Paper Citation Record · LEDGER

A Graph Neural Network deep-dive into successful counterattacks

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 3 inbound Pith citation observations for arXiv:2411.17450.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17450 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:10:45.623035Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T23:09:19.444392Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-06-29T22:44:01.524819Z

Reference resolution

38 of 38 outbound references displayed

verified exact4
verified fuzzy26
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ab990fc-f81f-49a5-b099-f6a730083d35 · outbound

This paper cites Permutation impor- tance: a corrected feature importance measure.

A Graph Neural Network deep-dive into successful counterattacks Permutation impor- tance: a corrected feature importance measure

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:46.288711Z

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Observation 7ca4fbaf-1aa7-47f2-b262-f371a4396840 · outbound

This paper cites an unresolved cited work.

A Graph Neural Network deep-dive into successful counterattacks Unresolved cited work

Reference 2

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Observation f5772631-57e8-4be5-9fbf-7e2c103fcb00 · outbound

This paper cites Putting team formations in association football into context.

A Graph Neural Network deep-dive into successful counterattacks Putting team formations in association football into context

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:46.267294Z

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Observation a6440cd0-b71a-4d91-9e1a-b111b1642684 · outbound

This paper cites an unresolved cited work.

A Graph Neural Network deep-dive into successful counterattacks Unresolved cited work

Reference 4

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Observation df04cea7-9334-4ca0-8ecb-020b7dfebc9e · outbound

This paper cites Pass maps 2.0, 2017.

A Graph Neural Network deep-dive into successful counterattacks Pass maps 2.0, 2017

Reference 5

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Observation b71321a7-69ce-411d-b373-f361a0b65135 · outbound

This paper cites Interactive digital tactics board, 2022.

A Graph Neural Network deep-dive into successful counterattacks Interactive digital tactics board, 2022

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:46.228704Z

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Observation 05e2e6bc-00ee-4329-a304-4c541c97a5da · outbound

This paper cites Flow motifs in soccer: What can passing behavior tell us? Journal of Sports Analytics, 5(4):299–311, 2019.

A Graph Neural Network deep-dive into successful counterattacks Flow motifs in soccer: What can passing behavior tell us? Journal of Sports Analytics, 5(4):299–311, 2019

Reference 7

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Observation 20101539-23c9-4a9c-b99b-a766106a3edc · outbound

This paper cites Women’s football analyzed: Interpretable expected goals models for women.

A Graph Neural Network deep-dive into successful counterattacks Women’s football analyzed: Interpretable expected goals models for women

Reference 8

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Observation 5a233bdd-d3b4-4e3c-823a-ae9ff2d7f5e8 · outbound

This paper cites Using network metrics in soccer: a macro-analysis.

A Graph Neural Network deep-dive into successful counterattacks Using network metrics in soccer: a macro-analysis

Reference 9

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Observation 52621f30-b79b-4cb1-a4ab-41e2b71ba84e · outbound

This paper cites Vaep: An objective approach to valuing on-the-ball actions in soccer.

A Graph Neural Network deep-dive into successful counterattacks Vaep: An objective approach to valuing on-the-ball actions in soccer

Reference 10

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Observation ae41a979-f6f2-4c4d-b07e-020bffb188a9 · outbound

This paper cites Soccer Federation.

A Graph Neural Network deep-dive into successful counterattacks Soccer Federation

Reference 11

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Source-reported events for the cited work

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Observation d54ff150-538c-40d9-8065-9332fc7fa20a · outbound

This paper cites Soccer Federation.

A Graph Neural Network deep-dive into successful counterattacks Soccer Federation

Reference 12

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Observation 25bff969-4dfd-4d31-98d8-0cae87805ee3 · outbound

This paper cites Soccermap: A deep learning architecture for visually- interpretable analysis in soccer.

A Graph Neural Network deep-dive into successful counterattacks Soccermap: A deep learning architecture for visually- interpretable analysis in soccer

Reference 13

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Observation 4abb3900-cc2c-4a54-8475-a0f489982110 · outbound

This paper cites A framework for the analytical and visual interpretation of complex spatiotemporal dynamics in soccer.

A Graph Neural Network deep-dive into successful counterattacks A framework for the analytical and visual interpretation of complex spatiotemporal dynamics in soccer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:46.120274Z

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Observation 312c846b-536c-4dfc-a76f-020dd4f5ff34 · outbound

This paper cites Fast Graph Representation Learning with PyTorch Geometric.

A Graph Neural Network deep-dive into successful counterattacks Fast Graph Representation Learning with PyTorch Geometric

Reference 15

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Observation e491c406-112e-4dd1-aa0e-ee18a5a693c3 · outbound

This paper cites Fifa women’s world cup 2019 ™ watched by more than 1 billion, 2019.

A Graph Neural Network deep-dive into successful counterattacks Fifa women’s world cup 2019 ™ watched by more than 1 billion, 2019

Reference 16

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Observation 4cfb8611-f6be-4bf6-8fb2-2ae2c66038b4 · outbound

This paper cites Fifa council makes key decisions for the future of football development, 2019.

A Graph Neural Network deep-dive into successful counterattacks Fifa council makes key decisions for the future of football development, 2019

Reference 17

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Observation 9551bce0-1ddd-4af8-ab75-13a09e2250c8 · outbound

This paper cites Graph neural networks in tensorflow and keras with spektral [application notes].

A Graph Neural Network deep-dive into successful counterattacks Graph neural networks in tensorflow and keras with spektral [application notes]

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:46.080390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation aa54d00d-882c-4586-a451-c8eca1707168 · outbound

This paper cites Searching for a Unique Style in Soccer.

A Graph Neural Network deep-dive into successful counterattacks Searching for a Unique Style in Soccer

Reference 19

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Observation 37995852-05a6-4a73-9e98-e5139e7673af · outbound

This paper cites Open graph benchmark: Datasets for machine learning on graphs.

A Graph Neural Network deep-dive into successful counterattacks Open graph benchmark: Datasets for machine learning on graphs

Reference 20

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This paper cites Those are some numbers the 2022 #nwsl championship drew in 915,000 viewers saturday, a +71% jump from last, 2022.

A Graph Neural Network deep-dive into successful counterattacks Those are some numbers the 2022 #nwsl championship drew in 915,000 viewers saturday, a +71% jump from last, 2022

Reference 21

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Observation a95772ea-ba92-4b48-9660-aec31e346faa · outbound

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A Graph Neural Network deep-dive into successful counterattacks Unresolved cited work

Reference 22

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A Graph Neural Network deep-dive into successful counterattacks Unresolved cited work

Reference 23

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Observation 4fb86177-286f-4263-ba42-0c04fef454fb · outbound

This paper cites Mullenberg.

A Graph Neural Network deep-dive into successful counterattacks Mullenberg

Reference 24

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Observation 9e228b38-23ad-41d8-a30f-a31d01a1188d · outbound

This paper cites Paul power: neural networks for understanding defending [video], 2021.

A Graph Neural Network deep-dive into successful counterattacks Paul power: neural networks for understanding defending [video], 2021

Reference 25

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Observation 92de82e0-bb10-4d71-bc46-23e87630d811 · outbound

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A Graph Neural Network deep-dive into successful counterattacks Osmanbaˇ si´ c

Reference 26

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Observation f335d7f3-7f27-4147-a621-b7beaf0ad394 · outbound

This paper cites Explaining the differ- ence between men’s and women’s football.

A Graph Neural Network deep-dive into successful counterattacks Explaining the differ- ence between men’s and women’s football

Reference 27

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Observation 7be315b3-bc45-4d19-b861-52c3bfc2faba · outbound

This paper cites A network theory analysis of football strategies.

A Graph Neural Network deep-dive into successful counterattacks A network theory analysis of football strategies

Reference 28

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A Graph Neural Network deep-dive into successful counterattacks Unresolved cited work

Reference 29

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Observation ed2a5cb9-a0ae-4eed-b4c1-60d724c583c3 · outbound

This paper cites Physics-based modeling of pass probabilities in soccer.

A Graph Neural Network deep-dive into successful counterattacks Physics-based modeling of pass probabilities in soccer

Reference 30

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A Graph Neural Network deep-dive into successful counterattacks Analytics and modelling in women’s football, 2022

Reference 31

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A Graph Neural Network deep-dive into successful counterattacks Statsbomb conference 2022: Dr

Reference 32

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This paper cites Optaproanalyticsforum– learning to watch football: Self-supervised repre- sentations for tracking data [video], 2020.

A Graph Neural Network deep-dive into successful counterattacks Optaproanalyticsforum– learning to watch football: Self-supervised repre- sentations for tracking data [video], 2020

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:45.927403Z

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Observation d7192332-54ac-4259-ab50-6c5ddded3131 · outbound

This paper cites Making offensive play predictable-using a graph convolutional network to understand defensive performance in soccer.

A Graph Neural Network deep-dive into successful counterattacks Making offensive play predictable-using a graph convolutional network to understand defensive performance in soccer

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:45.914628Z

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Observation 70dddea1-0e3f-4285-a0b2-68d205a35ad0 · outbound

This paper cites Women’s euro watched by over 365 million people globally, 2022.

A Graph Neural Network deep-dive into successful counterattacks Women’s euro watched by over 365 million people globally, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:45.901918Z

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A Graph Neural Network deep-dive into successful counterattacks Worville

Reference 36

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Observation 37ee35c3-2d8b-4a54-99b9-01226eab10d1 · outbound

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A Graph Neural Network deep-dive into successful counterattacks Graph neural networks to predict sports outcomes

Reference 37

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Observation a19f0174-92ec-4835-a28c-a1b5553ec25f · outbound

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A Graph Neural Network deep-dive into successful counterattacks Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:10:45.875706Z

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Pith citing papers

Observation 3fe643e8-9323-4d8c-b89f-6b6c5f76e282 · inbound

Pressing Intensity: An Intuitive Measure for Pressing in Soccer cites this paper.

Pressing Intensity: An Intuitive Measure for Pressing in Soccer A Graph Neural Network deep-dive into successful counterattacks

Reference 3

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Observation 93a6e18f-d845-493b-9a49-94826b28e2ba · inbound

EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment cites this paper.

EFPI: Elastic Formation and Position Identification in Football (Soccer) using Template Matching and Linear Assignment A Graph Neural Network deep-dive into successful counterattacks

Reference 3

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Observation a8b08205-9f53-41c6-839d-f88fd29ccb90 · inbound

Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection cites this paper.

Evaluating passing decision-making in professional football: An enhanced MPNN approach to Receiver Selection A Graph Neural Network deep-dive into successful counterattacks

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:44:01.526162Z

Source-reported events for the cited work

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