IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v10y2014i2p16n16.html
   My bibliography  Save this article

Skill importance in women’s soccer

Author

Listed:
  • Heiner Matthew
  • Fellingham Gilbert W.

    (Statistics, Brigham Young University, Provo, UT, USA)

  • Thomas Camille

    (Physical Education and Human Performance, Southern Utah University, Cedar City, UT, USA)

Abstract

Soccer analytics often follow one of two approaches: 1) regression models on number of shots taken or goals scored to predict match winners, or 2) spatial and/or temporal analysis of plays for evaluation of strategy. We propose a new model to evaluate skill importance in soccer. Play by play data were collected on 22 NCAA Division I Women’s Soccer matches with a new skill notation system. Using a Bayesian approach, we model play sequences as discrete absorbing Markov chains. Using posterior distributions, we estimate the probability of 35 distinct offensive skills leading to a shot during a single possession.

Suggested Citation

  • Heiner Matthew & Fellingham Gilbert W. & Thomas Camille, 2014. "Skill importance in women’s soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 10(2), pages 1-16, June.
    • Handle: RePEc:bpj:jqsprt:v:10:y:2014:i:2:p:16:n:16
    DOI: 10.1515/jqas-2013-0119
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/jqas-2013-0119
    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-2013-0119?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. Miskin Michelle A & Fellingham Gilbert W & Florence Lindsay W, 2010. "Skill Importance in Women's Volleyball," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-14, April.
    2. Ian McHale & Phil Scarf, 2007. "Modelling soccer matches using bivariate discrete distributions with general dependence structure," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 432-445, November.
    3. Brillinger David R, 2007. "A Potential Function Approach to the Flow of Play in Soccer," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(1), pages 1-21, January.
    4. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    5. Hamilton Howard H, 2011. "An Extension of the Pythagorean Expectation for Association Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-18, May.
    6. Goldner Keith, 2012. "A Markov Model of Football: Using Stochastic Processes to Model a Football Drive," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(1), pages 1-18, March.
    7. Goddard, John, 2005. "Regression models for forecasting goals and match results in association football," International Journal of Forecasting, Elsevier, vol. 21(2), pages 331-340.
    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. Santos-Fernandez Edgar & Wu Paul & Mengersen Kerrie L., 2019. "Bayesian statistics meets sports: a comprehensive review," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 15(4), pages 289-312, December.
    2. Corona, Francisco & Forrest, David & Tena, J.D. & Wiper, Michael, 2019. "Bayesian forecasting of UEFA Champions League under alternative seeding regimes," International Journal of Forecasting, Elsevier, vol. 35(2), pages 722-732.
    3. Corona, Francisco & Forrest, David & Tena Horrillo, Juan de Dios & Wiper, Michael Peter, 2017. "Evaluating significant effects from alternative seeding systems : a Bayesian approach, with an application to the UEFA Champions League," DES - Working Papers. Statistics and Econometrics. WS 24521, Universidad Carlos III de Madrid. Departamento de Estadística.
    4. Hengtao Zhang & Guosheng Yin, 2021. "Response‐adaptive rerandomization," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1281-1298, November.
    5. Andrew Gelman & Daniel Lee & Jiqiang Guo, 2015. "Stan," Journal of Educational and Behavioral Statistics, , vol. 40(5), pages 530-543, October.
    6. Zuoxian Gan & Tao Feng & Min Yang, 2018. "Exploring the Effects of Car Ownership and Commuting on Subjective Well-Being: A Nationwide Questionnaire Study," Sustainability, MDPI, vol. 11(1), pages 1-20, December.
    7. Rolando de la Cruz & Oslando Padilla & Mauricio A. Valle & Gonzalo A. Ruz, 2021. "Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
    8. Scarf, Philip & Yusof, Muhammad Mat & Bilbao, Mark, 2009. "A numerical study of designs for sporting contests," European Journal of Operational Research, Elsevier, vol. 198(1), pages 190-198, October.
    9. Chao Xu & Didit O Pribadi & Dagmar Haase & Stephan Pauleit, 2020. "Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model," Environment and Planning B, , vol. 47(7), pages 1184-1200, September.
    10. Regina Madalozzo & Rodrigo Berber Villar, 2009. "Brazilian Football," Journal of Sports Economics, , vol. 10(6), pages 639-650, December.
    11. J. James Reade & Carl Singleton & Alasdair Brown, 2021. "Evaluating strange forecasts: The curious case of football match scorelines," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(2), pages 261-285, May.
    12. Federico ANDREIS & Pier Alda FERRARI, 2015. "Customer Satisfaction Evaluation Using Multidimensional Item Response Theory Models," Departmental Working Papers 2015-25, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    13. Gross, Johannes & Rebeggiani, Luca, 2018. "Chance or Ability? The Efficiency of the Football Betting Market Revisited," MPRA Paper 87230, University Library of Munich, Germany.
    14. Antonio Parisi & B. Liseo, 2018. "Objective Bayesian analysis for the multivariate skew-t model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 277-295, June.
    15. Andrei Shynkevich, 2022. "Informational efficiency of football transfer market," Economics Bulletin, AccessEcon, vol. 42(2), pages 1032-1039.
    16. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    17. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    18. So, Sunha & Lee, Dong-Hee & Jung, Byoung Cheol, 2011. "An alternative bivariate zero-inflated negative binomial regression model using a copula," Economics Letters, Elsevier, vol. 113(2), pages 183-185.
    19. Schwarz Wolf, 2012. "Predicting the Maximum Lead from Final Scores in Basketball: A Diffusion Model," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 8(4), pages 1-15, November.
    20. Van Niekerk, Janet & Krainski, Elias & Rustand, Denis & Rue, Håvard, 2023. "A new avenue for Bayesian inference with INLA," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).

    More about this item

    Statistics

    Access and download statistics

    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:10:y:2014:i:2:p:16:n:16. 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.