Motivated by a substantial difference between standard OPR and scouting point contribution estimates for my team at the 2020 Palmetto regional, I developed and studied some modifications to OPR with the goals of getting it closer to scouting estimates and also making it more accurate for playoff match score prediction.
For Infinite Recharge, the field management system records several per-robot match actions such that those scoring components do not need to be estimated with OPR. This enables hybrid OPR, which combines exactly known per-robot scoring modes from the FMS with component OPRs for scoring modes recorded only for the alliance.
The results of some matches can disinform the OPR calculation. For example, my team was involved in an alliance where one robot did not move for the entire match. To make the situation worse, that robot turned out to be the highest scoring robot at the event. The dramatically lower score than “expected” in that match significantly reduced the OPR of all three alliance robots, because the math has no way of knowing that the problem was due to only one robot. If outlier alliance performances like this could be identified and removed from OPR calculation, the resulting robust OPR would hopefully be a better estimate of point contribution and be more useful in predicting future scoring, such as in playoff matches.
Hybrid, robust, and hybrid robust OPR were studied for all 52 events completed for Infinite Recharge. Each technique improved playoff alliance score prediction in a statistically significant way. Hybrid reduced the sum of squared errors for event playoff score predictions by an average of over 6% across all events. Robust reduced SSE by 2.3%. Combined hybrid robust reduced SSE by nearly 7%. While my team does not claim to conduct perfect scouting, especially with the difficulty of discerning inner vs outer port scores and determining which shot bounce-outs came from which robot, it was good to see that hybrid robust OPR reduced the sum of squared difference to scouting point contribution by a factor of almost 3 compared to standard OPR at 2020 Palmetto.
The accuracy advantage of hybrid OPR will vary from season-to-season depending on how much of the scoring can be directly ascribed to individual robots based on what is recorded in the FMS. However, for the 2021 season and a replay of some form of Infinite Recharge, it seems likely that hybrid OPR will continue to have a significant advantage over standard OPR.
Plenty of detail on hybrid and robust OPR is available in a paper.
A GitHub repo also contains supporting figures, result data files, and Python code to generate hybrid, robust, and hybrid robust OPR.