Esport games comprise a sizeable fraction of the global games market, and is
the fastest growing segment in games. This has given rise to the domain of
esports analytics, which uses telemetry data from games to inform players,
coaches, broadcasters and other stakeholders. Compared to traditional sports,
esport titles change rapidly, in terms of mechanics as well as rules. Due to
these frequent changes to the parameters of the game, esport analytics models
can have a short life-spam, a problem which is largely ignored within the
literature. This paper extracts information from game design (i.e. patch notes)
and utilises clustering techniques to propose a new form of character
representation. As a case study, a neural network model is trained to predict
the number of kills in a Dota 2 match utilising this novel character
representation technique. The performance of this model is then evaluated
against two distinct baselines, including conventional techniques. Not only did
the model significantly outperform the baselines in terms of accuracy (85%
AUC), but the model also maintains the accuracy in two newer iterations of the
game that introduced one new character and a brand new character type. These
changes introduced to the design of the game would typically break conventional
techniques that are commonly used within the literature. Therefore, the
proposed methodology for representing characters can increase the life-spam of
machine learning models as well as contribute to a higher performance when
compared to traditional techniques typically employed within the literature