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Post Subject: Predicting Professional Players for Starcraft 2 (2023-US-PO-1512) T1217
Post Body:
Nishita Rao, Graduate Student, University of Connecticut
Neehar Namjoshi, Graduate Student, University of Connecticut
Shivam Kumar, Graduate Student, University of Connecticut
Pooja Shah, Graduate Student, University of Connecticut
This project explores the business case of esports teams profiting from high-skill players in Starcraft 2, a real-time strategy video game. The goal was to accurately identify the elite players who excel at the game. Models were built to classify this small cohort of high-performing players, who make up just 2.7% of the data set. The model enabled these organizations to scout and recruit high-skill Starcraft 2 players, thus increasing the probability of winning championships.
The team created and compared different models in JMP to define complex relationships between the target variable (high-skill Starcraft 2 players) and several predictor variables, such as actions per timestamp, mini-map attacks, etc. While the in-built models were used to make predictions, the model used add-ins like the Stratified Split Balance and Alternate Cut-off Confusion Matrix to tune the model further.
The insights gained from this model should help esports organizations make more strategic decisions and better investment choices so they can optimize performance and profitability in their ecosystem. These organizations can then provide targeted training and development opportunities, along with retention plans, for high-potential players.
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Link to post: Predicting Professional Players for Starcraft 2 (2023-US-PO-1512) T1217
