BP Introduces New Offensive Statistic in DRC+

[Spanky99]

Can we all just agree that everyone in this thread is a trike and move along?

 

Well that's just gross and wrong, a trike falls under pedophilia.

[Boxcar]

Well that's just gross and wrong, a trike falls under pedophilia.

 

You're absolutely f***ed in the head

[Spanky99]

You're absolutely f***ed in the head

 

It's a joke you retard? See the tongue in cheek emoji? What's the slang of a bike, figure it out?

[Boxcar]

It's a joke you retard? See the tongue in cheek emoji? What's the slang of a bike, figure it out?

 

I thought bike already was slang in whatever weird ass new world Viking town you grew up in in Northern Labrador or whatever

[jays4life19]

This was a really interesting read so i thought i would share

 

_____

 

What does it take for a new advanced stat to go mainstream?

 

One month​ ago,​ Baseball​ Prospectus​ introduced a new​ total-offense statistic called Deserved​ Runs Created Plus (DRC+).​​ Although the site’s related pitching statistic, Deserved Run Average (DRA), has appeared in my Awards Watch columns since 2016 — at times with behind-the-scenes assistance from the statistic’s co-creator, Jonathan Judge — the launch of DRC+ caught me by surprise.

 

The introduction of DRA in early 2015 addressed a need. Sabermetrics continued, and continues, to struggle with the erratic nature of pitching performances and the challenge of separating pitcher-influenced results from fielder-influenced results. Offensive contributions, by comparison, are more predictable, and our existing alphabet soup of total-offense statistics —OPS+, wRC+, wOBA, and Baseball Prospectus’ own, now deposed, True Average, to name a few— seemed to be doing a sufficient job in capturing it.

 

That got me thinking about how new baseball statistics gain traction, and about the chances of DRC+ gaining the necessary currency to obtain equal footing with, if not supplant, those other metrics. To find out what can make or break a new statistic, I turned to some of the sabermetric community’s foremost gatekeepers, statisticians and thought leaders for their opinions.

 

“I don’t think there’s any one lead thing that determines what gets accepted and what doesn’t,” said Sean Forman, founder and president of Sports Reference, the parent company of Baseball-Reference.com. “It’s the quality of the stat. It’s the marketing of the stat. It’s the availability of the stat. You have to have all of those things present in order for something like that to take off.”

 

Launching a new statistic in today’s crowded stat-scape is certainly very different than it was 15 to 20 years ago, when sabermetrics was still the work of baseball outsiders, and the baseball blogosphere, as well as now-essential sites such as Baseball-Reference, Baseball Prospectus and FanGraphs, was still finding its feet.

 

“I think, initially, the decision-making process was just, whatever seems kind of cool,” said FanGraphs CEO David Appelman, who launched the site with a focus on fantasy baseball in the summer of 2005. “But that was in the days when there weren’t so many baseball stats out there. So, it was just kind of like, let’s just throw stuff at the wall and see what sticks. Now, I think it’s a considerably different process.”

 

“I think that most of the things that we’ve seen that have become popular have stayed popular because it was deemed that there was a need for them in the first place,” said Rob Neyer, whose column on ESPN.com in the late 1990s and early 2000s helped introduce a generation of baseball fans to sabermetric thought, concepts and statistics. “That’s what’s happened with WAR [Wins Above Replacement]. A lot of people wanted one number to look at.”

 

Meeting existing needs has been the primary motivating factor for the statisticians working with Statcast, the most recent fount of new baseball statistics, according to one of the men behind the numbers, MLB.com Statcast analyst Mike Petriello. “We start with the question we are trying to answer,” said Petriello. “Someone will say, ‘hey, we’re really interested in being able to measure this,’ and then we’ll go back and look at the data and say, ‘how can we best do that?’”

 

Forman takes a similar approach in deciding what new statistics to add to Baseball-Reference’s player pages. “Our whole goal is to answer user questions,” he said. “So, if a user wants to know who was the best player in the National League last year, that’s a question that we feel we have the data to answer, and so we want to help answer that question for them with WAR or whatever other advanced stats they would find useful.”

 

That general focus on need is why the launch of DRC+ caught me by surprise. It uses the same 100-as-average scale as wRC+ and OPS+ and attempts to answer the same question as those two stats. Judge and company argued convincingly that DRC+ is better than the similar statistics that were previously available, but the degree of improvement is a crucial factor.

 

“I think the jump in improvement of stats is something that’s really important when introducing a new one,” said Appelman. “Because if it’s very, very marginal, people are going to tune out or say ‘why do I need to learn this new thing?’ And then, from a media standpoint, let’s say you’re writing an article, do you want to take the time to introduce a new stat that you think really isn’t going to increase your readers’ understanding of something? So, that’s kind of the thought process now, at least when introducing a stat. How much better are they then the existing stats we have, and are they going to tell you something new about the game of baseball that we didn’t already know? If the answer’s no, there’s going to have to be some other reason why we’re using it.”

 

Baseball Prospectus Editor in Chief Aaron Gleeman doesn’t disagree. “For me, it’s mostly about whether a new stat tells a more accurate story and/or adds important context. With hitting stats, we all know the basics by now. There’s no need to introduce something new if it really only advances things a tiny bit or not at all. Our hope with DRC+ is that it’s not only more accurate than what’s previously been available, but that it’s more predictive and adds a level of context that advances things considerably.”

 

Adds Judge, “Once it became clear the metric would be a real upgrade, and not just another abbreviation for everyone to remember, we decided we wanted to release it.”

 

Still, unless and until that superiority becomes clear to its most influential users — teams, writers, broadcasters, fellow sabermetricians — DRC+ may have to fight for attention in what Appelman calls the “platform wars,”

 

“WAR, for instance, is where you have these platform wars, where you‘re like, ‘okay, well, which one is easier to access?’ I don’t think, when it comes down to one or the other, you’re looking at, really, a superior stat. But that is an instance where it comes down to, which one can I get to easier, not so much which one do I trust more. Because you can have one that maybe you prefer to the other, but, at the end of the day, you could flip a coin.”

 

A site like Baseball Prospectus, that puts most of its content behind a paywall and has kept the same look and functionality of its stats pages for more than a decade, tends to come up short in those comparisons. (Full disclosure: I worked on Baseball Prospectus’ books from 2006 to 2012.) Jay Jaffe, who wrote for Baseball Prospectus from 2004 to 2012 and is now a senior writer for FanGraphs, elaborates.

 

“I think Baseball Prospectus has always struggled to have its metrics penetrate the mainstream. Because both Baseball-Reference and FanGraphs have invested so much more in presenting statistics to the public, which is really what drives their models, they have always had the upper hand when it comes to their particular flavors of metric getting widespread use. WARP [baseball Prospectus’s Wins Above Replacement Player] beat the various conceptions of WAR to market, but it didn’t catch on because not many people were looking to Baseball Prospectus for the encyclopedia-type stats in the way they were using FanGraphs and Baseball-Reference.”

 

“It has a lot to do with just the general interface,” Jaffe continues. “You go to Baseball-Reference, the first thing you’re given is the option of looking up a million player cards. FanGraphs, you’ve got editorial content there, but it’s still riding atop this tremendous statistical resource. Baseball Prospectus was, I think, more content-forward and really suffered from the lack of investment in interface infrastructure. BP today, when you’re looking for statistics, still looks a lot like BP did in 2001. Whereas FanGraphs, while it is a content company, it is, I think, first and foremost a stats-delivery company, and likewise with Baseball-Reference.”

 

That may change. Gleeman told me that Baseball Prospectus is, “planning a further rollout on a soon-to-be-improved BP stat section that will allow people to access the data in much cleaner, more fluid ways.”

 

If that comes to fruition, BP’s current leadership will have succeeded where previous iterations failed.

 

“Baseball Prospectus was sort of a loose confederacy during its heyday,” Colin Wyers, who was director of statistical operations for the site from late 2009 to late 2013 and is now the senior architect in the Astros’ research and development department, told The Athletic via email. “BP had a lot of very brilliant people sort of doing their own thing. The problem was that a lot of things didn’t mesh well together. You had WARP and then you had VORP [the offense-only Value Over Replacement Player] and then you had [pitching metric] SNLVAR [support-Neutral Lineup-adjusted Value Added above Replacement], and, at one point, they all had different definitions of replacement. It all got confusing and overwhelming.”

 

“At some point, it’s too much for anyone to be expected to know what stats to use,” continued Wyers. “The other thing is that all of those people worked in their own fashion and with their own tools, and so you’d have some code in FORTRAN that nobody knows how to run anymore, and you have some stuff that’s running on Oracle databases, which is really expensive to run, and we wanted to move to cheaper database solutions. We just didn’t have enough staffing to port and maintain everything, so we had to be judicious in keeping what seemed to be the best of what we had.”

 

That confusion — compounded by the departures of nearly all of the Baseball Prospectus founders (only Dave Pease, who joined after the first annual book to help launch the website, remains), and the rapid turnover of staff, due in part to the poaching of the site’s top statisticians by major-league teams, including VORP creator and SNLVAR custodian Keith Woolner by the Indians in 2007 — caused many once-valuable statistics to wither on the vine. One such stat was Clay Davenport’s Equivalent Average, which was renamed True Average in 2010 in an attempt to boost its appeal. Davenport left BP in 2011. DRC+ has now arrived to replace True Average entirely.

 

That lack of lasting stewardship contrasts sharply with the consistency provided by Forman and Appelman at Baseball-Reference and FanGraphs, respectively. Both sites have also maintained ongoing relationships with sabermetricians such as Tom Tango, who helps both keep their statistics current.

 

“Anyone who is a friend of sabermetrics is a friend of mine,” Tango wrote via email. “Every now and then I’ll find something small that no one might notice, but it stands out to me, and I’d like to get that corrected on B-R and FanGraphs since I am a heavy user of both sites. In other words, it benefits me just as it benefits everyone else to make sure the data is as correct as possible.”

 

The DRA stat received criticism for growing up in public, with multiple significant alterations to its formula after its early 2015 release (no, Jason Schmidt’s 2004 is no longer among the greatest pitching seasons of all time according to the statistic), but such continued improvements are an important part of any statistic’s ongoing relevance.

 

“We have been very clear that our philosophy is of continuous improvement,” wrote Judge, who is now a co-owner of Baseball Prospectus, in an email. “We don’t think it is unreasonable to want something to be forever stable, but that is not a realistic goal unless you are willing to say, at some point, we are done learning and working. That’s a remarkable thing to declare, and we’re not interested in creating credibility by pretending our work is done when it isn’t. I also think that, if your position is that no statistic should ever be updated with new information or learning, then the most recent statistic you can use was probably created circa 1984. Virtually all statistics in wide use created since then have undergone major revisions at some point, including some ‘down-was-up’ moments as people have realized they were overlooking something important about this sport. Our objective is to get things right.”

 

A notable, non-BP example of a statistic falling out of favor because of an absent creator and a lack of public upgrades is Voros McCracken’s Defense Independent Pitching Stats (DIPS). In our email conversation, Tango called McCracken’s discovery of the lack of impact pitchers have on balls in play, first popularized via Baseball Prospectus and Neyer’s ESPN column in January 2001, “the most important sabermetric finding of the last 30 years.” Indeed, it was so monumental that McCracken went from relative obscurity to a job as a consultant with the Red Sox in less than two years.

 

“When the Red Sox hired me,” McCracken said, “basically all of my public work had to stop. And, at that time, by the fall of 2002, people were still very much in the thick of kicking around exactly what this all meant, and so forth and so on, and that discussion kind of had to go on without me contributing to it.”

 

With McCracken’s permission, Jaffe continued to post DIPS leaderboards on his personal site, FutilityInfielder.com, using the last publicly available version of the formula. However, with McCracken out of the public eye, DIPS was rather quickly replaced by Tango’s Fielding Independent Pitching (FIP).

 

Then again, McCracken, Jaffe and Tango all believe that FIP’s relative simplicity may have carried the day, even if McCracken had been around to keep DIPS current.

 

“[DIPS] was fairly arduous,” said Jaffe. “And then we learned, after a couple of years of doing that, a handy formula called FIP*, which you could do in seconds. I think it comes down to, you’ve got something that takes a whole spreadsheet to calculate and has a lot of complex variables in it, versus something that you can do with a pocket calculator, or even almost in your head, if you’re so inclined. Just the simplicity is what carried the day for FIP. You didn’t need an instruction manual to do it.”

 

*the formula for FIP is (13*HR+3(HBP+BB)-2*K)/IP + a constant, usually around 3.2, to put it on the ERA scale

 

“FIP is a shortcut to all that,” wrote Tango, “with a correlation of . . . I don’t remember what, but something like r=0.98 or r=0.99 [r=1 is total positive linear correlation]. While DIPS and FIP are both relevant, FIP makes the whole process completely clear and relatable. You can totally see how much of an effect one more walk or one more strikeout or home run will have on the final number. It’s the pure clarity of what it is doing.”

 

“I think that FIP, from Jump Street, was a better idea than DIPS,” said McCracken, who is now a consultant for another major-league team, “because it was much simpler to calculate. Looking back on it now, I probably should have done it differently. Just because complicated statistical things are not necessarily friendly for mass consumption.”

 

Simplicity and ease of understanding was a theme that those with whom I spoke returned to repeatedly.

 

“I think of the Statcast stats that have come out,” said Appelman. “Exit velocity and launch angle are clearly the two which have caught on the most because they’re just really simple. It’s stuff that people always wanted to know and now there’s a hard measurement on it.”

 

“A big part of our job is to translate this stuff to the general public,” said Petriello. “How can we use this on MLB.com, or on social media, or on television? So it’s got to be, not simplistic, necessarily, but in a way that can be easily explainable and understandable to people. Like hit probability, right now, is exit velocity and launch angle. I think people understand that. We could get 10 levels deeper than that if we wanted to, and get into all sorts of modeling and complicated stuff. And we may yet do that, but for right now, when this stuff is still so new to so many people, we’re trying to understand that our audience isn’t just the one percent of the mass of nerds, who we love, but obviously casual baseball fans, as well.”

 

Enter Tom Tango, again. Tango joined the Statcast team as senior data architect in June 2016. Among his tasks since arriving has been improving the seemingly abandoned route-efficiency stat.

 

“I knew right away that the idea was right, but the implementation wasn’t going to work,” wrote Tango. “That metric was simply total distance needed divided by total distance traveled, and you’d get back a number like 97 percent or 88 percent. Other than ‘high good, low bad,’ it was meaningless. The simple change was to take the difference between the two. That simple change makes it relevant because we are keeping the unit in feet. You run 50 feet, you need to cover 45 feet, so that’s an extra five feet added to your route. And it’s relatable because we can see how much a fielder missed the ball by. If he missed it by one step, three feet, then we can clearly determine that the extra five feet on the route prevented him from catching the ball. We can actually have a conversation about it.”

 

That new route-efficiency statistic is among those Tango, Petriello and the rest of the Statcast team hope to roll out before Opening Day this year.

 

Another crucial factor in ease of use and understanding is the name.

 

“I think the name is highly important,” said Neyer, “because you don’t have time to explain it. Nothing’s going to become truly popular unless it’s on TV or the radio, because that’s how 90-some percent of people still consume sports. They’re not going to read an article on Baseball Prospectus, a 1,000-word article on whatever their latest thing is. So, it’s got to have a name that people understand immediately.”

 

“We spend a lot of time thinking about that,” admitted Petriello. “Naming things is really, really difficult. I’ll be the first to say, we’re not always great at it. I think our most powerful stat is probably expected weighted on-base, xwOBA. It’s a tough thing to explain to anybody. It’s hard. We want to make sure that the name is something that people understand, but the deeper that you go with crazy acronyms, it might be more descriptive, but then it’s harder for people that actually want to know what it is. That is, for me, the most difficult thing we have, is trying to figure out how to name these things, and hopefully we’ve done a little bit better. I’m not sure we’re all the way there yet, but that’s an ongoing concern.”

 

Every now and then, a stat checks every box: a good name, simple to understand, responds to a question in need of an answer, moves our understanding forward significantly, benefits from an easily accessible presentation on a popular platform and is maintained and kept relevant by its creator. Jaffe’s Hall of Fame measuring stick JAWS (Jaffe WAR Score) is such a statistic.

 

Jaffe created the statistic in January 2004 to establish a total-value-based benchmark for Hall-worthiness, and later gave it its catchy name at the insistence of then-BP Editor in Chief Christina Kahrl. It still took years to catch on. Joe Posnanski was among the first mainstream writers to cite Jaffe’s Hall stats, in late 2010. The next fall, Brian Kenny brought Jaffe on the MLB Network to discuss JAWS and the Hall of Fame. Then, in 2012, Jaffe was hired by SI.com (full disclosure: I recommended him for the job) and Forman, at Jaffe’s suggestion, added sortable JAWS leaderboards to Baseball-Reference, which prompted a change in the base statistic for the calculation from BP’s WARP to B-Ref’s WAR. JAWS is now an essential part of the conversation about any player’s Hall of Fame candidacy, but not every statistic can satisfy every requirement so fully, and it still took the better part of a decade for JAWS to gain mainstream acceptance.

 

So, what are the most important attributes for a new statistic to have in order to gain traction in and beyond the sabermetric community? This is where opinions diverged most.

 

Tango listed the platform first among the factors that help a new statistic gain traction. “Leverage Index, WAR, wOBA and FIP are good examples of metrics I created or had a strong hand in shaping,” he wrote. “Without FanGraphs and B-Ref, they’d be relegated to the niche market, and so would take at least a decade to gain widespread use.”

 

To FanGraphs’ Appelman, however, the platform is a secondary concern. “I think with new stuff, it doesn’t really matter what the platform is so much,” he said. “I think it’s going to take a while sometimes for stats or unknown platforms to percolate into the public consciousness just because people are going to have to find the site, but if it’s really good, regardless of the platform, people will find it and use it.”

 

Tango and Appelman don’t actually disagree as much as they have different opinions about the time it can take a statistic to gain currency.

 

“The one factor people often forget about is time,” wrote Judge. “Metrics like OPS+ and UZR and wRC+ were once themselves weird and new, used only by nerds on the cutting edge of something and ignored by even many savvy writers. Now, all of these are in common usage despite having involved fairly substantial changes at times. Even the best metric is unlikely to simply shove existing ones aside after a few months. At least a season or two is usually needed to have people get comfortable seeing it in action and having it provide information that readers confirm is useful.”

 

Sabermetricians may have to learn patience, but the potential user base for a new statistic isn’t likely to show much. As a result, when I asked about the most important attributes for a new statistic hoping to gain traction, the word that came up most often was “accessible,” referring to both how easy it is to find and access the stat itself on whatever platform is offering it, as well as how easy it is to understand what the stat is and how it works.

 

“I think you want to have a good name,” said Neyer, “it’s got to be reasonably explainable — I can explain WAR in one sentence — and, at this point, it’s got to be accessible, which means you can find what you want in two or three clicks. I think those three ingredients. I wouldn’t have any way of saying which of those is the most important. I think you need all three.”

 

“It has to be accessible,” added Jaffe. “It certainly helps to have a memorable, catchy acronym, whether it actually tells you what it is or whether it’s reverse-engineered, like PECOTA. It’s those two things, and it’s also, ‘what is this for? Does it answer a question?’ I think there’s all of those things in there.”

 

“Is it unique is probably the first thing,” said Petriello, “and, yeah, is it accessible. For us, we do have an advantage where we have direct contact with a lot of the broadcasters, and we can get it on TV, and we can get it on the front page of MLB.com, and that certainly helps. I also think having the sabermetric community buy into it and pick it up and write about it on blogs and sites and articles, I think that helps a lot. You’ve certainly seen Sprint Speed on a lot of team blogs and all that kind of stuff. So, for me, those two things are maybe the most important. Is it unique? Is it available? And is the math behind it good?”

 

Though it may not always prove true, one still hopes that, ultimately, the deciding factor in a stat’s ultimate fate is the quality of the idea itself.

 

“I would like to think that it’s the importance of the concept behind it,” said McCracken. “In my experience, when you’re talking about statistics, you’re starting to talk about a subset of baseball fandom that is looking a little deeper than the talk radio crowd, and, therefore, I do think it is important that the concept has a good meaning and helps you understand baseball in a better way. I do think that that is, probably, in terms of long-term staying power, the most important. Now, other things matter. The platform certainly. The ease of understanding the statistic matters. But I really do think the No. 1 factor is, ‘how does this help us understand the game of baseball?’”

[P2F]

This is also an interesting read, so I thought I would share it:

 

The Evolution of Batting Statistics

Baseball statistics, and subsequently the way in which we evaluate the contributions of baseball players to their teams, have undergone many changes and innovations over the years – especially in the past decade or so.

 

The strides forward that the game of baseball have taken in recent years with the implementation of MLB Advanced Media’s Statcast, for example, speaks very strongly to where we are now collectively. What was once believed to be a game best evaluated using only our eyes has now become a game that is very much dissected and evaluated through an analytical lens. Every MLB team has an analytics department in their front office that they lean on heavily for cutting-edge information. The hope is that it can give them a competitive advantage, if only for a short window before another organization inevitably evens the playing field.

 

It was during the recording of our latest podcast episode that it dawned on me that the stats some of us use on a regular basis are not second nature to everyone. I would very much like to help familiarize you with the latest, best, and all-encompassing statistics in which to evaluate a hitter. For those of you with a moderate or extensive grasp of advanced statistics, my hope is that you’ll come away with either having learned something from this article, or that you will have at least found it informative or well-written. No matter your level of knowledge, let’s have a discussion and I encourage you to leave your thoughts in the comments section below.

 

I truly believe that there is so much more to the game than what you can assess with your eyes or through the most basic of statistics like batting average, home runs, and runs batted in. Baseball is a game of nuances and the seemingly smallest of contributions can ultimately play a significant role in helping to identify the game’s top players throughout the entirety of a grueling 162-game schedule. That’s a lot of baseball, friends.

Before we get to the reason we’re here, I’d like to, for mostly contextual reasons, run through a brief history of a few of the most frequently used basic batting statistics and how they helped lay the groundwork for advanced statistics. Having said that, I’m going to operate under the assumption that, if you’re reading this, you have at least some knowledge of baseball statistics, even if it’s just the basics. That’s okay!

 

AVG

 

Batting average (usually abbreviated as either AVG or BA) is a statistic that every fan or player of the game since the turn of the 20th century is likely familiar with. It’s the average performance of a batter, expressed as a ratio of a batter’s safe hits per official at-bats. For years, it was thought of as the holy grail, the most truly indicative statistic in which to measure a player’s hitting abilities. We now know that, while AVG has some merits, it’s generally a very shallow and often luck-based statistic. It is not predictive of future success, nor does it incorporate a hitter’s ability to get on base in ways other than a hit. It completely ignores the walk – which is obviously a very important aspect of batting – and the hit by pitch – which can, and often should, be considered a skill of the hitter.

Justin Smoak’s AVG in 2018 was .242, which, if evaluated through the scope of this statistic, is downright awful.

 

Throughout the article, I’ll continue to elaborate using Justin Smoak as an example, and you’ll eventually come to see the relevance of this.

OBP

 

After decades of uncontested reign atop the hierarchy of baseball statistics, AVG was finally usurped by On Base Percentage (OBP). OBP is much better than AVG is at telling us how often a hitter avoids making an out, which is obviously important for a hitter. Most of you have probably either read or watched Moneyball, which highlighted the importance of not making an out. It also depicted the competitive advantage in which capitalizing on a market inefficiency, like OBP at the time, can provide a team. OBP incorporates hits, walks, and that consolatory base awarded for getting plunked with the ball (ouch!).

 

To the formula!

 

 

Justin Smoak’s OBP in 2018 was .350, which is comfortably above average (.318). Even though Smoak’s AVG was as low as it was, he more than made up for that unfortunate fact by safely reaching base more than his fair share. While OBP is leaps and bounds better than AVG, as evidenced by Smoak, it still pales in comparison to some of the stats we will look at momentarily. Where OBP fails is that it doesn’t assign a proper value to each outcome individually and instead all non-out events are treated equally – a walk is not distinguished from a home run, for example. That’s not the fault of on-base percentage, as that’s not what it endeavours to measure. OBP is a pretty useful stat when used appropriately and in conjunction with other stats, and it should be preferred to AVG in virtually every scenario.

 

SLG

 

Around the same time that OBP was gaining traction within the baseball community, slugging percentage (SLG) began its rise to quasi-prominence. SLG measures the total number of bases per at-bat; the higher the number, the better. For example:

 

In 2018, Justin Smoak had 63 singles, 34 doubles, 0 triples, and 25 home runs. To break this down a little further, that's 63 bases for the singles, 68 bases for the doubles (34 x 2), and 100 bases for the home runs (25 x 4). This is 231 bases in total, which, when divided by the 505 at bats he had, equals a slugging percentage of .457.

 

SLG is relevant to us because it accounts for one half of the combination-statistic known as OPS (On-base Plus Slugging), which we will come back to in a little bit. The knock against OPS is that it treats OBP and SLG as equal, but, in regards to its effect on run scoring, OBP is almost twice as important as SLG.

 

*****

 

While some people still prefer to use surface statistics like AVG, OBP, Home Runs (HR), and Runs Batted In (RBI) to measure a hitter’s overall contributions to his team, I’m of the opinion that using more advanced stats allows for a much more accurate portrayal of what happened – and, perhaps much more importantly, what is likely to happen in the future.

 

In recent years, the mainstream adoption of more advanced metrics such as wOBA, OPS+, and wRC+ has helped change the landscape of statistical evaluation in baseball for the better. These metrics have been proven to be more reliable year-to-year, more descriptive, and more predictive than the rate stats we just discussed – AVG, OBP, and SLG.

 

wOBA

 

Those of you that are familiar with the sabermetric movement likely know who Tom Tango is – co-author of The Book and creator of numerous baseball stats, including wOBA (Weighted On-Base Average). For familiarity’s sake, wOBA is scaled to resemble OBP, although it’s calculated very differently. As I pointed out earlier in the article, OBP doesn’t attempt to weigh or distinguish between baseball batting events – it treats all non-outs the same, regardless of the result. wOBA attempts to do the opposite. It does a better job than AVG, OBP and SLG of calculating a hitter’s total offensive output.

 

We know that all ways in which to avoid making an out are not equal. A home run is worth more than a triple, double, single, walk, or hit by pitch; a triple is worth more than a single; a double is worth more than a hit by pitch; and so on. wOBA assigns each outcome a linear weight and calculates it into one easily-digestible statistic:

 

 

The league average wOBA in 2018 was .315. As I mentioned before, wOBA and OBP are scaled similarly – the league average OBP was .318. Let’s again use Justin Smoak as a comparative, since we know that he’s a pretty good hitter: Smoak’s wOBA in 2018 was .349, which was 34 points higher than league average – this is a good mark but not a great mark. This number seems to pass the sniff test based on the calibre of hitter that we know Justin Smoak is and when considering how he did at the plate in 2018 – he was good but not great.

 

wOBA is not park-adjusted, meaning that hitters playing in bandboxes such as Coors Field will have generally inflated wOBAs. However, the biggest knock against wOBA is that it is outperformed at the team level – in terms of reliability, descriptiveness, and predictiveness – ever-so-slightly by OPS. This was a shocking revelation when originally unveiled by Cyril Morong and it was later confirmed by Baseball Prospectus.

 

OPS+

 

Baseball-Reference’s OPS+ stat, as with all ‘plus’ stats, is put on an easily digestible scale – 100 is league average and a higher number is better (as indicated by the ‘+’). Let’s turn to ol’ Smoakie again for an example – he had a 123 OPS+ in 2018. This indicates that, according to OPS+, he was 23% better than league average as a hitter this past season. OPS+ is obviously OPS-based, but it’s adjusted for park effects and other small variables that add up to a big difference in how accurately and reliably the stat correlates to team runs, historically.

 

Although OPS+ is less effective than wRC+, it’s well ahead of AVG, OBP, and SLG. Yet for some reason, it’s been seemingly more widely-adopted to the mainstream than the superior wRC+. This is probably because of the familiarity that a lot of the community already have with OPS as a somewhat commonly-used statistic in television broadcasts.

 

wRC+

 

wRC+, or Weighted Runs Created Plus (uses the same scale as OPS+ and other ‘plus’ stats), has, over the past few years, become the preferred hitting statistic of sabermetricians and staunch supporters of advanced statistics everywhere, and rightfully so. It correlates more tightly to team runs/PA than any other publicly-available statistic. It’s also complex in nature, although I say that with great admiration.

 

From FanGraphs’ Glossary:

 

Weighted Runs Created Plus (wRC+) is a rate statistic which attempts to credit a hitter for the value of each outcome (single, double, etc.) rather than treating all hits or times on base equally, while also controlling for park effects and the current run environment. wRC+ is scaled so that league average is 100 each year and every point above or below is equal to one percentage point better or worse than league average. This makes wRC+ a better representation of offensive value than batting average, RBI, OPS, or wOBA.

 

And here’s how it's calculated:

 

 

To provide a little bit of backstory, wRC+ is an adjusted and scaled version of Tom Tango’s wRC (Weighted Runs Created), which is an improved version of Bill James’ Runs Created (RC) statistic. Per FanGraphs, RC attempted to quantify a player’s total offensive output and measure it by runs. While the idea was sound, James’ idea has since been replaced with Tango’s wRC. wRAA, seen in the calculation of wRC+ above, is essentially just wRC with league average scaled to zero. LgR/PA is League Runs per Plate Appearance, which is the total number of runs scored divided by the total number of plate appearances in MLB during that season. The Park Factor for all previous seasons can be found here.

 

As previously mentioned, wRC+ works on a scale where 100 represents league average. So even if you don’t quite grasp how it’s calculated, it’s constructed to be incredibly easy to use and understand. Let’s look at good guy Smoak again for an example of wRC+ in action.

 

Over the meadow and through the woods, to the Fangraphs graphic we go:

 

 

Smoak’s wRC+ in 2018 was 121, indicating that he was 21 percent better than a league average hitter this past season. It was also the 49th-best mark in all of baseball among qualified hitters, sandwiched between Andrew Benintendi’s 122 and Cody Bellinger’s 120. Smoak is good at smashing baseballs! We already knew this, but a statistic like wRC+ helps capture Smoak’s true total offensive output that a stat like batting average – or even a combo of AVG, HR, and RBI – fails to do. Sincerely, that is an example of one of the biggest benefits to using sabermetrics to evaluate baseball.

 

When looking at Justin Smoak’s surface stats - .242 AVG/.350 OBP/.457 SLG/25 HR/77 RBI, I feel like it would be easy to overlook the positive contributions that Justin Smoak made to the Blue Jays from the plate in 2018. This was not an article about Justin Smoak, but I did indeed use Smoak as a recurring example to highlight the disparity between basic and advanced statistics. And now that we’ve taken more than just a fleeting glance at some of the most-used advanced statistics in baseball, I’m hopeful that this article has proven to be helpful to you in some way, shape, or form.

[jays4life19]

This too, may be the most interesting so i thought i would share it.

 

Dynasty Rankings: The Top 50 Signees from 2018

Bret Sayre

The easiest way to know that it’s a new calendar year, besides all that time you’ve been spending at the gym these last two days, is another iteration of ranking the players who enter our dynasty-league player pools. It’s a beautiful thing—a new wave of fresh-faced youth who haven’t been around long enough to disappoint us yet. Yet, ultimately they will (on the whole). What we’re attempting to do here is to give you the best chance at making the most of your drafts in the aggregate, and that requires a combination of both picking the right players and making the most of your draft spots.

 

There are years when it’s better to have high-end picks in dynasty drafts, and there are years when it’s better to have more picks. This year is the latter. I may personally adore the player who graces the top spot on this list, but he might be the weakest to occupy it since I started putting together these lists nearly a decade ago. What’s going to end up happening due to this flattening is that this year’s drafts are going to be unpredictable. It’s not hyperbole to say that you could talk yourself into taking any of the first 15 names below with a top-three pick. In fact, if you sample enough leagues, you’d probably find that to be true in practice. And it doesn’t get much clearer after that. The depth of this draft class is strong, but it’s incredibly fluid.

 

Before we get to the most exciting part, it’s the paragraph that frames the list and it’s utility. The below is intended for dynasty leagues of approximately 14-16 teams, with one catcher. It assumes a separate farm team, and if your league does not have a separate farm team, feel free to bump up the players with faster timetables. If you’re in a deeper league, prioritize safety. If you’re in a shallower league, load up on risk. And, finally, each player’s situation is factored into their values. This can mean organizational history of developing players and/or future home ballpark (though the latter is discounted a bit since these players are generally pretty far away and park factors are not constants). You know your league best, and now you know how to translate what is about to come to your individual circumstances. And if you’re not sure, this is why we have comments.

 

Now, the part of the article you actually came for (though I do appreciate you either reading the intro or lying to me about it): the 50 best players who entered professional baseball during the 2018 calendar year:

 

1) Nick Madrigal, 2B, Chicago White Sox

If we’ve learned anything over the last few years of baseball, it’s that power comes both from the traditional and non-traditional places. Sure, the top of the leaderboard looks as muscular as always—with Khris Davis, J.D. Martinez and Joey Gallo—but Jose Ramirez, Francisco Lindor, Mookie Betts and Alex Bregman all eclipsing 30 bombs? That’s the power of quality contact , and it ties in with why Madrigal sits at the top of the list this year. There’s no one better in this class at making quality contact than the diminutive second baseman from Oregon State. I mean, he only struck out five times in 173 plate appearances in his pro debut across three levels. In a previous life, we’d assume that with his height and relatively linear swing, Madrigal would top out at around five homers a year. That, combined with the ability to hit over .300 and steal 30-plus bases, would be attractive enough without the power. Add 15-homer potential to that, though, and you basically have Whit Merrifield with a pedigree. Madrigal has the raw power to get there, despite his small frame, and we know the frame is no longer the impediment it once was.

 

2) Trevor Larnach, OF, Minnesota Twins

This is the first time I’ve ever seen the top two players available in dynasty drafts come from the same school—not surprisingly the team that won the NCAA Championship last spring. (They also have a candidate for next year’s top spot, but I digress.) The big question around Larnach heading into his junior season was whether he could turn his strength into over-the-fence power, and boy did he. Larnach hit 19 homers after just three in his previous two seasons, and kept it going with a .200 ISO in his pro debut. He’ll get compared to fellow Beaver Michael Conforto more than he should, but from a fantasy sense, it’s not far off.

 

3) Victor Victor Mesa, OF, Miami Marlins

What to do with the most recent Cuban export? There are those who would have him as the top overall pick, and those who would have him barely in the top-10. Partially it depends on your league type, as Mesa is more valuable in a deeper league due to his slightly more limited ceiling than the names around him. (In a mixed league shallower than 16 teams, I’d probably take the next two players over him.) Yet that ceiling still includes a potential .290 average, strong on-base skills and 25-30 steals. The power may top out at around 10-12 dingers annually, but Ender Inciarte was a borderline top-20 outfielder with the kind of production that Mesa could reach as soon as 2020. Don’t let the struggles of other recent Cuban players make you trigger-shy on Mesa.

 

4) Jordyn Adams, OF, Los Angeles Angels

5) Nolan Gorman, 3B, St Louis Cardinals

The two best prep bats in this draft, Adams and Gorman couldn’t be much more different. The former is the prototypical Angels outfield prospect: dripping with tools including plus-plus speed and the potential to reach average or better in both hit and power. There are multiple paths for Adams to be a high-end fantasy outfielder, even if all of the above don’t come together (they likely won’t), which makes him a little less risky than the typical “athlete” profile would suggest. On the other hand, Gorman has more thunder in his bat than any other player on this list, and is likely the only one who could one day hit 40-plus bombs in the majors. Of course, like you’d expect, it comes with a lot of swing and miss—yet it wasn’t egregious in the Appy League and it’s foolish to judge an 18-year-old on how much he struck out in full-season ball during his draft year. There’s no wrong way to order these two, and it’s reasonable that they might both be available in the second half of the first round in most dynasty drafts.

 

6) Alec Bohm, 3B, Philadelphia Phillies

7) Travis Swaggerty, OF, Pittsburgh Pirates

8) Jonathan India, 3B, Cincinnati Reds

This is a natural tier here, as it’s three college bats with more question marks than the two who sit at the top of the list. Bohm hints at an above-average hit tool and plus power, which could lead him to a few .270-30-100 campaigns at Citizens Bank Park. On the other hand, he’ll turn 23 during the 2019 season and he hasn’t seen a single at-bat in full-season ball. Swaggerty has the most athletic profile of this group and he’s a potential 20/20 contributor, with more upside in steals than that, but offers more swing-and-miss than fantasy owners are quite comfortable with. Not to mention that he doesn’t have a long track record of high-end performance. India is the safest of this group, but he’s also the most boring. It’s average tools across the board, and if any aspect of his game drops below average, the whole profile becomes much less palatable, regardless of what position he ultimately plays.

 

9) Casey Mize, RHP, Detroit Tigers

And we finally get to the first-overall pick in the draft. Going from pitching prospect to SP2 is really hard while keeping your arm in one piece, and while Mize certainly has that upside, so did Kyle Wright, A.J. Puk, Dillon Tate, Carlos Rodon, Mark Appel, and Kevin Gausman. What do all those pitchers have in common? They were the first college arms selected in their respective drafts going back to 2012. Buyer beware.

 

10) Yusei Kikuchi, LHP, Seattle Mariners

This isn’t a Darvish or Ohtani situation, where Kikuchi pushes himself up to the top of draft lists due to top-of-the-rotation upside. Instead, he offers immediate production and reasonable mixed-league certainty. In terms of landing spots, Seattle certainly isn’t ideal, especially in the short term. Their defense is shaping up to be below-average, but the kicker is Omar Narvaez and his bottom-of-the-barrel framing. Expect an ERA north of 3.50, a WHIP around 1.20 and 170-ish strikeouts if he can pitch a full season. He’s talented enough to be a top-30 starter, but sometimes it takes more than just talent.

 

11) Xavier Edwards, SS, San Diego Padres

As the two of the most impactful (yes this is a word, Craig) speedsters in the draft, it’s natural to compare Adams and Edwards. The Padres supplemental pick has 40-plus steal potential and showed off an extremely strong approach in his pro debut, walking more times than he struck out across the Arizona League and the Northwest League. The thing that keeps Edwards out of the top-10 for me is his limited pop, and while he could add strength and approach double-digit homers, it would likely come at the expense of some of those steals. Nits, they must be picked.

 

12) Kyler Murray, OF, Oakland Athletics

There’s no question who the most famous person on this list is, as Murray won the Heisman Trophy last month (it’s a football thing). And while he’s saying all the right things about committing to baseball and the Athletics, there’s always going to be the risk he ends up in the NFL instead. Murray’s speed is his calling card, but he’s strong enough to muscle more than a few out of the park as well—he hit 10 homers in his final season at Oklahoma and could get to 20-plus as a pro. He’ll have plenty of eyes on him as he starts his baseball career in 2019.

 

13) Julio Pablo Martinez, OF, Texas Rangers

While Martinez will be 23 before Opening Day this season and he stands a paltry 5-foot-9, he’s dripping with tools and the Rangers should move him relatively quickly through the minors so long as he can hold his own in 2019. His strong defensive profile will continue to get him looks and opportunities to work through the swing-and-miss and tap into his 25-homer power.

 

14) Jarred Kelenic, OF, Seattle Mariners

The lone player on this list who’s already been traded, Kelenic will look for a strong hit tool to carry his profile in Seattle but complements that nicely with 20-homer pop and even some speed to get to 10-15 steals. It’s not the sexiest profile, but the sum of its parts could make him a strong OF3 in time.

 

15) Joey Bart, C, San Francisco Giants

The only thing more frustrating than pitching prospects are catching prospects. Bart hit *checks notes* a billion homers in the Northwest League after being drafted second overall last year, and he’s a good bet to stick behind the plate. Yet, the track record of catching prospects is just brutal—with Buster Posey standing tall as the lone college catcher to go from the first round of the draft to a consistent top-10 option at the position. Maybe Bart can break the trend, but, well, you know the definition of insanity.

 

16) Alek Thomas, OF, Arizona Diamondbacks

17) Connor Scott, OF, Miami Marlins

For those of you counting at home, this now makes eight first names in a row. The only things that separate Thomas and Scott are 50 spots in the draft and five inches. Both carry speed-hit profiles leaving them capable of hitting .280-plus with 30 steals. The power lingers behind, but neither are scrubs in the department—and you wouldn’t need to flinch much to see one of them developing 20-homer pop. It’s just a starter kit at this point, as they’re both unlikely to reach the majors until 2022, but if you’re going to pick a starter kit, might as well be the Andrew Benintendi one.

 

18) Brice Turang, SS, Milwaukee Brewers

19) Matthew Liberatore, LHP, Tampa Bay Rays

Both Turang and Liberatore were potential top-five picks early last spring before ultimately settling into the back half of the first round. Turang can work a count and steal a base, but he’ll have to develop some power in order to be a starting fantasy shortstop. Liberatore may not have traditional overpowering stuff, but he throws four pitches that can be above-average and shows advanced command and pitchability for his age. If he can tick up his fastball velocity like some scouts believe he can, he’ll make this ranking look low.

 

20) Grant Lavigne, 1B, Colorado Rockies

21) Triston Casas, 3B, Boston Red Sox

22) Jordan Groshans, 3B/SS, Toronto Blue Jays

23) Seth Beer, DH, Houston Astros

For the power-hungry of you out there, this group is for you. Lavigne is a cold-weather prep bat with a great approach and very strong raw power. Plus, it won’t hurt that he’ll call Coors home in a few short years. Casas is forgotten about a little because he only got five plate appearances in before a season-ending injury in his pro debut, but if Gorman had the most power in the prep ranks, the Sox first-rounder falls right behind him in line. He’ll have to fight some potential contact issues, but he won’t be the first or last prep bat to do so. Groshans has a chance to be a more balanced hitter between his average and power than the two players he is sandwiched between, but don’t race him up your draft board just because the Blue Jays are experimenting with him at shortstop. Unless you’re a catcher, positions don’t matter anymore. The same applies to Beer, who profiles best as someone who should never pick up a glove save to hand it to a teammate in a friendly fashion. His biggest problem is that there’s a lot of concern the power he showed in college won’t transfer to wood, and it’s also going to be long road if he wants to hit lefties. In a perfect world, he’s Schwarber-esque. In most realistic worlds, he’ll top out as a Logan Morrison type.

 

24) Bo Naylor, C/3B, Cleveland Indians

The thing about fantasy catching prospects is that their bats have to be able to carry them at another position, and they ideally should be athletic enough to play somewhere other than the cold corner. Naylor fits both of these descriptors as he projects for a plus hit tool and has the arm/range for third if Cleveland moves him out from behind the plate.

 

25) Nico Hoerner, SS, Chicago Cubs

The Cubs’ first-rounder is a scrappy hitter with underrated strength, and while that gives him limited upside in our circles, a strong showing in the AFL has his name on more tongues this winter than expected. He’s another who is a better selection in deeper leagues, as he’s more of a .290-15-10 ceiling type.

 

26) Mike Siani, OF, Cincinnati Reds

27) Marco Luciano, SS, San Francisco Giants

28) Brady Singer, RHP, Kansas City Royals

29) Mason Denaburg, RHP, Washington Nationals

30) Merrill Kelly, RHP, Arizona Diamondbacks

The Reds may not have taken Siani until the fourth round, but he didn’t fall on talent, he fell on price tag. His strong center field skills will give him lots of opportunity to develop with the bat, and his plus-plus speed will provide him many chances with fantasy owners as well. Luciano is the top traditional J2 signee from 2018, and while he may not have the polish that Vladito or Wander Franco had, he’s got power, speed and a good approach at the plate. Singer was a potential 1-1 pick at the start of the spring and he had a strong showing at Florida in his junior season, but lacks the ceiling of the arms before him (all three of them). He’s hurt the most by my unease about investing in pitchers early in dynasty drafts. The Nationals love to draft pitchers with injury questions, and while Denaburg doesn’t have the lofty ceiling of a Lucas Giolito or Jesus Luzardo, they have a strong track record here. He has the talent to ride a strong fastball/curve combo to SP3 status with the potential for 200 strikeouts, if he’s healthy. Kelly is a fun story, but expecting him to be the next Miles Mikolas is extremely far-fetched. That said, he’s only 30 and he could be a reasonable SP5 this year in mixed leagues, and that has value in its own right.

 

31) Malcom Nunez, 3B, St Louis Cardinals

32) Ryan Weathers, LHP, San Diego Padres

33) Jameson Hannah, OF, Oakland Athletics

34) Jeremiah Jackson, SS, Los Angeles Angels

35) Logan Gilbert, RHP, Seattle Mariners

36) Ethan Hankins, RHP, Cleveland Indians

37) Blaze Alexander, SS, Arizona Diamondbacks

38) Greyson Jenista, OF, Atlanta Braves

39) Jackson Kowar, RHP, Kansas City Royals

40) Grayson Rodriguez, RHP, Baltimore Orioles

This is the part of the draft where you should be taking pitchers—in the third through fifth rounds. Some of these arms will end up being strong major-league contributors, but there’s so much development necessary between draft day and a major-league debut both in terms of physical strength and coaching—and that’s in addition to figuring out which of these guys holds their stuff while pitching every fifth day. Among these arms, Hankins has the highest ceiling and greatest risk—his fastball before the shoulder injury that caused him to drop in the draft was a true 80 pitch. Gilbert, on the other hand, has the highest floor if such a thing existed among pitching prospects. Rodriguez would probably be inside the top 30 were he drafted by almost any other organization, as I don’t trust the Orioles to develop a roll of film. Nunez set the Dominican Summer League ablaze after signing with the Cardinals in the spring, but he’s a true bat-only profile and though the Cardinals have had some success here, it’s tough to push him much higher than this. He may seem out of place here as an 11th-round pick, but Alexander was a top-100 name pre-draft and got a bonus five times slot. He’ll get chances to hit based on his defense (his arm at short was probably the best in the draft class), and he got off on the right foot in the Arizona and Pioneer Leagues.

 

41) Nick Schnell, OF, Tampa Bay Rays

42) Cole Winn, RHP, Texas Rangers

43) Noelvi Marte, OF, Seattle Mariners

44) Tristan Pompey, OF, Miami Marlins

45) Jeremy Eierman, SS/3B, Oakland Athletics

46) Griffin Conine, OF, Toronto Blue Jays

47) Diego Cartaya, C, Los Angeles Dodgers

48) Shane McClanahan, LHP, Tampa Bay Rays

49) Joe Gray, OF, Milwaukee Brewers

50) Gabriel Rodriguez, SS, Cleveland Indians

Schnell is a fun name to keep an eye on late, if he falls in drafts, but the overall ceiling is what drops him to this tier despite having average, power, and speed at his disposal. Winn is another arm who would push the top 30 if he’d been drafted elsewhere, as Rangers have had almost as good of a time developing pitching prospects as the Orioles. Pompey, Eierman and Conine were strong college bats with limited upside—though they balance it by having slightly more advanced timelines. (Of course, Conine went and messed that latter part up a bit by getting suspended for PEDs.) Pompey, the brother of oft-injured Jays prospect Dalton, has already gotten 101 PA in High-A, showing strong on-base skills and flashing some speed. I know I shouldn’t put a J2 catching prospect on here, but Cartaya is extremely advanced in his approach and the Dodgers have a pretty good track record with catching prospects.

 

Honorable Mention (in alphabetical order):

Luken Baker, 1B, St Louis Cardinals

Richard Gallardo, RHP, Chicago Cubs

Sandy Gaston, RHP, Tampa Bay Rays

Cadyn Grenier, SS, Baltimore Orioles

Adam Kloffenstein, RHP, Toronto Blue Jays

Victor Mesa Jr., OF, Miami Marlins

Jake McCarthy, OF, Arizona Diamondbacks

Misael Urbina, OF, Minnesota Twins

Steele Walker, OF, Chicago White Sox

Cole Wilcox, RHP, Washington Nationals

[P2F]

Wow

[Laika]

Can you imagine actually caring about that guy's opinion about the content of that list? wow

 

There is literally no novel information there. Nothing interesting at all. It's just a random reorganization of (mostly) the top players in the 2018 draft, with some stream of conscious text sprinkled in.

[Boxcar]

Can you imagine actually caring about that guy's opinion about the content of that list? wow

 

There is literally no novel information there. Nothing interesting at all. It's just a random reorganization of (mostly) the top players in the 2018 draft, with some stream of conscious text sprinkled in.

 

Bret Sayre needs to be stopped.

 

I don't want to say that he should be fired and find a new career but he needs to be fired and find a new career.

[Boxcar]

Also, WTF is an OF3? Is Bret suggesting that you should try to have 3 OF that are each in a different tier? Why?

[Dr. Dinger]

Bret Sayre needs to be stopped.

 

I don't want to say that he should be fired and find a new career but he needs to be fired and find a new career.

 

I legitimately feel that todd and tercet could write more useful fantasy rankings than Bret Sayre.

[Boxcar]

I legitimately feel that todd and tercet could write more useful fantasy rankings than Bret Sayre.

 

I have a sudden urge to write a critique of Bret Sayre's work and his person in weblog form.

[Dr. Dinger]

I have a sudden urge to write a critique of Bret Sayre's work and his person in weblog form.

 

Maybe a ranking of Bret Sayre's worst rankings.

[Boxcar]

Maybe a ranking of Bret Sayre's worst rankings.

 

I'd need to include that time he dunked the ball on our buddy BTS over Ja Rule Cotton