Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Steve Rigdon, PhD

4 Issues per year

CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

See all formats and pricing
More options …
Volume 9, Issue 2

Around the goal: examining the effect of the first goal on the second goal in soccer using survival analysis methods

Daniel Nevo / Ya’acov Ritov
Published Online: 2013-06-08 | DOI: https://doi.org/10.1515/jqas-2012-0004


In this paper we apply survival techniques to soccer data, treating a goal scoring as the event of interest. It specifically concerns the relationship between the time of the first goal in the game and the time of the second goal. In order to do so, the relevant survival analysis concepts are readjusted to fit the problem and a Cox model is developed for the hazard function. Time dependent covariates and a frailty term are also considered. We also use a reliable propensity score to summarize the pre-game covariates. The conclusions derived from the results are that a first goal occurrence could either expedite or impede the next goal scoring, depending on the time it was scored. Moreover, once a goal is scored, another goal scoring becomes more and more likely as the game progresses. Furthermore, the first goal effect is the same whether the goal was scored or conceded.

Keywords: Cox model; frailty; goals; soccer; survival analysis


  • Carmichael, F. and D. Thomas. 2005. “Home-Field Effect and Team Performance.” Journal of Sports Economics 6:264–281.CrossrefGoogle Scholar

  • Clarke, S. R. and J. M. Norman. 1995. “Home Ground Advantage of Individual Clubs in English Soccer.” Journal of the Royal Statistical Society, Series D (The Statistician) 44: 509–521.Google Scholar

  • Cox, D. R. 1972. “Regression and Life-Tables.” Journal of the Royal Statistical Society: Series B (Statistical Methodology) 34:187–220.Google Scholar

  • Dixon, M. J. and S. G. Coles. 1997. “Modelling Association Football Scores and Inefficiencies in the Football Betting Market.” Journal of the Royal Statistical Society: Series C (Applied Statistics) 46:265–280.CrossrefGoogle Scholar

  • Dixon, M. and M. Robinson. 1998. A Birth Process Model for Association Football Matches.” Journal of the Royal Statistical Society: Series D (The Statistician) 47:523–538.CrossrefGoogle Scholar

  • Fox, J. 2002. Cox Proportional-Hazards Regression for Survival Data, Appendix to an R and S-PLUS Companion to Applied Regression. Sage Publications, pp. 1–18.Google Scholar

  • Jones, M. B. 2011. “Responses to Scoring or Conceding the First Goal in the nhl.” Journal of Quantitative Analysis in Sports 7:15.Google Scholar

  • Kaplan, E. L. and P. Meier. 1958. “Nonparametric Estimation from Incomplete Observations.” Journal of the American Statistical Association 53:457–481.CrossrefGoogle Scholar

  • Klein, J. P. 1992. “Semiparametric Estimation of Random Effects Using the Cox Model Based on The Em Algorithm.” Biometrics 48:795–806.CrossrefPubMedGoogle Scholar

  • Klein, J. P. and M. L. Moeschberger. 2003. Survival Analysis: Techniques for Censored and Truncated Data. 2nd ed. Springer.Google Scholar

  • Maher, M. J. 1982. “Modelling Association Football Scores.” Statistica Neerlandica 36:109–118.CrossrefGoogle Scholar

  • Nielsen, G. G., R. D. Gill, P. K. Andersen, and T. I. A. S. Sørensen. 1992. “A Counting Process Approach to Maximum Likelihood Estimation in Frailty Model.” Scandunavian Journal of Statistics 19:25–43.Google Scholar

  • Pollard, R. 1986. “Home Advantage in Soccer: A Retrospective Analysis.” Journal of Sports Sciences 4:237–248.CrossrefGoogle Scholar

  • Ridder, G., J. S. Cramer, and P. Hopstaken. 1994. “Estimating The Effect of a Red Card in Soccer.” Journal of the American Statistical Association 89:1124–1127.CrossrefGoogle Scholar

  • Therneau, T. 2012. A Package for Survival Analysis in S, R Package Version 2.36-14.Google Scholar

  • Therneau, T. M., P. M. Grambsch, and T. R. Fleming. 1990. “Martingale-Based Residuals for Survival Models.” Biometrika 77:147–160.CrossrefGoogle Scholar

  • Volf, P. 2009. “A Random Point Process Model for the Score in Sport Matches.” IMA Journal of Management Mathematics 20:121–131.Web of ScienceGoogle Scholar

About the article

Corresponding author: Daniel Nevo, The Hebrew University of Jerusalem – Statistics, Jerusalem, Israel

Published Online: 2013-06-08

Published in Print: 2013-06-01

Citation Information: Journal of Quantitative Analysis in Sports, Volume 9, Issue 2, Pages 165–177, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2012-0004.

Export Citation

©2013 by Walter de Gruyter Berlin Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Marcel Ausloos, Adam Gadomski, and Nikolay K Vitanov
Physica Scripta, 2014, Volume 89, Number 10, Page 108002

Comments (0)

Please log in or register to comment.
Log in