Match statistics that discriminate between winning and losing teams in ODI and T20I cricket
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Date
2018-01
Authors
Schaefer, Mark Christopher
Journal Title
Journal ISSN
Volume Title
Publisher
University of the Free State
Abstract
Background
Cricket players and teams have a different strategy for batting for the different
formats of cricket, namely Twenty-Twenty International (T20I) and One Day
International (ODI). Different application of skills is required for each format of cricket
can clearly be seen as mostly a different team is selected for each format of the
game in professional cricket. Analysis of performance variables such as boundaries
hit by batsmen and runs scored during the power play can be used to predict future
success or failure of a cricket team based on the match outcome. This study will
provide batting statistics that discriminate between winning and losing teams in ODI
and T20I cricket. Furthermore, the study will reveal which variables correlate the
highest with successful performance within the different formats of the game.
Aims
The aim of this study was twofold, firstly to analyse batting data in ODI cricket that
discriminate between winning and losing teams. Secondly to analyse batting data in
T20I cricket that discriminate between winning and losing teams.
Method
Sample
Ten international teams were selected for the purpose of this study. The ten teams
were 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 randomly
selected and observed (3 batting first, 3 batting second). The first aim consisted of
conducting analysis of a total of 60 professional ODI cricket matches resulting in 120
records (innings) (both teams involved per match). The second aim consisted of
conducting analysis of a total of 60 professional T20I cricket matches resulting in 120
records (both teams involved per match). Drawn matches, and those which
employed the Duckworth-Lewis method, were excluded from the study.
Measuring instruments
Retrospective data from the 2014 and 2015 international cricket season was
collected from ESPN Cricinfo website.
Data analysis
In this research, a strong and reliable data source is needed which was found in
Statsguru. Statsguru is ESPN Cricinfo's cricket statistics maintenance database. The
data was then analyzed using the SAS statistical software (SAS, 2013).
Because of the fundamentally different match situation faced by the team batting first
and second, respectively, the data were analysed separately for the team batting first
and 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 of
the potential predictor variables with the match outcome was analyzed using
univariate logistic regression, fitting each predictor variable, one at a time. The
statistical significance of each predictor variable was tested using an exact test
(exact conditional logistic regression); the exact P-value is reported. The analysis
was carried out using SAS procedure LOGISTIC (see SAS, 2013).
Results
For aim 1 the significant predictors of winning an ODI cricket match when batting first
were: 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 top
four batsmen (p=0.0015); For aim 1 the significant predictors of winning an ODI
cricket 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 runs
scored between the lower order batsmen (p=0.0222). Variables that predict success
in ODI cricket differed for teams batting first and second, respectively. For aim 2
significant predictors of winning a T20I cricket match when batting first were: runs
scored in the first 5 overs (p=0.0035), runs scored in the last 7 overs (p=<0.0001),
Description
Keywords
Batting, Cricket one-day international, Twenty-twenty international, Runs scored, Boundaries scored, Dissertation (M.A. (Exercise and Sport Sciences))--University of the Free State, 2018