Is the performance of a cricket team really unpredictable? A case study on Pakistan team using machine learning

Abstract

Cricket is the second most popular game around the globe, particularly it breeds a high level of enthusiasm in Asia, Australia and UK. However, it is generally known and globally mentioned that Pakistan is an "unpredictable" cricket team, which leads to extreme reactions from the citizens in case of a loss, e.g., verbal anger, breaking of television sets and burning of players' effigies. Objectives: In this study, we leverage machine learning techniques to demonstrate that the use of the "unpredictable" tag for Pakistan's cricket performance is unjustified as the match outcome can be predicted with a pretty high confidence. Method: We produce a new dataset by scrapping latest statistics from cricinfo.com, the most reliable online source. Also, we propose a novel feature "consecutive wins" that incorporates recent performance trend of the team. With extensive experimental setup, state-of-the-art machine learning methodology was employed to prove effectiveness of proposed tool. Findings: Pakistan's cricket performance can be predicted with 82% accuracy, i.e., it is possible to understand the patterns (in advance) which may lead to a winning or losing situation. Hence, using pre-match analysis, it is possible to avoid any prejudiced opinion or potentially dangerous reactions. Novelty: We employ state-of-the-art machine learning methodology based on application of various algorithms, feature selection and data splitting methods. Eventually, state-ofthe-art prediction accuracy is achieved by exploiting all potential avenues in a structured way.

Compact CNN Structure Learning by Knowledge Distillation

“…Recent years have seen remarkable performance breakthroughs achieved in machine learning [1] and computer vision applications using deep Convolutional Neural Networks (CNNs) [2]- [4]. However, the CNNs are computationally expensive, memory-intensive, and power hungry.…”