In the rapidly evolving world of esports, one technology stands out as a potential game-changer: predictive analytics. This branch of advanced analytics, powered by artificial intelligence (AI), has the power to predict, at least theoretically, almost anything, including how games are played and won. This post delves into how predictive models already have improved to the point that they can anticipate opponent behavior and analyze historical data, player tendencies, and map dynamics to predict enemy rotations, site pushes, and eco rounds.
Predictive Models in Esports
Predictive models in esports use machine learning algorithms to analyze vast amounts of data from past games. These models can predict outcomes based on player performance, draft selection, and features from the game itself, including character information and real-time game process statistics. For instance, a study on Dota 2 outcomes prediction compared Linear Regression, Neural Networks, and Long Short-Term Memory (LSTM) models. As it turns out, neural networks seem to be the most promising when it comes to predicting DOTA 2 match outcomes.
There have already been successful real world applications of predictive analytics in esports. For example, a machine learning approach was used to outsmart the esports market, achieving a return rate of around 14% for Dota and around 98% for CS:GO.
Anticipating Enemy Rotations
One key aspect to game sense is the ability to correctly anticipate enemy rotations. This means there is vast untapped potential in utilizing AI to do exactly that. There is plenty of data available – Omnic Forge’s AI systems already analyze player movements and strategies. All it takes is to push a little bit further and utilize predictive models to forecast where the enemy team might move next. This ability to predict common types of enemy movement while, importantly, maintaining coordination with your own team would give your team a huge competitive advantage.
Predicting Teamfight Outcomes
In games like Overwatch 2, and League of Legends, winning teamfights is essential to winning matches. Given enough data, predictive models may begin to understand the nuts and bolts of what goes into winning a teamfight. Right now, it is up to coaches to ensure each member of the team is doing what is most optimal – using their abilities at the exact right time in the exact right way. With the assistance of predictive AI, imagine the new heights professionals and amateurs alike will be able to achieve.
Forecasting Eco Rounds
In games like Valorant and CS:GO, managing the in-game economy is a critical aspect of strategy. In fact, Omnic.AI has already done a study with grad student Sophia Cofone on the importance of economy when determining match outcomes. These findings, when combined with more advanced predictive modeling, very well could lead to an ultra-efficient level of Valorant economy management the likes of which we have not seen in any esport.
Predictive analytics has already begun revolutionizing esports, providing teams with unprecedented insights and a competitive edge. As AI continues to evolve, we can expect its impact on esports to grow, making the games we love even more exciting and strategic.
Omnic.AI is an AI-powered performance gaming tool designed to help you game smarter. Our technology provides resources for professional esports players, content creators, production teams and every day gamers to do what they love — faster and smarter through the power of AI. If you would like to take your game to the next level with Omnic Forge click here.