BP 2025: Key Highlights & Controversies

Translated by José M. Hernández Lagunes

There was, believe me, a time before the internet.If you wanted to stay up to date with the news, you had to listen to the radio, watch television, or read a newspaper or magazine. Specifically, if you followed baseball, the newspaper was the only place you could find daily updates on stats and standings.

And if you wanted a more complete list of baseball statistics than an occasional top 10 listing in some category of interest to you, you had to wait for the Sunday paper. There, buried in the sports section, they published a list of batting statistics (at-bats, runs scored, hits, home runs, runs batted in, and batting average) covering teams and hitters with more than a certain number of at-bats. They were ordered by average. For pitchers,in ERA sequence,you got innings pitched,hits,walks,strikeouts,wins,losses,and ERA,first for teams and then for all pitchers above the decision threshold (not per inning!). Here is a fairly illegible sample of the diary Gainesville Sun of July 10, 1983.

Sorry how difficult it is to see them. But you get the idea. Before Baseball Prospectus,FanGraphs,baseball ReferenceMLB.com and other sites, the place to get complete baseball statistics was the Sunday newspaper. He Sporting News I also had them, but you had to wait for them to arrive at the kiosk or for them to be delivered to your mailbox. the Sunday newspaper was immediate: it had everything up to the Friday night games!

In the American League, the aforementioned leading hitter was Rod carewof the California Angels, with a .403 batting average at that point in the season. (He would finish with a .339 batting average, second to Wade Boggs).At the other end was Julio Cruzfrom Chicago, with a.234 batting average. However, the Sundue to space limitations, removed the list. Gorman Thomasfrom Cleveland,such as,was hitting .191,but did not appear on the list. It also didn’t appear Tom Brunanskyfrom Minnesota, with a .199 batting average. Newspapers in larger cities, with more column space, included more names. Thus, although Thomas and Brunansky were not mentioned in Gainesville, they did appear in the newspapers of the cities where the teams play. And the players saw the statistics, as did the fans.

In 1979, Seattle shortstop Mario Mendozawas at the bottom of those lists. On May 13 of that year, the Sunday newspapers published his batting average at .202. It was .189 the following two Sundays,.186 the following week, and .185 in the newspapers on Sunday, June 10. (He finished the season with a .198 average.)

Bruce BendMendoza’s teammate on the Mariners, coined the term “Mendoza Line,” which indicated the batting average of the light-hitting shortstop. If your batting average was worse than Mario Mendozaat the bottom of the list on Sunday, you were below the Mendoza Line. Reportedly, when Kansas City traveled to Seattle for a four-game series beginning May 14, Mariners left fielder, Tom Paciorekwarned George Brettof the Royals, who risked falling below the Mendoza Line. (Brett was hitting just .257 at the time, coming into the series on an 8-for-38 slump. He went 6-for-16 in the series and finished the year with a .329 average.) Brett mentioned it to ESPN host Chris Berman, and the term entered the baseball lexicon. (Mendoza, by the way, finished his MLB career in 1982 with the Rangers, returned to his native Mexico, where he played for seven more years, and ended up being enshrined in the Mexican Baseball Hall of Fame in 2000.)

The Mendoza Line has come to represent a .200 batting average. If you hit below .200,like they did Bo Naylor (.195) y Michael Conforto (.199) among players with at least 400 plate appearances this year, you are saeid to be below the Mendoza Line.

Of course, 1979 was almost half a century ago. Back then, Sunday newspapers published batting averages, as that’s how hitters were evaluated. We have come a long way as then. So, at the suggestion of my friend José Hernández Lagunes, editor of BP in Spanish, I set out to define a new standard for ineffective batting.

As you probably know, Bill James pointed out that teams’ on-base percentage correlates much better with their run production than with their batting average. I found that while James’ observation was true at the time he wrote it, slugging percentage has surpassed OBP in recent years. Here are the correlations between a team’s runs and five commonly used hitting measures for 810 team-seasons in the 30-team era (as 1998), excluding 2020.

Slugging percentage has the advantage of being easy to calculate (just two figures: total bases and at-bats). But OPS and wOBA have better correlations.The problem is that not all fans know what OPS and wOBA are like they know AVG or even SLG. However, OPS is mentioned in broadcasts and articles more frequently than wOBA, so if we’re going to use a simple rule of thumb for the public, I think it’s best to use the more familiar figure, even if it’s a little less accurate than wOBA.

The question then becomes: what do we use for the OPS equivalent of a .200 batting average? Specifically, what easy-to-remember value for OPS produces a level below which almost no one falls (but some do)?

I looked at various OPS levels in the five post-pandemic seasons and the number of players with at least 400 plate appearances who didn’t meet them.

Here’s a rewrite of your provided data into an engaging, SEO-optimized sports article for archysports.com, focusing on American sports enthusiasts.

Beyond the Box Score: Unpacking the hidden Metrics that Define Elite Performance

by [Your name/ArchySports Staff Writer]

In the ever-evolving landscape of sports analytics, we’re constantly seeking new ways to quantify what makes athletes truly exceptional. While traditional statistics like points, rebounds, and touchdowns tell a story, they often miss the nuanced contributions that seperate the good from the truly great. at archysports.com, we’re diving deep into the metrics that go beyond the obvious, exploring how advanced data can unlock a deeper understanding of athletic prowess, particularly for the discerning American sports fan.

Think about it: a basketball player might not lead the league in scoring, but their ability to consistently draw double teams, creating open looks for teammates, is invaluable.Or consider a football lineman who consistently forces double-team blocks, freeing up a star running back for explosive plays. these are the “invisible” contributions that analytics are increasingly helping us to measure and appreciate.

The data snippet you see below, while seemingly raw, hints at this very concept. it appears to be a breakdown of performance metrics, possibly related to efficiency, impact, or a specific skill set, across different levels of play or perhaps even different games. Let’s break down what these numbers could represent and why they matter to us as fans.

(Note: The specific meaning of each column header is not provided, so we’re inferring potential interpretations based on common sports analytics.)

Decoding the Data: What Are We Really Looking At?

Let’s hypothesize what these columns might represent. The first column, with values like “.610” and “.600,” could be a success rate or efficiency percentage. In baseball, this might be a batting average or on-base percentage. In basketball, it might very well be field goal percentage or assist-to-turnover ratio. In football, it might relate to completion percentage or yards per carry.

The subsequent columns, with single-digit numbers, could represent various contributing factors. For instance, in basketball, they might be:

* Value 1: Assists
* Value 2: Rebounds
* Value 3: Steals
* Value 4: Blocks
* Value 5: Turnovers

And the final column, “Impact Score,” is the real prize. This is where the magic happens – an attempt to synthesize multiple contributions into a single, digestible metric that reflects an athlete’s overall positive influence on the game.

The “Impact score”: A Modern MVP Metric?

The “Impact Score” is particularly intriguing. It suggests a sophisticated algorithm at play, aiming to assign a quantifiable value to an athlete’s performance beyond just raw output. This is akin to how advanced metrics like WAR (Wins Above Replacement) in baseball or PER (Player Efficiency Rating) in basketball attempt to capture a player’s total value.

imagine a scenario in basketball: a player might have 15 points, 5 rebounds, and 3 assists. That’s a solid stat line.but what if they also had 4 deflections, forced 2 turnovers through active defense, and made 3 “hockey assists” (passes that lead to an assist)? the “Impact Score” aims to capture that extra layer of defensive pressure and playmaking that doesn’t always show up in the traditional box score.

This is where the analogy to a coach’s trust comes in. A coach knows that certain players, even if their individual stats aren’t eye-popping, consistently make the right plays, elevate their teammates, and contribute to winning. This “Impact Score” is essentially an attempt to put a number on that intangible quality.

Why this Matters to You, the Sports Fan

For us as fans, understanding these advanced metrics enriches our viewing experience. it allows us to

=”middle”>1.2 .550 1 1 0 0 1 0.6

Clearly, .550 is too low. (The three players with an OPS below .550 in the last five years are Jackie Bradley Jr. (2021,.497), Nick Allen (2025, .535) y Geraldo Perdomo (2022, fourth in NL MVP voting three years later, .547). For the same reason, .650 covers too much.

Consider half of that distribution. The best number, I think, is .590: only two or three players on average, but almost never zero. It is an achievable standard,although undesirable. But .590 is a clumsy number; .600, a nice round number, is not. So let’s go with .600. Any OPS below .600 is below… wait, we have to find a name for the line!

The first player I thought of was Allen. He is the modern Mendoza: good glove in the box,chewing gum baton. The problem is that he has taken the issue of the güango tolete to the extreme. He has never achieved an OPS higher than .600. He hasn’t even posted an OPS above .550. His lifetime average is .536. Mendoza’s career batting average was .215. A batting average below .200 can be named after someone with a batting average of.215. You can’t name a sub-.600 OPS after someone who has never come close to that level.

Among active players with at least 1,000 plate appearances, those closest to .600 are Taylor Walls (.584), Sandy León (.585), Luke Maile (.597),Billy Hamilton (.617) and Martín Maldonado (.620). The problem with that quintet is that Walls is more of a utility player than a regular one, and the others are near the end of their careers.Something about Walls’ line and Maile’s line sounds good, but it might not be.

There is something else (bonus points if you understand the reference). Jonah Heim he is, to be clear, a useful player. As recently as 2023, he generated 4.1 WARP thanks to a .259/.318/.439 offensive line, 18 home runs, 95 RBI and 17.1 defensive runs prevented. He has framed well with his mascot, and although his arm is not the best, it has been good enough to deter base stealers. In total, he has generated 9.1 WARP in just under five full seasons,always above replacement level.

But his play has gotten worse since 2023. His defense hasn’t been as solid, and more to the point, his OPS has been identical at .602 in both 2024 and 2025.Almost exactly.600. And as nice as the Walls Line or the Maile Line may be, it can’t compete with the Heim Line. It sounds like “hemline” o “timeline“! ¡rima!

This is our 21st century analogue to the Mendoza Line. This year, Nick allen (.535),Joey Ortiz (.593) and Ke’Bryan Hayes (.595) had an OPS below.600.

They were below the Heim Line.

Well, that’s what it’s called.