BiRating -- Iterative averaging on a bipartite graph of Beat Saber scores, player skills, and map difficulties
Juan Casanova

TL;DR
This paper introduces a simple iterative averaging algorithm on a bipartite graph to estimate player skills and map difficulties in Beat Saber, demonstrating accurate, convergent results aligned with community perceptions and outperforming existing methods in certain cases.
Contribution
The paper presents a novel iterative averaging method that jointly estimates player skills and map difficulties using only score data, improving accuracy over previous approaches.
Findings
Algorithm converges to low estimation error in practice.
Aligns well with community perceptions of difficulty.
Outperforms existing methods on problematic maps.
Abstract
Difficulty estimation of Beat Saber maps is an interesting data analysis problem and valuable to the Beat Saber competitive scene. We present a simple algorithm that iteratively averages player skill and map difficulty estimations in a bipartite graph of players and maps, connected by scores, using scores only as input. This approach simultaneously estimates player skills and map difficulties, exploiting each of them to improve the estimation of the other, exploitng the relation of multiple scores by different players on the same map, or on different maps by the same player. While we have been unable to prove or characterize theoretical convergence, the implementation exhibits convergent behaviour to low estimation error in all instances, producing accurate results. An informal qualitative evaluation involving experienced Beat Saber community members was carried out, comparing the…
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Taxonomy
TopicsArtificial Intelligence in Games · Sports Performance and Training · Sports Analytics and Performance
