  League of Legends (LoL) has been a dominant esport for a decade, yet the
inherent complexity of the game has stymied the creation of analytical measures
of player skill and performance. Current industry standards are limited to
easy-to-procure individual player statistics that are incomplete and lacking
context as they do not take into account teamplay or game state. We present a
unified performance model for League of Legends which blends together measures
of a player's contribution within the context of their team, insights from
traditional sports metrics such as the Plus-Minus model, and the intricacies of
LoL as a complex team invasion sport. Using hierarchical Bayesian models, we
outline the use of gold and damage dealt as a measure of skill, detailing
players' impact on their own-, their allies'- and their enemies' statistics
throughout the course of the game. Our results showcase the model's increased
efficacy in separating professional players when compared to a Plus-Minus model
and to current esports industry standards, while metric quality is rigorously
assessed for discrimination, independence, and stability. Readers might also
find additional qualitative analytics which explore champion proficiency and
the impact of collaborative team-play. Future work is proposed to refine and
expand the SIDO performance model, offering a comprehensive framework for
esports analytics in team performance management, scouting and research realms.
