IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v16y2020i4p311-323n2.html
   My bibliography  Save this article

Evaluating the effectiveness of different network flow motifs in association football

Author

Listed:
  • Håland Else Marie

    (Industrial Economics and Technology Management, NTNU, Trondheim, Norway)

  • Wiig Astrid Salte

    (Industrial Economics and Technology Management, NTNU, Trondheim, Norway)

  • Hvattum Lars Magnus

    (Molde University College, Molde, Norway)

  • Stålhane Magnus

    (Industrial Economics and Technology Management, NTNU, Trondheim, Norway)

Abstract

In association football, a network flow motif describes how distinct players from a team are involved in a passing sequence. The flow motif encodes whether the same players appear several times in a passing sequence, and in which order the players make passes. This information has previously been used to classify the passing style of different teams. In this work, flow motifs are analyzed in terms of their effectiveness in terms of generating shots. Data from four seasons of the Norwegian top division are analyzed, using flow motifs representing subsequences of three passes. The analysis is performed with a generalized additive model (GAM), with a range of explanatory variables included. Findings include that motifs with fewer distinct players are less effective, and that motifs are more likely to lead to shots if the passes in the motif utilize a bigger area of the pitch.

Suggested Citation

  • Håland Else Marie & Wiig Astrid Salte & Hvattum Lars Magnus & Stålhane Magnus, 2020. "Evaluating the effectiveness of different network flow motifs in association football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 311-323, December.
  • Handle: RePEc:bpj:jqsprt:v:16:y:2020:i:4:p:311-323:n:2
    DOI: 10.1515/jqas-2019-0097
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2019-0097
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.1515/jqas-2019-0097?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McHale, Ian G. & Relton, Samuel D., 2018. "Identifying key players in soccer teams using network analysis and pass difficulty," European Journal of Operational Research, Elsevier, vol. 268(1), pages 339-347.
    2. Simon N. Wood, 2011. "Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 3-36, January.
    3. Łukasz Szczepański & Ian McHale, 2016. "Beyond completion rate: evaluating the passing ability of footballers," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 513-533, February.
    4. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Håland Else Marie & Wiig Astrid Salte & Stålhane Magnus & Hvattum Lars Magnus, 2020. "Evaluating the effectiveness of different network flow motifs in association football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(4), pages 311-323, December.
    2. Isabel Proença & Stefan Sperlich & Duygu Savaşcı, 2015. "Semi-mixed effects gravity models for bilateral trade," Empirical Economics, Springer, vol. 48(1), pages 361-387, February.
    3. Øystein Sørensen & Anders M. Fjell & Kristine B. Walhovd, 2023. "Longitudinal Modeling of Age-Dependent Latent Traits with Generalized Additive Latent and Mixed Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 456-486, June.
    4. Jorge Tovar & Andrés Clavijo & Julián Cárdenas, 2017. "A strategy to predict association football players’ passing skills," Documentos CEDE 15821, Universidad de los Andes, Facultad de Economía, CEDE.
    5. Umlauf, Nikolaus & Adler, Daniel & Kneib, Thomas & Lang, Stefan & Zeileis, Achim, 2015. "Structured Additive Regression Models: An R Interface to BayesX," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i21).
    6. Karina Jansone & Anna Eichler & Peter A. Fasching & Johannes Kornhuber & Anna Kaiser & Sabina Millenet & Tobias Banaschewski & Frauke Nees & on behalf of the IMAC-Mind Consortium, 2023. "Association of Maternal Smoking during Pregnancy with Neurophysiological and ADHD-Related Outcomes in School-Aged Children," IJERPH, MDPI, vol. 20(6), pages 1-14, March.
    7. Rodríguez-Álvarez, María Xosé & Lee, Dae-Jin & Kneib, Thomas & Durbán, María & Eilers, Paul, 2013. "Fast algorithm for smoothing parameter selection in multidimensional generalized P-splines," DES - Working Papers. Statistics and Econometrics. WS ws133026, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Gerhard Tutz & Moritz Berger, 2018. "Tree-structured modelling of categorical predictors in generalized additive regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 737-758, September.
    9. Tommaso Luzzati & Angela Parenti & Tommaso Rughi, 2017. "Spatial error regressions for testing the Cancer-EKC," Discussion Papers 2017/218, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    10. Davide Fiaschi & Andrea Mario Lavezzi & Angela Parenti, 2020. "Deep and Proximate Determinants of the World Income Distribution," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 66(3), pages 677-710, September.
    11. Conor Waldock & Bernhard Wegscheider & Dario Josi & Bárbara Borges Calegari & Jakob Brodersen & Luiz Jardim de Queiroz & Ole Seehausen, 2024. "Deconstructing the geography of human impacts on species’ natural distribution," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    12. Xiao Ni & Daowen Zhang & Hao Helen Zhang, 2010. "Variable Selection for Semiparametric Mixed Models in Longitudinal Studies," Biometrics, The International Biometric Society, vol. 66(1), pages 79-88, March.
    13. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    14. Sihvonen, Markus, 2021. "Yield curve momentum," Research Discussion Papers 15/2021, Bank of Finland.
    15. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    16. Dillon T. Fogarty & Caleb P. Roberts & Daniel R. Uden & Victoria M. Donovan & Craig R. Allen & David E. Naugle & Matthew O. Jones & Brady W. Allred & Dirac Twidwell, 2020. "Woody Plant Encroachment and the Sustainability of Priority Conservation Areas," Sustainability, MDPI, vol. 12(20), pages 1-15, October.
    17. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.
    18. Daniel Melser & Robert J. Hill, 2019. "Residential Real Estate, Risk, Return and Diversification: Some Empirical Evidence," The Journal of Real Estate Finance and Economics, Springer, vol. 59(1), pages 111-146, July.
    19. Ji, Shujuan & Liu, Xiaojie & Wang, Yuanqing, 2024. "The role of road infrastructures in the usage of bikeshare and private bicycle," Transport Policy, Elsevier, vol. 149(C), pages 234-246.
    20. Ding, Hui & Zhang, Jian & Zhang, Riquan, 2022. "Nonparametric variable screening for multivariate additive models," Journal of Multivariate Analysis, Elsevier, vol. 192(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:16:y:2020:i:4:p:311-323:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.