Receiver Operating Characteristic (ROC) Curves for Measuring the Quality of Decisions in Cricket
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Ananda B. W. Manage
A receiver operating characteristic (ROC) curve visually demonstrates the tradeoff between sensitivity and specificity as a function of varying a classification threshold. It is a common practice to use ROC curves to measure the accuracy of predictions by different methods. Although this method has been used primarily in medical and engineering fields, it could be used effectively in sports as well. More precisely, an ROC plots the sensitivity versus (1 - specificity), and the area under the curve gives a measure of the prediction. So, the ideal best prediction should have one square unit of area under the ROC curve, where it achieves both 100% sensitivity and 100% specificity (which, in reality, rarely happens). Consequently, when we compare two methods, the one with the greater area under its ROC is judged best. This paper shows the effectiveness of using the ROC curves in analyzing cricket data. In particular, the quality of the decisions made by umpires is investigated. Also, the comparison of the accuracy of the methods for revising the target for matches shortening due to weather interruptions is the key interest in our investigation.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
- Conference Paper
- Predicting Overtime with the Pythagorean Formula
- Using Game Theory to Optimize Performance in a Best-of-N Set Match
- A Spatial Multidimensional Unfolding Choice Model for Examining the Heterogeneous Expressions of Sports Fan Avidity
- Anomalies in Tournament Design: The Madness of March Madness
- Skill Importance in Women's Volleyball
- Quantifying the Effect of Performance-Enhancing Drug Use on Fastball Velocity in Major League Baseball
- A New Iterative Method for Ranking College Football Teams
- Receiver Operating Characteristic (ROC) Curves for Measuring the Quality of Decisions in Cricket
- Visualization of Crew Race Performance: Drives and Duels
- Valuations of Soccer Players from Statistical Performance Data
- Measuring Risk in NFL Playcalling
- Using Random Forests and Simulated Annealing to Predict Probabilities of Election to the Baseball Hall of Fame
- The 2009 New England Symposium on Statistics in Sports
