Baseball stats for dummies part 1: OPS

[Southwest Brewer]

OPS = OBP + SLG

 

Now it makes sense... http://forum.brewerfan.net/images/smilies/frown.gif

 

From how I understand:

 

OBP (on base percent) = (H+BB+HBP) / (AB+BB+HBP+Sacrifice Flies)

 

PLUS

 

SLG = (TB/AB)

 

So these two stats seem to overlap in what they count and I don't see that as a big deal b/c this is baseball and not science. So what makes a average/good/great OPS?

 

What do you expect from a leadoff, clean up, 5 spot, 8 spot in the batting order... approximate estimate with a short reasonable explaination would be great!!! http://forum.brewerfan.net/images/smilies/smile.gif

 

What's more important OBP or the SLG....is one of the two more important for leadoff than clean up (yeah I expect OBP to be #1 for leadoff and SLG to be best for cleanup)? But it can't be so simple so how about a brewer related explaination or super-simple so my brain can get a little bit of what's going on with this thing.

 

Is expected OBP more related to position played rather than spot batted in the lineup?

 

I assume I'm not seeing some of the most important points of OBP so feel free to point them out.

 

Thanks y muchas gracias

[Southwest Brewer]

Just to be clear.... I'm the dummie and I'm looking for a little help.

[JoeH33]

As far as OPS goes, I have always graded players this way. This is about as in depth as I go, but I feel its a good place to start.

 

 .600 - .700 - Poor .701 - .800 - Average .801 - .900 - Good .901 - 1.000 - Elite 1.001+ - Superstar

[1992casey]

Southwest Brewer,

 

I think you've found the way to get your answers! Start threads over here one stat at a time. That would seem to be one of the purposes of this forum that's been underutilized.

 

In the case of OPS in general, I'd say JoeH33's breakdown is a good way to get going. It's simple and to the point.

 

After getting used to that, it pays to look at positions on an individual basis. Baseball Prospectus offers league positional averages going back to 1972. On that site, you'll have to add OBP and SLG to get OPS.

 

Keep in mind that positional numbers bounce around somewhat from year to year. If I'm trying to determine what a shortstop should be hitting, for instance, I might look at both leagues for the past 5 years. You end up with a good feel for the numbers that way. The extra work wouldn't be necessary if all you want to do is back up a statement like, "Hardy's offense was above average among NL shortstops in 2005."

 

Run Estimation for the Masses (Hardball Times, Dan Fox) is nice for getting people going, too.

[brettac1]

I'd also say that the O is more important than the S. When I try to explain to someone why OBP is the most important, I just ask - "how many runs would you score if you never made an out?". They then say "infinite" - and I say "correct, so doesn't it just make sense to build a team full of the guys who make the least outs?". That seems to be the simplest way to understand the importance of OBP, and I guess in part, OPS. SLG is important, too, because that's the advancement of runners, but a guy can put up an .800 OPS with a .300 OBP and .500 SLG, and that guy's still not that great of a player because he consumes too many outs.

[Southwest Brewer]

so are you saying a .500 slg is low? or that the .300 obp is low?

 

I'm guessing you want the numbers to be close to eachother and the .300 OBP is kind of low? And obviously we want both as high as possible.

 

This is exactly the situation I have problems with in a thread that "goes stats."

 

Would .800 OPS be better if it was .380 OBP and .420 SLG? Would that for some reason be incredible OR just the same?

[brettac1]

The .300 OBP is low, the .500 SLG is nice. The league is littered with guys like that, like Wily Mo Pena, Soriano, Carlos Lee to a degree, etc. It'd be better because the guy made less outs. For instance, a high OBP guy like a Giambi (.535 SLG) made only 312 outs last year. A low OBP guy like Soriano (.512 SLG) made 479. That's huge. A .380/.420 wouldn't be incredible, that's somewhere around what Brady Clark hit - he made 399 outs. That's a nice year.

 

Using simple OXS (OBP*SLG*AB) with the Brewers total AB's last year (5448 AB's), the .300/.500 line would have resulted in 817 RS, the .380/.420 would have been about 870 RS. The Red Sox hit .357/.454 and scored 910.

[Southwest Brewer]

Quote:

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Using simple OXS (OBP*SLG*AB) with the Brewers total AB's last year (5448 AB's), the .300/.500 line would have resulted in 817 RS, the .380/.420 would have been about 870 RS.

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http://forum.brewerfan.net/images/smilies/frown.gif Huh? Could you explain why OX is used? Same with RS?

 

I just saw something fly over my head.

[brettac1]

OXS is just on-base times slugging times AB's. It gives you a (very) rough estimate of what a teams runs scored (RS) should be, it weights OBP and SLG equally, whereas OPS generally gives more weight to SLG, because it is nearly always higher, that's why not all .800 OPS's are created equal. You use it more for teams than individual players, though.

 

I was just using it to show how a higher OBP would result in more runs scored than the higher SLG, even if both had that very same .800 OPS, or to demonstrate that the OBP is more important.

[Southwest Brewer]

Thanks for the extra info. That really helped a lot and it makes sense.

 

Before you explained, I had no idea what that meant.

 

I plan on asking plenty of these questions for a while, all the input is appreciated.

[1992casey]

I agree with Brett when he says that OPS doesn't weigh OBP like it should. But it doesn't affect the OPS to the point where we have to disgard it. Despite the flaw, OPS stacks up quite well. Check out the graph included in Dan Fox's article linked to above:

 

http://www.hardballtimes.com/images/uploads/dlf_ops1.JPG

[brettac1]

I actually really prefer to look at all three "slash stats" (AVG/OBP/SLG) because it helps provide context. I like to subtract the AVG from the OBP to see how patient a guy is, because just like not all .800 OPS's are equal, neither are all .380 OBP's. OPS is really a nice number, and I use it a bunch, but I think seeing all three seperate gives a truer, more complete picture of what kind of hitter a guy is - like if he's AVG dependent, for instance, or if he draws a bunch of walks, or hits a lot of homers and not much else, or a bunch of singles, etc.

[sbrylski]

From Linked Article Above:

Some analysts have noted, as discussed by Michael Lewis in Moneyball, that each point of On-Base Percentage is more valuable than each point of Slugging Percentage. How much more has been the topic of some discussion over the years, but a multiplier of 1.8 has been suggested. This turns out to be the value that results in the maximum correlation coefficient.

 

OBP is about 1.8 times more important.

 

OBP of .350 helps score runs as much as a .630 Slugging %.

 

Brady Clark's .372 OBP helped the team score the same as a player with a .670 SLG, which would be an amazing SLG. Compare it to Jenkins' .512 and Lee's .487. Albert Pujols' career SLG stands at .621.

 

Of course, it is also very relative to where the player bats in the lineup. A .550 SLG doesn't help score as many runs when you're batting leadoff, because there are less baserunners, ect.

[rluzinski]

The relative value of OBP and SLG was talked in this thread a bit:

 

LINK

[1992casey]

I fully agree with Brett: I like the "slash stat" (BA/OBP/SLG) idea, too. We can see a picture which includes a batter's walk rate, extra base hit rate (isolated power), and also, of course, his OPS. What OPS offers is accuracy relative to a player's total value. The other numbers tell us about a hitter's qualitative aspects.

 

I also have no problem with stating that OPS overvalues slugging percentage (the idea that a point of OBP is worth more than a point of SLG). The thing is that even with that flaw, OPS stands up as being extremely close accuracy-wise to the stats that attempt to weigh OBP more appropriately and be more inclusive. In short, we're not losing enough to worry much about.

 

If all we're doing is trying to answer the question, "Who's been the better player?" we can often use OPS (or one of the stats to its right on the above graph) and stop there. If two OPSs are close, we're going to want the "slash stats" at our fingertips, too.

 

If we're trying to predict a player's future, we should probably be looking at the "slash stats", then breaking them down into their components even further. In other words, we'd be looking at bits and pieces to answer the ultimate question, "What's going to happen to this player's OPS?" Predictive stats are a different sort of animal and probably deserve a thread of their own.

 

One of the great things about OPS is that it's ready for prime time. Its accuracy has been demonstrated. Unlike stats like RC, its formula is never going to change. It's easy to use (adding is simple). It's hitting the mainstream; even Tom Haudricourt is starting to mention it on occasion. I believe it's time for MLB to make OPS an official stat.

 

None of this would exclude using the stats to the right of OPS on the above graph. They're just more work to compute and somewhat harder to find, while not always offering enough advantage to justify the extra effort. There are times I dig those stats out. RC27 (runs created per 27 outs), for instance, gives us a number that makes sense at a glance as it looks a lot like an ERA. OXS (which is actually a variation of RC) offers the opportunity to do some cool calculations.

 

In short, everything has some kind of limitation, but we can use OPS a lot and manage to stay out of trouble in the process.

[Southwest Brewer]

Thanks guys. I think these posts are pretty good to get me a basic understanding. The links are also great and I'm working my way through them.

 

My overall conclusion:

 

1. OBP is a little more important than SLG% (about 1.6-1.8X as important).

 

2. Both are important and to look at a stat line of avg/obp/slg can tell you what kind of patience, power, and hitting ability a guy has.

 

3. A rough estimate of how good a batter's OPS is:

.600 - .700 - Poor

.701 - .800 - Average

.801 - .900 - Good

.901 - 1.000 - Elite

1.001+ - Superstar

 

But sill have to look at the numbers mentioned in #3 b/c not all OPS are created equal..

 

Thanks again, I think I have the basics on this one. (up next...WHIP)

[sbrylski]

Question for the stat guys:

 

Is there a stat that is calculated like this:

 

OBP * 1.8 + SLG = ???

 

(OBP times that magically number that makes its value equal to SLG) + SLG

 

What would that be called? Wouldn't it be more accurate than OPS?

[rluzinski]

Hardball Times does. They call it GPA.

[Picciolo]

Quote:

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From Linked Article Above:

Some analysts have noted, as discussed by Michael Lewis in Moneyball, that each point of On-Base Percentage is more valuable than each point of Slugging Percentage. How much more has been the topic of some discussion over the years, but a multiplier of 1.8 has been suggested. This turns out to be the value that results in the maximum correlation coefficient.

 

OBP is about 1.8 times more important.

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The problem with the moneyball analysis, and the one linked by rluz below (I mention this in the linked discussion) is that OBP is constrained between 0 and 1. SLG is constrained b/w 0 and 4. You can't divide the coefficients of two variables to get relative weights when they are of different scales.

[Southwest Brewer]

I'm undecided right now....but why? Isn't that part of the reason to give the value a weight so they even out?

[Picciolo]

Quote:

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I'm undecided right now....but why? Isn't that part of the reason to give the value a weight so they even out?

---

 

Coefficients aren't relative weights. Basically, without going into too much statistical jargon, they are sample means. I'll throw out an example...see if this clears it up:

 

Suppose I wanted to do a homerun study, and I decided to regress the number of homeruns a player hits on the players' weight, and the percentage of balls he puts in play so that:

 

HR = X1*weight + X2*Percent

 

Before I even run my regression, I pretty much know which coefficient will be smaller. It will be the players weight because each individual player weight is going to be very large compared to a percentage. Say I run my regression on my sample and suppose I get the following numbers...

 

HR = 4 + .1*Weight + 16*Percent

 

So that someone who weighed 200 lbs and put 50% of his balls in play would work out to 32 HR's (just plugging the numbers in). Now, if coefficients were relative weights, we would say that each percentage point of balls in play is 160 times more important than another pound of weight.

 

Now say that instead of entering the balls in play number as a percentage, I decide to enter them in as whole numbers. In other words, I multiply the percentages by 100. Guess what happens to the coefficients. The intercept (4) stays the same as does X1. The only number that changes is X2 so that I'd get something that looked like:

 

HR = 4 + .1*Weight + .16*Percent

 

Someone that weighs 200 lbs and puts 50% in play still works out to 32 HR's. But now, each percentage point increase is only worth 1.6 times a pound of weight. All I did was change the scale. This is why you can't use coefficients as relative weights. It's apples & oranges.

[Southwest Brewer]

OK so I have another question.....isn't that how every multiple regression equation works?

 

You are trying to estimate Y, right? And putting a weight before each variable X1, X2 is just used to make your model run, right?

 

I think all these things go back to Bill James and he just tried to make sense...not necessarily statistically sound equations.

 

I agree with you that they are apples/oranges. ....Wondering, back....what was the point of saying OBP*1.8 is equal to each point of SLG? Was it to determine wins?

[Southwest Brewer]

I don't think it's to compare OBP to SLG....it's multiplyint OBP*1.8 with SLG.....to get a GPA Gross Production Average to compare players.

 

I don't understand why they decided OBP is multiplied by 1.8....but if you do the same thing for both players....I don't see the problem for comparing players.

[Picciolo]

Quote:

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You are trying to estimate Y, right? And putting a weight before each variable X1, X2 is just used to make your model run, right?

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Forget about multiple regression for a second. Let's look at an equation with just one X variable. Hopefully this won't get too confusing...this is tough to do in this format, but I'll give it a shot.

 

Let's say my Y variable is homeruns and player's body weight is my X variable.

 

HR=X*BodyWeight

 

Let's say I'm looking at 5 players. So I have 5 homerun numbers and the body weight for the 5 players. For example, my five pairs of numbers are (X,Y): (220,10), (230,15), 235,12), (215,27) and (220,28 ) . We can calculate the intercept and the X coefficients using these numbers. To do so, we need to calculate some additional numbers.

 

1) I need the mean of the X numbers, which is (220+230+235+215+220)/5=224.

 

2) I also need the mean of the Y, which is 18.4.

 

3) Next, I need to subtract the mean of the X from each individual X, and the mean of Y from each individual Y.

 

4) I need to square the difference between the X's and the X mean (this is where the term least "squares" comes from).

 

5) I need to multiply each (X-224) by its corresponding (Y-18.4).

 

Hopefully the formatting don't get too screwed up...

 

Y........X........ X-224........ (X-224)^2........Y-18.4........(X-224)*(Y-18.4)

10........220........-4............ 16............ -8.4.............. 33.6

15........230........ 6............ 36............ -3.4..............-20.4

12........235........11...........121............ -6.4..............-70.4

27........215........-9............ 81............. 8.6.............. -77.4

28........220........ -4............16............. 9.6.............. -38.4

Total.................................270.................................-173

 

To calculate the X in the regression equation divide the total of the last column by the sum of the X-squares:

 

-173/270= -.64

 

I won't go into the details of how to calculate the intercept, as it's not really important here, but the calculations are similar. So from my observations, I get the following equation:

 

HR=162 - .64*BodyWeight

 

So a couple of observations here:

1. As you can see, we're not assigning weights to anything. We are calculating the actual coefficient from the numbers in our sample of 5.

 

2. Coefficients can be negative.

 

3. We can use the equation to predict homeruns for someone who weighs 185 pounds. We take 162-.64*185lbs and we get 43.6 HR's.

 

4. The coefficients are really slopes. For each 1 pound increase in body weight, the model predicts that homeruns will decrease by .64. That's what least squares regression does, it finds the best slope for the data we have.

 

5. The OPS analysis was trying to predict runs scored I believe. So people were saying that each point of OBP is 1.8 times better than SLG. They calculated the coefficients for the two numbers the same way we just did in the example above and divided a couple of the coefficients -- which I've already shown is absolutely, positively wrong.

 

6. All of the calculations can be done in Excel.

 

7. Multiple regression uses the same type of calculations to get as many coefficients as is necessary (if you thought the above calcs were long, the work increases geometrically with each add'l variable).

 

Quote:

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I think all these things go back to Bill James and he just tried to make sense...not necessarily statistically sound equations.

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Yes, I know. http://forum.brewerfan.net/images/smilies/frown.gif

 

People can make sense without cutting corners in their analysis (I may have failed here, but I'm winging it). It's something I find incredibly frustrating. Not so much on this board as for most of us, this is a hobby. But for people that do this for a living, I pull my hair out over a lot of the analysis -- especially when the analysis violates very basic regression rules. Many of the conclusions I see out there cannot be drawn from the methods used. Moneyball was an incredibly frustrating book to get thru for me.

[Southwest Brewer]

Well, Pic I think I've talked with you on the badgernation board.

 

And I've just spent the last 10 hours working through my stats for my M.S. project including a couple hours with one of my professors....I'm pretty much convinced that I hate stats, specifically how to write code for something that is nested and why GLM will give you an ANOVA table, while MIXED won't. Why not just give me the dang ANOVA table either way, it's the same table. And MSgroups that go up to T...not pretty, for this little brain. There's more, but I'm starting to rant.

 

I want to understand what you're saying, but I may have to pick up this conversation when my brain is back to normal OR when I quit for the night.