As I explained in my August introduction post, I’m going to attempt to calculate FanGraphs WAR accurately for Chris Taylor’s 2017 season, in my own spreadsheet. To do this, I expect to make heavy use of FanGraphs’ documentation. I also have to give a big thanks to FanGraphs owner Dave Appelman as well as my sabermetric sage Matt Swartz. Here’s FanGraphs’ overview of WAR For Position Players. The basic formula is this:
WAR = (Batting Runs + Base Running Runs + Fielding Runs + Positional Adjustment + League Adjustment + Replacement Runs) / (Runs Per Win)
This doesn’t look too daunting. Add up the three different ways a position player can create value, make adjustments for position and league, and put it on the correct scale. OK, let’s calculate batting runs!
Show of hands, who knows anything about batting runs? Offhand, I couldn’t tell you how batting runs are tabulated, or what benchmarks for success are. So batting runs is a stat unto itself that requires a full exploration. Here’s the batting runs formula:
Batting Runs = wRAA + (lgR/PA – (PF*lgR/PA))*PA + (lgR/PA – (AL or NL non-pitcher wRC/PA))*PA
Huh. OK, when I look at that formula, the only acronym I’m familiar with is PA, which is plate appearances. We can all agree that we know what a plate appearance is.
I do not, however, know what wRAA is. FanGraphs says it stands for Weighted Runs Above Average. And, well, it has its own formula:
wRAA = ((wOBA – lgwOBA)/wOBA Scale) * PA
It seems that to calculate wRAA, we first need to calculate wOBA. Now, before I lose you in this sea of acronyms, wOBA is actually useful and fairly easy to understand. It stands for weighted on-base average. According to FanGraphs, wOBA “is a rate statistic that attempts to credit a hitter for the value of each outcome (single, double, etc) rather than treating all hits or times on base equally.” Intuitively, I find wOBA to be a simple and useful offensive statistic. At MLBTR, we often cite a batter’s “triple slash” line. Chris Taylor’s triple slash in 2017 was .288 (batting average)/.354 (on-base percentage)/.496 (slugging percentage). These days, people worry a lot less about batting average, since OBP counts a player’s hits, walks, and hit-by-pitches. But OBP fails to give a complete picture, since a walk is valued the same as a home run. That’s why we have slugging percentage, right? SLG is just total bases divided by at-bats, but it wrongly suggests a home run is worth four times as much as a single or twice as much as a double.
The purpose of that aside was to illustrate that wOBA is indeed a strong foundation for the batting runs component of WAR. Here’s the wOBA formula for 2017:
wOBA = (0.693×uBB + 0.723×HBP + 0.877×1B + 1.232×2B + 1.552×3B +
1.980×HR) / (AB + BB – IBB + SF + HBP)
In this formula, there are six things a batter can do to create value: draw an unintentional walk, get hit by a pitch, or hit a single, double, triple, or home run. As I learned from Appelman, and by just playing around with some example numbers, the batter also gets credit for intentional walks, by virtue of those being subtracted in the denominator.
You can see there is a weight assigned to each possibility, like 0.877 for a single or 1.980 for a home run. These weights change a little bit each year, and can be found here at FanGraphs. The concept of linear weights is explained well in this FanGraphs article. There are 24 different base-out states, such as “runner on second with one out” or “bases loaded, nobody out.” FanGraphs explains, “In order to calculate the run expectancy for that base-out state, we need to find all instances of that base-out state from the entire season (or set of seasons) and find the total number of runs scored from the time that base-out state occurred until the end of the innings in which they occurred. Then we divide by the total number of instances to get the average.” So if you know that the bases are loaded with nobody out in the year 2017, you should expect 2.32 runs to score. 50 years prior, you would have expected 2.13 runs to score in that situation.
We have 24 different run expectancy numbers, and each plate appearance moves the team from one box to another. The difference between the two is the run expectancy for that plate appearance. With this information, we can get the linear weights for each of the six batting outcomes. This concept dates back well before FanGraphs and is worth exploring.
One thing to note, from Neil Weinberg of FanGraphs: “the inventors of wOBA decided that it would probably be best to scale it to something familiar to make it easier to understand,” so they made the “aesthetic choice” to scale wOBA to on-base percentage. As we’ll see later in the wRAA calculation, this scaling choice has to be undone to get us back on a run scale. That seems needlessly convoluted, but I’m probably the only one trying to do this by hand.
In theory, one could create a version of wOBA that doesn’t just include these six positive batting outcomes, but rather every batting outcome. To quote Weinberg, “If you wanted to, you could build wOBA with more nuanced stats like fly ball outs, ground outs, strikeouts, etc; it would just get more complicated without much added value.” Well, hold up. First off, we shouldn’t care about making wOBA more complicated, since (this exercise aside), no one is computing it by hand. In fact, in a different FanGraphs wOBA explainer, the author says, “OBP or SLG might be easier to calculate with pencil and paper, but wOBA is extremely easy to find and use on our site, meaning any computational costs of moving to wOBA are minuscule.” I agree with that point, and since WAR is already a very complicated stat, why not incorporate the nuances of all batting events into it by using the most advanced wOBA possible? For example, take two players who have the exact same number of unintentional walks, HBPs, singles, doubles, triples, and home runs. Say those players each also made 400 outs in a season, but one player made every out by strikeout and the other made every out by flyball. Wouldn’t the flyball guy be a more valuable hitter?
In response to that question, Dave Appelman pointed me to this link, a seven-year-old Hardball Times article in which JT Jordan re-calculated wOBA with strikeouts included for batters. Jordan concluded, “The difference is incredibly small. So really, it’s not a big deal to ignore strikeouts when using a context-neutral method like linear weights and wOBA. But it can be done. When all is said and done, we’re talking about a run or two of difference.” Swartz remarked, “I have never gotten a beat on when sabermetricians deem it okay to call something ’close enough.'” Bottom line: wOBA could be made a tiny bit more accurate, but the keepers of the stat must feel that there is little added value in incorporating other batting outcomes.
Ultimately, a batter’s wOBA is a strong foundation for calculating his offensive value. Let’s calculate that number for Chris Taylor. If we want to cheat, we can just pull up his FanGraphs page to see that his wOBA was .361 in 2017. We don’t want to cheat, though.
wOBA = (0.693×50 + 0.723×3 + 0.877×88 + 1.232×34 + 1.552×5 + 1.980×21) / (514 + 50 – 0 + 1 + 3)
wOBA = 0.3613
Now, we need to turn wOBA into wRAA. wRAA is a counting stat that “measures the number of offensive runs a player contributes to their team compared to the average player.” Here’s the formula again:
wRAA = ((wOBA – lgwOBA)/wOBA Scale) * PA
I feel pretty good about my understanding of wOBA, which required only the number of unintentional walks, hit-by-pitches, singles, doubles, triples, and home runs Taylor hit, as well as the linear weights of each of those events in 2017. I can understand the league average wOBA as well, which FanGraphs shows was .321 in 2017. Keep in mind that lgwOBA does not refer to the National and American Leagues; it refers to all of MLB for that year.
Our next step, wRAA, isn’t that hard to comprehend either. It uses the aforementioned linear weights but presents its results in a cumulative manner, unlike wOBA. wRAA is also scaled such that zero is the league average, so it can be compared across different seasons. Finally, wRAA uses a number called the “wOBA scale” to undo the “scale to OBP” choice that is baked into wOBA. I know from Taylor’s player page that his wRAA in 2017 was 19.3.
wRAA = ((0.3613 – .321)/1.185) * 568
wRAA = 19.317
So far, we’ve found our way to the correct “weighted runs above average” amount for Chris Taylor. It’s worth pausing to appreciate that nothing overly complicated or debatable has been done so far: Taylor received the correct amount of credit (linear weights) for each of the positive batting outcomes (single, double, etc.) and that was scaled against the league’s offensive production since the value of a home run was very different in 2017 vs. 1917. We are most of the way to Batting Runs, which along with fielding and baserunning is one of the three pillars of WAR. What we need to do next is adjust these batting runs for Taylor’s ballpark and league. Here’s the batting runs formula again:
Batting Runs = wRAA + (lgR/PA – (PF*lgR/PA))*PA + (lgR/PA – (AL or NL non-pitcher wRC/PA))*PA
I believe the number we’re aiming for, based on Taylor’s FanGraphs player page, is 18.7, which suggests minimal adjustments were needed to his 19.3 wRAA.
- wRAA = 19.317
- lgR = all the runs scored in all of baseball in 2017 = 22,582
- PA = all the plate appearances in all of baseball in 2017 = 185,295
- lgR/PA = 0.1219
At this point, we need to pause and talk about park factors. Neil Weinberg wrote an informative beginner’s guide to park factors here. Intuitively, it’s logical to make an adjustment for the player’s home stadium. In the case of Taylor, Dodger Stadium suppressed overall run scoring by about 8% from 2013-17, so we apply half of that under the assumption that he played half his games at home. Taylor actually did play half of his games at home in 2017, but even if he didn’t, the park factor would be applied as if he did. Additionally, as Weinberg explains in his article, “parks don’t affect every player evenly and our park factors sort of assume that they do.” If for some reason Dodger Stadium actually improves Taylor’s hitting (due to handedness, batted ball profile, weather, or any number of things) he’d still get a boost in this WAR calculation to account for Dodger Stadium suppressing offense on average. An assumption is also being made that the player played his road games in “a pretty average setting,” which is not necessarily true.
Weinberg wrote his park factor article in January 2015, noting, “We want to know how parks influence each moment of the game, but we simply don’t have granular enough data to really get there. A ball hit at 15 degrees directly over the shortstop while traveling at 93 miles per hour will travel how far and land where? That’s basically what we want to know for every possible angle and velocity, but we just don’t have the data and we don’t have it for every type of weather in every park.” In 2018, we do have most of that data, due to Statcast. I asked Appelman about potential efforts to reboot the park factor component in WAR using Statcast data, and he replied, “I have not personally done much work on park factors. They are in my opinion, very annoying. I just don’t really like dealing with them and they make everything much more complicated. However, they’re obviously good to have.” Swartz was of the same mind, explaining that park factors are “very noisy” and while you could possibly improve them with Statcast or weather data, the precision gained would be minimal. Imperfect as park factors are, Swartz told me it would be “disastrous” to leave them out.
- PF = 2013-17 park factor for Dodgers Stadium = 0.955055 (Good luck finding a park factor this precise. FanGraphs’ Guts page just gives you .96 for the Dodgers. Were I not able to speak directly to Appelman, I wouldn’t know how to get the more precise figure, nor would I know that 2013-17 is the current time period used on the listed five-year park factor).
In this example we added a significant amount of batting runs to account for Taylor playing half his games in Dodgers Stadium – about 3, to the 19 we started with.
Now, we need to talk about one more mini-calculation, for which a custom FanGraphs league-level, non-pitcher leaderboard is needed.
- NL non-pitcher wRC = 11,282
- NL non-pitcher plate appearances = 87,753
Batting Runs = 19.317 + (.1219 – 11.64)*568 + (.1219 – .1286)*568
Batting Runs = 19.317 + 3.111 + (-3.803) = 18.625
That last part of the formula, where we ended up subtracting 3.8 batting runs? That comes from this part:
(lgR/PA – (AL or NL non-pitcher wRC/PA))*PA
I asked Swartz exactly what is being adjusted there, and why it exists. He answered, “What it appears to be doing is some sort of league adjustment (AL vs. NL), but I’m not sure it really makes sense.” He added, “It’s really a very specific approach, so I have to imagine whoever put that together had something in mind. And it needs to be some sort of league adjustment, even if the adjustment is only about the run environment of the league.” I’m left without a clear understanding of the purpose of this part of the batting runs formula.
In the end, I didn’t quite arrive at the 18.7 listed under the Batting section on Taylor’s FanGraphs page. While I used unrounded numbers wherever possible, I believe rounding is the reason I’m slightly off. Getting this close to the correct batting runs number was arduous. Perhaps that’s because WAR isn’t meant to be calculated by hand, but attempting to do so increased by understanding of batting runs well beyond just looking at the formula. It’s easy to read an explanation and think you understand, even when you don’t. I hope MLBTR readers will learn and ask questions along with me. We’ll tackle the baserunning component of FanGraphs WAR next time.
the “read more button” really needs to be utilized more – incredibly long post that I have to scroll past ( no time to read it all now but will read it)
Thanks, Tim. I thought you abandoned this project, and wouldn’t have blamed you. Firm believer in WAR since it incorporates defense and offense into evaluating a player, and I don’t believe any other stat does that.
To all the WAR naysayers: I know change is difficult, but a lot of work was put to attempt (by Tim’s own admission) explaining WAR in layman’s terms. Try to be polite in your criticism of the stat, or Tim’s work. Thanks again, man! I was eagerly awaiting this.
I have one request, and it involves going into depths of defensive stats like DRS (Defensive Runs Saved) UZR (Ultimate Zone Rating) their differences, and how they’re calculated etc. Who knows? Might help further explain WAR. I know you’ve barely tapped into WAR, so take as long you want. Just something to consider.
WAR isn’t needed and UZR is for people who can’t tell who is good at defense or not. I can create my own version of WAR that values doubles, triples, and stolen bases that make Jonathan Villar, Ketel Marte, etc look better than they are. It’s all relative. Value is relative. WAR is okay but still very fickle in terms of shifts and relating the human element. Sometimes s*** happens you can’t explain in baseball. Not bashing it, but not a fan of it.
So THIS is why I needed algebra! Crap.
Agreed and I was great in math except for trig. Took college calc II while still in high school yet this is gibberish to me. That said, kudos to Tim for trying to explain it to those that care. Since none of us scribers will become a GM or have voting rights for post season awards and HOF ballots in the near future, my guess is those that value sabermetrics are invested in fantasy pools. To me, the eye is most important as the game is played on the field. Basic stats including match ups is all you need. A few years ago Bill James almost ruined NESN telecasts with his temperature gauge. A gimmicky visual wasn’t needed as if a batter was 22 for his last 56 ABs with 9 walks and 25 RBIs, I think the average fan would understand the hitter was in a groove.
Preach. Preach.
Really great idea for an article, as well as in the execution. It is a little concerning that you got a [shrugs shoulders] response to the question about the 3.8 run reduction in the runs created part of this example, and you’ve teed someone up with a research idea for revisiting the question of whether the makeup of a batter’s outs has a meaningful impact on their value. I’d have to imagine with launch angles and bat exit velocity there might be some interesting analysis to be done there.
I’ve always been skeptical of how defensive wins are calculated – guys get dWar results that are way higher than I’d intuitively expect – so I will be very interested to see that part of the analysis.
Well, their bsr rating is based on UBR + wGDP + wSB.
Great post, and I learned a lot, so thank you for the effort.
I am curious about when you discussed strikeouts vs flyball outs. If there is a runner on first or second and a batter grounds out, thereby advancing the runner, that has value. A strikeout is a wasted out. Why are productive outs not taken into account? I think this might be one of the reasons you see guys like Gallo these days, cause people don’t take into account the value of a productive out.
Using Nichols run expectancy table, Gallo strikes out with a runner on 1st, now there is 1 out which produces 0.52 runs, but if Gallo advanced the runner to second with 1 out it creates 0.69 runs. That is an increase of 0.17 runs. Meaning for every 5 times a batter moves a runner over instead of striking out when there is 0 outs they create almost an additional run. So how can productive at bats not be counted towards these productivity stats? I am meaning to sound quisitorial rather than aggressive here.
Closer to every 6 times a run is produced. It could be a small change for each player’s WAR when thinking about it.
It also doesn’t take errors into account. A batter has a 0.015 chance to reach base via an error. If a player goes from 200 strikeouts to 100, and puts those 100 balls into play, but does not get a hit, he still will get on base an average of 1.5 times. Not a huge amount, but take into account the advancing of runners, and you have theoretically created an estimated 4-6 runs, by just not striking out.
But the benefit of potentially reaching on an error will be attenuated by the potential to hit into a double-play. That’s part of why there’s not as much of a difference between the negative value of a strikeout compared to other types of outs as one might initially think: it’s not always beneficial to put a ball in play.
Very good point, I did not take the likelihood of a double play into account.
Thank you for this. Although some of the math is stuff I have to really parse because math isn’t my strong suit (thank goodness my partner is a math professor!) I like that you are explaining this for all those “WAR isn’t based on anything” types. The inputs are real, and important, and I’m glad a catch-all number exists as a quick-and-dirty way to explain value. Thanks again.
I gave up at “Batting Runs = wRAA + (lgR/PA – (PF*lgR/PA))*PA + (lgR/PA – (AL or NL non-pitcher wRC/PA))*PA”
How long did everyone else last?
Same. If you have to do that much math for a single player, it is not worth it for the average fan. Even just reading on how it is determined takes the fun out of the game. WAR is an good stat but I just leave it to the statisticians of the world to figure it out and I will trust the answer that they give me.
Through the end. Tim did a pretty good job of laying out what the equations meant in the narrative that followed each one. That’s largely why it was such a lengthy piece: he spent a paragraph or two translating a single equation into digestible text.
IMHO, what could have been better explained was how Fangraphs park factors were calculated because IIRC they do something a little bit different than what Palmer, BBRef, and others have done. Although that could probably be an entire post in itself.
I read the whole thing. With as many people here who post “WAR is garbage, give me my RBIs and batting average” and such, It’s quite important to me to understand all the inputs that go into how WAR is calculated. Nothing ever got worse because you had more information about it.
Why is it important to you to understand? Are you putting a team together and have limited resources? . Are you really seeing a difference in players that’s measurable to the average fan? Or is it you’re looking for an edge in putting together a fantasy team? I laugh reading articles trying to compare players of different generations based on advanced metrics. Don’t you think players would have performed the same way if they knew they’d be judged differently?
I’ve never played fantasy baseball. I doubt any of this has any bearing on how players perform. I just don’t think information is bad. It’s a sport in which nearly every event that happens on-field can be measured, so why not measure it and put that information out there? If you are in love with RBIs and BA and the “baseball card” stats, then great. You can ignore this excess information all you want.
I’m not a fan of war. I look at the Orioles and see that Joey Rickard is “better” than Adam Jones according to war. I think it’s a flawed stat and is used too much by baseball writers, voters, etc.
Is this still an accurate portrayal of wOBA scale?
Excellent: ——- .400
Great: ———— .370
Above Average: .340
Average: — ——.320
Below Average: .310
Poor: ———— .300
Awful: ————-.290
Fangraphs says this wOBA chart is meant as an estimate, and that league-average wOBA varies on a year-by-year basis. Is it this chart far off?
More or less. Keep in mind that wOBA is not position adjusted, so a .320 wOBA catcher might be more valuable than a .340 wOBA 1B (holding everything else equal).
Thanks! No one but Fangraph provided a chart. Helped understand substantially, but if the article was dated, I didn’t know if the wOBA #’s changed much since then.
It’s interesting how it’s calculated if you are into math, but I think the average fan just likes reading the end result. WAR may not tell the entire story but I like how it can summarize how a player is doing in general.
Now this is an article right up my street. One question: Why is a hit by pitch worth more than a walk in the wOBA statistic? I can’t really see a reason for it.
I would guess because HBP really rare, (and painful) whereas walks are commonplace, and painless.
In my book, HBP should be worth more since you risk missing the whole season to get that stat.
At a guess, it’s probably due to situational weighting. A hit by pitch is more likely to be an undesired outcome for the pitcher, for that particular situation. Where as a walk, while technically achieving the same result as a HBP, can situationally be less impactful.
“In the end, I didn’t quite arrive at the 18.7 listed under the Batting section on Taylor’s FanGraphs page.”
In the Value section of Taylor’s page, 18.6 is listed, which matches your calcukations.
Is that adjustment that you couldn’t get an explanation for an adjustment that attempts to filter out pitcher at bats? I’m thinking it’s keeping actual batters from being assigned too much batting value against average when part of that average is being attributed to pitchers who suck at hitting. Just a thought. Thanks for the article. It was very interesting.
baseball is all about stats.
this is about as confusing as “white sox math”
This is why the layman does not like war. As a child in the 60s a big part of the fun was calculating players average, era and ops. Then as a player in high school and college those were fun tools. WAR takes that out of it. Sure it’s a legit and interesting stat, but the average baseball follower thinks it’s another egg head attempt to trivialize the game. WAR takes the fan aspect out of the game and for this it will never be fully embraced.
This illustrates my biggest problem with WAR. It’s not the calculations, I can handle them. My problem is the numbers like positional adjustment and league adjustment that just get pulled out of nowhere and added to the formula. Batting average and ERA might not be complicated stats, but at least they only include what actually happens in a game.
These Ivy League mathletes have worked their way into front offices using formula analysis that no one but themselves can understand. This stuff is painful to read and has no place in evaluating players.
Don’t the Dodgers have about a half dozen sabetmetrically driven ex-GM types in their front office? And what has it produced, a team that is about 18 games over .500?
Player evaluation should be left to people that have played the game and know what to observe as far as a quality MLB ballplayer is concerned. That teams drop millions on these stat ” experts ” is laughable. It’s a fad that I eagerly look forward to ending in the near future.
This over analyzing of players is hurting the game. The launch angle garbage has turned most players into HR seeking, strikeout prone bores. MLB has become a chore to watch and it’s going to be detrimental to the sport unless there is a quick pivot back to the line drive/contact hitting game I grew up with.
Another issue with the game is the lack of talent. There are probably 6 players on each team that are really not MLB players. Teams like the Rays are not using bullpen days because it is necessarily more effective, but because there is such a dearth of starting pitching that can throw 6 or 7 effective innings. They have no choice. If they had 5 Blake Snell types, do you really believe they’d be having a reliever start the game?
Those nerds you speak of gave Chicago and Houston championships.
No, the players on the fields gave Chicago and Houston championships.
Old man yells at cloud, in a nutshell.
The fact that every Team in MLB is “dropping millions” on analytics, should probably be a sign that they’re onto something. Unless, everyone is wrong else, and you’re right.
It’s quite clear you can’t separate your criticisms of analytics, from your attachment to some of baseball’s fabric. “I want players hitting for contact” does not equal “analytics are bad!”.
Don’t hate because there are people looking for ways to better understand something they love – it’s ugly.
Don’t hate on someone because they are older than you. I know you can do better than that, so how about it?
No matter how you feel about analytics, pretty clearly they’ve turned into an arms race, and like all arms races, they develop a logic of their own, and it is not enough to say that they are worthwhile because they are happening. It’s much more the case that they are happening because they can’t be stopped.
I’m not hating because he’s older than me. I’m hating because he’s ignorant.
Not good enough, sorry. So very disappointing. Bring on the downvotes.
I’m not sure what your issue is. I’m happy to listen, but, make some sense first.
I did already. Feel free to respond to what I’ve already said. Please refrain from name calling and insults, if you are serious.
It’s a simpsons reference….
Exactly what Cam said. We don’t diminish or lessen our love for something by learning more about it. That’s what we do with the things we love.
Assuming we are learning more. This deep dive into WAR is instructional in the way it reveals the difficulty of understanding what assumptions go into the model and why they were made. If we’re going to trust that the model tells us anything about the real world then the first thing to understand is how statistical models work and why assumptions are everything, and not just incidental. These issues have come up innumerable times here. I don’t find a lot of receptivity f0r these concepts, honestly.
You and Cam want to point the finger at others calling them ignorant, yet you want them to be tolerant of your opinion? It has to work bothways and you’re showing zero tolerance.
An undeniable fact about WAR is their mathematics has hypothetical, non-concrete figures within their mathematical equation, which should automatically turn any non-WAR fan off. Positional Adjustment? Are you kidding me? That number is a guess, at best.
Let’s also add the poor scorekeeping, where errors are now hits. That messes up the defensive numbers within WAR.
Too many assumptions and fake numbers thrown into the mix to be taken as seriously as you want people to take it. You WAR fans need to relax, seriously.
You could say scorekeeping screws up all stats then. You can’t just punish one and not the other. And a lot of the defense part of the equation comes from range anyways so it’s not like a couple extra or fewer errors will screw up a number too much.
Adjustment numbers are theoretical, not fact. You clearly read nothing except for what you wanted to see. Hey, that’s kinda like WAR….
But you don’t deny that counting stats are just as flawed due to scorekeeping or variables?
And I’m sorry man but you’re coming off as pretty condescending because I read your whole post. Sorry if it came off another way, maybe I misunderstood the point you were trying to make
Meh whatever.
To answer your first post, tons of flaws in a lot of numbers in-general. WAR or no WAR. WAR has been pushed on every fan as some kind of gospel and it’s factually incorrect. Yes, teams use sabremetrics and mathematical figures to determine who’s valuable to their team. Ultimately you still need to draft well, trade well and spend money. If you do neither you will not win. Oakland can attest to that, even Cleveland.
No, Cam, what’s ugly is the dozens of players in MLB batting in the low 200s/ high 100s, striking out 25 -30% of the time and being evaluated as good ballplayers because they can draw walks.
What sabermetrics seems to do, in my opinion, is come up with numbers tjay exclaim a bad player isn’t as bad as he seems and good players aren’t as good as they seem. It’s laughable.
Baeeball is not rocket science. Trying to evaluate every player down to the atom using mathematical models is overkill. Ever heard the phrase, ” Paralysis by analysis “?.
My guess is that most of the people commenting on this board have never played the game at any competitive level, thus praying at the sabr altar because they have no ability to judge a player on his physical merits.
Sabr is far from perfect. As a Yankees fan, I don’t recall any pre-season analytics site predicting Gary Sanchez batting .180 or Luke Voit relegating Bird to a bench warmer. The game is more than just numbers.
Well said.
Yes it is painful to read but luckily there are those Mathletes as you call them that are well equipped to understand them and use them to build teams. I believe WAR and all,of the other metrics are good for analyzing players. I know what the formulas are designed to tell us but it is not all that important to me to really know all of the nuts and bolts of it all. As jjd002 said, several teams gained WS wins based on what the math told them.
“This over analyzing of players is hurting the game. The launch angle garbage has turned most players into HR seeking, strikeout prone bores.“
JD Martinez embraced launch angle, and credited it with transforming his career:
“It was an out-of-body experience,” said Martinez. “Like, ‘This is so crazy. Everything I’ve been taught for so long has been so wrong. Now, I see everything we’re trying to do and how this makes sense.’
“You take a million swings one way and all of a sudden you’ve got to tell your body you can’t do that anymore and have to take a million swings this way.”
Martinez played in the Venezuelan Winter League in November 2013, and almost immediately, he realized he’d forged a new career path. In his first round of batting practice with Caracas, he started launching ball after ball in the air, clearing the fences with ease from foul line to foul line. He turned to Astros bullpen catcher, in shock.
“I was like, ‘What in the world? What is this?’ ” said Martinez. “I’m looking around like, ‘I’m cheating.’ Honest to God, I felt like I was cheating.”
In his first two games for Caracas, Martinez launched three homers, teeing off opitches that had beaten him previously.
Martinez was released in March 2014 by the Astros, during spring training, and signed a minor league deal with Detroit. He went on to hit 33 homers in 140 games for Detroit’s Triple A and big league teams that season.
apps.bostonglobe.com/sports/graphics/2018/01/launc…
Why?
Yikes, what an effort! One of the main takeaways should be that all mathematical models involve a series of assumptions. Of the many, linear vs. exponential is major assumption as few phenomena can be expected to behavior in a linear way (Regression Analysis 101). The only way to know whether your assumptions are valid is to have a means of comparing what you’ve predicted against actual results. This is why in the sciences the data sets are published as are the models and the assumptions that go into them. Not so much here, where reverse-engineering is required just to get close to the model output. I would like to see an evaluation of what WAR predicts.
WAR is essentially incomprehensible to the average fan, as are many of the advanced statistics in the current era. We are left to rely upon number crunchers who devise the formulas, based on values assigned arbitrarily, to decide the whole value of a player. The very development of WAR as an ultimate value taking into account a variety of esoteric statistics suggests that the field of evaluation has become so crowded with imponderable numbers that interested parties are desperate for a single statistic that provides at least the illusion of a summative value. My ignorance renders me unable to question their unstated assumptions or to dispute their findings. I am totally dependent on their calculations. Players, their agents, and club officials can debate them as their interests dictate. Yet I remain suspicious of such unchecked authority, and my guess is that in spite of WAR, an everyday player who hits .280, with 20 home runs and 80 RBIs, is going to get paid a lot of money. As a fan, I can understand that.
The market has strongly indicated otherwise. There is a litany of 20HR pop guys out there who have had their market significantly suppressed in recent years.
….we’re waiting for the names of these .280/20/80 guys…..
Chris Carter, Mark Reynolds were more sluggers who had trouble. But if you want guys with a higher average, Shin Soo Choo and Matt Kemp would have trouble getting big deals. I’d assume Aguilar wouldn’t get as big as a deal as most would expect. Adam Jones for sure. Probably Jed Lowrie. Probably Starlin Castro.
Choo and Kemp did NOT have trouble pulling in big deals. Carter never hit .280. Reynolds did, thanks to Colorado. Starlin’s been well-paid. Lowrie’s injury-prone.
Figured you’d take the bait JDGoat.
I’m talking about if they became free agents today. There’s no way Choo and Kemp get their deals (regardless of age) in today’s MLB free agency with their current performance. Castro also wouldn’t get the same amount and I’m pretty sure it was either an arb or pre arb deal anyways, before he really shown what he was. I dont really get what bait either? Are you intentionally trolling the internet?
The line was, `an everyday player who hits .280, with 20 home runs and 80 RBIs, is going to get paid a lot of money“
Now, how much is a lot of money to you? Choo, a sabremetric wet dream, would have still commanded big money if his free agent year was this year. 8-figure deal? You bet. Current situation is not what the guy was talking about, if you bothered to read carefully.
Matt Kemp too. Treated like a superstar. 8-figure deal? Yep.
If Castro were to command $6, 7-mil, is that not a lot of money?
Do you intentionally skip every third word of every post before you respond?
The MLB average salary for FA’s that everyday players or pitchers in a rotation makes usually ranges from 8-12 million. I can’t see any of those players making over that. I consider a lot of money in MLB terms as 12< because honestly, MLB teams are fine throwing around 10 million dollar gambles Ex. Smyly, Liriano, Pineda etc.
Trim, I don’t understand why you’re always so hostile. There’s absolutely no need for it. I’m sure you can discuss baseball without throwing an unneeded jab in every post.
Something happened there but I said teams have no problem throwing around 10 million dollar gambles ex. Smyly, Arroyo, Liriano, Pineda, etc.
Shelve the math. If those numbers were important, they’d be on the TV screen when a batter’s stats are displayed.
BA – HR – RBI tell the whole story for me.
That’s like asking a Forbes 500 business to do their tax return on the back of a receipt.
I think you’re being sarcastic, but if you aren’t, well, either way that’s funny stuff. I bet even GMs of teams don’t even look at BA or RBIs anymore.
You’re getting told a pretty awful story then.
Better than a story that bores you and makes you want to change the channel and watch something else. The whole point of professional baseball not to determine a mathematical formula for winning. It is to get as many people as possible to watch. Sure these formulas and stuff will appeal to mathletes everywhere, but that is a very small percentage of the audience that MLB is trying to appeal to.
No, I don’t think that’s true. Look at the Brewers. They didn’t bring in the most flashy players in Cain, Shaw and Yelich yet look where they’ve led them.
And anyways, if BA-HR-RBI somehow tells you the whole story, you are not being told even a quarter of the whole story.
The adjustment to league nonpitchers is huge for NL hitters, because NL pitchers dramatically reduce their teams performance relative to NL averages. There are only TWO NL teams this year that have a teamwide 100 wRC+ (Dodgers, 110, Cubs, 100). 9 teams have a non-pitcher wRC+ above 100. The average reduction is -eight points-. NP only, the Dodgers have a 117 wRC+ and the Cubs 108.
A team with all average hitters that has the pitcher hit will be 8% below average.
So adjusting league-wide offense to compare position players to only other position players costs NL players a substantial amount of value, because the league wide batting line in the NL is kept artificially low by pitchers.
Leaguewide, the AL has a 99 wRC+ and the NL has a 94. Non-pitchers both leagues have a 100 (which makes sense since league-wide average is intended to be 100 for non-pitchers).
Adjusting to the nonpitcher environment moves league average northward by an enormous amount, so it isn’t surprising it costs Chris Taylor 3 runs of value.
Comparing players to the league average line with pitchers included just artificially lowers the league average batting line- the league average position player is substantially better.
The team stats always really get me; by overall wRC+ the Dodgers are 2nd in MLB in offense, but with pitchers excluded they have a 7 point lead on the 2nd place offense in MLB (Athletics). Their offense looks comparable to AL offenses only because the pitcher bats. In reality, it is substantially better: it is one of the top 30 NL offenses of all time.
What a waste of time! Baseball is a simple game & you nerds want to make it a scientific project.
Simply said, baseball as many other sports is a game that whomever scores the most runs after 9 innings or when it goes in extra innings, wins the game. What a bunch of garbage to determine when a player is productive.
Yeah. I bet these guys are a lot of fun at a game.
Great post! I appreciate all of the effort that went into it. I know some ppl don’t like “advanced stats”, but they have a place and if they provide an edge to a team…good on that team for investing in science.
I was really excited about this series until I realized I failed High School Math
To all those geniuses out there, ¿when Wally Pipp got hurt and he was replaced by Lou Gehrig, who had never played in the big leagues, what was Lou Gehrig’s War?
I’d assume it was 0.0 if he never played. You don’t really have to be a genius to figure that out
WAR is like God; a person has to have faith that it’s real.
This was a great read. Always wanted to know what actually went into WAR (mostly blindly trusted it before). Appreciate the hard work! This is the reason Trade Rumors is great.
Nice run down of this, and thanks for taking on this calculation. I have bookmarked it, and will reread as necessary to understand all this.
One thing I would like to add, since this is kind of an intro article, but, from what I read on these comment boards, some people seem to believe that batting average is not all that important, with all the emphasis on OBP, and especially with all the talk about how BA is lacking compared to OBP, as it includes a valuable component of run creation, walks. I believe that can’t be further from the truth.
I would like to remind everyone that Batting Average is still very important because it actually contributes to both OBP as well as SLG. Each extra point of batting average means about an extra point each to OBP and SLG (a little less to OBP because of walks, a little more to SLG because of extra base hits).
It is just that, by itself, BA does not tell as much as OBP does. So it is not that BA is not important, but that BA is a component of both OBP and SLG, and thus its effects are captured from those two important bits of information. And a low BA, once considered the domain of bad hitters, can be countered by a high rate of walks and extra base hits.