Data Entry: OPS and OPS+

In the first of a new series on understanding advanced metrics used in baseball, I explain OPS and OPS+ and why they are useful in helping to understand player performance. This series is aimed primarily at those who are either new to baseball or still learning the basics of the game, so might be of questionable interest to those who have Fangraphs and Baseball Reference bookmarked in their web browser! Feel free to use the comments section to feedback or seek clarification on anything.

Whilst one of the most appealing things about baseball is its rich history, there are a number of creases originating in the past which have still not been properly ironed. One of those oddities is the batting average stat (AVG).

In the dead ball era, where walks were like hens’ teeth and the hitter told the pitcher where he wanted the ball pitched, it was rare for a hitter to be able to regularly do more than to slap hit for singles. Hitting for power was a rare commodity. It was into that run-scoring environment that cricket-mad English expat Henry Chadwick stumbled, and the box score he devised, and his batting average stat which calculated the number of hits divided by the number of at-bats, still proliferates today.

For nearly a hundred years now, though, and largely thanks to the change in approach pioneered and exemplified by Babe Ruth (in 1919 when he topped the home run leaderboard with 29, the second-best slugger that year got 12), hitting for power and extra bases has become more common. Considering that doubles are more valuable than singles in terms of run production, and triples and home runs even more so, it has always made sense to value extra-base hits more than singles in judging players. So for roughly a hundred years(!), despite being outdated, the mainstream baseball media and many fans have clung on to it to the extent that it decides batting titles today.

Whilst it is significantly more effective at measuring performance, On-base plus slugging (OPS) is pretty much as simple as batting average. So simple, in fact, that it barely warrants an ‘advanced metrics’ tag. It is a hybrid of two stats, On-base percentage (OBP) and Slugging percentage (SLG), that have existed for years.

On-base percentage simply measures how often a hitter reaches base, by dividing all hits and walks by plate appearances. Slugging percentage is the number of total bases divided by the number of at-bats. It treats doubles, triples and home runs as twice, three times and four times more valuable than singles respectively.

Votto is currently ranked 19th all-time in career OPS+

OPS simply adds together a player’s OBP and SLG, to produce a crude but reliable measure of player performance. It was touted by John Thorn and Pete Palmer in their excellent 1984 book, The Hidden Game of Baseball, and it has gradually gained popularity over the years.

It is not perfect by any means, and there are other more advanced measures of run production and value which I will cover in future posts, but it far outweighs batting average, and either OBP and SLG individually, as measures of performance. Its critics point to the fact that OBP is more important to run-scoring than SLG, and therefore should be weighted more than SLG, rather than the 50-50 split afforded it in OBP, but it is so simple to use that it has more universal appeal than more advanced metrics.

In 2017 the league average OPS was .750. Anything above .800 is considered good, with anything above .900 considered elite. In the last three seasons (I’m petitioning to have it dubbed ‘The Juiced Ball Era’) the top five performers look familiar, and it is hard to argue that they are the best performances of late. The limitations of the stat are highlighted perfectly by Joey Votto‘s fifth place because although his slugging percentage is dwarfed by that of Martinez in third on the list below, his hugely superior OBP made him a more valuable player overall in 2017.

1 Bryce Harper (2015) 1.109 .460 .649 .330
2 Mike Trout (2017) 1.071 .442 .629 .306
3 JD Martinez (2017) 1.066 .376 .690 .303
4 Aaron Judge (2017) 1.049 .422 .627 .284
5 Joey Votto (2017) 1.032 .454 .578 .320

For historical context (or to shoehorn a bit of Barry Bonds stat porn into the article!), here are the best all-time seasons by OPS.

1. Barry Bonds, 1.4217 (2004)
2. Barry Bonds, 1.3807 (2002)
3. Babe Ruth, 1.3791 (1920)
4. Barry Bonds, 1.3785 (2001)
5. Babe Ruth, 1.3586 (1921)

OPS is increasingly being used in mainstream baseball media. Recently some more analytics-driven teams have been displaying OPS on their scoreboards instead of AVG.

An even more useful but more complex related statistic is OPS+, which adjusts OPS for park factors and run-scoring environment. The statistic is scaled so that an OPS+ of 100 is league average, whereas 105 is 5% above league average, and 95 is 5% below league average, and so on.

The obvious advantage of OPS+ is that it supports more accurate analysis of player seasons in a historical context. Given its increased complexity, it is not possible to calculate simply by glancing at a player’s slash line, and the simplicity of OPS makes it a stat of choice for many when discussing the game.

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