IDEAS home Printed from https://ideas.repec.org/a/spr/alstar/v99y2015i2p209-227.html
   My bibliography  Save this article

Extended ordered paired comparison models with application to football data from German Bundesliga

Author

Listed:
  • Gerhard Tutz
  • Gunther Schauberger

Abstract

A general paired comparison model for the evaluation of sport competitions is proposed. It efficiently uses the available information by allowing for ordered response categories and team-specific home advantage effects. Penalized estimation techniques are used to identify clusters of teams that share the same ability. The model is extended to include team-specific explanatory variables. It is shown that regularization techniques allow to identify the contribution of explanatory variables to the success of teams. The usefulness of the methods is demonstrated by investigating the performance and its dependence on the budget for football teams of the German Bundesliga. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Gerhard Tutz & Gunther Schauberger, 2015. "Extended ordered paired comparison models with application to football data from German Bundesliga," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 209-227, April.
    • Handle: RePEc:spr:alstar:v:99:y:2015:i:2:p:209-227
    DOI: 10.1007/s10182-014-0237-1
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10182-014-0237-1
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10182-014-0237-1?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. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    2. A. Springall, 1973. "Response Surface Fitting Using a Generalization of the Bradley‐Terry Paired Comparison Model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 22(1), pages 59-68, March.
    3. Hatzinger, Reinhold & Dittrich, Regina, 2012. "prefmod: An R Package for Modeling Preferences Based on Paired Comparisons, Rankings, or Ratings," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 48(i10).
    4. Manuela Cattelan & Cristiano Varin & David Firth, 2013. "Dynamic Bradley–Terry modelling of sports tournaments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(1), pages 135-150, January.
    5. Howard D. Bondell & Brian J. Reich, 2009. "Simultaneous Factor Selection and Collapsing Levels in ANOVA," Biometrics, The International Biometric Society, vol. 65(1), pages 169-177, March.
    6. Alan Agresti, 1992. "Analysis of Ordinal Paired Comparison Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 287-297, June.
    7. Yee, Thomas W., 2010. "The VGAM Package for Categorical Data Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i10).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Christophe Ley & Yves Dominicy, 2017. "Mutual Point-winning Probabilities (MPW): a New Performance Measure for Table Tennis," Working Papers ECARES ECARES 2017-23, ULB -- Universite Libre de Bruxelles.
    2. László Csató, 2020. "Optimal Tournament Design: Lessons From the Men’s Handball Champions League," Journal of Sports Economics, , vol. 21(8), pages 848-868, December.
    3. Riccardo Ievoli & Aldo Gardini & Lucio Palazzo, 2023. "The role of passing network indicators in modeling football outcomes: an application using Bayesian hierarchical models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(1), pages 153-175, March.
    4. Mammen, Enno & Wilke, Ralf A. & Zapp, Kristina Maria, 2022. "Estimation of group structures in panel models with individual fixed effects," ZEW Discussion Papers 22-023, ZEW - Leibniz Centre for European Economic Research.
    5. Gunther Schauberger & Andreas Groll & Gerhard Tutz, 2018. "Analysis of the importance of on-field covariates in the German Bundesliga," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1561-1578, July.

    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. Weichen Wu & Nynke Niezink & Brian Junker, 2022. "A diagnostic framework for the Bradley–Terry model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(S2), pages 461-484, December.
    2. Jeon, Jong-June & Kim, Yongdai & Won, Sungho & Choi, Hosik, 2020. "Primal path algorithm for compositional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 148(C).
    3. Benjamin G. Stokell & Rajen D. Shah & Ryan J. Tibshirani, 2021. "Modelling high‐dimensional categorical data using nonconvex fusion penalties," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 579-611, July.
    4. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    5. D'Alberto, R. & Targetti, S. & Schaller, L. & Bartolini, F. & Eichhorn, T. & Haltia, E. & Harmanny, K. & Le Gloux, F. & Nikolov, D. & Runge, T. & Vergamini, D. & Viaggi, D., 2024. "A European perspective on acceptability of innovative agri-environment-climate contract solutions," Land Use Policy, Elsevier, vol. 141(C).
    6. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    7. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    8. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    9. 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.
    10. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    11. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    12. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    13. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    14. Perrot-Dockès Marie & Lévy-Leduc Céline & Chiquet Julien & Sansonnet Laure & Brégère Margaux & Étienne Marie-Pierre & Robin Stéphane & Genta-Jouve Grégory, 2018. "A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-14, October.
    15. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    16. Chuliá, Helena & Garrón, Ignacio & Uribe, Jorge M., 2024. "Daily growth at risk: Financial or real drivers? The answer is not always the same," International Journal of Forecasting, Elsevier, vol. 40(2), pages 762-776.
    17. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    18. Sung Jae Jun & Sokbae Lee, 2024. "Causal Inference Under Outcome-Based Sampling with Monotonicity Assumptions," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(3), pages 998-1009, July.
    19. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    20. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.

    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:spr:alstar:v:99:y:2015:i:2:p:209-227. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.