Using Local Correlation to Explain Success in Baseball
Statisticians have long employed linear regression models in a variety of circumstances, including the analysis of sports data, because of their flexibility, ease of interpretation, and computational tractability. However, advances in computing technology have made it possible to develop and employ more complicated, nonlinear, and nonparametric procedures. We propose a fully nonparametric nonlinear regression model that is associated to a local correlation function instead of the usual Pearson correlation coefficient. The proposed nonlinear regression model serves the same role as a traditional linear model, but generates deeper and more detailed information about the relationships between the variables being analyzed. We show how nonlinear regression and the local correlation function can be used to analyze sports data by presenting three examples from the game of baseball. In the first and second examples, we demonstrate use of nonlinear regression and the local correlation function as descriptive and inferential tools, respectively. In the third example, we show that nonlinear regression modeling can reveal that traditional linear models are, in fact, quite adequate. Finally, we provide a guide to software for implementing nonlinear regression. The purpose of this paper is to make nonlinear regression and local correlation analysis available as investigative tools for sports data enthusiasts.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 7 (2011)
Issue (Month): 4 (October)
|Contact details of provider:|| Web page: https://www.degruyter.com|
|Order Information:||Web: https://www.degruyter.com/view/j/jqas|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Young William A & Holland William S & Weckman Gary R, 2008. "Determining Hall of Fame Status for Major League Baseball Using an Artificial Neural Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 4(4), pages 1-46, October.
- Lawrence Hadley & John Ruggiero, 2006. "Final-offer arbitration in major league baseball: A nonparametric analysis," Annals of Operations Research, Springer, vol. 145(1), pages 201-209, July.
- Smith Lloyd & Downey James, 2009. "Predicting Baseball Hall of Fame Membership using a Radial Basis Function Network," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(1), pages 1-21, January.
- Freiman Michael H., 2010. "Using Random Forests and Simulated Annealing to Predict Probabilities of Election to the Baseball Hall of Fame," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(2), pages 1-37, April.
- Jahn K. Hakes & Raymond D. Sauer, 2006. "An Economic Evaluation of the Moneyball Hypothesis," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 173-186, Summer.
- Timothy Anderson & Gunter Sharp, 1997. "A new measure of baseball batters using DEA," Annals of Operations Research, Springer, vol. 73(0), pages 141-155, October.
- Kaplan David, 2006. "A Variance Decomposition of Individual Offensive Baseball Performance," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 2(3), pages 1-18, July.
When requesting a correction, please mention this item's handle: RePEc:bpj:jqsprt:v:7:y:2011:i:4:n:5. 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: (Peter Golla)
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.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with 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 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.