Last Updated on April 15, 2023 by Dave Farquhar
Baseball, perhaps more than any other sport, attempts to quantify how good or bad a player is with statistics. Most baseball statistics only measure a small part of a player’s ability. One statistic that gained popularity in recent decades is On-base Plus Slugging, or OPS. Here’s an explanation of OPS in baseball and what it means.
OPS is the sum of a player’s on base percentage plus slugging percentage. Modern analysts argue it’s a better measure of a player’s ability than traditional stats like batting average or runs batted in.
What is OPS in baseball?
OPS, like its non abbreviated name states, is the sum of on base percentage (OBP) plus slugging percentage (SLG). On base percentage measures the player’s ability to get on base. Slugging percentage, also called slugging average, measures the ability of a player to hit the ball a long distance and get into scoring position right away and/or clear the bases if anyone else is on base.
Modern analysts argue that OPS gives a better measure of a player’s value than traditional baseball statistics.
One of my friends summed up the problem with traditional statistics really well. He was at a Cardinal game, and Mark McGwire came up to the plate. McGwire was in his final season and not exactly tearing up the league due to failing health. “What’s the big deal with this guy?” one of the other attendees asked. “He’s not even hitting .200.”
“Sometimes players who can’t hit .200 can do other things,” my friend said as McGwire launched a baseball high over their heads and into the deck above them. He pointed upward toward the ball. “Like that.”
McGwire’s .187 batting average in his final Major League Baseball season understates his value. The same can be said for Babe Ruth’s final season. Traditional baseball statistics like batting average and runs batted in can obscure the value of useful players. Sometimes a faded star at the end of his career can still provide value. And many championship teams built themselves around unheralded players, either knowingly or unknowingly.
OPS helps you find underrated and overrated players
Advanced statisticians love Hal Morris’ 1998 season. He hit .309, a good batting average. He finished second on his team in batting average. But he wasn’t the team’s second best hitter. He got 146 hits that year, but 116 of them were singles. Making matters worse, he only walked 32 times. His OPS was only .731 due to his small number of walks and extra-base hits. The .309 batting average overstated Morris’ production. He was good at hitting singles but his overall ability to get on base wasn’t great. Nor was his ability to hit for extra bases. And he wasn’t an especially fast runner, so his ability to get to first base didn’t help the team score a lot of extra runs.
Al Pedrique, a one-year wonder for the Pittsburgh Pirates in 1987, is a similar example who had a much shorter career.
In 1974, Gene Tenace had a disappointing year. He only hit .211. But he walked 110 times and hit 26 home runs. His OPS that year was .778. Tenace didn’t hit the ball as much as Morris, but his walks did the team about as much good as Morris’ singles, and they were almost as numerous. When Tenace did hit the ball, it was a double, triple or home run about 40 percent of the time, which gave his team a much better chance of scoring. His batting average was a lot less impressive, but he had a much better year. His .211 batting average really understated Tenace’s production. Like Morris, he wasn’t a great runner, but it affected his game less. Neither of them were Ted Williams, but Tenace was closer in spite of his low batting average.
Gene Tenace was an underappreciated player in his day. OPS is one of the statistics analysts use to try to identify players like Gene Tenace, who can help your team while being affordable in terms of what it will take to acquire the player and what you’ll have to pay him. This sums up the “Moneyball” approach the Oakland Athletics made famous early in the 21st century.
What is a good OPS in baseball?
Another trick is to take OPS and divide it by three and compare it to the player’s batting average. If the OPS/3 is higher than the batting average, batting average is understating his value and you have an underrated player. If the OPS/3 is lower than the batting average, you have an overrated player.
Going back to the examples of Hal Morris and Gene Tenace, Morris’ OPS/3 in 1998 was .244. That’s well below his batting average and not the kind of production you look for in a first baseman. Hal Morris was something of a poor man’s Wade Boggs, but Boggs was more valuable because he drew more walks. Morris only exceeded an OPS of .800 in four of his 13 seasons in the major leagues.
Tenace’s OPS/3 in 1974 was .259, which was quite a bit higher than his batting average. He wasn’t great, and I’m sure people questioned why he played every day, but he deserved the playing time he got. He didn’t quite hit that .800 threshold in 1974, but like I said, it wasn’t his best year. In 11 of his 15 seasons, he did exceed an OPS of .800.
The problem with OPS in baseball
There’s one big, big mathematical problem with OPS in baseball. You’re adding two percentages. But when you express them as fractions, they have different denominators. As a result, you end up double-counting singles, but only single-counting extra-base hits and walks. A single can be better than a walk, but not all the time, and a single isn’t as valuable as a double. But in effect, by counting singles twice, OPS assigns the same value to singles and doubles.
A better statistic would be to take the sum of a player’s total bases, walks, and hit by pitches, and divide that by plate appearances minus sacrifice flies. It’s still an incomplete picture, but it’s mathematically fair, and gives a more complete picture of a player than raw OBP or slugging percentage. That’s the gist of the 1980s statistic Total Average, or TA, and the modern statistic of wOBA, which is weighted on base average.
But OPS is easy to calculate and most collections of statistics include both OBP and slugging percentage. So people use it. My favorite stat for pitchers, WHIP, is imperfect too, but I still use it.
Better statistics than OPS
Adjusted OPS is an improvement over OPS by measuring the OPS of a player against the league average. It’s a very nice stat. A score of 100 is average. So it’s easy to tell how far or below average a player is.
If you want a really nice statistic, look at WAR. WAR stands for Wins Above Replacement. Some people describe replacement-level players as league average. But practically speaking, think of the type of player who gets released in September to open a roster spot for a prospect, and some contending team picks him up to see if he has anything left.
WAR estimates how many more games a team would win if they replaced a player like that with a different player.
When you look at Hal Morris in 1998, his WAR was 0.4. He was barely better than Daniel Nava’s in 2015. Nava, late in his career, was the kind of player contending teams would acquire in September as a bench bat. Most teams didn’t play him every day.
The 1974 Gene Tenace, in spite of having only a slightly better OPS than the 1998 Hal Morris, had a WAR of 5. In spite of his unimpressive individual statistics, his overall value was better than some All Stars. A team that replaced the 1998 Hal Morris in the starting lineup with the 1974 Gene Tenace could expect to win 4-5 more games over the course of a season, which is pretty significant.
The problem with WAR is it’s hard to calculate. You have to know things like how often a player grounded into a double play and kept his team from scoring. But we have the data and we have the algorithms to make computers calculate it for us.
Fun with WAR
Thanks to WAR, we can answer lots of questions. Who was better, Mickey Mantle or Willie Mays? Well, in 1957, Mantle was slightly better than Mays was in his best season, 1965. When both of them were at their best for a single season, Mantle was better.
But Mays put together a stretch from 1962-1965 that was better than any four-year stretch of Mantle’s career. Cherry-pick Mantle’s four best seasons and they still weren’t quite as good as that stretch. We can still argue about raw ability, but if the Yankees had traded Mantle for Mays even up, they would have won a few more games with Mays, especially if they did it after 1957. We have the numbers to prove it.
According to WAR, the three greatest position players of all time, in order, are Babe Ruth, Barry Bonds, and Willie Mays. Before WAR, we knew Willie Mays was great. But greater than Ty Cobb? No one argued that in the ’80s, because Mays never hit .400. Cobb did. Crossing eras can be tricky, but WAR lets us compare better than we used to be able to.
Why use OPS, then?
And WAR is great for figuring out whether trading one player for another is a good trade, or figuring out who should play more often and who should play less. But it’s not a good situational statistic. When you want to figure out which bench player should pinch hit for a weak hitter late in the game, OPS works much better. And it’s more readily available to the fans.
Second guessing the managers is a big part of the fun of watching baseball, and OPS is a really good stat for that purpose. That’s why people care about OPS in baseball.
David Farquhar is a computer security professional, entrepreneur, and author. He started his career as a part-time computer technician in 1994, worked his way up to system administrator by 1997, and has specialized in vulnerability management since 2013. He invests in real estate on the side and his hobbies include O gauge trains, baseball cards, and retro computers and video games. A University of Missouri graduate, he holds CISSP and Security+ certifications. He lives in St. Louis with his family.