Multiplayer strategy games are examples of imperfect information games, where information about the game state can be retrieved through in-game mechanics. One such mechanic is vision. Within esports titles of this genre, such as League of Legends (LoL) and Dota 2, players often gather map information through the use of friendly units called wards. In LoL, one of the most popular esports title worldwide, warding has hitherto been evaluated only using a heuristic called vision score, provided by Riot, the game’s developer. In this paper, we examine the accuracy at LoL’s vision score at predicting the overall game-winner within the context supported by the game. We have ported LoL’s vision score to Dota 2, a similarly popular esports title, and compared its performance against a novel warding model. We have compared both models not only at predicting the overall winner, but also the current state of the game and their ability to predict and reflect short term game advantage and events. We found our model significantly outperformed LoL’s vision score. Additionally, we trained and evaluated a model for predicting the value of wards in real-time through the use of a Neural Network.