IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v35y2019i2p634-640.html
   My bibliography  Save this article

A generalized non-linear forecasting model for limited overs international cricket

Author

Listed:
  • Asif, M.
  • McHale, I.G.

Abstract

This paper proposes a generalized non-linear forecasting model (GNLM) for forecasting the number of runs remaining to be scored in an innings of cricket. The proposed model takes into account the numbers of overs left and wickets lost. The GNLFM can be used to build a model for any format of limited-overs international cricket. However, the purpose of its use in this paper is for building a forecasting model for projecting second innings total runs in Twenty-20 International cricket. Our model makes it possible to estimate the runs differential of the two competing teams whilst the match is in progress. The runs differential can be used not only to gauge the closeness of a game, but also to estimate the ratings of cricket teams that take into account the margin of victory. Furthermore, the well-known original Duckworth/Lewis (DL) model and the McHale/Asif version of it for revising targets in interrupted matches are special cases of our proposed generalized non-linear forecasting model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:2:p:634-640
    DOI: 10.1016/j.ijforecast.2018.12.003
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207019300068
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2018.12.003?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. Steven E. Stern, 2011. "Moderated paired comparisons: a generalized Bradley–Terry model for continuous data using a discontinuous penalized likelihood function," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 60(3), pages 397-415, May.
    2. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630, April.
    3. McHale, Ian G. & Asif, Muhammad, 2013. "A modified Duckworth–Lewis method for adjusting targets in interrupted limited overs cricket," European Journal of Operational Research, Elsevier, vol. 225(2), pages 353-362.
    4. Steven E Stern, 2016. "The Duckworth-Lewis-Stern method: extending the Duckworth-Lewis methodology to deal with modern scoring rates," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1469-1480, December.
    5. S R Clarke & P Allsopp, 2001. "Fair measures of performance: the World Cup of cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(4), pages 471-479, April.
    6. 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.
    7. Asif, Muhammad & McHale, Ian G., 2016. "In-play forecasting of win probability in One-Day International cricket: A dynamic logistic regression model," International Journal of Forecasting, Elsevier, vol. 32(1), pages 34-43.
    8. F C Duckworth & A J Lewis, 2004. "A successful operational research intervention in one-day cricket," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(7), pages 749-759, July.
    9. McHale, Ian & Morton, Alex, 2011. "A Bradley-Terry type model for forecasting tennis match results," International Journal of Forecasting, Elsevier, vol. 27(2), pages 619-630.
    10. P. E. Allsopp & Stephen R. Clarke, 2004. "Rating teams and analysing outcomes in one‐day and test cricket," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 657-667, November.
    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. 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.

    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. 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.
    2. Hubáček, Ondřej & Šír, Gustav, 2023. "Beating the market with a bad predictive model," International Journal of Forecasting, Elsevier, vol. 39(2), pages 691-719.
    3. 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.
    4. Wunderlich, Fabian & Memmert, Daniel, 2020. "Are betting returns a useful measure of accuracy in (sports) forecasting?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 713-722.
    5. Hemanta Saikia, 2020. "Quantifying the Current Form of Cricket Teams and Predicting the Match Winner," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 45(2), pages 151-158, May.
    6. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    7. Kovalchik, Stephanie, 2020. "Extension of the Elo rating system to margin of victory," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1329-1341.
    8. Silva, Rajitha M. & Manage, Ananda B.W. & Swartz, Tim B., 2015. "A study of the powerplay in one-day cricket," European Journal of Operational Research, Elsevier, vol. 244(3), pages 931-938.
    9. Marc Garnica-Caparrós & Daniel Memmert & Fabian Wunderlich, 2022. "Artificial data in sports forecasting: a simulation framework for analysing predictive models in sports," Information Systems and e-Business Management, Springer, vol. 20(3), pages 551-580, September.
    10. Baker, Rose D. & McHale, Ian G., 2014. "A dynamic paired comparisons model: Who is the greatest tennis player?," European Journal of Operational Research, Elsevier, vol. 236(2), pages 677-684.
    11. 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.
    12. Brown, Alasdair & Reade, J. James, 2019. "The wisdom of amateur crowds: Evidence from an online community of sports tipsters," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1073-1081.
    13. Ian G. McHale & Philip A. Scarf & David E. Folker, 2012. "On the Development of a Soccer Player Performance Rating System for the English Premier League," Interfaces, INFORMS, vol. 42(4), pages 339-351, August.
    14. Abhinav Sacheti & Ian Gregory-Smith & David Paton, 2016. "Managerial Decision Making Under Uncertainty," Journal of Sports Economics, , vol. 17(1), pages 44-63, January.
    15. Kovalchik Stephanie Ann, 2016. "Searching for the GOAT of tennis win prediction," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(3), pages 127-138, September.
    16. Sarah Jewell & J. James Reade & Carl Singleton, 2020. "It's Just Not Cricket: The Uncontested Toss and the Gentleman's Game," Economics Discussion Papers em-dp2020-10, Department of Economics, University of Reading.
    17. Clive B Beggs & Alexander J Bond & Stacey Emmonds & Ben Jones, 2019. "Hidden dynamics of soccer leagues: The predictive ‘power’ of partial standings," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-28, December.
    18. Ramirez, Philip & Reade, J. James & Singleton, Carl, 2023. "Betting on a buzz: Mispricing and inefficiency in online sportsbooks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1413-1423.
    19. Halkos, George & Tzeremes, Nickolaos, 2012. "Evaluating professional tennis players’ career performance: A Data Envelopment Analysis approach," MPRA Paper 41516, University Library of Munich, Germany.
    20. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.

    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:intfor:v:35:y:2019:i:2:p:634-640. 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/ijforecast .

    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.