Using Support Vector Regression Kernel Models for Cricket Performance Prediction in the Womens Premier League 2024
Background. The interest in women’s premier league cricket has caused the need for advanced analytics to understand the multifaceted dynamics of the sport. Study Purpose. This study aimed to contribute to sports analytics by assessing the efficacy of Support Vector Regression (SVR) kernel models in...
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| Published in: | Teorìâ ta metodika fìzičnogo vihovannâ Vol. 24; no. 1; pp. 72 - 78 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
OVS LLC
29-02-2024
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | Background. The interest in women’s premier league cricket has caused the need for advanced analytics to understand the multifaceted dynamics of the sport.
Study Purpose. This study aimed to contribute to sports analytics by assessing the efficacy of Support Vector Regression (SVR) kernel models in predicting the most valuable player. Such research methods as ANOVA, Bessel function, and Inverse MultiQuadratic kernel application have been deliberately chosen for their diverse mathematical approaches, aligning with the nuanced intricacies of women's premier league cricket.
Materials and methods. Player performance was analyzed by using the following study methods: ANOVA, Bessel function and Inverse MultiQuadratic kernel application. The data, sourced from espncricinfo.com and the International Cricket Council, includes essential metrics for five teams. Rigorous preprocessing techniques, such as imputation and outlier removal, enhance data reliability, ensuring robust predictive models.
Results. The application of the Inverse MultiQuadratic kernel exhibits exceptional predictive performance, surpassing ANOVA and Bessel function models. The kernels radial basis function proves effective in capturing the intricate dynamics of women’s premier league cricket. The findings underscore the suitability of kernel method for predicting standout performers in the Womenʼs Premier League 2024 season.
Conclusions. The study revealed the dynamic interplay between sports analytics and machine learning in women's premier league cricket. The application of the Inverse MultiQuadratic kernel stands out as the most effective model, providing key insights into player predictions. This emphasizes the continual integration of advanced analytical techniques to enhance our understanding of the evolving landscape of women’s premier league cricket. As the sport gains prominence on the global stage, such analytical endeavors become imperative for strategic decision-making and sustained growth. |
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| ISSN: | 1993-7989 1993-7997 |
| DOI: | 10.17309/tmfv.2024.1.09 |