Match statistics that discriminate between winning and losing teams in ODI and T20I cricket

[摘要] BackgroundCricket players and teams have a different strategy for batting for the differentformats of cricket, namely Twenty-Twenty International (T20I) and One DayInternational (ODI). Different application of skills is required for each format of cricketcan clearly be seen as mostly a different team is selected for each format of thegame in professional cricket. Analysis of performance variables such as boundarieshit by batsmen and runs scored during the power play can be used to predict futuresuccess or failure of a cricket team based on the match outcome. This study willprovide batting statistics that discriminate between winning and losing teams in ODIand T20I cricket. Furthermore, the study will reveal which variables correlate thehighest with successful performance within the different formats of the game.AimsThe aim of this study was twofold, firstly to analyse batting data in ODI cricket thatdiscriminate between winning and losing teams. Secondly to analyse batting data inT20I cricket that discriminate between winning and losing teams.MethodSampleTen international teams were selected for the purpose of this study. The ten teamswere selected because they all participate in all three formats of cricket namely ODI,T20I, and test cricket. Six matches from each team's records were randomlyselected and observed (3 batting first, 3 batting second). The first aim consisted ofconducting analysis of a total of 60 professional ODI cricket matches resulting in 120records (innings) (both teams involved per match). The second aim consisted ofconducting analysis of a total of 60 professional T20I cricket matches resulting in 120records (both teams involved per match). Drawn matches, and those whichemployed the Duckworth-Lewis method, were excluded from the study.Measuring instrumentsRetrospective data from the 2014 and 2015 international cricket season wascollected from ESPN Cricinfo website.Data analysisIn this research, a strong and reliable data source is needed which was found inStatsguru. Statsguru is ESPN Cricinfo's cricket statistics maintenance database. Thedata was then analyzed using the SAS statistical software (SAS, 2013).Because of the fundamentally different match situation faced by the team batting firstand second, respectively, the data were analysed separately for the team batting firstand for the team batting second. The outcome of the match is a binary variable(win/lose) since drawn matches were excluded from the analysis. The association ofthe potential predictor variables with the match outcome was analyzed usingunivariate logistic regression, fitting each predictor variable, one at a time. Thestatistical significance of each predictor variable was tested using an exact test(exact conditional logistic regression); the exact P-value is reported. The analysiswas carried out using SAS procedure LOGISTIC (see SAS, 2013).ResultsFor aim 1 the significant predictors of winning an ODI cricket match when batting firstwere: runs scored in the first 20 overs (p=0.0019), runs scored in the last 12 overs(p=0.0004), sixes scored (p=0.0017), and the number of runs scored among the topfour batsmen (p=0.0015); For aim 1 the significant predictors of winning an ODIcricket match when batting second were: fours scored (p=0.0024), sixes scored(p=0.00277), runs scored between the top order batsmen (p=0.0197), and runsscored between the lower order batsmen (p=0.0222). Variables that predict successin ODI cricket differed for teams batting first and second, respectively. For aim 2significant predictors of winning a T20I cricket match when batting first were: runsscored in the first 5 overs (p=0.0035), runs scored in the last 7 overs (p=<0.0001),

[发布机构] University of the Free State