Design and Implementation of Statistical Estimation Based Model for Fair Assessment of Rain Interrupted Cricket Matches
Abstract
Cricket has achieved the status of a religion in India due to its huge popularity. The huge amounts of money and interest that cricket garners is increasing the spotlight on making the cricket experience for avid fans more seamless and enjoyable. There is a immediate requirement to come up with a fair assessment method which at any point of the game can decide the winner considering all relevant factors influencing the match. The current model used in rain interrupted matches is the Duckworth-Lewis (D/L) method. In interrupted matches a decision has to be reached within an allocated time of the game and the game cannot be postponed to another day. It has been reported that the D/L method delivers unrealistic target scores for certain cases exhibiting its unfairness and bias towards teams batting second. The proposed algorithm formulated is an alternate approach that could serve well to reset the target score overcoming this intrinsic problem of the D/L method. This algorithm demands extensive data cleaning and structuring of the raw available data, followed by feature extraction. Exploratory analysis and statistical tests have then been carried out on the independent variables. The developed mathematical functions work for both batting and bowling teams and the neural networks are trained to learn these functions. The developed algorithm is trained and validated for all the completed ODI matches as well as for D/L matches. Accuracy of the model tested on completed ODI matches and for rain interrupted matches is 57 % and 61% respectively. The implemented algorithm can be extended to player selection, modelling using other features (apart from batting and bowling related) to improve the prediction for the rain interrupted matchesimplementing a D/L method - for fairer evaluation of outcomes.
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