyatt Smith Jan Van Haaren
This is my paper · ORCID
classification cs.DBcs.AI
keywords CommonDataFormatfootballmatchstandardizationeventschematrackingJSONLinessheetinteroperability
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

During a football match, several companies collect overlapping data about the same game, and each one uses its own event definitions, coordinate conventions, time formats, and file structures. This paper argues that these incompatibilities — more than any shortage of data — are what force clubs, federations, and researchers to spend time and money on data wrangling before analysis can even begin. To remove that barrier, it proposes the Common Data Format (CDF), a concrete v1.0.0 schema that standardizes five match-data types — match sheet data, video footage, event data, tracking data, and match meta data — together with delivery conventions. The CDF keeps its mandatory event set deliberately small, admitting only events that are objective or have clear consensus, so the base format stays unambiguous while optional fields leave room for the ecosystem to grow.

What carries the argument

The central object is the CDF schema itself: a minimal, versioned JSON and JSON Lines specification covering match sheet data, video footage, event data, tracking data, and match meta data, plus representational conventions that make it unambiguous — metric units, UTC timestamps, a pitch-centered coordinate system, home team playing left-to-right, explicit nulls for missing values, monotonically increasing frame identifiers, and per-vendor entity IDs. The load-bearing mechanism is the pair of availability rules (match sheet data is always required, and match meta data is mandatory whenever event or tracking data are provided) together with the restriction of mandatory event types to objective or consensual definitions. Together these rules guarantee that any compliant dataset carries enough context to be interpreted, while optional fields provide room for vendor-specific information.

What would settle it

Take two vendors' CDF-compliant exports of the same match and attempt to automatically join player identities and event definitions using only the mandatory CDF fields; if the same action receives different event types, or the same player cannot be matched across files without manual mapping, the claim that the format makes data unambiguous and complete is falsified.

Watch

Extended reading notes

Core claim

The central claim is that a single, minimal, versioned schema can serve as a common interface for football match data across providers. The CDF specifies mandatory fields for each of the five data types, adopts fixed representational conventions (metric units, UTC timestamps, a pitch-centered coordinate system with the home team playing left-to-right, null for missing values, and per-vendor unique identifiers), and prescribes JSON for static files and JSON Lines for streaming event and tracking data. The design choice that carries the argument is restricting mandatory events to those that are objective or consensual, leaving subjective events as optional extensions, so the base format is unambiguous even as the ecosystem evolves. The paper positions this as Version 1.0.0, with semantic versioning and an accompanying validation tool to keep implementations aligned.

Load-bearing premise

The format's promise rests on providers adopting it, and on the deliberately small set of objective events plus per-vendor identifiers being sufficient to make cross-vendor data genuinely interoperable.

Editorial extensions

If this is right

  • Clubs, federations, and researchers will be able to ingest data from multiple vendors through a single parsing layer, cutting development and maintenance costs.
  • Time and location synchronization between event and tracking data becomes more straightforward because the CDF mandates a common coordinate system, UTC timestamps, and a flag indicating whether the data are synced.
  • The format's semantic versioning and validation tooling give providers a clear upgrade path as new data types or standardized event definitions emerge.
  • Because subjective events are optional rather than mandatory, the base CDF remains objective and stable while the football data landscape evolves.
  • Live and post-match processing can share one file structure: JSON Lines for streaming data and JSON for static data.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If a critical mass of vendors adopted the CDF, competition would likely shift from raw data delivery toward analytics quality, since the integration barrier that currently fragments the market would largely disappear.
  • The choice of per-vendor identifiers rather than vendor-agnostic IDs means the format solves representation but not entity resolution; a future version that incorporates shared player and team identifiers would close the remaining alignment gap.
  • The same schema-driven structure could be adapted to other invasion sports whose match data share the same five-source pattern, though the event taxonomy would need to be rebuilt for each sport.
  • A concrete completeness test would be to re-implement a published analysis using only CDF-compliant files; if the analysis cannot be reproduced without extra vendor-specific information, the 'sufficient' principle would need revision.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes the Common Data Format (CDF) Version 1.0.0, a standardized schema for five types of football match data: match sheet data, video footage, event data, tracking data, and match meta data. It specifies mandatory and optional fields, representational conventions (metric units, UTC, a center-origin pitch coordinate system, playing direction, unique identifiers, null handling), and delivery conventions (JSON for non-streamed data, JSON Lines for event and tracking data). The paper motivates the format by cataloging provider differences in specification, representation, delivery, and data quality, and it provides tables, figures, and examples intended as a precise, actionable specification.

Significance. If the CDF schema were internally consistent and its semantic scope were clearly delineated, this would be a valuable community resource: it is a concrete, versioned, multi-stakeholder proposal with a companion validator package and explicit extension mechanisms. The paper makes no empirical or circular claims, and its design choices are transparently argued. However, the manuscript's central claim of providing an unambiguous, precise interface is currently undercut by concrete schema errors and by unresolved semantic gaps that the paper itself identifies as core pain points. The contribution is therefore promising but not yet acceptable as a v1.0.0 specification.

major comments (3)
  1. [§5.1, Table 3] Table 3 specifies `event type` as an open string with examples ("shot, pass, referee, misc, etc.") and provides no normative definitions for `pass`, `shot`, or their subtypes. The paper states in §3.1 that passes are not defined by the Laws of the Game and that providers use their own definitions, and §5.1 claims CDF includes only events that are objective or have clear consensus. As written, a conforming file can label the same on-ball action as "pass" in one vendor feed and "shot" in another, so the format does not deliver the unambiguity promised in §1 and §5. This needs a controlled vocabulary with operational definitions, or an explicit narrowing of the unambiguity claim to structural aspects of the schema.
  2. [§5.2, Footnote 12] The CDF requires only vendor-unique identifiers and Footnote 12 concedes that vendor-agnostic IDs are out of scope. Yet §3.2 identifies unique identifiers assigned by each provider as one of the pain points, and the abstract/introduction claim that the format mitigates interoperability barriers. Retaining per-vendor IDs means cross-vendor entity alignment remains entirely on the consumer. The authors should either add an optional canonical or cross-reference identifier field, specify a mapping mechanism, or explicitly scope the CDF as standardizing file structure and field names rather than entity identity.
  3. [Table 12 and Figure 4] There are load-bearing consistency errors in the specification itself. Table 12 lists `periods/{i} type`, `periods/{i} time start`, `periods/{i} time end`, `periods/{i} frame id start`, and `periods/{i} frame id end` as type Float, although the values are documented as strings (period names, UTC timestamps) or integers (frame ids). Figure 4 has a JSON syntax error: `"event" {` is missing the colon before the opening brace. These errors in a document whose purpose is to be a precise schema undermine the claim of being a well-defined interface and must be corrected before the format can be implemented reliably.
minor comments (5)
  1. [§5.3] The claim that JSON Lines is "relatively compact in terms of memory used per file (compared to other commonly used file formats like JSON or XML)" is misleading: JSON Lines is not inherently more compact than JSON, and the statement should be reworded to emphasize streaming and line-oriented processing.
  2. [Table 6 vs. Figure 3] Table 6 names the field `match/periods/{i} type` and `match/periods/{i} playdirection`, but Figure 3's example uses `"period"` and `"play_direction"` at a different nesting level. The field names in examples and tables should agree exactly, since the CDF defines field names as part of the contract.
  3. [Table 1] The descriptions for `events/substitutions/{i} in player id` and `events/substitutions/{i} out player id` appear swapped: the "in" row says "Identifier of the player that is substituted out" and the "out" row says "Identifier of the player that is substituted in".
  4. [Table 10] The optional tracking fields `teams/{home|away} name` and `teams/{home|away} formation` are each described as "Name of the away team" and "Formation label of the away team"; the descriptions should say "home or away" to match the field path.
  5. [Table 11] The description for `match/misc is open roof` says "closed (false) or open (false)"; the second value should presumably be "open (true)".

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the CDF is a proposed schema specification with no fitted inputs, predictions, or derivation that reduces to its own assumptions.

full rationale

This paper is a format specification rather than a derived empirical result. It does not fit any parameters, make predictions, or claim a mathematical derivation whose output is equivalent to its input by construction. The central content is a proposed schema for match sheet data, video footage, event data, tracking data, and match meta data, presented as Version 1.0.0 of the Common Data Format. The motivating challenges in Sections 3.1 and 3.2 are contextual motivation, not premises from which the schema is logically forced. The paper's self-citations, including references [2], [3], [4], [7], [8], and [26], are used for context or as pointers to synchronization and position-modeling work; none of them is invoked as a uniqueness theorem, and none carries the weight of the specification itself. A limitation worth noting explicitly is that Section 5.1 states that the CDF includes only events that are 'objective' or have 'clear consensus,' yet Table 3 defines 'event type' as an open string with examples such as 'shot' and 'pass' without operational definitions, and Section 5.2 defers vendor-agnostic identifiers to future work in Footnote 12. These are substantive correctness and scope concerns about whether the format achieves its stated goal of unambiguity, but they are not circularity: the schema does not define its own success measure in terms of those fields, and no claim is made that the format's effectiveness is demonstrated by results that reduce to its own assumptions. The paper is self-contained as a proposal, and any evaluation of its real-world utility would be external and empirical. Therefore, no significant circularity is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The paper introduces no free parameters or invented physical entities. It rests on domain assumptions about the suitability of JSON formats, the feasibility of objective event definitions, the adequacy of absolute coordinates, and the authority of IFAB. None of these are tested empirically in the paper.

assumptions (4)
  • domain assumption JSON and JSON Lines with UTF-8 encoding are appropriate delivery formats for both static and live football data.
    Section 5.3 selects these formats for compactness and tool support; no empirical comparison with alternatives (e.g., Parquet, protobuf) is provided.
  • domain assumption A set of objective events can be identified by consensus among stakeholders, and these events can be labeled unambiguously by human operators.
    Section 5.1 states that the CDF includes only events that are objective or have clear consensus, citing ongoing FIFA work [14]; the paper does not demonstrate that this consensus exists or that annotation is reliable.
  • domain assumption Absolute pitch coordinates in meters, together with pitch length/width metadata, provide an unambiguous representation across varying pitch sizes.
    Section 5.2 defines the coordinate system with origin at pitch center and boundaries derived from Lpitch and Wpitch; this assumes all consumers will correctly use the metadata to interpret coordinates.
  • domain assumption The IFAB Laws of the Game provide an authoritative basis for the objective events included in the CDF.
    Section 3.1 references IFAB as the source for rule-related events (kick-offs, free kicks, goal kicks, offside rulings), but many standard event types (e.g., passes, tackles) are not defined by the Laws.

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Cite this review

Pith. "Pith review of Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)." pith.science (2026). https://pith.science/paper/JO3DQYH7

@misc{pith2026250515820,
  author       = {Pith},
  title        = {Pith review of: Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JO3DQYH7}},
  note         = {Machine review of arXiv:2505.15820}
}
read the original abstract

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.

Figures

Figures reproduced from arXiv: 2505.15820 by the authors.

Figure 1
Figure 1. Pitch coordinate system Playing direction CDF assumes that the home team always plays from left to right for the entire match. The meta data contains sufficient information (e.g., home/away teams, direction of play in each period) to enable a user to derive alternative representations. In the case of penalty shootouts, the penalty taker always shoots left to right (i.e., towards the goal on the right). 14 [PITH_FUL… view at source ↗
Figure 2
Figure 2. Simplified example of a partial JSON Object that depicts the structure of the match sheet data. In particular, it describes the format of the results in the this data source. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_2.png] view at source ↗
Figure 3
Figure 3. Example of a non-nested JSON (.json) meta data file with stadium, player, and team information. 17 [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: illustrates the first two events of the first half. Figures 5 shows a simplified example for two frames of player center of body tracking data. Note that we are only showing the mandatory CDF fields. These can be augmented with the optional fields (see Tables 9 and 10)…
Figure 5
Figure 5. Figure 5: Example of the JSON Lines (.jsonl) for the Center of Body Tracking Data representation. Two frames are shown and they are expanded over multiple lines for readability. We omit the positions of all players for readability. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_5.png]
Figure 6
Figure 6. Figure 6: Example of JSON Object containing the standardized skeletal hierarchy of the provider specific skeletal model. The coordinate system is the same used in 5.2. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_6.png]
Figure 7
Figure 7. Figure 7: Position groups and associated position labels 29 [PITH_FULL_IMAGE:figures/full_fig_p029_7.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PathCRF: Ball-Free Soccer Event Detection via Possession Path Inference from Player Trajectories

    cs.LG 2026-02 conditional novelty 6.0 of 10

    Using only player tracking, a dynamic masked CRF infers the possession path and detects soccer events with 75.7% F1, but on a single test match.

  2. AIx4Soccer: A Unified Platform Architecture for Football Club Management and Structured Athlete Development

    cs.CY 2026-07 conditional novelty 4.0 of 10

    A conceptual multi-tenant football-club SaaS design embeds a PDI development cycle and a 75/25 certified video-analyst marketplace on a proposed event-sourced knowledge-graph substrate.

Reference graph

Works this paper leans on

35 extracted references · 29 canonical work pages · cited by 2 Pith papers

  1. [1]

    Visual analysis of pressure in football

    Gennady Andrienko et al. “Visual analysis of pressure in football”. In: Data Mining and Knowledge Discovery 31.6 (2017), pp. 1793–1839.issn: 1573756X. doi: 10.1007/s10618-017-0513-2 . url: https://link.springer. com/article/10.1007/s10618-017-0513-2 (cit. on p. 5)

    doi:10.1007/s10618-017-0513-2 2017

  2. [2]

    A Goal Scoring Probability Model based on Synchronized Positional and Event Data

    Gabriel Anzer and Pascal Bauer. “A Goal Scoring Probability Model based on Synchronized Positional and Event Data”. In: Frontiers in Sports and Active Learning (Special Issue: Using Artificial Intelligence to Enhance Sport Performance) 3.0 (2021), pp. 1–18. doi: 10.3389/fspor.2021.624475. url: https://www.frontiersin. org/articles/10.3389/fspor.2021.62447...

    work page arXiv 2021

  3. [3]

    Expected Passes—Determining the Difficulty of a Pass in Football (Soccer) Using Spatio-Temporal Data

    Gabriel Anzer and Pascal Bauer. “Expected Passes—Determining the Difficulty of a Pass in Football (Soccer) Using Spatio-Temporal Data”. In: Data Mining and Knowledge Discovery, Springer US (2022). issn: 1573-756X. doi: 10.1007/s10618- 021- 00810- 3. url: https://link.springer.com/article/10.1007/s10618- 021- 00810-3 (cit. on pp. 5, 7)

    doi:10.1007/s10618- 2022

  4. [4]

    Putting Team Formations in Association Football into Context

    Pascal Bauer, Gabriel Anzer, and Laurie Shaw. “Putting Team Formations in Association Football into Context”. In: IEEE Transactions on Big Data (submitted) (2021) (cit. on p. 28)

    work page 2021

  5. [5]

    Statkeepers Call the Shots, But They Can ’t Agree on Them

    Carl Bialik. Statkeepers Call the Shots, But They Can ’t Agree on Them. 2014. url: https://fivethirtyeight. com/features/statkeepers- call- the- shots- but- they- cant- agree- on- them/(visited on 05/07/2014) (cit. on pp. 2, 5)

    work page 2014

  6. [6]

    The People Tracking Every Touch, Pass And Tackle in the World Cup

    Carl Bialik. The People Tracking Every Touch, Pass And Tackle in the World Cup . 2014. url: https : / / fivethirtyeight.com/features/the-people-tracking-every-touch-pass-and-tackle-in-the-world- cup/ (visited on 10/06/2014) (cit. on p. 5)

    work page 2014

  7. [7]

    Identifying team style in soccer using formations learned from spatiotemporal tracking data

    Alina Bialkowski et al. “Identifying team style in soccer using formations learned from spatiotemporal tracking data”. In: IEEE International Conference on Data Mining Workshops, ICDMW January (2015), pp. 9–14. issn: 23759259. doi: 10.1109/ICDMW.2014.167 (cit. on p. 28)

    work page doi:10.1109/icdmw.2014.167 2015

  8. [8]

    Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data

    Alina Bialkowski et al. “Large-Scale Analysis of Soccer Matches Using Spatiotemporal Tracking Data”. In: IEEE International Conference on Data Mining, ICDM (Proceeding)January (2014), pp. 725–730. issn: 15504786. doi: 10.1109/ICDM.2014.133 (cit. on p. 28)

    doi:10.1109/icdm.2014.133 2014

Show all 35 references
  1. [9]

    Psychophysical bases of perceived exertion

    Gunnar Borg. “Psychophysical bases of perceived exertion”. In: Med Sci Sports Exer 14.5 (1982), pp. 377–381 (cit. on p. 4)

    1982

  2. [10]

    A goal-aligned coordinate system for invasion games

    Ulrik Brandes. “A goal-aligned coordinate system for invasion games”. In: Journal of Sports Analytics 9.4 (2023), pp. 261–271. issn: 2215020X. doi: 10.3233/jsa-220706 (cit. on p. 5)

    2023 doi

  3. [11]

    Psychometric and physiological responses to a pre- season competitive camp in the heat with a 6-hour time difference in elite soccer players

    Martin Buchheit, Yannick Cholley, and Philippe Lambert. “Psychometric and physiological responses to a pre- season competitive camp in the heat with a 6-hour time difference in elite soccer players”. In: International Journal of Sports Physiology and Performance 11.2 (2016), p...

    2016

  4. [12]

    Actions speak louder than goals: Valuing player actions in soccer

    Tom Decroos et al. “Actions speak louder than goals: Valuing player actions in soccer”. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1 (2019), pp. 1851–1861. doi: 10.1145/3292500.3330758 (cit. on p. 6)

    2019

  5. [13]

    Electronic Performance and Tracking Systems (EPTS) Standards

    FIF A. Electronic Performance and Tracking Systems (EPTS) Standards . url: \url{https://inside.fifa. com/technical/football-technology/standards/epts} (cit. on p. 7)

  6. [14]

    White Paper: Event Data Harmonisation-Level 1

    FIF A. “White Paper: Event Data Harmonisation-Level 1”. 2023 (cit. on p. 7)

    2023

  7. [15]

    glTF 2.0 Specification

    Khronos Group. glTF 2.0 Specification . Accessed: 2024-12-03. 2017. url: https://registry.khronos.org/ glTF/specs/2.0/glTF-2.0.html (cit. on p. 15)

    2024

  8. [16]

    Moneyball: The Art of Winning an Unfair Game

    Micheal Lewis. Moneyball: The Art of Winning an Unfair Game . New York: Norton, 2003 (cit. on p. 1)

    2003

  9. [17]

    Sports Analytics: How (commercial) sports data create new opportunities for sports science

    Daniel Link. “Sports Analytics: How (commercial) sports data create new opportunities for sports science”. In: German Journal of Exercise and Sport Research 48.1 (2018), pp. 13–25. issn: 25093150. doi: 10.1007/s12662- 017-0487-7 (cit. on p. 1)

    2018 doi

  10. [18]

    Football-specific validity of TRACAB’s optical video tracking systems

    Daniel Linke, Daniel Link, and Martin Lames. “Football-specific validity of TRACAB’s optical video tracking systems”. In: PLoS ONE 15.3 (2020), pp. 1–17. issn: 19326203. doi: 10.1371/journal.pone.0230179 (cit. on p. 4)

    2020 doi

  11. [19]

    Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world

    Lorenzo Lolli et al. “Data analytics in the football industry: a survey investigating operational frameworks and practices in professional clubs and national federations from around the world”. In: Science and Medicine in Football (May 2024), pp. 1–10. issn: 2473-3938. doi: 10...

    2024

  12. [20]

    A survey on player tracking in soccer videos

    M. Manafifard, H. Ebadi, and H. Abrishami Moghaddam. “A survey on player tracking in soccer videos”. In: Computer Vision and Image Understanding 159 (2017), pp. 19–46. issn: 1090235X. doi: 10.1016/j.cviu. 2017.02.002 (cit. on p. 2)

    2017 doi

  13. [21]

    DataBallPy: Load, Synchronize, and Analyzing Soccer Data

    Alexander Oonk. DataBallPy: Load, Synchronize, and Analyzing Soccer Data . url: \url{https://pypi.org/ project/databallpy/} (cit. on p. 7)

  14. [22]

    A public data set of spatio-temporal match events in soccer competitions

    Luca Pappalardo et al. “A public data set of spatio-temporal match events in soccer competitions”. In: Scientific Data 6.1 (2019), pp. 1–15. issn: 20524463. doi: 10.1038/s41597-019-0247-7 . url: http://dx.doi.org/10. 1038/s41597-019-0247-7 (cit. on pp. 2, 3)

    2019 doi

  15. [23]

    Floodlight–A high-level, data-driven sports analytics framework

    Dominik Raabe et al. “Floodlight–A high-level, data-driven sports analytics framework”. In: Journal of Open Source Software (2022) (cit. on p. 7)

    2022

  16. [24]

    Big data and tactical analysis in elite soccer: future challenges and oppor- tunities for sports science

    Robert Rein and Daniel Memmert. “Big data and tactical analysis in elite soccer: future challenges and oppor- tunities for sports science”. In: SpringerPlus 5.1 (2016). issn: 21931801. doi: 10.1186/s40064- 016- 3108- 2 (cit. on p. 2)

    2016 doi

  17. [25]

    How data availability affects the ability to learn good xG models

    Pieter Robberechts and Jesse Davis. “How data availability affects the ability to learn good xG models”. In: Proceedings of the 7th Workshop on Machine Learning and Data Mining for Sports Analytics . 2020, pp. 17–27 (cit. on p. 7)

    2020

  18. [26]

    Development of a sports technology quality framework

    Samuel Robertson et al. “Development of a sports technology quality framework”. In: Journal of sports sciences 41.22 (2023), pp. 1983–1993 (cit. on p. 7)

    2023

  19. [27]

    A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains

    Sarah Rudd. “A Framework for Tactical Analysis and Individual Offensive Production Assessment in Soccer Using Markov Chains”. In: New England Symposium on Statistics in Sports . 2011. url: http://nessis.org/ nessis11/rudd.pdf (cit. on p. 7)

    2011

  20. [28]

    Introducing Expected Threat

    Karun Singh. Introducing Expected Threat. https://karun.in/blog/expected-threat.html. 2019 (cit. on p. 7)

    2019

  21. [29]

    Interchangeability of position tracking technologies; can we merge the data?

    Matt Taberner et al. “Interchangeability of position tracking technologies; can we merge the data?” In: Science and Medicine in Football 4.1 (2020), pp. 76–81. issn: 24734446. doi: 10.1080/24733938.2019.1634279 . url: https://doi.org/10.1080/24733938.2019.1634279 (cit. on p. 3)

    2020

  22. [30]

    ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization

    Maaike Van Roy, Lorenzo Cascioli, and Jesse Davis. “ETSY: A Rule-Based Approach to Event and Tracking Data SYnchronization”. In: Machine Learning and Data Mining for Sports Analytics Workshop at ECML/PKDD

  23. [31]

    Automatic event detection in football using tracking data

    Ferran Vidal-Codina et al. “Automatic event detection in football using tracking data”. In: Sports Engineering 25.1 (Dec. 2022). issn: 14602687. doi: 10.1007/s12283-022-00381-6 (cit. on p. 3)

    2022 doi

  24. [32]

    kloppy: Standardizing soccer tracking and event data

    Koen Vossen. kloppy: Standardizing soccer tracking and event data. url: \url{https://kloppy.pysport.org/} (cit. on p. 7)

  25. [33]

    Modeling basketball play-by-play data

    Petar Vraˇ car, Erik ˇStrumbelj, and Igor Kononenko. “Modeling basketball play-by-play data”. In: Expert Systems with Applications 44 (2016), pp. 58–66. issn: 09574174. doi: 10.1016/j.eswa.2015.09.004 (cit. on p. 3)

    2016 doi

  26. [34]

    Documentation for the JSON Lines text file format

    Ian Ward. Documentation for the JSON Lines text file format . url: \url{https://jsonlines.org}) (cit. on p. 18). 31

  27. [2023]

    Vol. 2035. Communications in Computer and Information Science. Springer Nature Switzerland, 2023, pp. 11–23 (cit. on pp. 2, 7)

    2023

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