Efficient Feature Representations for Cricket Data Analysis using Deep Learning based Multi-Modal Fusion Model
This work addresses efficient data analysis for cricket team management and predictions, but it is incremental as it applies known representation learning methods to a specific domain.
The study tackled the problem of representing cricket features like players and teams for data analysis by developing a deep learning framework that learns adaptive embeddings through contrastive loss, achieving reliable overall run rate prediction.
Data analysis has become a necessity in the modern era of cricket. Everything from effective team management to match win predictions use some form of analytics. Meaningful data representations are necessary for efficient analysis of data. In this study we investigate the use of adaptive (learnable) embeddings to represent inter-related features (such as players, teams, etc). The data used for this study is collected from a classical T20 tournament IPL (Indian Premier League). To naturally facilitate the learning of meaningful representations of features for accurate data analysis, we formulate a deep representation learning framework which jointly learns a custom set of embeddings (which represents our features of interest) through the minimization of a contrastive loss. We base our objective on a set of classes obtained as a result of hierarchical clustering on the overall run rate of an innings. It's been assessed that the framework ensures greater generality in the obtained embeddings, on top of which a task based analysis of overall run rate prediction was done to show the reliability of the framework.