Advertisement

Statistical modelling of goalkicking performance in the Australian Football League

      Abstract

      Objectives

      Australian football goal kicking is vital to team success, but its study is limited. Develop and apply Bayesian models incorporating temporal, spatial and situational variables to predict shot outcomes. The models aim to (i) rank players on their goal kicking and (ii) create clusters of statistically similar players and rank these clusters to provide generalised recommendations about player types.

      Design

      Retrospective longitudinal study with goal kicking data from three seasons, 2018–2020, 576 official Australian Football League matches, containing 26,818 attempts at goal from 778 players.

      Methods

      The Bayesian ordinal regression model enables descriptive analysis of goal kicking performance. The models include spatial variables of distance and kick angle, situational variables of shot type and player or cluster with interaction terms. Alternative models included situational variables of weather and player characteristics, spatial variables of stadium location and temporal variables of time and quarter. Approximate leave-one-out cross validation was used to test the model.

      Results

      Overall goal rate of 47% (12,600), behind rate of 35% (9373) with misses the remaining 18% (4845). Accuracy of both player and cluster model achieved 0.51 against an uninformed (predict goal) model result of 0.47. The models allow for analysis of goal kicking accuracy by distance and angle and analysis of player and player-type performance.

      Conclusions

      While credible intervals for all players for set shots and general play were relatively large, some 95% credible intervals excluded zero. Therefore, it may be concluded that some players' goal kicking skill can be quantified and differentiated from other players.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Journal of Science and Medicine in Sport
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Gray A.J.
        • Jenkins D.G.
        Match analysis and the physiological demands of Australian football.
        Sports Med. 2010; 40: 347-360
        • Anderson D.
        • Breed R.
        • Spittle M.
        • et al.
        Factors affecting set shot goal-kicking performance in the Australian football league.
        Percept Mot Skills. 2018; 125: 817-833
        • Robertson S.
        • Back N.
        • Bartlett J.D.
        Explaining match outcome in elite Australian rules football using team performance indicators.
        J Sports Sci. 2016; 34: 637-644
        • AFL Tables
        Player Stats.
        (Available at)
        https://afltables.com/afl/stats/2020.html
        Date: 2020
        Date accessed: June 25, 2021
        • Champion Data Pty Ltd
        Joly C. AFL Prospectus: The Essential Number-Cruncher for Season 2018. 13th ed. Champion Data, Southbank Victoria2018
        • Champion Data Pty Ltd
        Joly C. AFL Prospectus: The Essential Number-Cruncher for Season 2019. 14th ed. Champion Data, Southbank Victoria2019
        • Champion Data Pty Ltd
        Joly C. AFL Prospectus: The Essential Number-Cruncher for Season 2020. 15th ed. Champion Data, Southbank Victoria2020
        • Hypometer Technologies Pty Ltd
        AFL Shot Charting.
        (Available at)
        https://statsinsider.com.au/afl/shot-charting
        Date accessed: November 10, 2020
        • Day J.
        • Nguyen R.
        • Lane O.
        fitzRoy: Easily Scrape and Process AFL Data. R package version 1.0.0.
        (Available at)
        https://CRAN.R-project.org/package=fitzRoy
        Date accessed: December 15, 2020
        • Quarrie K.L.
        • Hopkins W.G.
        Evaluation of goal kicking performance in international rugby union matches.
        J Sci Med Sport. 2015; 18: 195-198
        • Pasteur R.D.
        • Cunningham-Rhoads K.
        An expectation-based metric for NFL field goal kickers.
        J Quant Anal Sports. 2014; 10: 49-66
        • Hsu N.-W.
        • Liu K.-S.
        • Chang S.-C.
        Choking under the pressure of competition: a complete statistical investigation of pressure kicks in the NFL, 2000–2017.
        PLoS One. 2019; 14: 1-18
        • Goldschmied N.
        • Nankin M.
        • Cafri G.
        Pressure kicks in the NFL: an archival exploration into the deployment of time-outs and other environmental correlates.
        Sport Psychol. 2010; 24: 300-312
        • Nicholls M.E.R.
        • Loetscher T.
        • Rademacher M.
        Miss to the right: the effect of attentional asymmetries on goal-kicking.
        PLoS One. 2010; 5: 1-6
        • Galbraith P.L.
        • Lockwood T.
        Things may not always be as they seem: the set shot in AFL football.
        Aust Senior Math J. 2010; 24: 29-42
        • Cowgill M.
        Classifying players' positions using public data.
        (Available at)
        • Corke T.
        Classifying Recent AFL Players by Position.
        (Available at)
        • Blair S.
        • Robertson S.
        • Duthie G.
        • et al.
        Biomechanics of accurate and inaccurate goal-kicking in Australian football: group-based analysis.
        PLoS One. 2020; 15: 1-17
        • Elliott S.
        • Whitehead A.
        • Magias T.
        Thought processes during set shot goalkicking in Australian rules football: an analysis of youth and semi-professional footballers using think aloud.
        Psychol Sport Exerc. 2020; 48: 10169-10178
        • Harrell Jr., F.E.
        Ordinal logistic regression, chapter 13.
        in: Regression Modeling Strategies. 2nd ed. Springer, New York2015
        • R Core Team
        R: A language and Environment for Statistical Computing.
        R. Foundation for Statistical Computing, Vienna, Austria2021 (URL)
        • Lago-Peñas C.
        The role of situational variables in analysing physical performance in soccer.
        J Hum Kinet. 2012; 35: 89-95
        • AFL
        Stats glossary: every stat explained.
        (Available at)
        • Charrad M.
        • Ghazzali N.
        • Boiteau V.
        • et al.
        NbClust: an R package for determining the relevant number of clusters in a data set.
        J Stat Softw. 2015; 61: 1-36
        • Bürkner P.-C.
        Brms: an R package for Bayesian multilevel models using Stan.
        J Stat Softw. 2017; 80: 1-28
        • Bürkner P.-C.
        Advanced Bayesian multilevel modeling with the R package brms.
        R I Dent J. 2021; 10: 395-411
        • Vehtari A.
        • Gelman A.
        • Gabry J.
        Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC.
        Stat Comput. 2017; 27: 1413-1432
        • Vehtari A.
        • Gabry J.
        • Magnusson M.
        • et al.
        loo: efficient leave-one-out cross-validation and WAIC for Bayesian models. R package version 2.4.1.
        (Available at)
        https://mc-stan.org/loo/
        Date accessed: July 10, 2021
        • Gelman A.
        • Hwang J.
        • Vehtari A.
        Understanding predictive information criteria for Bayesian models.
        Stat Comput. 2014; 24: 997-1016