Baseball is arguably the most number-dependent sport. A batter is judged on his batting average (Hits divided by at-bats), and the highest batting average wins the batting title. A pitcher is deemed good or bad based almost exclusively on his ERA (Nine times earned runs allowed divided by innings pitched).

There is no eye test in baseball; performance drives numbers and numbers drive our perception of players.

However, the modern interpretation of the numbers that we base our analysis on are often flawed; a realization the general baseball populous has collectively come to over the past decade or so. BA treats all hits as equal when in reality a home run is an estimated 2.33-times more valuable than a single. ERA is a defense-dependent statistic and yet we use it to evaluate pitching performances.

Alternatives to these have come into vogue. OBP and WHIP, for example, are better (but still incredibly imperfect) options.

The replacement of these potentially harmfully inaccurate statistics is an ever-evolving science. Critically analyzing box scores and interpolating them in a meaningful way, often called sabermetrics, has birthed some accurate (yet terribly complex) statistics such as xFIP, SIERA, and xwOBA.

Of these incredulously useful statistics, one seldom used batting number stands above the rest. Base-out percentage (BOP) is the closest thing we have to a tell-all offensive statistic and should become a regular part of baseball conversation.

**What is BOP?**

BOP stands for base-out percentage. It was first introduced Barry Codell in 1979. BOP is, as the name does a poor job alluding to, a metric used to discern how many bases a batter earns for each out he records. In short, bases per out.

*BOP = (TB+BB+HBP+SB+SH+SF)/(AB-H+SH+SF+CS+GDP)*

In Codell’s words, “the base-out percentage (BOP) does not avoid anything a player does offensively.” While it is expected that one would say such a thing of his own creation, Codell is not wrong. BOP holds a batter accountable for each of the ever-valuable outs for which he is responsible while rewarding him for each base he creates.

**How do I use BOP?**

It is important to remember that because BOP only considers a player’s batting numbers, it is exclusively an offensive metric. Mike Trout, for example, is widely regarded as the best player in MLB, but his 2020 BOP was 11^{th} in the league. Marcell Ozuna, on the other hand, was not even an All-Star in 2020 but was second in BOP. This is all to reaffirm that BOP should not be used as an evaluation of a player’s entire team, but rather just his offensive capability.

The above chart is MLB’s BOP distribution from 2020. The average BOP among eligible batters was .773. Nearly half of the eligible batters fell within the .600-.799 range. Anything over .800 is very good, plus-.900 is strong, and over 1.000 is elite. Codell called batters who surpassed the one base per out mark a “Big BOPer;” there were 16 Big BOPers in 2020.

Juan Soto led MLB in BOP with a staggering 1.369 BOP. The next highest was NL MVP Freddie Freeman at 1.218.

On the other end of the spectrum, Nicky Lopez was the worst BOPer. His .410 mark was the only one in 2020 to below the .500 line.

**Does BOP work?**

But how effective is BOP? Does it truly generate a single number to summarize in full a player’s offensive capability, a metric that has eluded baseball statisticians for over a century?

If BOP wishes to be such a stat, it must correlate to offensive success. Unfortunately, there is no offensive catch-all for individual players; that is what BOP is trying to be after all. However, on a macro scale, you can measure offensive success via runs per game for a team.

Comparing team-wide BOPs to each team’s runs per game creates this graph.

At first glance, it appears obvious. There appears to be a strong relation between BOP and how well teams are performing offensively. Compare this to graphs of the more popular offensive measures, batting average and on-base percentage.

As you may have noticed, the plots for BA and OBP appear much looser: good news for BOP.

However, there is a way to measure what we’re seeing. In statistics, there are countless ways to measure if two sets of data correlate. The most simple uses a relatively complex formula to give us a correlation coefficient, represented by an *r*.

A correlation coefficient is measured on a scale from -1 to 1. If *r* is greater than 0, there is a positive correlation between the data, meaning as one goes up so does the other. If *r* is less than 0, there is still correlation, but a negative one; as one goes up the other goes down. The closer to the extremes your *r* is, the stronger a correlation between the two.

By finding a correlation coefficient for BOP we can discern how closely related it is to offensive success.

For frame of reference, batting average and runs per game had an *r*-value of .745 in 2020. Admittedly, that’s pretty strong. OBP further improves on that number; its *r*-value was .844. Both have an incredibly strong positive correlation with run-scoring.

BOP blows both out of the water. With an r-value of .928, BOP is the most closely related to run-scoring of the three.

But this doesn’t apply to just the COVID-impacted 2020 season. Using data from 2000-present, we learn that BOP and run-scoring are indeed correlated; the 2000-now *r*-value clocks in at an eerily similar .927.

Both samples prove that a team with a higher BOP scored more runs; it holds that a player with a higher BOP generates more runs. Nearly 100,000 games worth of data says so.

This argument does create a reason to take a batter’s runs and RBI totals as their offensive metric. However, an R+RBI stat entirely neglects non-scoring sacrifices, stolen bases, and the dreadful grounded into double play, to name a few.

**Why is BOP better than…?**

There are a few keys that make BOP stand out above other batting statistics.

Batting average is baseball’s favorite redheaded step-sibling; it tells us nothing but is among the most prestigious stats in sports. BA, as previously mentioned, neglects the difference in types of hits; a homer is the same as a single in the eyes of batting average. However, it additionally throws out the door the ever-useful walk. A walk is just as good as a hit, and yet BA pretends it never happened.

On-base percentage includes the walk but now begins to exclude advanced runners via sacrifices. The sac fly is one of the most common strategic plays in baseball, but OBP throws it out the window.

Slugging just measures power, wRC uses arbitrary measurement for offensive events… every statistic has its problem.

While BOP deals with none of these plagues, it also uniquely accounts for two often-forgotten plays; the stolen base and the double play. A successful stolen base moves a runner from first to scoring position, or from second to a sac fly away from home. The double play, meanwhile, is the second-worst outcome for a batter second only to its elusive brother the triple play. They are two of the most game-changing plays in baseball and while other statistics treat them as nonexistent, BOP ensures they are accounted for.

**Why isn’t BOP more popular?**

If BOP really is the ultimate batting statistic, why has it seemingly been lost to time? Googling “base-out percentage” yields just 12,700 results and you won’t be seeing BOP on the back of any baseball cards.

To be honest, there appears to be little to no explanation available. The few arguments available online all seem to follow the same line of thinking: BOP, like batting average, just doesn’t reflect real production.

But as shown, this is untrue. Those that make this argument are likely falling victim to one of two fallacies:

- They are unaware/ignoring how closely BOP relates to scoring runs
- They are attempting to use BOP as more than an offensive statistic

Regarding the first option, this is entirely avoidable by looking at the numbers. As shown, BOP is closely correlated to creating runs, more so than any other standard statistic. In 2020, BOP was 25% more correlated to runs than batting average; an unignorable increase.

The second argument is based on fact but is still foolish to make. It can be conceded that BOP does not reflect winning. Statistically speaking, the two are less likely to be related than not. Using the now oft-used 2020 season, we learn that winning percentage and BOP have just a .418 *r-*value. BOP is an offensive statistic and should only be used to measure such. A team won’t win because their BOP is high, nor is a winning team guaranteed a high BOP; but the best offensive teams are.

**What players BOPs best?**

BOP appears to favor no type of batter; no counting statistic has any relevant correlation. While not concrete evidence, this implies that good BOP players are well-rounded. This does not mean a master of none will out-BOP specialists, but rather that an all-around player like Trout will BOP better than a player with his power but no other tools.

**Who are the BOP leaders?**

As mentioned, Juan Soto was the 2020 BOP leader; the Best BOPer, if you will.

2021’s Best BOPer will likely come down to the current top two: of Shohei Ohtani (1.248) and Fernando Tatis Jr. (1.216). Byron Buxton blows both Ohtani and Tatis out of the water with his 1.662 BOP, but two long-term injuries this season means he falls short of minimum plate appearances to qualify.

By my research, there is an undeniable King of BOP. Babe Ruth not only holds the first, second, fifth, and eighth-best BOP season since 1919, he is also the only player to ever BOP over 1.650; he did so twice in 1920 and 1921.

Ruth’s 1.383 career BOP is well above Rogers Hornsby’s second-place mark of 1.243. Ruth and Hornsby are among the 32 career Big BOPers (1.000+ career BOP). That list includes names such as Lou Gehrig, Ted Williams, Barry Bonds and Mickey Mantle.

Mike Trout is seventh all-time and has the highest career BOP among active players with 1.109. Pete Alonso and Jeff McNeil are the only other career Big BOPers; interestingly both clock in at a career 1.000.

On the other end of the spectrum is Mickey Grasso. Grasso has just one season that reached the minimum plate appearances. That season was 1952, where he was the starting catcher for the Washington Senators. He BOPed .339 in the only sub-.345 season in MLB history.

**What is next for BOP?**

Ideally, everyone from managers to casual fans would pick up BOP right now and introduce it into the everyday baseball jargon. However, the statistic predates the Rubik’s Cube and still sees no usage. Either it just never caught traction or there is some hidden flaw that makes it useless.

Barring those two exceptions, BOP should become the highest regarded baseball statistic.

*For more on BOP, read Codell’s 1979 articleRaw data acquired from Baseball-Reference Lahman’s Baseball Database*

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