FALL 2024

AUTHORS

Gabriel Krotkov, Carnegie Mellon University, The Robotics Institute
Anuva Ghosalkar, Carnegie Mellon University, The Robotics Institute
Sienna Li, Carnegie Mellon University, The Robotics Institute
Anushka Prabhu, Carnegie Mellon University, The Robotics Institute
Lily Tang, Carnegie Mellon University, The Robotics Institute
Audrey Zheng, Carnegie Mellon University, The Robotics Institute
Aashi Bhatt, Carnegie Mellon University, The Robotics Institute

ABSTRACT

In this paper, we present an improvement to Offensive Power Rating (OPR), a popular linear regression model for assessing team performance at a given event. One key assumption of linear regression is the independence of the errors, but in the FIRST® Robotics Competition (FRC) context, this assumption is not exactly true. Using data from all district events between 2009 and 2024, we model the unweighted errors as a function of tournament progression and generate weightings to improve the regression fit through Weighted Least Squares (WLS). The best weightings show that the most representative matches for a team’s overall performance are midway through the tournament. That is, the real clutch matches are in the middle.