Mets Merized Online » sabermetrics Fri, 20 Jan 2017 14:30:23 +0000 en-US hourly 1 Know Your Stats: Weighted Runs Above Average (wRAA) Tue, 28 Jun 2016 16:00:17 +0000 wilmer flores

We continue our widely-beloved “Know Your Stats” series with a “gateway stat:” Weighted Runs Above Average. wRAA is essentially wOBA converted into runs, but understanding wRAA is crucial to understanding Wins Above Replacement and Weighted Runs Created Plus, which we’ll be talking about this afternoon.

One of the biggest issues with rate statistics like wOBA and On-Base Percentage is that they don’t put the production into baseball terms. What does a .340 wOBA really mean? wRAA makes it easy by putting it into the “currency” of baseball: runs. Before we get started, here is the wOBA formula this year

wOBA = (0.688×uBB + 0.719×HBP + 0.878×1B + 1.245×2B + 1.576×3B + 2.030×HR) / (AB + BB – IBB + SF + HBP)

The formula for wRAA is pretty simple. To convert a .350 wOBA to wRAA, you simply subtract the league wOBA (.313 in 2015), and divide that by the wOBA scale for that year, which slightly changes based on the particular weights for that season. Then, to adjust for playing time, you multiply by plate appearances. Here is the formula:

wRAA = ((wOBA – league wOBA) / wOBA scale) × PA

Now here is the formula for someone from last with 600 plate appearances and a .350 wOBA:

wRAA =  ((.350 – .313) / 1.251) × 600

wRAA = (.037/1.251) x 600

wRAA= 17.7 runs above average

Just as it sounds, this player would be worth 17.7 runs above average at the plate. How good is that exactly? Here is a “rule of thumb” chart for the stat, courtesy of Fangraphs:

wRAA chart 1Of course, it’s important to remember that like wOBA and any traditional rate stat, wRAA is context-neutral. Also, wRAA is critical for understanding Wins Above Replacement, since it is the offensive component. You won’t see it used much in articles because it doesn’t have all that much practical use on its own, but as you’ll see, knowing it makes it easier to wrap your head around a few more widely-used stats. That’s why it’s in some ways a “gateway stat.”

In Context

wraa chart 2

Further Reading

Other installments

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Know Your Stats: OPS/OPS+ Thu, 23 Jun 2016 17:00:04 +0000 simpsons sabermetrics

For the next few days, I will be bringing back my “Know Your Stats” series that I began a few years back to highlight some important sabermetric stats and concepts. We begin this afternoon with OPS and OPS+.

OPS, or On Base Plus Slugging was one of the first sabermetric stats to go mainstream. It is, as the name implies, On-Base Percentage plus Slugging Percentage. It’s crude and simple, but it’s a good quick and dirty reference tool

OPS is expanded on even further when made into an index, OPS+. OPS+ does something very important: puts the OPS into context. The stat makes it possible to compare players from different eras, different teams, and different ballparks.

OPS+ is set on a percentage point scale. Essentially it is the percentage of league OPS. 100 (or 100% of the league average) is the league average, while a 110 mark is ten percent better than league average, and 90 is ten percent worse.

There are many issues with the crude OPS and OPS+. Is one point of OBP worth the same as one point of SLG? The math says no. In fact, the math says a point of OBP is worth 1.7 times what a point of Slugging is. Neither OPS nor OPS+ tell you the composition of OBP or Slugging and thus overvalues extra base hits.

OPS as I mentioned, is crude and the most basic sabermetric stat out there. It has its flaws, but it is a great way to get people to start thinking about sabermetrics. OPS and OPS+ are solid stats and certainly better than batting average, although not as good as wOBA or wRC+.

More thoughts

  • Anytime there is a stat with a “+” at the end, that means it is an index and adjusted for park factors. I get a lot of questions and concerns about the fact that these park factors sometimes change from year to year. However, these changes are so miniscule from year to year that they don’t really effect the stat. Here are Yankee Stadium’s park factors going back to 2009:

Screen Shot 2016-06-23 at 12.44.30 AM


OPS= ((H +BB+HBP)/PA) + (TB/AB)

OPS+=100 x (OBP/lgOBP*+SLG/lgSLG*- 1) then park adjusted

In Context

ops chart 3ops chart 1

Further Reading

Up Next: wOBA

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Mets Are More ‘Clutch’ Than The Nats, Statistically Speaking Sat, 16 Aug 2014 16:16:33 +0000 eric campbell homers

Judging from comments on my twitter feed, I’d venture to guess that the prevailing emotion among Mets fans out there for this season is abject frustration. While there are times when we are offered glimmers of hope and slivers of consistency, they tend to be quickly snuffed out. The team itself is probably more likeable than it has been in a while, but the constant RISP and LOB trends tend to put a proverbial damper on these good feelings like a wet wool blanket tossed on uncle Jimmy after his little bbq accident at the 4th Of July get together. We’re left with that nasty smell of burnt forearm hair and losses that should have been wins.

I can’t remember a season so full of “could have beens.” I honestly believe we might have easily been 8 or 10 wins up in the win column if only we’d had a few things break our way, if only we had a few more clutch hits. The Mets, post 2006, have been plagued with the worst label you can have as a team, they are perceived as unclutch. More recently they seem capable of pitching well enough, they’ve repaired a chronically leaky pen, but the team as a whole continues to struggle with scoring runs in high leverage situations. Is this apparent perception borne out statistically?

The world of sabermetrics seems to put out a new stat every week, and each one more complex than the last. I actually read an article a couple of days ago that asked you to refer back to first year calculus, that’s like asking me to refer back to my time in the birth canal. There are some things I’d rather not remember. There is actually a stat now called “clutch.” Clutch = (WPA / pLI) – WPA/LI. It is a measure loosely based on something called sequencing and “performance bunching.” In a nutshell, clutch measures a team’s ability to group fortuitous events together with productive results.

Take the Nats series for instance, the Mets left a bunch of runners on base, and inning after inning seemed to string hits together only after getting two outs, which resulted in being repeatedly turned away without scoring — they had a clutch score of -0.07 (0 = average). The Mets, ostensibly, appear to be extremely unclutch, however, when you take a closer look, the numbers don’t exactly bear this out. They have a whopping -44 rdif and according to fangraphs, offensively are the tenth most “clutch” team in baseball with a rating of -0.20. Are you kidding me?

Mets hitters are actually more “clutch” than the Nationals (the Nats have a clutch rating of -0.44) … Why?? I don’t know … THIRD BASE! But before I add another “and I don’t give a damn,” Lou Costelloism, I should mention that given what the Mets have accomplished statistically, they have won more games than they should have. Wonderful, so the Mets are actually pretty clutch given how bad they are. That makes about as much sense as saying a pig can fly fairly well considering he’s a pig … but I get it.

This my friends is why we may be stuck with Terry Collins. The numbers gurus are quick to dump out buckets of stats showing that given what the Mets have produced, they’ve actually won more games than they should have. Right now the most clutch teams in baseball are the Royals, the Orioles, the Red Sox (really?), the Yankees, and the Braves, the least clutch teams are the Twins, the Rockies, the Angels, the Rays, and the Cubs. The Mets currently have a .471 winning percentage, however, BaseRuns a statistic that strips away variation from performance and tells you in a sense what a team should have done were it not for sequencing and “clutch events,” says the Mets should have a .458 winning percentage.

Statisticians are also quick to point out that clutch is meaningless, primarily due to the inordinately high probability of regression. According to their theory, the Giants were never really better than the Dodgers, they have simply been extremely clutch, similarly they cite the Orioles and the Royals as examples of teams that are currently running ahead of their competition contrary to actual on field performance … again mostly because they’ve been lucky enough to group or sequence productive events (they’ve been clutch). Regression, however, is unavoidable. Jeff Sullivan of fangraphs recently showed in convincing detail that there is no such thing as clutch … clutch is simply a grouping of productive events that happens to coincide with high leverage situations, it is random and thus highly vulnerable to regression. Which means the Mets, given their production, may end up losing at an even higher rate than they have thus far.

Now I am not familiar enough with what goes into these statistics to comment on whether clutch performances are anomalies on a team level and whether regression is inevitable. If it is then the Royals will not win their division and the Rays will make a run at some point, but some teams, the Orioles and Giants come to mind, appear to be consistently “clutch” which runs contrary to league regression trends. There are outlying examples of teams that do not regress. In these instances there may be opportunities for determining whether there are in fact occasional examples of teams that have been for whatever reason capable of bunching improbable productive events together consistently. We saw this first hand with the Giants. An error here, a passed ball there, and poof, they snatch a win from the jaws of defeat. Maybe clutch has something to do with not being bright enough to be nervous in a situation where you should be nervous … Hunter Pence, that poor man’s wanna-be Brandon Nimmo, comes to mind. Who knows. I sure don’t.

If I had to guess I would say that clutch hitting does not correspond accordingly with clutch pitching, if it did I’m sure the Nats with their 17 WAR for pitching would have a higher overall clutch rating. While the Mets have failed to produce on par with a .471 winning percentage, their pitching has been stifling at times and I think clutch pitching performances are more difficult to qualify than clutch hitting performances because of all the myriad situational nuances that go into a pitcher’s mound presence and execution. Tom Seaver by most accounts would be considered a clutch pitcher, but he was also really really good. I would wager the 1969 Mets were an extremely clutch team if you go off of their production, but I’d also wager the teams who faced the Mets down the stretch and in the playoffs in 1969 didn’t think clutchness had anything to do with it … they simply overwhelmed you with pitching.

What worries me in light of all this is that our current front office’s adherence to sabermetrics dictates that the team is performing above it’s capabilities, which would imply their coaching staff and manager are doing a great job. The problem with this approach is it is an after the fact analysis. The Mets have not produced but have somehow won more than they should have given their production … unfortunately one of the reasons they’ve failed to produce is a problematic roster and an even more problematic allocation of playing time from said roster. It is akin to a baker using the wrong ingredients in a cake that ends up tasting terrible and giving him a pass because, well it isn’t fair to expect a cake with the wrong ingredients to taste good.

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The True Value of Juan Lagares Sat, 16 Aug 2014 14:42:55 +0000 juan lagaresAs the New York Mets near the end of their ongoing rebuilding process, it’s time for Sandy Alderson to re-evaluate the entire team and see who can help the team on a day to day basis over the next few years. Once that task is completed, it’s time for the front office to work to get rid of the dead weight, or those who aren’t seen as useful in the coming years, and to plug up the holes with quality talent. They also need to look at where there’s a surplus of talent, and where there’s a dearth to plan accordingly.

Some of the answers are rather obvious. David Wright looks to be regressing, but for now at least, he’s still a very useful player. Obviously, the pitching depth is incredible both at the major league level and coming up through the farm system. Guys like Noah Syndergaard, Rafael Montero, and Steven Matz are well-known to Mets fans, but even in smaller prospects like Jack Leathersich and Cory Mazzoni, the Mets have the ability to put together a historically great rotation and bullpen filled with homegrown talent.

It’s clear that the team needs some more bats in the lineup. The question is, how many? Shortstop is an issue because while Ruben Tejada has improved upon an absymal 2013, he still hasn’t been anything to write home about. He’s been getting on base at a very nice pace, flashing a pretty .355 OBP, but his complete disregard for extra base hits, as evidenced by his .285 SLG, is an issue.

Outfield help is a necessity. The Young boys in left field have been nothing short of a disappointment, which is why Chris Young found himself Designated for Assignment last week. The really interesting debate begins when looking at who stands next to either of them in the outfield grass.

Juan Lagares has been an elite defensive player since his career began last April. The problem is that his offensive success has come and gone periodically. Is it worth locking him in as the center fielder when there’s no guarantee that he’ll ever develop into anything more than an inconsistent hitter?

Throughout his minor league career, Lagares was a largely uninspiring hitter. Since signing with the Mets as an amateur free agent at 17 years old in 2006, Lagares hit .281/.322/.403/.725 over the past nine seasons in all levels of the minors.

His best offensive season came in 2011, when he played 82 games in high-A and 38 games in Double-A, posting an overall .349/.383/.500/.883 line. And prior to making his major league debut in April 2013, he looked like he was on his way to a nice season in Las Vegas, hitting .346/.378/.551/.929 in a very tiny sample of 17 games.

There are some trends when looking at his professional career as a whole. First of all, Lagares has been a relatively free swinging hitter, as seen by his low walk totals. Over his first 203 games in the MLB, he’s drawn only 33 walks, which translates to one base-on-balls about every 6 games. His walk rate is 4.6%, which is well below the major league average of 7.9% over the past two years.

He also tends to swing at bad pitches more than most. According to Baseball Info Solutions, Lagares has swung at 35.8% of pitches outside of the strike zone during his major league career, compared to the major league average of 31%.

What makes his impatience somewhat bearable is the fact that he’s been a pretty decent contact hitter, which levels out his OBP to an acceptable level. While his .281 minor league batting average may not look like the greatest, considering the competition that he’s facing, it looks better when compared to the fact that he’s hitting .283 in 83 major league games this year.

While he may be over-aggressive in swinging at pitches outside of the strike zone, he also successfully makes contact at an above average clip. He makes contact with outside pitches 72% of the time, well above the MLB average of 66.2%. While the quality of that contact isn’t necessarily great, it’s good to know that if he’s going to keep chasing balls out of the zone, he’s not simply whiffing all of the time.

At times, he is able to flash a good amount of power. Some of that is due to his speed allowing him to stretch singles in the doubles, and doubles into triples, but as always, what happens off the bat is the most important thing to evaluate. A .403 slugging percentage in the minor leagues doesn’t suggest that he’ll hit for much power in the majors, but overall, his power has been coming in inconsistent bursts.

The problem is, I’m not sure how much of his moderate offensive success will continue. That .283 average in 2014 is heavily augmented by a ridiculous .363 BABIP, well above his major league mark from last year, which stood at .310 by year’s end. As a result, his .242 average from his rookie season has spiked significantly.

BABIP, or batting average on balls in play, can be a very telling stat when looking at a player’s future. A very high BABIP usually suggests that the batter is set for some regression, as they have been getting very good luck with bad defense, cheap infield singles, and bloopers in no-man’s land. A very famous case disproving this theory is Mariners’ center fielder Austin Jackson, who is able to use his speed to maintain a high BABIP mark. It’s entirely possible that Lagares’s speed is aiding him this season much more than last, but any wild fluctuation in BABIP is a red flag. It’s definitely something to keep in mind when evaluating Lagares’s career to this point and projecting forward.

Overall, Juan Lagares’s MLB averages of .260/.299/.367/.667 look to be right in the ballpark of what his slash line will look like for the forseeable future. He’s only 25 years old, meaning that he has yet to enter his offensive prime, but it’s unlikely that any player will get drastically better in their prime if their past doesn’t show anything that should get people excited. Juan Lagares will almost certainly be at best a mediocre hitter with some stretches of greatness, and many more slumps.

So, of course that means that he shouldn’t be part of the Mets’ long term plans. If there’s almost no hope for him offensively, then he needs to go, right?

I won’t blame anyone for thinking that way about Lagares. But their mind will change when they watch what the man can do with a glove. Watching Lagares field is one of the best things about the Mets. He’s easily the best center fielder to come to Flushing since Carlos Beltran‘s prime ended and he was shipped out of town.

Die-hard baseball fans already know about the other-worldy defense of Braves shortstop Andrelton Simmons, but Lagares deserves that type of mainstream attention for the way he plays in center.

So what if his defense is great? If he can’t hit, then does it really matter?

That’s true depending on what position the player in question fields. If we’re talking about a first or second baseman, or a corner outfielder, defense is much less important, but still needs to be factored into the player’s overall value. But when the amazing defensive player is a center fielder, the value of that defense is much higher. Center field is probably the most important defensive position in baseball (except maybe catcher), so having a guy out there with the talent of Lagares of extremely valuable and can save many runs.

Speaking of saving runs, Lagares has been near the top of the leaderboard in Defensive Runs Saved since making his debut. Among all fielders, Lagares is second to the aforementioned Andrelton Simmons with 49 DRS. In fact, had Lagares not missed significant time due to injuries over the past couple of seasons, he would be far and away the league leader in this category. Lagares has only played 1485.2 innings since the beginning of the 2013 season, while Simmons has played 2302 innings.

Because DRS is a cumulative stat, playing less innings hurts Lagares overall, but on a per inning basis Lagares has been much better than Simmons. He has saved .033 runs per inning compared to .024 runs per inning for the Braves’ defensive whiz. When I extrapolated those per inning averages over a full season of 1455 innings, Lagares turns out to be much better than Simmons, racking up an incredible 48 DRS while Simmons only saves 34 runs above the average fielder.

Another well-known defensive metric, UZR speaks very well of Lagares’s abilities in the field. His Ultimate Zone Rating has been tops in the major leagues at 37.6 during his career.

Defensive statistics aren’t perfect by any stretch of the imagination, but they do aid people in forming a broad opinion on a player’s defensive skills.

There are quite a few reasons why Lagares is such an incredible defensive player. These spray charts gathered by FanGraphs illustrate his talent:


The percentages seen in the second chart are measured by Inside Edge, in an attempt to put into perspective how easy each defensive play is to make. Do you see that huge gap in the middle of the second chart, which is almost all green in the first chart? That’s because Lagares does not mess up easy fly balls. His only missed play right in the heart of center field was considered by Inside Edge to be impossible to field. According to this chart, he’s missed quite a few fly outs with a 40% or higher chance of being fielded. But, when the time parameters are shortened to include only his 2014 results, you get this:


All but one of the yellow and green dots completely vanish, suggesting that Lagares has been even more reliable in the field this season than last. It’s a scary thought that someone already so good can keep improving. He’s a very sure-handed fielder, and tends not to mess up routine plays. He also won’t make the spectacular, diving grabs that get replayed all over the internet and ESPN, but I’d trade that for his inability to make mistakes on easy fly balls.

FanGraphs’ Jeff Sullivan wrote an amazing piece of Lagares’s defensive prowess last September, and I encourage everyone to read it. In a quick analysis of Lagares in center, one would be able to see that his starting position is routinely more shallow than most center fielders in the MLB. He’s able to do that because his speed allows him to catch up to the balls that go over his head, as seen here:

Photo courtesy of CBS Sports

And here:

GIF courtesy of Amazin Avenue

As Sullivan wrote, playing so shallow, along with a quick first step and very sound fundamentals enables Lagares to catch up to balls quicker than usual, and closer to the infield. Once there, he’s able to fire the ball in and, despite not having the strongest arm in the world, overall has one of the best. Starting his throws so much closer to the infield lessens the need for a cannon arm like what Yasiel Puig and Yoenis Cespedes are known for. Shorter, more accurate throws makes Lagares nearly impossible to run on.

Teams have started to take notice of this, as Lagares’s 15 assists in 2013 have been nearly eliminated from his stat line this season, as the number now only stands at 4 in the middle of August. That’s because everyone is so deathly afraid of Lagares’s arm that they won’t run on him unless the circumstances are perfect. They know that he isn’t any average center fielder out there.

There are few center fielders in the league that can even come close to matching Lagares’s value. The only players that come anywhere near him in center are Milwaukee’s Carlos Gomez, Texas’s Leonys Martin, Arizona’s A.J. Pollock, and Kansas City’s Jarrod Dyson and Lorenzo Cain. No, not even my boy Mike Trout, who has admittedly fallen off defensively over the last couple of years, can sniff Lagares’s defensive value. And while these other guys may come close, Lagares is clearly better than all of them.

What does all of this mean for the Mets? 

A remarkable defensive center fielder is rare, and any team with a player like that should make sure that they do their best to retain that player, even if their offense is subpar. Watching Lagares at the plate might be a tough pill to swallow at times, but as long as the other seven position players are quality hitters, Lagares’s issues will be much less of a factor. Hitting him eighth and surrounding him with good bats will lessen the blow. In the case of Lagares, his exceptional defense more than makes up for his lackluster offense.

All statistics courtesy of Baseball-Reference and FanGraphs. All graphs courtesy of FanGraphs. 

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Emerson, Jackson, and Baseball Statistics Thu, 01 May 2014 13:00:02 +0000

Our age is retrospective. It builds the sepulchres of the fathers. It writes biographies, histories, and criticism. The foregoing generations beheld God and nature face to face; we, through their eyes. Why should not we also enjoy an original relation to the universe? Why should not we have a poetry and philosophy of insight and not of tradition, and a religion by revelation to us, and not the history of theirs? Embosomed for a season in nature, whose floods of life stream around and through us, and invite us by the powers they supply, to action proportioned to nature, why should we grope among the dry bones of the past, or put the living generation into masquerade out of its faded wardrobe? The sun shines to-day also. There is more wool and flax in the fields. There are new lands, new men, new thoughts. Let us demand our own works and laws and worship.

-Ralph Waldo Emerson, “Nature” 1836

Ralph Waldo Emerson was  one of the leading figures in the Transcendentalism movement of literature and philosophy. Transcendentalism was a smaller part of a larger cultural era in American history known as the Second Great Awakening, a religious revival that defied economic and social structures. Advocates for the common man sprung up throughout the countryside, pleading the case of the poor yeoman farmer or the landless laborer. Andrew Jackson, among other political leaders, took this to heart, bringing then-revolutionary ideas, such as universal white male suffrage to the frontburner in what would later be thought of as the golden age of American politics and society.

What made this era so great was one key characteristic: it challenged the status quo. While there was still, of course, was economic stratification, previously held economic and political beliefs were turned upside down in a way that changed American history forever.

The idea of challenging the established order is not a new idea. Even before these heralded figures, the United States was a country of firsts, one that went against the grain in almost every respect. That attitude spread to much of the rest of the world, and today is prevalent in every scientific, mathematical, and philosophical field you can think of.

Times have certainly changed since the 1830s, and the demand is now for empirical evidence. This evidence has revolutionized biology, medicine, technology, you name it. Hard proof, not just logic or rhetoric, needs to be there to prove something to be true.

This has more recently been applied to Major League Baseball front offices where, like it or not, people were forced to look at new, statistical-based ideas about how the game is played.

Every single MLB front office has some form of an advanced statistical analysis department at the moment, yet there is still a large segment of fans and those who cover the game who are holding out, refusing to even acknowledge that this new set of beliefs are relevant. Many say teams still don’t use them, when in fact, the Cardinals and Red Soc have each attributed their recent success to an ideal combination of scouting and advanced statistics. It’s here to stay.

This last but still significant segment of baseball followers is not going to affect the way executives run their organizations anymore, but they, especially the writers who still have major influence, still need to be convinced. It’s always difficult to cast aside long-held beliefs and to find new perspective, especially after decades of being convinced something is superior.

The biggest barriers for many are the thoughts of current and former players on sabermetrics. They played the game for sometimes as much as 20 years. Shouldn’t they know more than anyone else? One would think that an experienced professional would know the trade inside and out, but baseball is a unique case in which there are two distinct jobs: evaluating talent and playing. The former players know how to play the game, but not necessarily evaluate it. They may know what worked for them or what worked for others they knew, but that doesn’t mean their way is the best way. That is where the people who have done hardcore research and analysis come in.

We have seen a huge influx of Wall Street GMs, and for good reason. While they are not former players, they both love baseball and can apply their statistical expertise to it. They are able to cast aside their predispositions far easier than players can.

Bill James is credited as the creator of the sabermetric movement in baseball.

This is a touchy subject, but education is an important factor as well. So many professional baseball players never went to college, and many more from foreign countries left home at 16, never even completing high school. In contrast, the analyst-types who didn’t play often have undergraduate or graduate degrees in mathematical fields, which will help them look at that data objectively. Bill James, the grandfather of sabermetrics, has a phD in statistics. Who would you rather hire to build a team, someone who both never got a college education and relies heavily on their personal experience or a highly-educated person who is able to take an objective look at things? The answer should be obvious.

Now, that’s not to say former players don’t have a place in the game, because they absolutely do. They often make great coaches and can relate to players like no other non-baseball person could. If a former player is able to combine their knowledge of the grind of a baseball season as well as knowledge of mechanics with objectivity and data interpretation, that’s even better, but it’s also uncommon. When it comes down to running a ballclub, the best person for the job is often someone who never played baseball professionally, as that combination in a player is so hard to come by.

The revolutionary figures in history are those who stood up to challenge traditions. New may not always mean better, but doing research and thoroughly examining an idea is always beneficial. Baseball is one of the few areas where new ideas are frowned upon by fans. The common “baseball card stats” were created in the 19th century, long before computers could crunch data in seconds, even before the rules of baseball were even set in stone.

I am not demanding those holdouts to “convert,” only to acknowledge that there is some validity to applying empirical evidence to baseball. As the old saying goes, you never know until you try it.

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Willie Randolph Says Better Days Are Coming for the Mets Sat, 08 Mar 2014 12:00:39 +0000 willie randolph 2

Twenty men have sat in the Met managerial hot seat. Some have served as momentary stopgaps, Mike Cubbage for only seven games. None survived long enough to rival the longest surviving managers of the modern game, guys like Bobby Cox and Tony LaRussa. In fact, none wore the Met orange and blue for more than seven seasons. And, only five of the twenty; Davey Johnson, Gil Hodges, Bobby Valentine, Willie Randolph and Bud Harrelson won more games than they lost commandeering the Mets.

On Friday afternoon, Willie Randolph was a radio guest on WFAN. Randolph is working at the Yankee camp in Tampa and shared his perspectives about the changing face of the team in pinstripes, a team and franchise in transition.

Invariably, the discussion turned to whether or not Willie would like to manage again some day. Of course, that meant the subject included Randolph’s time as manager of the Mets.

The radio team tossed Randolph a gopher ball providing Willie the opportunity to slam his former team when it was suggested that the Mets certainly haven’t found a winning stride since management forced his departure. Willie was too classy to take the bait.

Randolph spoke fondly of his opportunity to manage the Mets. He voiced pride in the work he and his staff accomplished in changing the culture around the Mets, and felt the Mets were taking strides forward when he was at the helm.

Willie reported he follows the Mets each and every day to keep tabs on the performance of ‘his boy,’ David Wright. Like everyone else, Randolph is impressed with the stable of young Met pitchers and predicts if his former team can keep those young power pitchers healthy there will be better days ahead for his former team.

Randolph is hoping to get another shot to lead a major league baseball team from the dugout. Willie took time to discuss the importance of sabermetrics in a modern day baseball manager’s approach. He worried that some might have a perception that he’s an old school guy who doesn’t understand or appreciate the value of sabermetrics in modern baseball noting that’s simply not the case.

Here’s hoping Willie gets the chance he’s looking for. I always appreciated the class Willie brought to the Mets during his short stay as our skipper and his understanding of what it takes for a franchise to win. Willie’s departure and the way it was handled were an embarrassment at the time. Against that backdrop it was reassuring to hear Willie talking with excitement about the positive possibilities of our current Met team and it’s future.

Winningest Met Managers

Manager           Won - Lost  Pct.
Davey Johnson     595 - 417   .588
Gil Hodges        339 - 279   .549
Willie Randolph   302 - 253   .544
Bobby Valentine   536 - 467   .534
Bud Harrelson     145 - 129   .529
Yogi Berra        292 - 296   .497
Roy McMillan       26 - 27    .491
Jerry Manuel      204 - 213   .489
Joe Frazier       101 - 106   .488

Presented By Diehards

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Oldies But Not Goodies: Discarding RBI, Runs Scored, and Pitching Wins Thu, 06 Mar 2014 18:00:17 +0000 While most traditionalist stats are all but completely gone from most Major League Baseball front offices, the casual fan and the older generations of die-hard fans have stuck with the statistics. Classic and easy to remember, a century of celebrating records has some people holding on to these numbers for good. Sometimes, classic theories and ideas can still be relevant, but in this case, that isn’t true.

baseball glove benchI have nothing against people who like these stats. Many of them have spent their entire lives having RBI records, batting average records, win-loss records, and other statistical feats drilled into their heads. These were the only options, so people didn’t question them.

There are three stats in particular, however, that need to go. This may be obvious to many fans but they can’t hold a candle to some of the new metrics that have popped up in recent decades. RBI, runs scored, and pitching wins and losses are arguably the most irrelevant and useless popular statistics.

Before diving in to each one, consider one thing. What is the goal of using statistics for individual players? The answer is simple: to isolate production. Simply, to tell how talented a player is. In theory, the best statistics are affected only minimally by other players, otherwise a statistic can be as much a measure of a team’s ability as the individual player’s. If you were a general manager signing a player from the Red Sox for $200 million, given only statistics that are significantly affected by other Red Sox players, would you consider making the deal.

Runs Batted In (RBI)

The Goal: Runs Batted in has two joint goals (or at least it is perceived to have two): to evaluate how good a player is in important situations and how productive he is overall.

The flaws: This statistic rose to mass use in the 1920s. This is not a problem, but it shows that the statistic was created before baseball was understood as much as it is today.

RBI is as much of a team statistic as it is an individual one, it’s main problem. There is only one method of getting an RBI without any baserunners and that’s hitting a home run. Say two hitters each hit home runs, except the second hitter did it with a runner on first base. Does the second batter deserve twice as much “credit” as the first? No, not at all. The second home run hitter probably had little to no effect at all on whether that runner reached base, but he still gets credit for “driving in” that person. Not only that, but players on better teams tend to get more opportunities to drive runners in. Two players may be driving in the same percentage of baserunners but one may have far fewer RBI than the other.

One more thing to consider about RBI is that it treats every situation equally. What good is a second inning RBI single when your team is down 9-0? Which leads me to…

Alternatives: There are a number of different alternatives for RBI but the most popular is probably Win Probability Added. Remember how every RBI is treated the same regardless of situation? That is where WPA comes in. Baseball has been played for over a century and almost every situation imaginable has repeated itself over and over again. One thing is certain: there are always calculable odds of who is more likely to win. Every action affects a team’s odds of winning a game, whether it is small or large. A walk-off home run obviously has a bigger impact than a one out single in the third inning with nobody on. WPA uses linear weights, a complex way of saying the odds of winning added (or lost) from each action. Players with a higher WPA tend to have had bigger impacts on games (although it is not predictive), specifically in high-pressure situations, which statisticians have debated the effects of with no real consensus yet. This is still a stat where a team must put a player into position to have a bigger impact, but it certainly quantifies that impact far better than RBI. (To read my article from last summer going in depth on WPA, click here.)

Runs Scored (Individual)

The Goal: This stat is rather murky in its presumed goal. Really, it is likely meant to measure both production overall and baserunning.

The flaws: Again, this is a stat in which it depends so much on the surrounding team, probably even more than RBI. A player can bat 1.000 and still never score a run. Of course, these theoretical situations aren’t relevant to the real world of baseball, but the idea holds true: teams set you up to score a run. Sure, the player may have successfully made it to home plate without falling flat on his face, or he could have even dove into home plate well. However, the hitting  team still had to do something to allow him to cross home plate and even the team in the field often time chooses not to throw to home, instead opting to hit the cutoff man and settle at that.

Alternatives: There are a ton of alternatives to Runs Scored, satisfying both purposes. Getting into them could take another thousand words, but there are plenty of viable alternatives. For overall production, OPS, OPS+, all the way down to wOBA and wRC+ do the job better than runs scored as they isolate that particular player more. For baserunning, there are complex metrics like UBR out there, as well as some of Baseball-Reference’s statistics that even include a player’s ability to avoid getting thrown out at first on a double play. There is some very interesting stuff out there that can even break down the type of baserunning a hitter is good at.

Wins and Losses (For Pitchers)

The Goal: To evaluate the performance of an individual pitcher

The flaws: Where to begin? There are so many flaws with wins and losses. As a general rule, I say that wins and losses are half affected by the offense and some by defense as well. A pitcher can be on his game striking batters and getting weak ground balls and still get the loss. In order to get a win, the offense of the pitcher’s team must score more runs than the other. Say what you will about pitching to the score, but that’s what it comes down to.

Additionally, a pitcher’s defense behind him can let him down, whether measured in errors or not. Even if the pitcher allows only unearned runs, the loss is still given.

There are also plenty of situations where the pitcher throws a great game but leaves tied, giving a reliever an opportunity to get credit, even if he comes in only to pick off a baserunner. It has happened before, and it is so often the pitcher with the best night that gets cheated.

Alternatives: Rate stats are the way to go here. Looking at game logs is fine as well, but not all wins and losses are created equal, but even an undeserved win will show up in rate stats. Specifically, FIP and xFIP are great alternatives as they take out the fielding aspect as well as the hitting aspect, which ERA does not completely do.

*   *   *

There are a number of statistics like these that are very flawed and should be essentially discarded from use by the average fan. As someone who’s skeptical of almost anything, I noticed early on that there were flaws. With baseball especially, it’s important to not let tradition get in the way of realizing the flaws of the different ways people analyze the game.


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MLB Advanced Media Launching Revolutionary Data Gathering Program Sun, 02 Mar 2014 04:37:12 +0000 MLB Advanced Media announced today the launching of a new data gathering system to eventually be implemented in every MLB ballpark.

The new tracking system will gather data on every play of every game. For this year, systems will be ready in Milwaukee, Minnesota, and Citi Field, with every team getting systems by 2015.

The technology could revolutionize even further the way executives look at baseball. Hit F/x tools were already available for teams (for them to acquire on their own), but giving every team this technology is significant. We have heard, but not known too much about, Hit F/x and Field F/x.

The program could help teams evaluate players in a much more objective way than ever before. For example, fielders can now see the exact route they ran to a fly ball, how far their direct path to the baseball was, and how efficient their route was. Fielder speed and acceleration can also be evaluated, among other things. This could be especially important for positioning fielders as new data could, in theory, pinpoint the exact weaknesses of certain fielders, allowing the coaches to adjust accordingly.

Previous forms of this technology for hitter has already leaked out a bit, including through ESPN’s Home Run Tracker, in which fans can look at elevation angles and velocity of the ball for every home run hit.This new system will extend that to every batted ball, giving possibly even more information. Instead of relying on a 70 year old’s set of eyes watching from 50 feet away, batters, fielders, and pitchers can now see exactly what happened and exactly what they could have done better.

Dodgers All-Star Steve Sax praised the new system to’s Mark Newman, saying: ”Really, the future of baseball and able to quantify the great things about this game is here now. For players and coaches alike, to be able to judge distances and speeds and ranges and how fast people get there is just an amazing tool that they’re going to be able to use going forward. I just wish they had this when I played.”

It will be fascinating to see how MLB teams either hide or publicize this new data. Pitch F/x has been around for a few years and proven to be a very useful tool. However, recent technological advancements have been kept under wraps away from the public eye. Hopefully this data, or at least some of it, will be available to the public to dissect so they themselves can expand their analysis of the game.

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Mets Officially Announce Chris Young Signing Tue, 26 Nov 2013 21:59:46 +0000 chris-young

After passing his physical, the Mets officially announced the signing of our new rightfielder Chris Young this afternoon.

“Chris has had a distinguished career to this point and we think with regular playing time we think he can return to his all-star caliber performance,” GM Sandy Alderson said.

“He’s a tremendous defensive outfielder, brings some power, the approach we’re looking for offensively and some speed on the bases.”

Alderson said that Young could bat anywhere from 5-7 in the lineup.

Young said he spoke to David Wright multiple times on the phone before signing with the Mets, so credit the Captain for helping the Mets to land this sought after player who was being pursued by the Cubs, Giants, Royals and Red Sox before he decided to sign with the Mets..

Young Is Exactly Who We Think He Is

If you’ve read anything I’ve written here, you know that I try my best to support this organization in their decisions. I’m a fan of the team – I supported Minaya to the end, I will support Alderson to the end. Why? Because if I’m gonna be a fan of a team, I’m doing it with the mindset that in order for me to be happy – I need the men who make decisions to succeed.

Ever since Chris Young signed, there seems to be an onslaught of opinions being tossed around. I tried to look at them all, and come up with as informed a decision as I could as to whether or not I liked the acquisition.

This move brought to the forefront the one thing I cannot stand about sabermetrics. At times, people use it as a way to polish a turd. It’s not used to say “let’s look at just how good this guy is,” but merely “let’s look to see if it’s as bad as people really think?” It’s almost at times used in a way a publicist might react in a crisis situation.

“Oh no! The Mets just signed a borderline terrible outfielder, quick – find some numbers that will divert everybody’s eyes!”

That is what is happening, and that is why I cannot stand sabermetrics sometimes.

byrd  hr 2Chris Young is often being compared to the 2013 Marlon Byrd. I find this comical because what NOBODY could have predicted would happen in Byrd – everybody is seemingly predicting will happen for Young. We saw one fluke season from a corner outfielder, so hey why can’t it happen twice in a row? Because if you know anything about flukes – they are often consistent, right?

This isn’t to say Young has no positive attributes. I recognize he has some speed, some defensive ability and what I’d consider borderline power. That isn’t the problem.

The problem is, this signing says two things to your average Mets fans.

#1 Chris Young is our starting corner outfielder that we desperately needed. Oh by the way, he is a center fielder.

#2 This contract was done with the “just in case” mindset of, “If we’re terrible in 2014 but he bounces back – we can always trade him.”

This idea that they are finding “undervalued” talent to me is exhausting. $7.5million for a below average outfielder is overpaying for his talent – so calling him undervalued is ridiculous.

I’ve also heard conspiracy theories that this signing was done because of potential bad press for the Mets lack of signings so far this off-season. I don’t buy into that at all. Can’t they just make a poor decision without some other motives for that decision? No GM is ever going to be right about every player – they aren’t signing Chris Young because some fans are calling up WFAN complaining – they signed him because they think he’s going to be better than he has been for years.

What really grinds my gears about this move is that I would bet the majority of people who are in favor of Chris Young, are the same people who were against Jeff Francoeur. Francoeur had a great 2009 with the Mets, had his detractors but they really unleashed the fury in 2010. Francoeur in 2010 is very comparable to Chris Young in 2013.

I do not need a bunch of charts to tell me that Chris Young as a starting outfielder is a bad idea. That doesn’t mean it won’t work – bad ideas work at times.

But, that doesn’t mean we should be happy when a bad idea is put into place. For starters, many Arizona fans believe Chris Young hasn’t been the same player (which isn’t saying a lot) since a shoulder injury in 2012 when he crashed into the wall. Great, because if there is one thing Mets fans know all too well it’s that players who have declined due to injury always bounce back for the Mets (right?).

Chris Young is terrible against right handed pitching, he benefited from playing in Arizona, and he’s had 2 good years and five below average years. As Bill Parcells once famously said, “you are what your record says you are.”

We can use whatever advanced stats we want to make ourselves feel better about the fact that Chris Young, as of now is one of our starting outfielders – but sometimes the eye test is valid too. Young has been a below average hitter for the majority of his career – and right now he is a starting outfielder for the 2014 Mets. That’s all you need to know.

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(Updated) Know Your Stats: Win Probability Added (WPA) Fri, 19 Jul 2013 01:40:49 +0000 Every play in every game has some effect on the odds of winning a baseball game. Whether you are down by ten runs in the second inning or tied up in the bottom of the ninth, every action can be measured.

Baseball has been played for over a century and a half. Major League Baseball has been around for over a century, and most situations have been played out over and over again hundreds of times, so we have a good idea of how every action affects a team’s chances. This is called Win Expectancy. We can use those thousands upon thousands of games from history, and look at a team in a particular situation and know their odds. We know that if a team is down by one run in the bottom of the seventh inning with runners on second and third with no outs, they will win approximately 66% of the time, because roughly 66% of teams in the past have won in those cases.

To utilize these win expectancies in analyzing how each action affects the odds, we simply take the difference between the old and new win expectancy and award it to the player who accomplished the action, and penalize the pitcher who allowed it. It’s simple and easy.

There are some issues with WPA, however. While it finally solves the problem of context-neutral stats like Batting Average, On-Base Percentage, and Weighted On Base Average, it’s only good at telling a story, not predicting the future. Many of the new sabermetric statistics are predictive, but this one is certainly not. It also brings up an issue that comes up frequently when talking about RBI and Runs Scored. A player can only change the course of a game so much with his bat if he isn’t put in the right spot at the right time by his teammates. Someone might not get the opportunity to hit a walk-off home run just by chance, therefore losing out on the opportunity to pad their WPA numbers.

One thing WPA definitely solves is this: No more arguing about the turning point of the game. Instead of looking at a play as a “clutch RBI hit,” you can now define how much that run-scoring double in the eighth inning meant to the overall outcome. Take game 4 of the 2004 ALCS for example. That was the year the Red Sox were down 3-0 in the series, and won a hard-fought 12-inning game to stay alive. Here is how the win expectancy shifted throughout the night:

wpa chart 1

This is a very handy chart from Fangraphs that shows the leverage (how important a moment in the game was going to be) and how the action that took place affected the odds of one team wining. As you can see, the game starts with each team at 50 percent and ends with one team at 100 percent, with fluctuations in between. The most important moment of the game was David Ortiz‘s walk-off home run, bringing the odds of Boston winning from a shade above 73 percent to 100 percent, ending the game.

Overall, Win Probability Added is a great way to see how influential moments in a game were, but it does not tell the story of how good a player really is. In order to conduct a complete evaluation, we must use context neutral statistics like wOBA and wRAA, as well as stats like WPA.

In Context

wpa chart 2

In every game, a player, in theory could have a WPA of -1 to 1, or even more extreme than those numbers. In practice, it is usually close to zero, but a player’s actions could almost completely determine the outcome of a game in rare scenarios. For David Ortiz’s walk-off home run in the 2004 ALCS, he added about 25% (73% WE to 100% WE) on that play alone. For that, he was awarded roughly 0.25 WPA. In every game, the WPA of each player on the winning team will amount to 1 and the WPA of each player on the losing team will always amount to -1. Over the course of a season, a player can have a negative WPA or positive, with each integer representing a win. Last season, Mike Trout finished the year with a 5.32 WPA, meaning in terms of win probability, he was worth over five wins.

Further Reading

(Both charts courtesy of Fangraphs.)

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Debunking The Myths About Sabermetrics Thu, 11 Jul 2013 15:30:38 +0000

There has always been a perceived tension between pro-analytics baseball fans and anti-analytics fans. The battle is already over and done with in front offices, as both have been able to coexist, but there are still fans who disagree. Really, most of the disagreements are caused by ignorance towards the other side, a lack of knowledge. For people to understand sabermetrics, they must get past some of the biggest myths. Here are a few of those myths, debunked.

Myth: We ignore human emotion and intangibles.

Absolutely false. Intangibles, clubhouse chemistry, and more are all part of the thought process of a sabermetric front office (and the ideal sabermetric fan). Look at Paul DePodesta, known by some traditionalists as anti-intangibles, he talked non-stop after the draft about how he loved the personalities of some of the organization’s draft picks. He says it is a big part of how he drafts, even with the Mets being one of the most sabermetric teams in the game.

When you look at traditional statistics, it doesn’t incorporate intangibles or emotion either. Neither traditional nor sabermetric stats do, so it’s not just a sabermetric problem. Every organization has their own method for evaluating personalities, with some weighing personality, intangibles, and clubhouse chemistry more than others. That has existed much longer than sabermetrics has.

Myth: It’s all about the numbers — and nothing else.

Sabermetrics-BreakdownThis covers a bit of the first point, but also enters another important topic: scouting. Some think that sabermetric teams (which now includes all but one or two teams in baseball) don’t emphasize scouting, or rely too much on their metrics. I can’t tell you how a front office operates in that regard, but I can tell you that as someone who embraces sabermetrics, I value scouting. Just as statistics can be predictive of a player’s future success, a scout can see the mechanics of a hitter’s swing and see unlimited potential, even when the numbers might say otherwise. Sometimes it’s the players that don’t succeed at first, but go on to have great careers. While some may think statisticians don’t take anything but numbers into account (They think this because former players never become analysts, they become scouts), it’s simply untrue.

Myth: The stats are subjective.

Here is where the importance of being informed comes in (if it hadn’t already). The purpose of sabermetrics is to look at the game of baseball in the least subjective way possible. The goal isn’t to take human emotion out of the game, but to take human emotion out of the way we evaluate the game. There are often players who are “counted out” and cast aside. Take Josh Satin, for example. Satin didn’t receive any significant big league playing time until he was 28 years old, mostly due to his age. Satin played four years in college, and wasn’t aggressively promoted. That meant he eventually fell out of “prospect” status and was never promoted. He put up minor league numbers on par with David Wright‘s, yet he didn’t get a promotion until years later because of one aspect. That’s what sabermetrics looks to eliminate. Players who are too fat, too short, have unique mechanics are frequently not given an opportunity. It really doesn’t matter if these players have these traits if they can provide the same production as a “normal” player.

Back to the point. People tend to look at the coefficients of a statistic like wOBA (weighted On Base Average) and say it’s subjective, that the creator of the stat just chose the coefficients. But why would a group of people who strive to be as least subjective as possible arbitrarily choose coefficients to put into their equations? That answer is: they don’t. Take my example of wOBA. The equation changes slightly every year, but here is the equation from the 2012 season:

wOBA = (0.691×uBB + 0.722×HBP + 0.884×1B + 1.257×2B + 1.593×3B +
2.058×HR) / (AB + BB – IBB + SF + HBP)

Those numbers may seem random, but they are actually based on run expectancy, or how a particular event influence’s a team’s chances of scoring, which is what baseball is all about. Slugging percentage tries to value events, but only does so for hits, and itself values the hits subjectively. Is a team with a man on third three times as likely to score as a team who only has a man on first? Over one hundred years of baseball tell us no. Notice how an unintentional walk (uBB) isn’t worth the same as a single. This is because runners on base only advance one base (and only on a force) when the batter walks while runners often advance multiple bases when the batter hits a single. See how this is all starting to come together?

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There is a stigma surrounding analytics in baseball, just as there is in the rest of the non-sports world. People don’t like to believe that what they see (or how they perceive what they think) could be wrong. It’s why people working for financial companies get frustrated when their economists put out bad projections, even when the economy is thriving. It’s really not about choosing one side or the other. It’s about acknowledging the pros and cons of each and using what you can see and what you can’t see together. That’s the best way to evaluate a baseball team.

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Fear & Loathing of Sabermetrics Tue, 09 Jul 2013 17:54:38 +0000 simpsons sabermetrics

People fear what they do not understand. There’s no way to deny that. The truth is, baseball is still a game where people continue to hold on to “the way things used to be” in their hearts.

It’s why there is no full replay yet, it’s why people are against the DH in the NL even though the NL is at a disadvantage during the regular season or AL during the World Series… or why people don’t like interleague play when in reality if every team plays every team an equal # of times, you’ll get a better idea of who the best teams are.

Stats are the same way. If you use ANY stats to evaluate players, then to me you shouldn’t turn your nose at people who use different stats that you.

When Henry Chadwick came up with Batting Average and ERA, you probably would have seen the same type of attitude about those stats that you do with some sabr stats today. Why? Because fans and executives knew what they knew without needing the help of some silly mathematical formula.

It’s information. Information isn’t the problem, it’s how you utilize that information that makes or breaks the value of the info. Most people I have found who “hate” sabermetrics, haven’t even given them a chance and just dislike a non-traditionalist view of the sport they grew up with.

If I write down step by step instructions on how to change a light bulb and you take that information and the light doesn’t turn on – that doesn’t mean the information I gave was wrong, it means the execution of the information was flawed. We’re all humans, and nobody has ever claimed that sabermetrics give you THE answer. You may evaluate a sabermetric stat differently than I do.

Gaining more information about a player can hurt or help a fan or executive. It depends on how much they rely on this information and how they interpret it that will dictate whether it helps or hurts.

In my experience, fans can go overboard with sabermetric analysis on both sides of the table. I believe in the value of information, and scouting. I believe a batting average can tell you as a fan enough about a player, but it might not be the best statistic to use when investing $100million on a player.

The truth is, every team has access to the basic information but they choose different ways to interpret and analyze the data. The Tampa Bay Rays in all likelihood have different statistical formulas for player evaluation than say the average fan has access to.

What sabermetrics do a better job at than “traditional” statistics is they try to explain in more detail why something is happening that perhaps is a fluke. For example, it might help you understand why a pitcher with bad statistics (traditional) appears to be struggling, but perhaps he isn’t as bad as those numbers make you believe. This can help a GM find talent that is perhaps underappreciated.

Where sabermetrics fails is defining and evaluating character. You cannot put a value on a player like Marco Scutaro or Yadier Molina using sabermetrics. They bring something to the table that statistical analysis cannot define. Besides their on the field presence, they clearly make all of the players around them better. You cannot replace that, and it’s very hard to find.

I rarely if ever use sabermetrics to evaluate a player I like. When it comes to pitchers, I like W, L, SV, ERA, IP/K, BB/K, HR, IR/IRS and WHIP. With hitters I like Doubles, Triples, HR, RBI, BB, K, SB, AVG, OBP, SLG, and OPS.

Those statistics put me almost all the way toward “traditionalist,” but you will never, ever find me scoffing at somebody who uses sabermetrics because the more information you have – the better you can understand what is in front of you.

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How Statistics Reshaped The Way We View Pitchers and Their Game Wed, 13 Feb 2013 05:14:23 +0000 baseball abstractIn 2003, Moneyball: The Art of Winning an Unfair Game, a book written by Michael Lewis, sparked the interest of not only baseball fans, but also management and statistical scholars across the country. [1] Recently in 2011, this book was introduced as a sports drama movie starring Brad Pitt.

Basically the storyline of the book encompasses how the main character Billy Beane, General Manager of the Oakland Athletics, implemented a measurement and feedback system based on a player’s ability to get on base. This system was based on the sabermetric principles and theories first introduced by Bill James in his comprehensive Historical Baseball Abstract, a rigorous statistical analysis used to track the traits most connected to a player’s value to a team. [2]

What is Sabermetrics?

The Moneyball book implies that when sabermetrics is used to identify players with superior abilities (who aren’t noticed by competitor scouts), it allows new players to be added to a team; thus, increasing the winning percentage for the team without paying premium salaries to those players. This results in a competitive advantage for the team’s league standing. For further understanding of sabermetrics and its theory in relation to baseball, please check the paper written by Jim Albert.

DiamondView and PlayersPlan

Several detailed programs have been implemented by teams like the Cleveland Indians MLB team. The Indians have implemented two programs: DiamondView and PlayersPlan with the hopes of increasing appraisal of a player’s performance and value. These types of plans are often used by MLB team managers to facilitate selection and recruitment possibilities and help determine the optimum team salary distributions. [3]

Systems like DiamondView and PlayersPlan keep track of each player in a database that is used for recruiting and selection, training and development, appraising player’s skills, and helping evaluate compensation for the players. For example, information on a baseball pitcher would include such pitching statistics as the number of times the pitcher allows a walk, how many members of the opposite team were pitched to at the plate, the number of times the pitcher entered the game with intent to save the game but failed, number of hits allowed in a game, home runs allowed, earned run average and many more. Statistics, along with some physics theory, has also suggested that left-handed pitchers get better results against left-handed hitters. Knowing which pitchers have the best odds against hitters based on this theory, managers can stratgically use relief hitters to counter pinch hitters substituted into a game at the last minute.

Others Question Usefulness of Sabermetrics

Many consider sabermetrics a valuable and objective means to gain an effective measure of a player’s value to the team. Others question the usefulness of such statistics in the prediction of future behavior of players.


Another measurement tool known as Defense-Independent Pitching Statistics (DIPS), introduced by Voros McCracken as early as 1999, measures a pitcher’s stats. These stats do not include plays that involve infielders or outfielders, but are based on stats that result strictly from the control of the pitcher alone, like walks and strikeouts. [4]


Others, like Tom Tippett [5], felt this DIPS evaluation tool was not entirely viable; while others have introduced new math formulae and statistics that keep track of innings pitched(IP), which measures how many outs were made while a pitcher was pitching. For instance, formulae such as Defense-Independent Component ERA (DICE) and FIP do consider factors that make them highly dependent on the defensive play of the fielders.

It appears that the focus on talent and statistics, along with the recent implementation of information technology, will continue to be implemented and will infuse team selection — including MLB pitchers — and salaries for awhile.

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Patricia Deming, is a graduate of the University of Texas at Austin, with a BA in Mathematics. She enjoys writing articles and contributing to sports and education blogs, such as She has been a baseball fan for many years and regularly attends Seattle Seahawks games.

[1] Lewis, M. (2004). Moneyball: The art of winning an unfair game. New York: Norton.

[2] James, B. (2001). The new Bill James historical baseball abstract. New York: Free Press.


[4] Voros McCracken, “Pitching and Defense: How Much Control Do Hurlers Have? January 23, 2001.


This Fan Shot was contributed by Patricia. Have something you want to say about the Mets? Share your opinions with over 15 thousand Mets fans who read this site daily. Send your Fan Shot to Or ask us about becoming a regular contributor.

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MMO Post of the Week: Are Advanced Statistics Hurting Or Helping The Game? Fri, 11 Jan 2013 20:28:26 +0000 mmo encore presentationSomewhere along the line baseball became more than just a game.

Once upon a time, baseball was a simple game. The goal is to score more runs than your opponent. Each team is given 27 outs to score as many runs as they can. In order to score runs, a team’s players have to get on base. Once a player gets on base it was the other players’ jobs to drive them home to score runs. On the other hand, the defense’s job is to get 27 outs allowing the opposition to score the fewest amount of runs. Whoever scores more runs in 9 innings of play wins – simple.

Now let’s fast forward to the 1980s. The 1980s were famous for Nintendo, big hair bands, Reaganomics, and the invention of rotisserie baseball.

Fantasy baseball exploded onto the scene in the 80s, and the men that played this game were looking for ways to build better teams. They wanted to build better teams in order to take home the lucrative prize money that came along with winning their rotisserie league. They used different combinations of stats to form equations, which in return would spew out which players they should select on their team.

Yes, the advanced stats that the game uses today were ultimately developed by men that maybe never even played the game. They were simply looking to build better fantasy teams. It leads the people with advanced knowledge of how the game is played on the field to butt heads with those that sat at their desk and computer doing all the math.


Photo Credit:

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Bill James, the father of advanced statistics and sabermetrics, didn’t start to gather a serious following until the mid to late 1990s. Until then, he published his yearly baseball reviews and would sell 500 copies per year if he was lucky.

The game wasn’t ready for the story he was trying to tell. James was basically telling everyone in the game that they have been looking at the game improperly for nearly 100 years. Advanced statistics were born. He broke down nearly every single aspect of the game, except defense, which he was never able to develop an accurate statistical rating for.

But did the game really need the advanced statistics?

The game had survived over 100 years just fine without advanced statistics. But now, in every team’s organization, there are mathematicians working in this area. The question is, is it really necessary?

The reason why sabermetrics and advanced statistics took over the game in the 90s is because that is the when player salaries started to get to the point where some sort of projection and analysis was needed. Owners wanted to know if it was really worth it to spend the money on player X.

Baseball had officially become a full-fledged business.

In every MBA program across America, students are often required to take a course dedicated to statistics and spreadsheet analysis. The students are taught how to use Excel spreadsheets and programs like Risk Solver to make business decisions. If you are under the assumption that the CEO of a big company makes decisions based on his/her gut you are mistaken.

More often than not, the decisions are made by a computer than runs simulations based on the data that the decision maker inputs. The program takes all the data and then it gives you the most logical decision after running all the simulations.

It’s actually pretty cool. You could build a model that can tell you the best location to build an ice cream shop, based on three different locations, with three different average yearly temperatures, three different traffic patterns, and three different populations in the towns they’re in. Not only will it tell you the best location to build your ice cream shop, but the expected revenue at each location.

The same thing can be done with baseball players – in theory that is.

Everyone knows that Billy Beane and Paul DePodesta’s use of advanced statistics and sabermetrics officially put them on the map. Their use was chronicled in the book Moneyball by Michael Lewis.

The book that exposed Billy Beane's strategies.

The book that exposed Billy Beane’s strategies.

Beane used the advanced statistics to remain competitive with a team that had the lowest payroll in the league. Once he started winning, people started to question how the heck the Athletics could be winning when they were only spending one-third of the money of the other teams. At that point, every Tom, Dick and Harry fell in love with sabermetrics.

Sabermetrics became the key to unlocking hidden baseball talent.

But here is the fundamental flaw with peoples’ understanding of what Billy Beane actually did – Beane wasn’t intentionally trying to win by spending the least amount of money he could. Beane wanted to spend money. He wasn’t trying to do his owner a favor by spending the least amount of money on building a team. He was simply in a situation where his hands were tied. He had to think outside of the box. He had to get more efficient with spending what little money he had. That’s it.

Somehow Beane’s strategy became an excuse for teams to spend less money, and try to build teams using a philosophy that Beane only developed because he had to and not because he wanted to.

Players are now investments, plain and simple. If a team is going to make an investment, the projections, spreadsheets, models and simulations have to all tell the same story – that the player is worth the investment.

However, there is a problem with advanced statistics – the game is still ultimately played on the field. You cannot remove the human element from the game, and no statistic can factor that in. And while past performance is a good indicator of future performance, there is only so much weight that advanced statistics should carry.

Advanced statistics paint an imperfect picture of the game when used improperly. Here is why:

Advanced statistics use inputs which are plugged into an equation and are determined by the person developing the statistics in order to arrive at a desired outcome. They often have to finagle with different stats until they get an answer that makes sense. What also comes into play is the developer’s bias.

If someone is playing with stats in order to make their equation work, how is that more accurate in telling me which player is better than if I used the old school statistics (OBP, AVG, ERA, etc.) which have been used for the past 100-plus years, and my eyes, used to watch the players play?

Let’s take a look at the Holy Bible. There is a show on TV that comes on one of the learning channels every once in awhile which basically alludes to the fact that the bible has a hidden code in it, which not only predicted things that happened in the past, but also can predict future events. Now on the surface, they did prove that there was a code in the bible. But is there really a code in the bible, or was it manipulation by the developer to come to a desired goal/outcome?

Odds are there isn’t a code in the bible, but this just shows how the manipulation of data can get to a desired outcome when played with long enough. One of the major issues with scientists to this day is trying to conduct scientific studies and not have their bias come into play. Bias alters outcomes.

The bottom line is that baseball is still a game where there is still a lot of luck involved. For instance, if a player is half a step to the left or right, a ball drops in that maybe shouldn’t have been a hit. Which stat factors any of these things in? The argument is the law of averages balances everything out. In the end, the math is the constant.

However, there are internal and external factors affecting the game constantly. These factors cannot be built into models. These factors cannot be accounted for statistically.

Where a card counter at the black jack table can turn the odds against the Casino by using probability and a system of advanced mathematical equations to gain an advantage, there is a set number of cards in the deck, and only a certain number of things can occur to account for. You can’t do that in baseball. In baseball, there are an infinite number of things all taking place simultaneously which affect the outcome of every pitch.

The problem at large is that the game has changed significantly since the introduction of advanced statistics. There are too many statistics which are complicating the game. They cause managers to over-manage situations.

For example, is a lefty specialist really necessary in a team’s bullpen? According to advanced statistics they are. But when it’s all said and done a bullpen pitcher is simply a pitcher who could not make it as a starting pitcher. Very few pitchers are groomed to be in the bullpen. In other words, why would I bring a pitcher into a game, and take out my better pitcher, simply because statistics show that one guy is better at getting left-handed batters out?

It doesn’t make sense. The best players should be on the field.

Statistics tell front offices they need lefty specialists. They tell the manager that they better go against their gut which tells them to leave their better pitcher in the game. It sounds crazy when you think about it. I’m going to take out my better pitcher because statistics show that over time, a pitcher of lesser quality has done a better job of getting left-handed hitters out? It doesn’t sound logical.

Now I have decided to take my best pitcher available out of the game to bring in a lefty specialist in order to get one hitter out. After he gets that batter out, I have to take him out of the game to put in an even lesser quality pitcher? Why not just leave my best pitcher in to get the lefty out. Now I have changed the odds of getting the remaining hitters out, all because stats have told me to take my best pitcher out of the game.

The entire landscape of the game changed because of a single stat.

Is there a stat that shows the odds of getting the remaining hitters out in a game after I made that decision? There is a stat that shows me that I should bring a lefty specialist into the game, but not a stat that shows the odds that I will get the remainder of hitters out now that I made that pitching change.

That is just one example of how stats have changed the game, but the question that still remains is – are all of these advanced statistics helping or hurting the game?

Cases can be made for both sides, but the truth of the matter is that all these stats are really good when looked at from the surface. It’s how the people behind the scenes use them that will ultimately determine whether they are good or bad for the game.

My daughter preparing for a front office job someday - you can never start them too early.

My daughter preparing for a front office job someday – you can never start them too early.

Advanced baseball statistics is very similar to the app market for smart phones. App developers are always looking to develop the next Angry Birds, and stat developers are looking to develop the next stat which proves that they have the secret formula to determine who the best player in the league is.

There is no secret formula. Baseball is played on the field, not in a laboratory, and not in a computer program. There isn’t a single stat or mathematical equation that can determine the outcomes on the field.

Nothing will ever change that.


Follow Mitch Petanick on Twitter.

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The Baltimore Orioles Are Defying Baseball Logic Tue, 04 Sep 2012 16:25:23 +0000

It’s September 4th, and the Orioles find themselves one game out of first place in the American League East. That statement, on it’s own, isn’t too ridiculous. It’s when you start looking at the stats that make that statement look ridiculous.

The Orioles are not a good baseball team according to the stats. In fact, if you were to look at the league statistical rankings, before looking at the standings, you would think they stink. They’re currently in the bottom half of teams for offense and defense, and the pitching is in the middle of the pack. It begs fans to question how this team is winning ball games.

I decided to look further into this anomaly, so I skipped over to to see if there was some sort of statistic that could explain why the Orioles were winning so many games this year. I found a statistic that, according to the sabremetric community, is supposed to provide the the most complete, and overall best analysis of both a player and the team. That statistic is Wins Above Replacement (WAR). I’m not going to go into detail about what WAR is, but if you would like an explanation of this statistical category, click here .

Finally, I thought to myself, I will find the answer I am looking for as to why the Orioles are playing so well this year. Their WAR must be off the charts, and that will explain everything. Boy was I wrong. Very, very wrong. The Orioles WAR isn’t just bad, it ranks in the bottom of the league. The only two teams with lower WAR, are the Cleveland Indians, and Houston Astros. So now the Orioles have not only defied our traditional baseball statistics, but also flipped the bird (no pun intended) to the sabermetric community.

What are we going to do about the Orioles? They’re defying baseball logic! They’re like Maximus Decimus Meridius defying the emperor in Gladiator. Something has to be done!

The Baltimore Orioles have their eyes set on the post-season. Statistics are supposed to give us a glimpse into why a team is successful or not. The Orioles laugh at that notion. Throw statistics out the window in the case of the 2012 Baltimore Orioles, they are irrelevant. However, the odds are that those statistics will catch up to them, most likely in the post-season, when playing against a superior opponent. With everything at stake, the post-season is generally the place where fairy tales end. The post-season is usually where these unlikely teams’ weaknesses get exposed. Then the baseball traditionalists smile and say I told you so.

The Orioles have the great baseball minds scratching their heads. They have fans of other MLB teams jealous that the Orioles are doing something that their team can’t – win. And the Orioles are throwing their hands up shouting are you not entertained?! Yes Baltimore, very entertaining. The baseball gods are smiling on you. There is no explanation for what the Orioles are doing this year except that’s baseball for you. The inexplicable things that happen are what makes this game great. We try to make sense of it all with statistics, but there are situations when not even statistics can help. That is the case with the 2012 Baltimore Orioles. And with the playoffs right around the corner, they better make a call to Jeffrey Maier and see if he’s available.

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Do You Still Believe In Moneyball? Mon, 13 Aug 2012 14:15:06 +0000 Moneyball is a ultimately a strategy that was developed by a small market team’s general manager in order to compete large market teams. When Billy Beane sat down and started to think about creative ways to stay competitive with large market teams, he didn’t write the word moneyball on a dry erase board, and tell everyone in the room that this was his new idea. Moneyball was a name, that was given by an author, to describe to the success that Beane experienced in the early 2000s. I’m here to tell you that much like Santa Claus, moneyball doesn’t exist.

Before you call me crazy, sit and think about it. The strategies that Beane developed and ultimately used do exist, but moneyball doesn’t. If it did ever exist, it ended once large market teams also started to implement Beane’s strategies once they saw how successful Beane’s Oakland A’s were. The Red Sox started implementing the small market strategies made famous by Beane, and what did it lead to? They finally broke the curse of the Bambino. But that wasn’t moneyball, because moneyball doesn’t exist.

There are basically two schools of thought in baseball – the old school scout mentality, and the moneyball school of thought. The old school thinkers say “wow that guy has all the tools,” but moneyballers ask “can he play ball?” Old school thinkers look for potential, while moneyballers look for performance. The old school mentality has driven me crazy for years. Year after year I watch teams draft players based on the coveted five tools, and then pay them upwards of $1 million just for signing a contract. They don’t even know if this guy can play, and simply because the guy can hit a baseball a country mile during batting practice, they invest millions. It doesn’t make sense to pass up on a guy that has shown he can play the game at a high level, for a guy who is visually or physically more impressive. That’s a stupid strategy even if you have a lot of money to spend. I guess that means my beliefs would make me a moneyballer, that is, if moneyball existed.

Remove the word moneyball from your vocabulary. Instead, call it performance based evaluation of players. Rather than looking at what this guy might be able to do for your team, you look at what this player can do for your team. You do that through evaluation of statistics, but also based on what you see on the field. You can not evaluate a player on statistics alone. The two schools of thought really should work hand in hand, not against each other. If you combine the schools of thought, you really have a total of six tools that players should be evaluated on (not the traditional five) – running speed, arm strength, hitting ability, quickness, mental acuity (patience at the plate), and ability to get on base. I firmly believe that teams should always value proven players over guys who have an array of tools but can’t apply them in game situations. I guess that would make me a moneyballer, that is, if moneyball existed.

One team that I think has been doing this well the past few years is the San Francisco Giants. They tend to draft guys that they can get through the minor leagues as quickly as possible to start helping the big league club. You can’t do that by drafting guys based on talent alone, so there has to be skill there. If a guy has tons of talent, but has to spend six years in the minor leagues developing the skill, then what’s the point? Just draft the guys with skill, and save yourself time and money developing them in the minors. That’s why Beane focused his draft on more polished college players – there is less development needed, and they can help the team in a shorter period of time (in most cases). Then you don’t have to spend big money in free-agency to address your needs. I completely agree with Beane’s drafting strategy. I guess that would make me a moneyballer, that is, if moneyball existed.

Moneyball doesn’t exist. There isn’t some magic formula, or mathematical equations, that a team can use to evaluate players and uncover undervalued players. If that’s what you think, get it out of your head. Teams can’t expect to win without spending money, unless they have a well developed minor league factory that is spitting out skilled players like Ford spits out Mustangs. This is an area the Mets are lacking. Every team uses the same analyses now, so those days of Beane’s A’s are all but over. But the Mets have a distinct advantage over those early 2000′s Oakland A’s – they aren’t a small market team. Those small market team rules don’t apply.

The New York Mets should focus on what the San Francisco Giants have done the past few years. They have to find a way to get guys to the major leagues, as fast as possible, because the team is in a complete state of disarray right now. There are guys playing out of position in order to plug holes. The problem is there are more holes than plugs, and we all know what happens when there are more holes than plugs – the ship sinks. The fix is simple enough – start drafting more polished players that will be able to help the team now, rather than later. Either that, or they have to pony up some cash and address their needs.

The Mets can turn this around, but they have to get their hands dirty, and re-evaluate their organization from the ground up. They better do it fast, because this is starting to remind me of the Mets teams of the early 1990s…and I don’t know how many of us can go through that again.

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Should The Mets Retire The Moneyball Project? Fri, 10 Aug 2012 14:30:10 +0000 The Mets currently have a front office in place that has earned them the nickname the “Moneyball Mets.” Mets G.M. Sandy Alderson was once Billy Beane’s mentor, and the Mets have also added a couple of other front office executives that once worked with Beane. As the Brady Bunch theme song goes – everyone sing along – that’s the way we became the Moneyball Mets.

Does that mean the Mets are on pace to have the success the A’s had ten years ago that was chronicled in the book and movie Moneyball? Not likely.

For those of you who haven’t read the book (or watched the movie), Moneyball is based on a form of analysis called sabermetrics. Simply stated, moneyball theorizes that in order to win games, a team has to score more runs than their opponent by getting on base more frequently. It goes further to analyze which players actually help you score more runs using a series of mathematical equations to develop advanced statistics called sabermetrics. This is obviously a very rudimentary explanation of moneyball, but it inevitably goes against everything the traditional scouts have been saying for over 100 years. Scouts search far and wide for the coveted five tool players which are as rare as unicorns and leprechauns. The search takes them around the globe with one goal in mind: to build the best teams they can by seeking out the best talent.

Sabermetrics allowed Beane to take advantage of players often ignored by other teams in order to build his historic 2002 team. They were ignored since teams didn’t understand their true value. This misunderstanding was due to not using sabermetrics to evaluate players. At least that is what we are led to believe. We will return to this later.

The movie alludes to the idea that Beane was looking for a way to analyze talent that was different from the traditional scouts. This was supposedly due to the fact that he was once considered a “can’t miss” five tool player. He was selected in the first round of the 1980 MLB draft (by the Mets coincidentally), but never lived up to expectations. The Mets had three first round picks that year, and held the number one pick. They used that number one pick on Daryl Strawberry after Beane signed on to play football and baseball with Stanford, even though scouts thought Beane was as close to a “sure thing” as you can get from a prospect. No teams wanted to risk a first round pick on a kid that was going to be John Elway’s heir at Stanford. The only team who could afford to take that risk was the New York Mets since they had two other first round picks.

To this day, scouts say Beane was the most gifted athlete in the 1980 draft class. But if Beane learned anything from his playing career, it’s that there is no such thing as a “sure thing.” This has him at odds with scouts who wanted to try and put the best overall players on the field, the way big market teams do.

Back to Beane’s 2002 Oakland Athletics team which was the basis of the book and movie Moneyball. First, let me say that the movie was entertaining. Unfortunately, it paints a picture of Beane building the entire 2002 A’s from a bunch of players that no other team wanted. It reminded me of the scene in the movie Major League when they are trying to build a team bad enough that will help the Indians move out of Cleveland. Nobody was previously playing in the California Penal League, and the team was actually stacked before Beane added the final few pieces of the puzzle using sabermetrics.

The movie fails to mention the fact that the pitching staff consisted of Barry Zito (2002 Cy Young Winner), Mark Mulder, and Tim Hudson who were affectionately known as the “Big 3.” Let’s put it this way, if Beane didn’t win the division with those three guys he should’ve lost his job. By the way, the closer was Billy Koch, and it gets even better. The A’s had Miguel Tejada (2002 AL MVP), Eric Chavez, Jermaine Dye, Ray Durham, and David Justice all in their lineup. So was the success of the A’s due to sabermetrics being used to add a few players that nobody even remembers from the team, or the fact that everything came together for the A’s due to great player development? And if you thought the 2002 pitching staff was scary, the 2003 & 2004 A’s added a young Rich Harden to the mix. How did the Athletics manage to never win a World Series with those guys on their pitching staff?

Now let’s get back to the Mets. I think everyone will agree the Mets don’t have the talent the A’s had in the early 2000s. Not only that, but the A’s are a small market team, so they had to come up with creative ways to compete with big market teams. Look at it this way - when a person with a lower income goes to buy a car, they look for different attributes in that car than a person with a higher income would. The person with lower income goes to buy a Honda. It will get you back and forth to work, it’s reliable and good on gas, but you aren’t winning any races. The person with higher income goes to buy a Corvette, and the license plate reads “eat my dust.”

The Mets are a large market team. They shouldn’t be shopping for Hondas. Their license plate should read “eat my dust.” It doesn’t make sense for them to use the strategies of the small market teams. Their strategy should be to use their revenue stream to crush their opponents. The Mets can certainly learn a thing or two about player development from the Athletics of the early 2000s, but I’m still not sold on the fact that sabermetrics had anything to do with the success of those teams after looking at the players on that roster.

Can the Mets build a winning team using sabermetrics and moneyball? I know one thing for certain – no small market teams have won the World Series using sabermetrics alone in the past ten years. So if the Mets want to start winning again, they better start taking the money out of Moneyball, and start spending it.

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R.I.P. Moneyball Part II –The UNDERVALUED STAT! Mon, 17 Oct 2011 16:36:36 +0000

Having explained more precisely what the central GOAL of Moneyball is (Save Money) I thought it might be good to talk about what it actually says it did and why it did what it did from the Statistical Analysis point of view.

Moneyball, as was stated previously, used Statistical analysis to find an UNDERVALUED METRIC that was then used to hunt for and create a MASTER LIST of players who exhibited that quality for consideration of acquisition.

How did they arrive at OBP as the undervalued metric and how would that work today?

Well the book leads us to believe that Sabermetrics were used to list and rank players and to determine which stat was prevalent in good players AND cheap players that was being overlooked by the rest of the league.

But did that really happen? Did it really show that OBP was the undervalued stat or did it by statistical bias automatically select OBP as the undervalued stat since Sabermetrics were used and invented to ILLUSTRATE the importance of OB in the game of baseball?

You see by using Sabermetrics as your analytical model you PRE-SELECTED OBP as valuable merely because Sabermetrics values that more than any other event and didn’t really let the analysis of numbers make the decision for you.

You would come up with OBP as the UNDERVALUED VALUE stat even in years when it was not.

If you didn’t construct the statistical model in a way that made OBP valuable before any comparison was made it would not have valued OBP in the first place and therefore you would not have come to the conclusion that OBP was UNDERVALUED!

Moneyball used an OB-centric statistical model and came up with OBP as the UNDERVALUED METRIC. WOW, What a SHOCK! They looked for something specific and then FOUND IT! Eureka moment or preselected destiny because they used a metric that favored the result before the result was even seen?

IE: If you ran a statistical analysis model that favored HRs above any other event you would come up with a list of GOOD and CHEAP HR hitters, and you might be left thinking HRs are the UNDERVALUED STAT, but would you be right? Would it mean that HRs are undervalued? No – what you did was lent value to something before any player comparison was made. You FOUND what you BIASED your search parameters to look for. HRs are a poor example granted because we all KNOW they are valued, but it does make the point that if you bias your stat model towards one event, it will only work if your biased model correctly ascertained the undervalued assets from the outset. Garbage in, Garbage out.

Now OB was undervalued at the time because OBP while well known, was not given the importance it is today thanks to the book Moneyball. Most teams all look at OBP now and it is no longer undervalued – it hasn’e been for quite a few years.

Sabermetrics were new, not widely used and therefore no majority of teams would come up with the same MASTER LIST as Oakland meaning they would not be FAVORING OBP and as a result OBP would be undervalued.

This is how MONEYBALL came to find that particular market inefficiency.

Would it work today?

What is the most UNDERVALUED STAT in today’s market?

Can Sabermetrics tell you what is undervalued these days?

Or will Sabermetrics come up with the SAME EXACT ANSWER as before because it values OB more than anything else?

It is believed that Sabermetrics actually found the UNDERVALUED stat but the truth is the guy who PICKED Sabermetrics as his statistical model picked OBP as the undervalued metric before any comparison, chart or calculation was made!

And it is for this reason none of the people who have a strong belief in Sabermetrics have been able to ascertain what the new UNDERVALUED metric is.

ASK them and they will not be able to tell you. Any of you sabers care to list a couple of undervalued metrics right now?

They will cite other metrics made BASED on the same OBP biased approach, leading to the same results.

They need to come up with a new statistical model that will not value one stat over another and not bias the results in favor of OBP. Otherwise just use OBP and trash the other stuff that doesn’t really lead you to anything undervalued assets.

Sabermetrics was primarily a form of statistical analysis that allowed OBP to be seen as underused and undervalued. That is all behind us now and what we now need is to discover that NEW undervalued Metric.

A NEW STATISTICAL APPROACH IS REQUIRED if that Moneyball UNDERVALUED approach is going to ever work again!

That can only occur by someone finding an event that is overlooked and creating a metric to find it.

If Sabermetrics is used by all teams then Sabermetrics will not be able to come up with an UNDERVALUED STAT because EVERYONE will be valuing it!

SO for MONEYBALL to succeed it has to abandon the Sabermetrics it used when no one knew about it, and it must invent a NEW Statistical model that no one else uses in order to get back its edge and find what no one else is looking for and placing VALUE on!

And if that’s not done?

R.I.P. Moneyball.

This Fan Shot was submitted by Mike (Metsie). Have something you want to say about the Mets? Share your opinions with over eleven-thousand Mets fans who read this site daily. Send your Fan Shot to

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R.I.P. Moneyball Thu, 13 Oct 2011 16:11:26 +0000 I think it is time to talk about what Moneyball ACTUALLY means and to show some of those who claim to have read the book what the book is all about. Apparently, reading doesn’t always lead to UNDERSTANDING.

I have read the book, it was interesting to learn about the little known “behind the scenes” process of team building and how a front office operates. It is for THAT reason the book sold so well not because it was chock-full of useful information, formulas and strategy. It did have some information which would be useful for ANY team regardless of targeted payroll ceiling, and it did help to show how Statistical Analysis can help you find some hidden value where none is actually perceived.

The book discusses quite well the methodology used but mostly because it would be a pretty short book if it just said we used statistical analysis to find cheap players. I suspect this is the main reason why people confuse Sabers with Moneyball. The book expended a great deal of effort explaining what methodology they used to find “cheap” players which was the central goal of Moneyball.

Sabermetrics or advanced statistics were one of the predominant tools used to rank and identify who had the quality that was deemed UNDERVALUED but also used to identify what quality it was that was undervalued.

Sabermetrics are based largely on Bill James’ work and his work is mostly about how important On-Base Percentage is to winning (right or wrong that’s what conclusion he came to in its most simplistic form).

By using that strategy, your initial ranking of ALL players is going to FAVOR players with high a OBP and rank them accordingly. If you use OBP alone to identify what players are undervalued many low cost players will not show up on your list of top players simply because you ignored so many other aspects of the game.

Using the Moneyball method to exploit market inefficiencies in the early 2000’s, doesn’t guarantee that you will come up with all the top low-cost players today. Markets change every year.  It worked great for a little while back in the day, but that was then and this is now.

Now onto the topic at hand, the processes which will no doubt cause much vitriol in the comments?

MONEYBALL no matter what methodology used to arrive at the goal is about NOT SPENDING MONEY or Spending as little as possible. The REASON for implementing it is varied:

  • Not having the money to spend due to low fan attendance and revenues.
  • Cheap Ownership who are more interested in profit than wins and force the GM to shop at Kmart.
  • Blind stupidity that convinces you that minimizing your player options translates to more success. I call this the “penny wise, pound foolish” syndrome!

Sabermetrics was used in the Book as a comparative means to identify players who fit the qualities they were looking for. But it was not the decider in who to get. Sabermetrics does not INCORPORATE player salary in their metrics. Two Players judged by Sabers can be equal while one gets paid 8 Million per year while the other gets the league Minimum!

So while the Sabers may have identified the players and created the initial MASTER LIST of candidates it did not DECIDE which one the team was going to get because there are probably a lot of HIGHLY PAID players on that list as well.

Look at the 2011 Top OBP leaders:

  • Joey Votto
  • Prince Fielder
  • Lance Berkman
  • Matt Kemp
  • Ryan Braun
  • Matt Holliday
  • Carlos Beltran
  • Troy Tulowitzki

Do you see any hidden gems or $1 million dollar a year players?

So you used OBP to identify all the players that you deemed were GOOD, but then eliminated all the players who were being paid accordingly for their talents, in this case all of them.

You see MONEYBALL is about REMOVING high salary players from the candidate list…

Sabermetrics are not the central driver of the Philosophy in Moneyball. MONEY is!

The Red Sox who are most often used as an example of a Moneyball team use advanced stats, but they SKIP the most important step needed for Moneyball… The removal of any players or options that command a high salary!

The Red Sox never removed those higher priced options from their list of targeted players… Oakland DID!

This is why the Red Sox actually won a WS and have the third highest Payroll in baseball while Oakland has never won a championship since they implemented Moneyball.

The Red Sox never limited their options based on Money.

Yes they both used sabermetrics, in fact most teams do, but the Red Sox did not ignore quality players because of money! They did not discard a better option merely because he made more than a cheaper and more inferior player!

Oakland did!

Even if a player had a superior OBP or SLG,Oakland would ignore those sabermetrics and that better player in favor of the lesser player and $$$$$.

Red Sox did no such thing! THEY ARE NOT A MONEYBALL TEAM! You can say they are a Sabermetric team as many teams are these days in some respect or another.


SABERMETRICS = A Limited Form of Statistical Analysis!

Statistical Analysis DOES NOT EQUAL SABERMETRICS! There are many ways to analyze stats and they don’t all subscribe to the theories put forth by Bill James and all those profiting in his footsteps.

Statistical Analysis is a means of calculating stats and placing importance on some stats over others but they do not show you the cheapest player nor compare price per performance in any way shape or form.

Now we COULD debate Sabermetrics in and of itself, but it really isn’t relevant to this conversation. Yes Sabers seem to be good at comparing players but Sabers themselves and the philosophy of Bill James is not required, important, or the be-all and end-all of Statistical Analysis!

Bill didn’t really INVENT statistical analysis we have ALWAYS looked at stats as a comparator. Bill James’ contribution was to create a few metrics that placed importance where he saw fit. I’m not going to debate if he’s right or wrong here, it is not the focus.

You do not need to read MONEYBALL nor any of Bill James books to create or use good metrics. Anyone can do it and if you work hard to ensure you are not biasing the data to show what you want, you will also come up with the right answers.

No single stat will ever give you the complete picture of any player. To say that OBP is a better metric than BA because it takes all PA into consideration doesn’t make it better. An even better metric can be achieved than the ones Bill James came up with.

How about a metric that takes into account moving the runner over or driving in a run regardless of an out being made? It would tell you a lot more about a player than either BA and OBP.

The thing that Moneyball SUCCESSFULLY showed was not that saving money is the way to go, but that DEEP STATISTICAL ANALYSIS is the key to making good decisions because you are making an EDUCATED Guess – an informed decision.

But MONEYBALL discards much of that information and the end result is as old as the game of baseball itself, how much they get paid!

Moneyball uses Sabermetrics to come up with answers, and then IGNORES the answers given based on COST!

Moneyball is not about Sabers or statistical analysis it is about NOT SPENDING MONEY!

The Braves have been used as an example of a team that did it the right way and they did it without the benefits of Moneyball. (They were pretty much done winning championships by the time Moneyball was invented!) They built a good team that was cheap because they developed it from scratch. Fine to do provided you have the patience to wait as long as it took them – decades of losing and a bit of good luck and timing and Greg Maddux. They finally built a team that carried them to five league championships and their one World Series title.

If they had spent some additional payroll to maintain their edge they might have won a few more WS and Titles.

The notion that spending less means winning more does not hold true. Building BETTER (regardless of methodology and COST) leads to better teams.

And by handcuffing and limiting your choices based on money means you make it that much harder to succeed. Because when you place limits on yourself that preclude you from many options,  you helped give the opposition who did not limit their choices an ADVANTAGE OVER YOU!

While Oakland might seem to have done well despite limiting themselves via implementing Moneyball, the bottom-line is Moneyball didn’t get the job done!

And while it might seem wise to a Moneyballer to point out how many playoffs Oakland went to while spending peanuts, you still have that little issue that the Yankees won more titles and World Series spending money.

1998 – 2011

Oakland A’s – AL League Championships 0, World Series Titles 0

NY Yankees – AL League Championships: 6, World Series Titles: 4

The great equalizer, Moneyball was not!

And what COULD/WOULD the story be if they had just spent a little to keep the cheap players they had worked so hard to find from walking away?

Or to compliment the team with players who would cost a little more, but would have increased profits due to WS ticket sales and victories?

How far might they have gone if they simply signed rather than ignored the best players they themselves calculated based on their UNDERVALUED Metrics?

When you LIMIT your choices based on self imposed financial limitations, you will not have the same success as those teams who use the same statistical analysis to pluck all the productive players that are out of YOUR price range!

Cheaper isn’t ALWAYS BETTER… You GET what you pay for!  Not in every circumstance but more often than not.


Especially now when all teams use advanced metrics everyday, but are willing to pay for the best available talent.

R.I.P. Moneyball

This Fan Shot was submitted by Mike (Metsie). Have something you want to say about the Mets? Share your opinions with over eleven-thousand Mets fans who read this site daily. Send your Fan Shot to

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Shut Up With The “Moneyball Mets”! Fri, 23 Sep 2011 14:05:55 +0000 I came across a piece yesterday (linked by Craig Lerner in his article) by Dave Lennon of Newsday that caught my eye.

Is the ”Moneyball” approach paying off for the Mets?

In some respects, the answer to that question is yes, as they have climbed to the top of the National League in targeted offensive categories such as walks and on-base percentage.

What a statement. Look, I’ll be honest here. I don’t like sabermetrics, and I don’t like the Moneyball approach. It worked with the Oakland A’s? That’s cute. They had to do it because they were forced into that situation. Necessity is the mother of creation, yes? Their approach to signing players, valuing prospects, and analyzing specific aspects of the game was impressive. They discounted traditional wisdom and found success for the short term by using sabermetrics to analyze. You can see Sandy Alderson employing those techniques by signing undervalued players such as Capuano, Bucholz, Young – which by the way, are three signings that I did and still do support because they were cheap and low risk. Hey, injuries happen and that will be that. (And for the record, no, I’m not supporting signing Chris Young again. It was worth a shot, though.)

Now think again about what Lennon said. So apparently, the philosophy for signing players and valuing them differently has a direct correlation to the fact that the team is walking more and getting on base more overall? Just because we have a GM who uses sabermetrics (focusing on getting on base more), the players magically got better at it? That’s Moneyball in action? Don’t feed me that crap. It’s more of a testament to the fact that the Mets played their hearts out for the majority of the season than it is to the Moneyball theory. What I am saying, is that the GM does NOT have 100% control of what happens on the field. He does not play the games nor is he some kind of puppet master that controls how his players perform. He can put together a team, but what happens after that is a combination of what that player is capable of plus the HUMAN ELEMENT.

That’s my problem with sabermetrics. It does not nor can it ever account for the human element. Things such as emotions, attitude, and even growth. I’ll allow the SABR crowd a few seconds to laugh at that. News Flash – Believe it or not, there was a lot of talent on this roster. Nobody could have accounted for the injuries to Davis and Daniel Murphy – and there was even hope that Santana could have been back in July. Even with not one significant addition during this offseason (releasing Perez and Castillo was probably the highlight), this team still is going to finish very close to a .500 season.

Many of the Mets younger players grew and progressed as MOST young players do: Murphy became a more patient and mature hitter, Ike was in the midst of a breakout campaign, and Tejada was no longer over-matched at the plate. Wright was returning to his old self, Reyes having the best season of his career, Beltran was swinging with authority, hell, even Bay showed some flashes this season!

So without the offensive and defensive contributions of Ike Davis, and without the contributions of Daniel Murphy, and without an ace or even number two starter, and without a closer for three months, and without Carlos Beltran – arguably the best hitter on the team at the time of the trade, this team is finishing close to .500 for the season. That’s about 81 wins. In my book, 90 wins is a damn good season and that’s only nine more than .500.

So let’s say the Mets finish with 78 wins. Don’t you think that half a season from Santana, Carlos Beltran staying in the lineup, and a non-injured Ike Davis and Daniel Murphy could have gotten this team to near 90 wins and at least kept us in the wild card chase?

Having Alderson at the helm can lead to a interesting future for the Mets. He was brought on to cut payroll, make smarter decisions in terms of long-term deals, and perhaps draft prospects who reflect their sabermetric ideals. The difference is: The A’s were forced to work with an extremely low payroll, the Mets are not.

If Alderson wants to cut payroll and not deal out long-term contracts (except maybe a 5 year in the direction of Jose Reyes), that’s fine with me. If he’s here to cut payroll and NOT try to help us win (which seems idiotic), I’d be pissed off. But on both sides, the jury is still out – at least until the end of this offseason.

Bottom line in my eyes – We will never be the Moneyball Mets because we have more payroll accessible to us and that’s great. I don’t want to see the Moneyball Mets.

So what happens in the offseason if Alderson simply signs Reyes and a closer like he said he would, and that’s all?

Is that his formula for winning or just some token moves while keeping payroll on the down low?

Obviously GMs are never hired to create a losing franchise, but after this offseason, we’ll know more as to whether the main priority for this front office is winning OR cutting payroll. It can be both, but there will be a point where they will clash.

As to the original point – No, the Mets aren’t walking more because of the result of some Moneyball experiment or the direct result of Alderson’s sabermetric Jedi Mind Tricks.

These current Mets players, almost all of which are Minaya’s and not Alderson’s, are simply progressing, learning and becoming better hitters. The players are evolving, fighting hard and showing resilience – if you’ve watched the games, you know what I mean.

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What Makes A Real Mets Fan? Fri, 20 May 2011 02:00:13 +0000 Since the hiring of Sandy Alderson there seems to be a an overwhelming sentiment among the fanbase that if you don’t 100% support everything Alderson and his front office do then you’re not a real fan of the New York Mets.

Gone are the days when having a different opinion was acceptable. Today, Mets fans who disagree with the front office are labeled haters and trolls. They are singled out and attacked on sites like this one or social mediums like Twitter.

I know there are Mets fans who like me don’t believe that Sandy Alderson was the right choice to be general manager. Some of us feel his moneyball ways are simply wrong and not cut out for a large market team like the Mets. The over-reliance on sabermetrics seems foolish and short-sighted to me. The game got along just fine for over 100 years without using sabermetrics to build a solid roster and a championship level team. I know I’m not the only one who feels that way, in fact most ballplayers – former and current – scoff at it.

Every team has a fanbase that likes certain players on the team and dislikes other players. I know there is a good number of fans who like myself dislike Carlos Beltran. Like me, they think he’s selfish, overrated and overpaid. Those fans are also bashed and called names that I can’t repeat on a site like this. There are also many who dislike Jose Reyes and/or David Wright too. Reyes and Wright are my two  favorite players. I believe they are the most important players on the team. Some of you would probably disagree with that and that’s just fine, you are entitled to your opinions. We don’t have to agree on everything and anything. However there’s a civil way to do it without resorting to name calling or worse.

The bottom line is we are all Mets fans, we all love our team, and we all want to see them win. We buy their merchandise, we buy their tickets and we watch them regularly. We are entitled to have different views and that’s what makes sports fun and worth debating. Let’s get back to having civil, adult discussions instead of acting like children and calling people names and making fun of them simply because they have different opinions on what’s right for the team and what isn’t.

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