IDEAS home Printed from
   My bibliography  Save this article

Principal component analysis-based frequent pattern evaluation on the object-relational data model of a cricket match database


  • P. UmaMaheswari
  • M. Rajaram


Frequent pattern evaluation is imperative for cricket match data to develop more proficient coaching strategies and progress the performance of individual players. The rapid growth in size of the match database far exceeds the human ability to analyse, thus creating an opportunity to extract knowledge from this database. Very few research efforts have been carried out on sports data (especially on cricket) and none of them focused on play patterns. Our work emphasises play patterns to discover interesting patterns from cricket matches and evaluate those patterns to turn them into knowledge that can further be used to modify the coaching process and play styles. Since real-time cricket data are too complex, an object-relational model is used here. In this work, Principal Component Analysis (PCA) is used to reduce high dimensional match data set into lower dimensional data set in order to improve predictive performance and to detect frequently occurring play patterns.

Suggested Citation

  • P. UmaMaheswari & M. Rajaram, 2009. "Principal component analysis-based frequent pattern evaluation on the object-relational data model of a cricket match database," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(4), pages 364-384.
  • Handle: RePEc:ids:injdan:v:1:y:2009:i:4:p:364-384

    Download full text from publisher

    File URL:
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to search for a different version of it.


    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:ids:injdan:v:1:y:2009:i:4:p:364-384. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Darren Simpson). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.