League of Legends (LoL) has established itself as one of the most watched
and played e-sports in the world. Our work examines the impact of the death
of an individual player on the probability of victory in League of Legends.
Using the official Riot Games application programming interface and pub-
lic sources, we compile a large dataset of Diamond-rank matches and derive
a minute-by-minute representation of each game state. Based on these fea-
tures, we construct and calibrate a win probability model. We then define
the concept of a death window —the interval between the state immediately
before a death and the first state after the player returns to the game. The
change in team win probability within this window is attributed to individual
players using a Performance Score, which depends on the role of the player
and by the global SHAP values.
Our empirical analysis covers 150,472 death windows. On average, the
probability of victory for the team of the deceased player decreases by 2.03%;
the probability falls in 58% of windows and rises in 42%. Our results show
that positive contributions from the deceased player occur more frequently
when the team secures objectives or achieves favorable trades during the
absence. The proposed approach enables rapid identification of the most
impactful deaths and learning opportunities.
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