  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.
