This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Who, really, is the best player in baseball? Ask five sports media outlets and you may get five different answers.
Rankings can vary dramatically from one source to another, change from year to year and leave out different players altogether, making it difficult to determine where there is genuine consensus and where opinions diverge. Now, Rice University statisticians, in collaboration with Cornell University, have developed a new method designed to find clearer answers in messy rankings data. The research was recently published in the Journal of Quantitative Analysis of Sports.
The approach, called Bayesian Multivariate Rank Regression, or BMRR, combines rankings from multiple sources and across multiple time points while accounting for disagreements among the rankers, incomplete lists and factors that might influence the rankings. Just as importantly, the method measures how certain or uncertain the resulting consensus is. "Rankings look simple on the surface, but statistically they're actually very complicated," said Rose Graves, a doctoral student studying statistics at Rice and the study's corresponding author.
"Two experts may rank different numbers of items, disagree about the order or even change their opinions over time. We wanted to create a way to bring all of that information together while accounting for uncertainty." For their real-world test case, the researchers turned to one of the most closely scrutinized ranking systems in sports: annual lists of Major League Baseball's best players. They analyzed preseason rankings published by ESPN, CBS, Bleacher Report, Yahoo Sports and MLB from 2021 through 2024.
Each outlet ranked its top 100 players, but the lists varied in both who was included and where players were ranked. To create a consistent pool for comparison, the researchers focused on 55 players who appeared in at least one outlet's rankings in each of the four years. Those differences illustrate exactly what makes ranking data difficult to analyze.
Simply averaging a player's position across several lists can mask meaningful information, especially when a player is omitted from one list altogether or when assessments shift substantially from year to year. BMRR instead treats the rankings as evidence about an underlying value and uses a hierarchical Bayesian framework to combine that evidence. The model can incorporate incomplete rankings and ties, borrow information across years and estimate how closely each source aligns with the overall consensus.
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