CoChat
Sign Up
Cited by 35Sports Performance and TrainingSports Analytics and PerformanceSport Psychology and Performance

The DOI matches its Crossref record.

Measuring Football Players’ On-the-Ball Contributions from Passes During Games

Open access
Bransen, Lotte, Van Haaren, Jan·2019·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-17274-9_1
Open this paper in CoChat — free accountOpen PDF

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/978-3-030-17274-9_1

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 12

  1. 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)
  2. 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)
  3. Decroos, T., Bransen, L., Van Haaren, J., Davis, J.: Actions speak louder than goals: valuing player actions in soccer. ArXiv e-prints (2018) · DOI
  4. 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
  5. 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)
  6. Gyarmati, L., Stanojevic, R.: QPass: a merit-based evaluation of soccer passes. ArXiv e-prints (2016)
  7. 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
  8. Knauf, K., Brefeld, U.: Spatio-temporal convolution kernels for clustering trajectories. In: Proceedings of the Workshop on Large-Scale Sports Analytics (2014)
  9. Liu, G., Schulte, O.: Deep reinforcement learning in ice hockey for context-aware player evaluation. ArXiv e-prints (2018) · DOI
  10. Maher, M.: Modelling association football scores. Stat. Neerl. 36(3), 109–118 (1982) · DOI
  11. 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
  12. 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

See all 35

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
DOI
10.1007/978-3-030-17274-9_1
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
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.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Measuring Football Players’ On-the-Ball Contributions from Passes During Games. https://app.cochat.ai/papers/10.1007/978-3-030-17274-9_1
The paper
Bransen, L., & Van Haaren, J. (2019). Measuring Football Players’ On-the-Ball Contributions from Passes During Games. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-17274-9_1