The DOI matches its Crossref record.
Measuring Football Players’ On-the-Ball Contributions from Passes During Games
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“Recent studies on pass quality assess factors like complexity, risk-reward, or expected impact on scoring [19, 20, 37].”
From Automating assist identification in football (soccer): a machine learning approach using event and tracking data (2026) · cites this paper as [37]
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In the citing paper
“For example, [37] analyze player involvement in chance creation but do not specifically identify potential assists.”
From Automating assist identification in football (soccer): a machine learning approach using event and tracking data (2026) · cites this paper as [37]
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No references could be resolved yet.
Research works printed but not linked: 12
- Bemdt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (1994)
- Cervone, D., D’Amour, A., Bornn, L., Goldsberry, K.: POINTWISE: predicting points and valuing decisions in real-time with NBA optical tracking data. In: MIT Sloan Sports Analytics Conference 2014 (2014)
- Decroos, T., Bransen, L., Van Haaren, J., Davis, J.: Actions speak louder than goals: valuing player actions in soccer. ArXiv e-prints (2018) · DOI
- Decroos, T., Dzyuba, V., Van Haaren, J., Davis, J.: Predicting soccer highlights from spatio-temporal match event streams. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp. 1302–1308 (2017) · DOI
- Decroos, T., Van Haaren, J., Dzyuba, V., Davis, J.: STARSS: a spatio-temporal action rating system for soccer. In: Proceedings of the 4th Workshop on Machine Learning and Data Mining for Sports Analytics, vol. 1971, pp. 11–20 (2017)
- Gyarmati, L., Stanojevic, R.: QPass: a merit-based evaluation of soccer passes. ArXiv e-prints (2016)
- Karlis, D., Ntzoufras, I.: Bayesian modelling of football outcomes: using the Skellam’s distribution for the goal difference. IMA J. Manag. Math. 20(2), 133–145 (2008) · DOI
- Knauf, K., Brefeld, U.: Spatio-temporal convolution kernels for clustering trajectories. In: Proceedings of the Workshop on Large-Scale Sports Analytics (2014)
- Liu, G., Schulte, O.: Deep reinforcement learning in ice hockey for context-aware player evaluation. ArXiv e-prints (2018) · DOI
- Maher, M.: Modelling association football scores. Stat. Neerl. 36(3), 109–118 (1982) · DOI
- Power, P., Ruiz, H., Wei, X., Lucey, P.: Not all passes are created equal: objectively measuring the risk and reward of passes in soccer from tracking data. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1605–1613. ACM (2017) · DOI · Paper page
- Schulte, O., Zhao, Z., Routley, K.: What is the value of an action in ice hockey? Learning a Q-function for the NHL. In: Proceedings of the 2nd Workshop on Machine Learning and Data Mining for Sports Analytics (2015)
Cited by
Sorted by citations
Klemmer, M., Arnsmeyer, K., Bauer, P., & Bücker, M. (2026) · Sports Engineering · doi:10.1007/s12283-025-00533-4 · Cited by 1
“Recent studies on pass quality assess factors like complexity, risk-reward, or expected impact on scoring [19, 20, 37 ].”
Metadata
- Authors
- Bransen, L., & Van Haaren, J.
- Corresponding author
- Bransen, Lotte
- ORCID
- Bransen, Lotte
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 3-15
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2894804864
- Cited by
- 35
- References
- 0
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 4 · 2022: 8 · 2021: 4 · 2020: 2
- FWCI
- 25.03
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Sports Analytics and Performance
- Social Sciences › Psychology › Developmental and Educational Psychology › Sport Psychology and Performance
- Keywords
- football, player performance metrics, PASS, match analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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