If you’ve been reading me for a while, you know that OPS+ is my go-to stat for a quick player evaluation. Here’s why.
Here are some actual player stats:
HR BA OBP SLG OPS 22 .259 .296 .452 .749 22 .261 .321 .428 .749 22 .250 .329 .416 .745 22 .249 .338 .411 .753
These are 4 seasons by 4 different guys in 4 different years. But they are quite similar, right?
Well, let’s take a look at 4 other seasons:
Player Year OPS+ PA R RBI Craig Pauqette 1996 87 462 61 67 Aubrey Huff 2005 99 636 70 92 John Mayberry 1978 108 587 51 70 Boog Powell 1968 127 634 60 85
Big differences here.
Huff and Mayberry had relatively league-average years. Pauquette was well below-average, and Powell had a very nice season.
As you’ve probably guessed, these are the same 4 players as the 1st list above. They all had 22 homers and all had raw OPS right around .750. It’s just so damned difficult to judge raw numbers without some sort of context. OPS+ does a really nice job of smoothing over the differences in ballparks and offensive eras.
Interesting that the players respective OBPs ended up being ordered the same as their OPS+. Which I might expect as OBP is more valuable than SLG in terms of player performance? Maybe I got that wrong.
Anyways, neat little piece here.
Interesting observation, E, but I’m afraid it’s just a coincedence. OPS+ is calculated by comparing OBP and SLG to league averages for that year, and then adding in park factors. When this is done, OBP and SLG are weighted equally, so that neither takes precedence over the other.
The phenomenon you see here is because of differences in years and park factors. Putting up a .750 OPS at the cavernous Oakland County Coliseum in 1968, a historically bad year for hitters, would have been quite a feat. On the contrary, putting up a .750 OPS at Fenway Park anytime during the Steroid Era would have been easily below average.
Actually, GrandyMan, there might be more to ERolf’s point than just coincidence. The players are initially listed in ascending order by OBP and descending order by SLG. If I may oversimplify park and era effects, they tend to have a small impact on OBP and a bigger impact on SLG (with several exceptions, of course). If we read a higher OBP as “better offensive player” and higher SLG as “beneficiary of hitter’s park/era”, the adjustments that add the + may scale Paquette’s and Huff’s SLG back to where they belong, while keeping Powell’s and Mayberry’s OBP intact, thereby rewarding the better hitters for getting on base more.
Never stopped to think about that, Bryan. You’ve made an excellent point.
Always loved how the player not named Barry Bonds who had the highest OPS+ for a season was Fred Dunlap back in 1884. It took 107 years for that record to be broken.
Dunlap was greatly aided by the miracle of the Union Association’s resurrection to major league status 85 years after its untimely and asynchronous dissolution. For Bonds it was the miracle of modern medicine.
Dunlap’s nickname was “Sure Shot” – yet another bond between them!
My go-to is WAR batting runs. OPS+ is a rate stat, so here’s how they translate with everyone evened out to 700 PA:
It is interesting to me that Mayberry was closer to “average” than Huff for Rbat/700. But then Huff was closer to “average” than Mayberry for OPS+. (Assuming 0 is “average” for Rbat/700, and 100 is “average” for OPS+)
To me it makes a difference if a player is above, below, or just average.
After thinking about it for a while AND not knowing how Rbat is calculated, I am realizing that 3 players probably could all have 108 OPS+ with 3 different Rbat values. So I guess you need to look at numerous stats (which we have) to evaluate average, above average or below average. And as you guys have shown, the more Sabermetrically educated of us would have a personal favorite stat for a quick and dirty assessment.
Adam, which is your A-number-one — Rbat, or Rbat/700?
Oh hey, John. Missed this until now. I’d call #1 Rbat. I have to factor in PAs as well, but Rbat is what I look at first when determining how good an offensive performance was.
I tend to use it as a quick and dirty as well. That being said, OPS+ has no clear meaning or interpretation. I’m not sure why B-R continues to use it whereas Fangraphs transitioned to the superior wRC+ in 2009. For those unfamiliar with it, wRC stands for weighted runs created and wRC+ is wRC compared to league average. Average is 100 and each point above or below 100 is a percentage point above or below league average. So a wRC+ of 120 means the player created 20% more runs than league average. wRC+ is also adjusted for park and league.
Just for kicks, here are the wRC+ for each of the players in Andy’s post:
Paquette: 81
Huff: 92
Mayberry: 106
Powell: 130
I’m a little dismayed also that B-Ref didn’t include wRC+ over OPS+ in their recent changes to their WAR calculation. They’re still way behind Fangraphs in that regard, although I think fWAR for pitchers is bad, especially for relievers.
There are two reasons that I continue to use OPS+:
1. B-Ref uses it and not wRC+. Kind of hard to do a search on the P-I involving wRC+ when it’s not available anywhere on the site.
2. Almost amazingly, there usually isn’t a big difference between OPS+ and wRC+. It’s very often less than 5%. Perhaps this is why Sean et al chose to not change it.
I tend to think of OPS+ as a good wRC+ estimator.
Bstar – I agree that OPS+ gets used a lot cause it’s “there” and easy to access. It’s why I use it. And Baseball Reference is simply 100x better than fangraphs for extracting data. Still, I think people should be clear on the limitations of the data which are at least 4 in my mind:
1) SLG% and OBP are weighted equally when we know they shouldn’t be. I think Tom Tango said it should be 1.2 for OBP and 0.8 for SLG%.
2) SLG% itself is a problematic statistic. It assumes that a double is worth twice as much as a single, a triple 3 times a much and a home run 4 times as much. We know that’s not true.
3) There’s a problem with how hits are counted relative to walks. Take a simple example using one game with two players. Player A has 2 walks and 0 hits in 4 PAs; Player B has 2 singles and 0 walks in 4 PAs. Both players have a .500 OBP which is correct. But Player A has a .000 SLG% whereas Player B has a .500 SLG%. Now clearly Player B’s singles are worth more than Player A’s walks. But not to a level of .500 vs. .000.
4) OPS+ is essentially meaningless. For example, what exactly does it mean to have a 110 OPS+???
Again, I’m not blaming anyone for using it: as I said, I use it myself. I think the fault lies with Baseball Reference for continuing to report a problematic and outdated stat (fangraphs has dropped OPS+ entirely). And of course, we now see OPS+ being used in HOF discussions not just among fans but among voters too.
As for Bstar’s point that wRC+ and OPS+ are often similar…a) while that might be true, I’d like to see a study of that before coming to any conclusions, b) there are probably certain types of players for whom OPS+ works better than others but right now we don’t know which types.
Tom Tango’s comments (along with Bill James) can be read here:
http://www.insidethebook.com/ee/index.php/site/comments/ops_and_ops/
You touched on a lot of important things, Ed, and I strongly agree with everything you’ve pointed out. I would add the following to your 4 points:
1) Actually, the proper weighting of OBP and SLG, according to Tango and Fangraphs, is
1.8*OBP + SLG=OPS(fangraphs) 1.7*OBP + SLG=OPS(Tango)
Here’s an article from Tango’s blog that argues why this is the proper weight. I wouldn’t bother getting bogged down with all the math:
http://www.insidethebook.com/ee/index.php/site/comments/why_does_17obpslg_make_sense/
2) Not only does SLG improperly weight hits, it also excludes walks entirely.
3) OBP not only has the problem you listed, but it also improperly weights hits/walks as equal to XBH.
4a) My assertion that OPS+ and wRC+ are very often quite similar is based on a lot of work I have done comparing the two metrics of different players, so no I haven’t done a comprehensive look at this; it’s just my personal observation. Again, I think the fact that the difference between these two very different metrics is very often negligible looks to me like a mathematical coincidence. It’s not like wRC+ was created to be an OPS+ proxy(at least to my knowledge).
4b) Tango sheds some light on what type of players OPS+ may favor in one of his comments in the link I posted above:
“OPS is a horrible stat, relatively speaking. OPS+ is a slight improvement. They “work” as long as you aren’t a power hitter with few walks, or a walk machine with no power.”
Thanks for the comments/additions. A few followups:
1) I wasn’t clear. The weights of 1.2 and 0.8 apply to the calculation of OPS+. Tango says using these weights would greatly improve OPS+.
3) I’ll have to take your word for that cause my brain doesn’t want to think that hard right now. 🙂
4a) Tango describes the general correspondence as “luck”.
4b) Another quote from Tango: “As you can see, OPS+ is biased toward power hitters, notably HR, and biased against guys who walk.”
Yup. You sure got it right, bstar. Usually, they’re about the same. Actually, I’m surprised you’ve seen it off by as much as 5% (though maybe with some of the bigger seasons, it is). Since most guys hang right around average, it’s usually the same, or 1-3 points off, if at all. Besides stats, though, different park factors MAY be influencing the differences, as well – so they may be even CLOSER than it looks!
As for the reason b-ref doesn’t switch? I’m pretty sure it’s because OPS+ is one of Sean’s own inventions, so he has a personal interest in keeping it on the site. Likewise, ERA- is a much better stat than ERA+, and it would be nice to see B-R switch over to that one, as well. Really the only reason to do this is that those versions are superior stats, and b-ref is used by more people than Fangraphs. But frankly, I think it’s really a big deal for those of us that are familiar with both sites to actually just go and look at the two different sites.
Gotta agree with your comments, Dr. D. Babe Ruth’s career OPS+ is 4.5% higher than his wRC+, while Bonds’ is 4% higher. I think you’re right about the big seasons showing the most difference, and those numbers are why I felt confident enough to throw out 5% as about the highest difference you’re going to find.
I also agree about ERA-. When a pitcher has an ERA+ of 130(and since ERA not lgERA is in the denominator), the only thing we can say about 130 is that the league average ERA is 30% worse than this pitcher’s ERA.
But ERA- flips the two so that ERA- = 100x(lgERA/ERA). Because of this, we can say that a pitcher with a 70 ERA- has an ERA that is 30% better than league average. This, plus the fact that ERA and ERA- both go down as the pitcher’s skill goes up makes more intuitive sense than the other way around.
Doom, in the article Ed linked to @13 Tango says that Pete Palmer invented OPS+.
Ed, I really appreciate your description of wRC+. My problem is that the Fangraphs site just blows. For most websites, there is a certain learning curve to figure out where stuff is, now to find it, read it, parse it, etc. (Remember when B-R revamped its layout about 3 years ago and moved all the stats around? It was really shocking at first but took only about a week to get used to). But Fangraphs I never get used to. Stuff is so damned hard to find. Explanations are hard to find. Data is hard to parse.
Great content, god awful execution.
Oh I agree Andy. As I noted in comment #13, Baseball Reference is 100x more user friendly than Fangraphs. My hope is that Sean will start including wRC+ in Baseball Reference. I’m not really sure what the barrier is. I’m sure he knows wRC+ exists and that it’s superior to OPS+. I doubt there are any proprietary issues re: wRC+. So I’m a bit stumped.
Ed:
Is there anything wrong with using baseball-reference’s RC/27 and dividing it by baseball-reference’s AIR to get a better picture of a player’s producticn within the scope of all environments?
BTW, I still believe dWar stinks. Darwin Barney is currently the 6th best position player by WAR (4.4) strengthened entirely by a dWAR of 3.3…Perhaps compared to peer NL 2B he is a superior defensive player, but that dWAR value is so over-the-top, it’s ridiculous, absurd, and strains all level and manner of credulity
Paul E: No idea, not my area of expertise but one of the other really smart people on this site might be able to answer your question. Regardless though it would be a fair amount of work if you had to do it on a regular basis.
Yeah dWAR is probably still a work in progress. BTW, you may know this already but dWAR and oWAR both contain the position adjustment which is why you can no longer add them together to get total WAR.
That drives me crazy. It would be much more intuitive to strip the positional adjustment out of BOTH oWAR and dWAR…. say, something like this for a player with six WAR in one year:
oWAR: 3.0
dWAR: 1.0
pos: 2.0 (translated from runs)
Rather a good season. It’s very easy to add it all up and know where the player stands relative to his peers. You can say to yourself, “Self, would I like my team to keep this six WAR player, or take the four WAR player at a discount? Because, Self, that four WAR guy got 2.5 oWAR and 0.5 dWAR with half the playing time. Is it worth the risk of his not recovering or getting hurt again? Because if he’s healthy the whole year he could be a 7-8 WAR guy for a discount!”
You could also say, “Gee, Self, that 2-WAR guy seems all right if there’s no other choice, but almost all of that was just sending him out there with no other good option. The team could take that September call-up with half a WAR and use him full-time next year, and he’ll top that easy.”
If you break it down to 3.0 for offense, 1.0 for defense, and 2.0 for position, you might as well just use the run components. They already break it down for you.
They never should have called it oWAR and dWAR. They should have had two numbers:
1. WAR
2. diWAR, or Defense Indpendent WAR. This is a flavor of WAR for those who don’t trust the TZ, UZR, or DRS numbers. It keeps the positional value but removes the +/- defensive numbers. This is what we know now as oWAR.
FWIW, I never ever ever look at oWAR or dWAR.
Adam – When BR originally retooled their WAR calculations this year, they renamed oWAR as ndWAR (no defense WAR). People found that confusing, so later the same day, they renamed it afWAR (Average Fielding WAR). Apparently this was confusing as well because a few days later they went back to calling it oWAR. I realize this isn’t exactly what you were suggesting since they never dropped dWAR, but they did try renaming oWAR.
Paul E,
I personally think defensive metrics, while not quite jokes, are so much in their infancy as to be virtually useless. The Barney rating is only one reason why. In fact, I’ve been watching Cub games to get an idea of why Barney is rated so highly, and one thing has become clear.
Barney is a very good fielder; he could probably become the equal of Sandberg defensively if he hits enough to stay in the league. But Sveum or the Cub defensive positioners are exceptional. They make it easy for Barney. It’s almost uncanny how well positioned he nearly always is: he’ll be behind second to steal a base hit when the ball goes up the middle, and in the hole to steal a base hit when it’s pulled. It seems the Cubs are employing some very advanced metrics on infield positioning, which definitely shows. Now if they can get Castro on the same page they will really have a superb up-the-middle defense. And if their pitchers can induce more ground balls instead of giving up so many homers they might actually become a decent team.
tag – but positioning, whether by the team or by the player, is an immensely useful skill. I don’t watch many Cubs games, but if the team’s defensive positioning is that good and Castro is NOT listening, doesn’t that signal that he is in some way hurting the club and costing them wins? And doesn’t Barney, by listening to their advice, help the team win? Positioning (and a strong arm) is what made Cal Ripken into the defensive shortstop that he was. Ripken didn’t have the flash of someone like Omar Vizquel, but he was a great defensive SS because he always seemed to be in the right spot.
I think about it this way – we don’t take anything away from a hitter because a hitting coach works out a flaw in the hitter’s swing because the hitter listens and then executes (I’m thinking of something along the lines of Curtis Granderson and Kevin Long). Why should we take things away from the defensive player if he just listens and puts himself in the right spot and then executes?
I dunno. These are the all time leaders in defensive runs:
Looks pretty good to me, no?
I think you have an issue with putting too much stock into small sample sizes. I can get behind that. Defensive numbers are GOING to fluctuate. Just like home run hitters don’t hit the same number of home runs ever year. These things even out over the long haul. I think the list above shows that.
That table you displayed above is why I ultimately think these metrics are doing a credible job of calculating defense. Doesn’t that list pass the eye test with flying colors? Aren’t our current defensive metrics, while still flawed, way better than assuming everyone is a league-average fielder? Haven’t we made improvements to catcher defense just in the last year by implementing pitch framing and pitch blocking into the equation?
Hopefully first baseman defense will be next on the list of improvements.
Piling on a bit, tag, since Artie and Adam have already made the essential points: If we followed your line of reasoning, wouldn’t we have to assess pitching success relative to catchers’ abilities to call a good game? I’ve always seen this as a huge gap in a primary category of assessment, but one we really will never have any way to measure outside of occasional anecdotal comments from players.
There are many areas of the game where I think we have some confusion about what we value: outcomes or skills (perhaps “earned accomplishments” would be better, but too long) – it seems to me we often take the former as a proxy for the latter, when our impulse is to assess players on their skills (unless there’s a really good story line of success beyond talents – the Eddie Yost/Ron Hunt Story). I think you’re pointing to this issue. That’s perfectly valid, but the ideal you’re implying for stats in this area seems to me as much beyond reach as batting stats that assess hitting outcomes against pitch/placement quality. When we emerge from that sort of statistical infancy I’ll be dead, so I’m not waiting for it.
Having spouted all that, the point you’ve raised about the Cubs is really interesting.
My imprecise wording is doubtless the reason for it, but I think there’s been some slight misapprehension of what I meant. I think defensive positioning is enormously useful and is, for instance, what is responsible for Barney’s high rating, along with (probably) the small sample size. I’m saying that Barney definitely deserves his high defensive rating, but it’s not necessarily because he has unheard-of range / otherworldly skills. It’s because he’s almost uncannily in the right place at the right time, either due to Sveum / the Cub staff’s positioning statistics or Barney’s own study and instincts. And there is high value in that, but it’s not necessarily what people think about when they think about defensive WAR. (About Castro: I wasn’t implying he wasn’t listening to Sveum / coaching staff; it just seems for some reason the statistics on positioning favor batted balls going to the right side of the diamond rather than the left. I don’t know if that’s because lefty hitters chart more dependably or what. At least that’s what I’ve observed in my limited viewing.)
My problem with defensive metrics in general, as Adam intuited, is that because of the small sample size they vary too greatly. For instance, from 1966-1973 Brooks Robinson’s Rfield was 4, 32, 33, 23, 5, 20, 18, 18, all in full seasons. Now, two of these numbers are not like the others and simply don’t belong. The home run totals of Aaron, Mays, Williams, Schmidt, etc. certainly varied, but none of them hit 45 homers one season and 7 the next.
And as for the defensive metrics confirming what we already know from observation, I don’t think that speaks so highly in their favor. Okay, on one level – yes – it does, but the value of advanced statistics in my book is when they can reveal or express or give genuine color to something not apparent to casual observation. Any fairly observant fan could see that Greg Luzinski was a horrendous outfielder, and conversely that Brooks was a superb one. Defensive metrics help to quantify that but only do so with any degree of accuracy by the time Luzinski’s been banned from picking up a glove ever again and Brooks is hanging up his fifth straight gold glove. I do like the idea of measuring defense on a team basis because I think that can add real insight, but I’m not confident yet of the results here either.
tag, one of the reasons you see blips in Brooks Robinson’s career fielding numbers is because they take longer than HR to stabilize. And by stabilize I don’t mean in the sense that we can make a general statement about a player’s abilities, I mean stabilize in the sense that we can take some sort of meaning from it at all. It’s well known that Fangraphs’ UZR takes 3 years to stabilize; I’m not sure about B-Ref’s DRS.
So consider the possibility that looking at Brooks’ one-year d numbers is like looking at 1/3 of a season’s worth of HR totals. It just takes longer to stabilize and make sense. So you’re going to see more bumps in the road.
As for positioning, absolutely it comes into play. If I’m not mistaken, the Rays led MLB in UZR for three straight seasons in 2009-11 and were universally credited with advanced placement of their defenders before it became the norm. Now it seems this year that almost everyone is doing it. But that still doesn’t mean that those Rays defenders weren’t skilled.
As for Barney himself, he’s very much impressed me with his athleticism this year. I mean, someone has to be leading the league in dWAR, don’t they?
In fact, raw OPS has absolutely nothing to do with OPS+. It is not used in the calculation whatsoever. The four variables that determine OPS+ are OBP, lgOBP, SLG, and lgSLG, where:
lgOBP = the OBP a league avg player would have in the same ballpark
lgSLG = the SLG% a league avg player would have in the same ballpark
A lot of people, including a lot of well-respected authors, make the mistake of saying that when a player has an OPS+ of 150, he has a raw OPS that is 50% better than league average. This is wrong. It suggests that:
OPS+ = 100x{(OPS-lgOPS)/lgOPS)}
It’s not. If OPS+ were calculated that way, the all-time leader would be Babe Ruth at 154. The top 5 would be as follows:
1. Babe Ruth 154
2. T Williams 146
3. Barry Bonds 141
4. Lou Gehrig 140
5. Rog Hornsby 138
Here is the actual top 5 for OPS+:
1. Babe Ruth 206
2. T Williams 190
3. Barry Bonds 182
4. Lou Gehrig 179
5. Rog Hornsby 175
Why? Because OPS+ is calculated this way:
100 + {(% OBP is better than lgOBP)x100} + {(% SLG is better than lg SLG)x100}
Here’s an example. Let’s look at, say, Corey Hart’s 2012 season. As of this writing, Hart currently has a 118 OPS+. But his raw OPS is only 10% better than his lgSLG. So why is Hart’s OPS+ not 110? To get the 118 mark:
OBP-.327 lgOBP-.333 SLG-.506 lgSLG-.424
OPS+ = 100 + {(.327-.333)/.333) x100} + {(.506-.424)/.424) x100}
OPS+ = 100 + (-1.8) + (19.3) = 117.5, which rounds to 118
The difference is that OBP and SLG are not added together before taking the % difference. Looking at the all-time top 5, you can see how this affects OPS+: it spreads the results out more. I think I like it this way, and I agree with Andy that’s it’s my go-to offensive stat.
You can find lgOBP, lgSLG, and lgOPS for individual players by clicking on the More Stats tab next to Standard Batting. From there, scroll down to Advanced Batting and you can calculate OPS+ yourself to see the individual OBP and SLG components of the final number.
Amendment to comment #5:
A lot of people think OPS+ = 100 x (OPS/lgOPS),
instead of a lot of people think OPS+ = 100 x (OPS-lgOPS)/lgOPS
I wish we could edit our posts.
OPS+ is my go to stat was well. That said, I do think that it tends to undervalue leadoff hitters, particularly if they’re high-percentage, prolific basestealers (Raines, Henderson, Molitor and a few others) so like almost any stat it does require a little context.
I think something special is happening this year, and that is Juan Pierre might lead the NL in stolen bases. Juan is awesome as we all know but he has been playing only part time for the great Philadelphia Phillies this year. Despite that he has a real chance at leading the league in SB’s. I think he has been helped by Victorino being shipped to LA.
I put every hitter with at least 502 PAs in 2011 in a spreadsheet with their OPS+ and their wRC+. In 2011, the correlation (using Excel’s CORREL function) between the two stats for this group of hitters was 98.7%; an extremely high correlation. I suspect most of the remaining difference is explained simply by the fact that RC includes stolen base value and OPS doesn’t. In percentage terms the guy with the largest 2011 advantage of OPS+ over wRC+ was Miguel Olivo and the guy with the biggest advantage of wRC+ over OPS+ was Brett Gardner. I think that confirms that most of the difference between the two is about base running, not hitting.
To me, OPS is much more intuitive than wRC. The conceptual simplicity of on-base percentage and slugging percentage as the base elements of the formula, as compared to the custom-fit numbers for each offensive event that go into wRC, has a greater, Occam’s razor sort of elegance. It is something of a coincidence — but a very happy coincidence — that when you combine the flaws of OBP with the flaws of SLG they mostly cancel each other out and give you a result (with respect to hitting, not base running) extremely similar to what you get with the more elaborate set of fractional adjustments that go into wRC.
Given all that, my personal preference when I’m interested in hitting numbers is for OPS and OPS+ over wOBA, wRC and wRC+ — of course the wonderful accessibility of OPS and OPS+ in the Play Index plays a role in that preference too.
The PI does give you access to Runs Created numbers if you want them, as I sometimes do if I want a sabr-friendly, offense-side counting stat that incorporates stolen base value and is measured in a range similar to R and RBI. It would be a nice added PI feature if PI would add rate-state versions (including a league and park adjusted version) of the b-ref RC counting stat. But that would be well down on my priority list of new toys I’d like to see from the PI (higher on my list: the ability to search team season stats, player and team split stats and existing advanced stats, rookie stats, minor league stats, season-level streaks, etc.)
To me, wRC+ is much more intuitive than OPS+.
What is RC? It is runs created,an estimate of the runs the particular batter we are looking at would be worth when inserted into an average team. Divide by PA and we have a rate stat. At today’s scoring levels, an average player who never missed a game would create about 80 runs in an average park situation. A really good hitter will create 120 runs. A great one maybe 150. wRC+ is a rate stat that is closely related to runs, and runs, (and wins) are central to sabermetrics. OPS+ is not closely related to either of these, or anything else of great importance.
Why add OBP and SLG? The only reason I can see is that it works weel enough, that when converted to OPS+ it gives us a pretty good, if unsupported by any theory, estimate of something we want. Bill James original RC formula was RC = OBP * TB. of course SLG = TB/AB, so TB = SLG * AB. That makes his original RC formula = OBP * SLG * BA. Which multiplies the 2 numbers, and by further multiplying by AB we get an estmate of something useful, runs. We don’t figure RC this way for the first reason we don’t use ops, the events are incorrectly weighted. But there is nothing that says that adding, rather than multiplying is the way to combine OPB and SLG to get something of use.
At some level, this debate becomes more a matter of personal esthetic judgment. To me, once you start multiplying one number by .34, another by .72, another by 1.18, etc. to get your stat, you have slipped out of the realm of the pleasingly intuitive. Not that there is anything wrong with complicated fractional calculations — I love the WAR family of uber-stats and nothing is more complicated than they are. I just find OPS elegant in its use baseball’s own unfiltered numbers — plate apps, times on base, extra bases — to produce results that happen to come out amazingly close to results produced with the elaborate, “fractional multiplication of each event” approach to RC.
In contrast to the more recent RC formulas, the original RC formula from Bill James, TB x OBP, struck me, and still does, as an amazingly elegant way to get almost all of what we want from a summary counting stat for hitters. I completely understand that the newer, more complex formulas for RC are more precise. But part of me still laments the loss of the beautiful simplicity of the original fomula.
What’s the best way to introduce something like this to people? Even the most basic, widely used, established, simple stuff like OPS+ seem to go over the heads of most baseball fans I talk to (aside from my wife, she gets it).
I make sure people understand that OPS is OBP + SLG, and then I say it’s normalized to their ballpark and league average, and that over 100 is better.
I also call it “ops” as opposed to “oh pee ess”. I think the latter has too many syllables and causes confusion for a lot of people.
Wow! For a budding sabrmetrician like myself, this post and the many comments were quite helpful. I did not know how wRC+ was determined – it was always pretty opaque to me, and OPS+ was more helpful. Does anyone else think baseball-reference.com needs an Android app? How helpful would that be for on-the-go analysis!
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Sorry for not having read every post. My quick point: OPS+ (for all its referenced [minor] faults) is, in my opinion, far and away, the best single statistic to gauge a player’s offensive production, compared to his peers (although I would certainly separate close scores by considering Grounded-into double-plays [which often changes one-on and one-out, if you just strike out or fly-out or beat the throw to first base, to none-on and two out] and Stolen Base Percentage (both of which reward you for speed). My reasoning is simple – there is not another stat that starts with Ruth, Williams, Gehrig, and Hornsby, in that order!! Note that pre-steroid Bonds would not be #3 (but the stat clearly identifies his ridiculous post-steroid years, which were magnified by expansion pitchers who belonged in the minor leagues and bonds’ lack of fear of being brushed bake [nothing like elbow pads!]). By looking at OPS+, we can see that Mike Trout appears to be the second coming of Mickey Mantle (and why I would take Mantle over Mays any day [not even mentioning Mays’ terrible World Series stats]).
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