{
  "id": 10535758,
  "title": "Who's really No. 1? Statisticians build a better way to make sense of competing sports rankings",
  "url": "https://urgent.news/2026/09/28/whos-really-no-1-statisticians-build-a-better-way-to-make-sense-of",
  "topic": "science",
  "section": "Science",
  "published": "2026-09-28T21:00:08.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-09-statisticians-sports.html"
  },
  "original_language": "en",
  "account": "Who truly holds the top spot in sports rankings? Statisticians have devised a novel approach to decipher the complexities of competing sports rankings. Traditional rankings present a seemingly simple surface, but they are fundamentally intricate. Experts may rank distinct quantities of items, disagree on order or alter opinions over time. The Rice University and Cornell University statisticians have addressed this issue through the development of Bayesian Multivariate Rank Regression (BMRR). This method amalgamates rankings from various sources and across multiple time periods while accounting for disagreements among the rankers, incomplete lists and factors that may influence the rankings. More importantly, BMRR quantifies the certainty or uncertainty of the resulting consensus. The researchers applied their method to Major League Baseball's annual rankings of the best players between 2021 and 2024. ESPN, CBS, Bleacher Report, Yahoo Sports and MLB produced annual rankings of the top 100 players, though the lists varied in content and ordering. The researchers focused on 55 players who appeared in at least one outlet's rankings each year. BMRR treats rankings as evidence about an underlying value and employs a hierarchical Bayesian framework to combine this evidence. It can handle incomplete rankings and ties, borrow information across years and estimate each source's alignment with the overall consensus. The method not only identifies consensus but also provides a measure of uncertainty, indicating when the data strongly supports one player and when two players are effectively equally ranked. This distinction is crucial as rankings can have real economic implications, such as influencing trades, salary negotiations, arbitration and draft decisions. BMRR can reveal characteristics associated with higher rankings, including age, base salary and wins above replacement (WAR). Higher WAR, age and salary are all positively correlated with rankings, with WAR having the strongest effect. The analysis also uncovered some predictable outcomes. The top four players in the 2023 season, according to the model, were Shohei Ohtani, Aaron Judge, Mike Trout and Mookie Betts, all of whom were All-Star starters that year and Ohtani winning the American League MVP Award. The model captured both Ohtani's eventual top-ranking and his dramatic rise from being omitted from the top 100 in 2021 to being ranked in the top 10 in 2022 and 2024. BMRR can also calculate the probability that one player should outrank another, potentially making it useful for head-to-head decisions. In terms of free agency decisions after the 2024 season, the analysis identified a strong consensus that Alex Bregman, Pete Alonso and Paul Goldschmidt ranked above Willy Adames. BMRR evaluates the players being ranked and can also reveal how individual rankers behave. MLB's rankings aligned most closely with the model's overall consensus, followed by ESPN. Bleacher Report and Yahoo ranked more distantly from the aggregate rankings, with Yahoo tending to elevate younger, rising players over established veterans. The model thus provides a more nuanced understanding of sports rankings, accounting for uncertainty, discrepancies among sources, and underlying player attributes.",
  "summary": "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.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}