dc.contributor.author |
Pathirana, O.D.R. |
|
dc.contributor.author |
Mallawa Arachchi, D.K. |
|
dc.date.accessioned |
2017-01-04T09:04:23Z |
|
dc.date.available |
2017-01-04T09:04:23Z |
|
dc.date.issued |
2016 |
|
dc.identifier.citation |
Pathirana, O.D.R. and Mallawa Arachchi, D.K. 2016. Categorizing T20 cricket grounds. In Proceedings of the International Research Symposium on Pure and Applied Sciences (IRSPAS 2016), Faculty of Science, University of Kelaniya, Sri Lanka. p 60. |
en_US |
dc.identifier.isbn |
978-955-704-008-0 |
|
dc.identifier.uri |
http://repository.kln.ac.lk/handle/123456789/15714 |
|
dc.description.abstract |
T20 cricket matches are played by all cricket playing countries. There are more than
80 grounds in various countries on which these games are played. It is hypothesized
that some of these grounds favor batsmen while others favor bowlers, or some
grounds are high-scoring while others are low-scoring. In this research work, we
perform a statistical analysis to determine whether those grounds can be categorized
based on the past data. Numerous factors can be considered for the analysis. Main
factors we have been considering are the total runs scored in both innings, humidity
level, gust, wind, air pressure and the temperature at the grounds when the matches
are played. Cluster analysis was used in investigating and determining the number of
categories.
This study helps identify the behavior of the T20 cricket grounds all over the world
and thus enables one to predict the winning possibilities.
Data were collected through Cricinfo website from 84 cricket grounds throughout
the world. Ward’s method of Hierarchical cluster analysis, which is a major statistical
method used in determining the relatively homogeneous clusters, was used.
We found that grounds can be clustered into 3 clusters according to the coefficients
of the Wards linkage table. When we consider the countries in which these grounds
are located, there is no evidence to conclude that grounds in some specific countries
are belonging to a particular category. For example there are grounds in India
belonging to all three categories.
SPSS statistical software was used in this analysis to categorize the grounds. The
research work is being carried out to identify how cluster changes with different
factors. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Faculty of Science, University of Kelaniya, Sri Lanka |
en_US |
dc.subject |
Cricket grounds |
en_US |
dc.subject |
Cluster analysis |
en_US |
dc.subject |
Hierarchical cluster analysis |
en_US |
dc.subject |
Gust |
en_US |
dc.title |
Categorizing T20 cricket grounds |
en_US |
dc.type |
Article |
en_US |