Classification of all-rounders in limited over cricket - a machine learning approach

In cricket, all-rounders play an important role. A good all-rounder should be able to contribute to the team by both bat and ball as needed. However, these players still have their dominant role by which we categorize them as batting all-rounders or bowling all-rounders. Current practice is to do so by mostly subjective methods. In this study, the authors have explored different machine learning techniques to classify all-rounders into bowling all-rounders or batting all-rounders based on their observed performance statistics. In particular, logistic regression, linear discriminant function, quadratic discriminant function, naïve Bayes, support vector machine, and random forest classification methods were explored. Evaluation of the performance of the classification methods was done using the metrics accuracy and area under the ROC curve. While all the six methods performed well, logistic regression, linear discriminant function, quadratic discriminant function, and support vector machine showed outstanding performance suggesting that these methods can be used to develop an automated classification rule to classify all-rounders in cricket. Given the rising popularity of cricket, and the increasing revenue generated by the sport, the use of such a prediction tool could be of tremendous benefit to decision-makers in cricket.

Annotations on the Relationship Among Discriminant Functions

Different forms of discriminant functions and the essence of their appearances were considered in this study. Various forms of classification problems were also considered, and in each of the cases mentioned, classification from simple functions of the observational vector rather than complicated regions in the higher-dimensional space of the original vector were made. Violation of condition of equal variance covariance matrix for Linear Discriminant Function (LDF) results to Quadratic Discriminant Function (QDF). The relationships among the classification statistics examined were established: The Anderson’s (W) and Rao’s (R) statistics are equivalent when the two sample sizes are equal, and when a constant is equal to 1, W, R and John-Kudo’s (Z) classification statistics are asymptotically comparable. A linear relationship is also established between W and Z classification statistics.

Sensitivity Analysis of the Quadratic Discriminant Function for Predicting Pregnancy Outcomes

The prevalence rate of stillbirth is ten times higher in developing countries relative to developed countries with a 2016 rate of 18 percent in Ghana. This study employed the Quadratic Discriminant Function for discriminating and classifying of pregnancy outcomes based on some predictors. The study further examined the sensitivity of the Quadratic Discriminant Function in predicting pregnancy outcomes with variations in the training and test samples of deliveries recorded in a hospital in Accra, Ghana. The study considered the scenarios; 50:50, 60:40, 70:30 and 75:25 ratios of training sets to testing sets. Predictor variables on both maternal factors (maternal age, parity and gravida) and fetus variables (weight at birth and gestational period) were all statistically significant (P < .01) in discriminating between live birth and stillbirth. Results showed that maternal age had a negative effect on the live birth outcomes, while parity, gravida, gestational period and fetus weight recorded positive effects on live birth outcomes. The 75:25 ratio outperformed the other ratios in discriminating between live and stillbirth based on the Actual Error Rate of 7.28% compared to 7.81%, 12.14% and 13.79% for the 50:50, 70:30 and 60:40 ratios respectively whereas, the receiver operating characteristic curve shows the 70:30 (AUC= 0.9233) ratio outperformed the others. The study recommend the use of either the 70:30 or 75:25 training to test ratios for classification and discrimination related problems. Moreover, further research to establish the power of the respective training to test sample ratios with other statistical classification tools and more socio-economic variables can be considered.

Trash or treasure? Use of sagittal otoliths partially composed of vaterite for hatchery stock discrimination in steelhead

Sagittal otoliths are normally deposited as the CaCO3 polymorph aragonite; however, a proportion of otoliths transitions to vaterite during growth. This transition can complicate otolith chemistry analyses, as differences in the crystalline structure (aragonite or vaterite) of otoliths causes variation in otolith chemistry signatures. To address this issue, we introduce a method to utilize sagittal otoliths partially composed of vaterite for stock discrimination. Using this method, we determined the hatchery origins of yearlings from five Lake Erie hatcheries by using Ba, Mg, Mn, and Sr concentrations in vaterite sections of steelhead (Oncorhynchus mykiss) otoliths. We then compared the classification accuracy of our vaterite method with a method in which otoliths composed entirely of aragonite were used. Overall, quadratic discriminant function analyses revealed similar classification success when elemental concentrations from vaterite (95% accuracy) and aragonite (94% accuracy) otolith regions were used. The methods introduced here could likely be used for other fish species that have otoliths that transition to vaterite as long as an adequate number of juvenile fish are available to develop vaterite otolith chemistry signatures.

On the Error Rate Comparison of the Quadratic Discriminant Function, Euclidean Distance Classifier, Fisher’s Linear Discriminant Function and the Vine Copulas

The estimation of the error rates is of vital importance in classification problems as this is used as a basis to choose the best discriminant function; that is, the one with a minimum misclassification error. The quadratic discriminant function (QDF), Euclidean Distance Classifier (EDC), and Fisher&rsquo;s Linear Discriminant Function (FLDC) have been in use for a long time for the purpose of classification. In this paper, we compare the misclassification error rate of the QDF, EDC, and FLDC with the Vine Copulas based on Gaussian and Clayton models. The results were obtained for the general case where the means are unequal and the covariance matrices are unequal.

ANALISIS DISKRIMINAN PADA KLASIFIKASI DESA DI KABUPATEN TABANAN MENGGUNAKAN METODE K-FOLD CROSS VALIDATION

Tabanan Regency is one of the eight regencies and one municipality in Bali Province. Administratively, it is divided into 10 districs and villages. There are rural areas and urban areas in the regions. Discriminant analysis is a technique related to the separation of objects into different groups that have been set previously. The purpose of this research is to classify villlages in Tabanan Regency into urban or rural groups with discriminant analysis. Linear discriminant analysis assumes that the covariance matrix of the two groups are equals, if the assumption of equality of covariance matrix is violated, quadratic discriminant analysis can be used for classification. This research uses k-fold crosss validation method for calculating the accuracy of quadratic discriminant function where . Quadratic discriminant function is obtained by with the smallest APER value (). All of classification results are stable and consistence.

Nursery areas and connectivity of the adults anadromous catfish (Genidens barbus) revealed by otolith-core microchemistry in the south-western Atlantic Ocean

The anadromous catfish, Genidens barbus, is a commercial and vulnerable species from South America. The aims of the present study were to assess whether the nursery areas can be discriminated by using microchemical signatures of lapillus otoliths, to assess the accuracy of classifying fish in relation to natal nursery area and to discuss the possibility of existence of homing behaviour. Thus, the otolith-core chemical signatures (barium (Ba):calcium (Ca), lithium (Li):Ca, magnesium (Mg):Ca, manganese (Mn):Ca, strontium (Sr):Ca, and zinc (Zn):Ca ratios) of adult fish were compared among different estuaries (De La Plata River in Argentina, and Patos Lagoon, Paranaguá Bay and Guanabara Bay in Brazil). PERMANOVA analysis showed significant differences in the multi-element signatures of the otolith core among sampling sites for all cohorts (2002, 2003, 2006 and 2007), indicating that the technique has considerable potential for use in future assessments of population connectivity and nursery areas of G. barbus. Via quadratic discriminant function analysis, fish were classified to natal nursery areas with 80–100% cross-validation classification accuracies. These results suggested that a high level of spatial segregation exists in adult catfish life and homing behaviour could not be ruled out on the basis of our data.