IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v268y2018i1p339-347.html
   My bibliography  Save this article

Identifying key players in soccer teams using network analysis and pass difficulty

Author

Listed:
  • McHale, Ian G.
  • Relton, Samuel D.

Abstract

We use a unique dataset to identify the key members of a football team. The methodology uses a statistical model to determine the difficulty of a pass from one player to another, and combines this information with results from network analysis, to identify which players are pivotal to each team in the English Premier League during the 2012–13 season. We demonstrate the methodology by looking closely at one game, whilst also summarising player performance for each team over the entire season. The analysis is hoped to be of use to managers and coaches in identifying the best team lineup, and in the analysis of opposition teams to identify their key players.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ejores:v:268:y:2018:i:1:p:339-347
    DOI: 10.1016/j.ejor.2018.01.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718300365
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2018.01.018?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. Boshnakov, Georgi & Kharrat, Tarak & McHale, Ian G., 2017. "A bivariate Weibull count model for forecasting association football scores," International Journal of Forecasting, Elsevier, vol. 33(2), pages 458-466.
    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. 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. 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.
    3. Ausloos, Marcel, 2024. "Hierarchy selection: New team ranking indicators for cyclist multi-stage races," European Journal of Operational Research, Elsevier, vol. 314(2), pages 807-816.
    4. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
    5. Sergio Caicedo-Parada & Carlos Lago-Peñas & Enrique Ortega-Toro, 2020. "Passing Networks and Tactical Action in Football: A Systematic Review," IJERPH, MDPI, vol. 17(18), pages 1-19, September.
    6. Gong, Bingnan & Zhou, Changjing & Gómez, Miguel-Ángel & Buldú, J.M., 2023. "Identifiability of Chinese football teams: A complex networks approach," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Ardekani, Aref Mahdavi & Distinguin, Isabelle & Tarazi, Amine, 2020. "Do banks change their liquidity ratios based on network characteristics?," European Journal of Operational Research, Elsevier, vol. 285(2), pages 789-803.

    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. Holmes, Benjamin & McHale, Ian G. & Żychaluk, Kamila, 2023. "A Markov chain model for forecasting results of mixed martial arts contests," International Journal of Forecasting, Elsevier, vol. 39(2), pages 623-640.
    2. Baboota, Rahul & Kaur, Harleen, 2019. "Predictive analysis and modelling football results using machine learning approach for English Premier League," International Journal of Forecasting, Elsevier, vol. 35(2), pages 741-755.
    3. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    4. Singleton, Carl & Reade, J. James & Brown, Alasdair, 2020. "Going with your gut: The (In)accuracy of forecast revisions in a football score prediction game," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 89(C).
    5. Groll Andreas & Kneib Thomas & Mayr Andreas & Schauberger Gunther, 2018. "On the dependency of soccer scores – a sparse bivariate Poisson model for the UEFA European football championship 2016," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 14(2), pages 65-79, June.
    6. Butler, David & Butler, Robert & Eakins, John, 2021. "Expert performance and crowd wisdom: Evidence from English Premier League predictions," European Journal of Operational Research, Elsevier, vol. 288(1), pages 170-182.
    7. Kharrat, Tarak & McHale, Ian G. & Peña, Javier López, 2020. "Plus–minus player ratings for soccer," European Journal of Operational Research, Elsevier, vol. 283(2), pages 726-736.
    8. Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," International Journal of Forecasting, Elsevier, vol. 36(3), pages 916-932.
    9. Scarf, Phil & Parma, Rishikesh & McHale, Ian, 2019. "On outcome uncertainty and scoring rates in sport: The case of international rugby union," European Journal of Operational Research, Elsevier, vol. 273(2), pages 721-730.
    10. 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.
    11. Lawrence Clegg & John Cartlidge, 2023. "Not feeling the buzz: Correction study of mispricing and inefficiency in online sportsbooks," Papers 2306.01740, arXiv.org, revised Jul 2024.
    12. J Reade & C Singleton & L Vaughan Williams, 2020. "Betting Markets for English Premier League Results and Scorelines: Evaluating a Simple Forecasting Model," Economic Issues Journal Articles, Economic Issues, vol. 25(1), pages 87-106, March.
    13. Pearson Mitchell & Jr Glen Livingston & King Robert, 2020. "An exploration of predictive football modelling," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 16(1), pages 27-39, March.
    14. Gavin A. Whitaker & Ricardo Silva & Daniel Edwards & Ioannis Kosmidis, 2021. "A Bayesian approach for determining player abilities in football," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 174-201, January.
    15. Song, Kai & Gao, Yiran & Shi, Jian, 2020. "Making real-time predictions for NBA basketball games by combining the historical data and bookmaker’s betting line," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    16. Federico Fioravanti & Fernando Delbianco & Fernando Tohmé, 2023. "The relative importance of ability, luck and motivation in team sports: a Bayesian model of performance in the English Rugby Premiership," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 715-731, September.
    17. Asif, M. & McHale, I.G., 2019. "A generalized non-linear forecasting model for limited overs international cricket," International Journal of Forecasting, Elsevier, vol. 35(2), pages 634-640.
    18. Koopman, Siem Jan & Lit, Rutger, 2019. "Forecasting football match results in national league competitions using score-driven time series models," International Journal of Forecasting, Elsevier, vol. 35(2), pages 797-809.
    19. Sharifah Farah Syed Yusoff Alhabshi & Zamira Hasanah Zamzuri & Siti Norafidah Mohd Ramli, 2021. "Monte Carlo Simulation of the Moments of a Copula-Dependent Risk Process with Weibull Interwaiting Time," Risks, MDPI, vol. 9(6), pages 1-21, June.
    20. Hubáček, Ondřej & Šourek, Gustav & Železný, Filip, 2019. "Exploiting sports-betting market using machine learning," International Journal of Forecasting, Elsevier, vol. 35(2), pages 783-796.

    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:eee:ejores:v:268:y:2018:i:1:p:339-347. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    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.