When Thomas and I first started dreaming podcast-related thoughts, we hoped to have listeners who were fans of the San Francisco Giants Baseball Club, not necessarily fans who had advanced mathematical and statistical training. That said, on the podcast we’ve been throwing out stats left and right, and some of them bear explaining.

First of all, you’re going to hear the term “Sabermetrics” a lot, which is the general term that has come to describe baseball statistics. It has nothing to do with sabers, or swords in any way. Rather, it comes from the Society for American Baseball Research (SABR), which was one of the first organizations to do complicated quantification of baseball.

JT over at Triples Alley does a great job of explaining the complicated stats in his glossary, but I’m going to do my best to explain what I can here on this blog too. So here goes:

**AVG/OBP/SLG – aka The “Slash” Line:**

Among the more oft-quoted batting statistics, you will often hear these three statistics together, which is made up of Batting Average (AVG), On-Base Percentage (OBP) and Slugging (SLG). Typically when we say three numbers in sequence, we’re talking about these stats.

**Batting Average** is probably the most common stat you’ll see when a player comes up to bat, even though it has fallen a bit out of style as more descriptive stats have come into use. It is derived by:

AVG = Hits/At-Bats

Yep, pretty simple. To tell the truth, batting average doesn’t describe much, because it fails to account for a player’s ability to draw walks or hit for extra bases. This can be a very misleading stat, because you can have a player with a great average but no patience and no power, or you can have a player like Pat Burrell who is great at drawing walks and crushing the ball, but only has an underwhelming .266 average. Don’t take the batting average too seriously.

**On-Base Percentage** is a statistic that measures a player’s ability, as you’d expect, to get on base. It’s measured as follows:

OBP = (Hits+Walks+Hit By Pitch)**/**(Plate Appearances)

On-Base is great for showing which players have good patience and a good eye at the plate. It can also be seen as the player’s ability to not get out, which is really quite useful. To use Pat Burrell as an example again, in 2007 he had a measly .256 average, but drew 114 walks and ended with an 0n-base of .400. That means that 4 times out of 10, he managed to get on base in whatever way. Again useful, but doesn’t tell the whole story.

**Slugging **is often referred to as “Slugging Percentage,” which is a bit of a misnomer, as it’s not actually a percentage of anything, and can be over 1, even though it never actually has. Slugging comes from the following formula:

SLG = [1*(Singles)+2*(Doubles)+3*(Triples)+4*(Home Runs)]/At-Bats

Which can also be written as:

SLG = (Total Bases from Hits)/(At-Bats)

While slugging also ignores walks, it can basically be defined as the average number of bases that a player gets every time they come to bat, excluding walks. A player who hits a lot of home runs or doubles would have a higher slugging than, say, Ichiro Suzuki. Ichiro has led the league in hits in 7 of his 10 MLB seasons and set the record for most hits in a single season, with 262. Still, of his 2,244 career hits, 1825 of them are singles, and he has led the league in singles *every year *of his MLB career. While his career batting average and on-base are .331 and .376, respectively, his career slugging is just .430, as opposed to Pat Burrell’s career .475 slugging (or Barry Bonds’ .607 slugging).

Don’t get me wrong, Ichiro is still one of the best players to ever wear the uniform, but still, I’ll take my shots at him when I can.

The other stat you’re going to hear quite often is **OPS**, which stands for On-base Plus Slugging, and serves as a general tool to compare a batter’s overall batting. It is derived from:

OPS = OBP + SLG

Anyway, I hope this guide has been a bit helpful. I’ll continue to post more layman’s guides as we keep going. Let me know if you have anything specific you’d like me to write about, and keep listening.

Go Giants!

[…] of statistical understanding, so if you don’t remember what OBP and SLG are, check out SFB Part 1, or here is Wikipedia’s dissertation […]

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who hears a mysterious voice and obeys its suggestion to build a baseball field in the middle of his corn crop, movie

director Phil Alden Robinson decided to bring the heartwarming tale about love, redemption and second chances to the

big screen. You don’t want less speed and more movement when throwing

to a teammate, do you.

Great article, thanks for taking the time to research and write.

One thing that might’ve been useful would have been explaining a bit further the utility of the OPS stat in evaluation a hitter’s overall performance, and also some of its pitfalls.

As the author stated, OPS is a good measure of a hitter’s overall ability at batting. When combined with Slugging Percentage (SLG)’ the stat becomes even more informative (if you know what you’re looking for, of course). SLG Assigns increasing weights to increasingly valuable hits: for instance, when calculating a player’s SLG, the number of singles hit by player is multiplied by one, doubles by three and home runs by four. This has the effect of rewarding players for power, or “slugging.”

OPS simply combines the stats of OBP and SLG, which is supposed to answer the question, “How effective is this player at both getting on base and hitting for power?” A powerful stat, indeed.

Some shortcomings in OPS exist, however. These differences are in the fact that, in reality, there are a significant number of times when a double isn’t in fact worth twice as much as a single, a triple not worth 1.3333 times more than a double, etc.

Also, it has been demonstrated that OBP has historically been more valuable to a team’s success than SLG, but the formula calculating OPS gives the two factors equal weight.

Despite these shortcomings, OPS is used everyday by MLB scouts, players and fans alike as a fairly reliable metric to compare hitters.

Drop me a line at dwindsor@gmail.com to discuss this further; I’d love to hear others’ opinions.

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