IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v7y2011i2n3.html
   My bibliography  Save this article

The Intra-Match Home Advantage in Australian Rules Football

Author

Listed:
  • Ryall Richard

    (Royal Melbourne Institute of Technology)

  • Bedford Anthony

    (Royal Melbourne Institute of Technology)

Abstract

The existence of home advantage in Australian Rules football (AFL) has been well documented in previous literature. This advantage typically refers to the net advantage of several factors which, generally speaking, have a positive effect on the home team and a negative effect on the away team. However, this practice excludes the in-course dynamics of home advantage throughout the match including the interrelationship between pre-game and in-game team characteristics. The aim of the present study is to calculate the intra-match home advantage for each quarter in AFL by incorporating the interaction between team quality and current score. Archival AFL data was obtained from seasons 2000 to 2009 which consisted of year, round, quarter, (nominal) home team, away team, home team score and away team score. Analysis of variance (ANOVA) on margin of victory was used to determine if there was a distinct difference between team quality (favourite/underdog) within current score (ahead/behind). Since the in-game team characteristics (current score) are likely to be caused by pre-game characteristics (team quality) the margin of victory is adjusted for team quality. The results provide marginal evidence that home underdogs in the third quarter irrespective of whether they were ahead or behind at half time receive a greater advantage than home favourites. Furthermore, home advantage is greatest in the final quarter when there is a high level of uncertainty about the outcome of the match.

Suggested Citation

  • Ryall Richard & Bedford Anthony, 2011. "The Intra-Match Home Advantage in Australian Rules Football," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(2), pages 1-14, May.
    • Handle: RePEc:bpj:jqsprt:v:7:y:2011:i:2:n:3
    DOI: 10.2202/1559-0410.1314
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1314
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1559-0410.1314?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. Jones Marshall B, 2007. "Home Advantage in the NBA as a Game-Long Process," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 3(4), pages 1-16, October.
    2. Ryall, Richard & Bedford, Anthony, 2010. "An optimized ratings-based model for forecasting Australian Rules football," International Journal of Forecasting, Elsevier, vol. 26(3), pages 511-517, July.
    3. Adi Schnytzer & Guy Weinberg, 2008. "Testing for Home Team and Favorite Biases in the Australian Rules Football Fixed-Odds and Point Spread Betting Markets," Journal of Sports Economics, , vol. 9(2), pages 173-190, April.
    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. Adi Schnytzer, 2011. "The Prediction Market for the Australian Football League," Working Papers 2011-15, Bar-Ilan University, Department of Economics.
    2. Jaume García & Levi Pérez & Plácido Rodríguez, 2017. "Forecasting football match results: are the many smarter than the few?," Chapters, in: Plácido Rodríguez & Brad R. Humphreys & Robert Simmons (ed.), The Economics of Sports Betting, chapter 5, pages 71-91, Edward Elgar Publishing.
    3. Delbianco Fernando & Fioravanti Federico & Tohmé Fernando, 2023. "Home advantage and crowd attendance: evidence from rugby during the Covid 19 pandemic," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(1), pages 15-26, March.
    4. Angelini, Giovanni & Candila, Vincenzo & De Angelis, Luca, 2022. "Weighted Elo rating for tennis match predictions," European Journal of Operational Research, Elsevier, vol. 297(1), pages 120-132.
    5. Christian Deutscher, 2011. "Productivity and New Audiences: Empirical Evidence From Professional Basketball," Journal of Sports Economics, , vol. 12(3), pages 391-403, June.
    6. Jones Marshall B, 2011. "Responses to Scoring or Conceding the First Goal in the NHL," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(3), pages 1-13, July.
    7. Trung Minh Dang & Ross Booth & Robert Brooks & Adi Schnytzer, 2015. "Do TV Viewers Value Uncertainty of Outcome? Evidence from the Australian Football League," The Economic Record, The Economic Society of Australia, vol. 91(295), pages 523-535, December.
    8. Christopher J. Boudreaux & Shane D. Sanders & Bhavneet Walia, 2017. "A Natural Experiment to Determine the Crowd Effect Upon Home Court Advantage," Journal of Sports Economics, , vol. 18(7), pages 737-749, October.
    9. Roberto Gásquez & Vicente Royuela, 2016. "The Determinants of International Football Success: A Panel Data Analysis of the Elo Rating," Social Science Quarterly, Southwestern Social Science Association, vol. 97(2), pages 125-141, June.
    10. Trandel Gregory A & Maxcy Joel G, 2011. "Adjusting Winning-Percentage Standard Deviations and a Measure of Competitive Balance for Home Advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 7(1), pages 1-17, January.
    11. Jinook Jeong & Jee Young Kim & Yoon Jae Ro, 2019. "On the efficiency of racetrack betting market: a new test for the favourite-longshot bias," Applied Economics, Taylor & Francis Journals, vol. 51(54), pages 5817-5828, November.
    12. Angelini, Giovanni & De Angelis, Luca, 2019. "Efficiency of online football betting markets," International Journal of Forecasting, Elsevier, vol. 35(2), pages 712-721.
    13. Jones Marshall B, 2008. "A Note on Team-Specific Home Advantage in the NBA," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(3), pages 1-15, July.
    14. Manner Hans, 2016. "Modeling and forecasting the outcomes of NBA basketball games," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 12(1), pages 31-41, March.
    15. Guy Elaad, 2020. "Home-field advantage and biased prediction markets in English soccer," Applied Economics Letters, Taylor & Francis Journals, vol. 27(14), pages 1170-1174, July.
    16. Massimiliano Ferraresi & Gianluca Gucciardi, 2020. "Team performance and audience: experimental evidence from the football sector," Working papers 94, Società Italiana di Economia Pubblica.
    17. Tai, Chung-Ching & Lin, Hung-Wen & Chie, Bin-Tzong & Tung, Chen-Yuan, 2019. "Predicting the failures of prediction markets: A procedure of decision making using classification models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 297-312.
    18. Chmait, Nader & Robertson, Sam & Westerbeek, Hans & Eime, Rochelle & Sellitto, Carmine & Reid, Machar, 2020. "Tennis superstars: The relationship between star status and demand for tickets," Sport Management Review, Elsevier, vol. 23(2), pages 330-347.
    19. Federico Fioravanti & Fernando Delbianco & Fernando Tohm'e, 2023. "Visitors Out! The Absence of Away Team Supporters as a Source of Home Advantage in Football," Papers 2308.06279, arXiv.org, revised Nov 2023.
    20. Kai Fischer & Justus Haucap, 2020. "Betting Market Efficiency in the Presence of Unfamiliar Shocks: The Case of Ghost Games during the Covid-19 Pandemic," CESifo Working Paper Series 8526, CESifo.

    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:7:y:2011:i:2:n:3. 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.