Rational Opinion-Forming

Uncertainty differentiates opinions and facts. In truth, there always exists a degree of uncertainty in any assertion. The overly skeptical among us point out that the only reason why we believe China exists is that other people have told us it does, and even if we traveled there, you cannot know that the place we actually went to was China, per se. The blurred line of where facts end and opinions begin further confound the question. Are “facts”, like the existence of China or photosynthesis, only those for which there is no disagreement outside of the solipsist circles? Can we also include historical events, such as the Appollo Moon Landing, which a sizable portion of westerners may disbelieve? Where exactly one draws the line is immaterial so long as one applies it consistently. The important thing is that uncertainty differentiates fact and opinion.

Let’s say you’re “line” is 99.5%, which would mean you are okay with believing something you take as fact to be false one in two hundred times [aside- that isn't quite mathematically true, as only the marginal -i.e. those that have exactly a probability of 99.5%- would be wrong one in two hundred times, but that is only important pedantically. this is for illustrative purposes.]. The probabilities to which you’ve assigned a theory, whether it is 99.9999999999% that the world is not flat or 75% that Obama would be a better candidate than McCain, have a specific statistical definition. They are known as a subjective, or Bayesian, probabilities. Even if you do not explicitly assign a number to how sure you are of any opinions, a truth-seeking mind invisibly thinks in such a matter.

This comes into conflict with how many pseudo-intellectuals view logical fallacies. Certain fallacies, such as guilty by association, appeal to the majority, appeal to authority and correlation does not imply causation do not firmly push an argument in one direction or another in any circumstance. However, this in turn does not mean that they should not affect your view at all. Subjective probabilities are all about bringing all relevant information into your mind, assigning the information weights, and coming to a conclusion. The reasonably strong (although not perfect) scientific consensus on global warming means something, even to an informed adult who can understand the literature and formulate a real opinion. The logical fallacy tells us wisely that the weight shouldn’t be infinite, but Bayesian inference tells us that it musn’t at the same time be zero.

There are times when all the evidence we’ve got in either direction are functions of intuition and “fallacies”. Proving or disproving the existence of God provides a watershed of such arguments that, while not argumentatively rigid, do mean something. Rather than listing them, I’ll point here and here. How much weight an individual places on each should determine the direction of their belief, which in turn results in a subjective probability.

The “wobbliness” of each proof does not necessitate agnosticism, but is very instructive of the uncertainty we share in forming any opinion at all. Analogously, this wobbliness appears in questions such as abortion, where we must weigh the fetus’s right to life against the woman’s right to her own body, whatever either of statements really mean. It doesn’t imply that we are to form no opinion whatsoever.

Informally, when we formulate opinions internally, some logical fallacies stop being fallacious. However, there are certain heuristics that appear to hold subjective meaning, but in reality mean literally nothing. Rather than bits and pieces of extraneous information that could conceivably point to truth, such as invoking the opinion of the masses, they are lies. Primarily,

1. Confirmation Bias. To selectively choose history and events that match preconcieved notions, rather than forming opinions through empirics or reason. Thomas Sowell refers to confirmation bias in the scope of empirics as “a-ha statistics”, providing the excellent imagery of a reader perusing the entirety of the newspaper for the few facts that he can point to and say, “a-ha, I knew it all along!”. Evidence to the contrary is subsequently considered chance.
2. Nassim Nicholas Taleb’s “Narrative Fallacy“. Wikipedia gives a very incomplete reference, so I’ll go into more detail on this one. It is the notion that humans look for history to fit into an easily explained narrative, rather than an inconcievably complex entity whose infinite factors and caprice bend in whichever direction for no other reason than they do. Taleb cites, as a starting point, an Italian academic who enjoyed one of Taleb’s earlier works and describes how well it verbalizes the role of randomness rather than reason in success. He then goes on to explain how Taleb could only see this through his upbringing in Lebenon rather than white, Protestant America. Taleb initially agrees, only to realize that such an explanation is ironically against the very role of randomness he argues. Locking onto a single explanatory variable, rather than accepting the existence of multitudes of reasons, gears an individual to a singular, simplistic understanding of what is true, one inflexible to any further incremental information. By perversely believing that whatever chosen variable is the entirety of the truth, he closes himself off to any further information.
3. Monday Morning Quarterback.  Taking past events as inevitable and providing baseless, haughty commentary. By itself, it isn’t all that dangerous besides being absolutely asinine, but when combined with a couple shots of confirmation bias, truth gets thrown out the window altogether. If you go through life thinking that everything is predictable, selecting for the instances that you may have possibly predicted it, all conversation stops and everything in the future becomes an exercise in reinforcing the opinions you already had.
4. Demonization. By ascribing the viewpoints of another to evilness rather than the argument they actually make or resort to pure epithets, no one is getting anywhere. If you actually start believing such perverions of truth (see: Bush), you’re going to think perpetually that half the country is evil and/or stupid. To categorically demonize a point of view is to shove your head in the sand.

These four aren’t the only failures in forming personal opinions, but they are the big ones. Their commonality is their desire to simplify the world into discrete, predictable events inevitably falling in tune with the grand score of life, rather than the cacophony we actually live. Evaluating events and facts as they occur, rather than fitting them to something we can readily understand, is the only way Bayesian reasoning can actually work.

There isn’t anything wrong with taking a piece of information that we honestly believe under consideration. To stuffy logicians, these pieces may be irrelevant, but in the real world of uncertainty, they aren’t. Yet, when our minds stop doing the interpreting and weighing of these facts, and instead ignore or force new information into preconcieved ideals, we lose everything. Subjective probability is the only way the human mind can understand the world, but even such forgiving standards are blemished by certain caveats.

Scouts versus Stats: A Case Study in the Diffuse Nature of Knowledge

Baseball is a game that lends itself especially well to the compilation of statistics. Almost any important event in baseball is strictly distinct, discrete, and countable. The cost of capturing the data is very low in comparison to other sports. In effect, if any sport may possibly be understood empirically and scientifically, it is baseball, after setting aside pseudo-sports such as poker. The study of the compilation of the data and its subsequent analysis has been coined “Sabermetrics” by its patriarch, Bill James. The best history of the movement is The Numbers Game by New York Times journalist Alan Schwarz.

In that book, Schwarz notes the interest fans shared in the countability and the elegantly discrete events of the game found in the box score. These numbers were eventually added together at the conclusion of the season, so the fan could learn who led the league in any countable category. It took until the 1960s for a scientist named George Lindsey to dig into the data. He discovered many tenets of Sabermetrics that were later independently re-discovered two decades later. Lindsey quickly faded into obscurity, or more accurately, never escaped from it. It took for a bored security guard named Bill James to get discovered by chance for such works to get published to a large audience.

The form of Sabermetric works were to challenge the prevailing wisdom of announcers, journalists, front office executives, and yeomen scouts. The archetypes of such works include,

• On-base percentage and Slugging Average, or how many men you put on base and how often you hit extra base hits, are the key determinants of offensive production.
• Managerial actions and “small ball” strategies were either pointless or deleterious to one’s chances of winning. This includes, but is not limited to, the choice of batting order, the stolen base, the hit-and-run, and the sacrifice bunt.
• “Clutch hitting”, situational hitting, and subjective factors were minuscule in effect, if they exist as a skill at all.
• Performance evaluation is more accurate than subjective, qualitative measures scouts use.

These points of view, the books and websites dedicated to them, and the rise of fantasy sports and companies like STATS, Inc. cemented their influence, which entreated upon the conscious of the mainstream baseball fan by the late 1990s. The watershed event, the publication of Michael Lewis’s Moneyball, came in 2003.

Moneyball follows the history and philosophy of Oakland Athletics General Manager, Billy Beane. It portrays him as a nearly psychopathic maven of Sabertmetric principles dedicated to winning, despite his franchise’s meager budget. Beane exploits the purported market inefficiencies of the undervaluation of players who walked frequently, players who had good defensive range, and college players in the amateur draft.

What is most memorable to many is the book’s abrasive attitude towards scouts. They are treated as stubborn, irrational traditionalists who don’t give teams anything that statistics could already tell them. They stupidly scorn certain categories of amateurs and minor leaguers such as short right handed pitchers, those who have “soft” bodies, and players without plus potential in either speed or power. Beane ignores these biases and concentrates solely on the performance of their amateur careers.

“The guy’s an athlete, Billy,” the old scout says. “There’s a lot of upside there.”

“He can’t hit,” says Billy.

“He’s not that bad a hitter,” say the old scout.

“Yeah, what happens when he doesn’t know a fastball is coming?” says Billy.

“He’s a tools guy,” says the old scout defensively. The old scouts aren’t built to argue; they are built to agree. They are part of a tightly woven class of former baseball players. The scout looks left and right for support. It doesn’t arrive.

“But can he hit?” asks Billy.

“He can hit,” says the old scout, unconvincingly.

Paul reads the player’s college batting statistics. They contain a conspicuous lack of extra base hits and walks.

“My only question is,” says Billy, “if he’s that good a hitter why doesn’t he hit better?”

“The swing needs some work. You have to reinvent him. But he can hit.”

“Pro baseball’s not real good at reinventing guys,” says Billy.

There was almost immediate backfire from the old guard. An analogous book, taking the opposite perspective, was written in 2005. Joe Morgan, a Hall of Fame second baseman and announcer, became known for criticizing the book every chance he had. The anti-intellectual mob grasped its moment to subjectively and stubbornly assert itself over the scientific, evidence-based minority favoring truth over tradition.

Then something weird happened.

Nate Silver of Baseball Prospectus began developing a projection system known PECOTA. The algorithm would look at the historical data of all players to find the most similar to today’s to create aging profiles. All explanatory variables were considered to build the most accurate model of the “shape of the player”. Being the good scientist, Silver, included a few variables that were often ignored by the sabermetric community, such as height and weight, in making these calculations. These variables were considered to be irrelevant and a vestige of the stubborn opinions of scouts.

Both variables were statistically and practically significant in the model.

A favorite prospect of the sabermetrics crowd in the early 2000s was Jackie Rexrode of the Arizona Diamondbacks. His performance was everything such analysts wanted in a leadoff hitter. He walked frequently, which, when coupled with a reasonably high batting average, portended to a perennial all star candidate when he reached the big leagues. He even at good speed, which while immaterial to most analysts, was pleasing aesthetically for someone at the top of the order. Scouts couldn’t get what the big deal was, insisting that his frequent walks were only the result of a decent batting eye. Once he reached a level where pitchers could throw strikes consistently, he would “get the bat knocked out of his hands” when forced to swing.

Jackie Rexrode never made the major leagues, not even with the Oakland A’s.
These two episodes, where the analysts we so irrefutably and unambiguously wrong about ideas they were very excited about, were not the only sharp objects maiming the hubris of sabermetrics. The pretensions to science attracted empiricists who actually deserved their pretensions to science. Major League Equivalences, statistics that attempted to morph the statistics of minor or foreign leagues into what they would be “equivalent” in the major leagues, were derided by Tom Tango on theoretical grounds. These equivalences, developed initially by Bill James and expanded later by others (PECOTA uses a modified form of Major League Equivalences known as Davenport Translations), are a keystone to many statistical projection systems, whether that be PECOTA or the assumptions that Billy Beane’s then-assistant, Paul DePodesta, made in Moneyball. Sabermetrics additionally became criticized for its over-reliance on linear regression elsewhere. A decidedly un-profound statement hit the community. Analytics is hard.

Shortly after Moneyball was published, Dayn Perry wrote an article that framed the irrationality of the “controversy” in what quickly became a race to state the obvious.

A question that’s sometimes posed goes something like this: “Should you run an organization with scouts or statistics?” My answer is the same it would be if someone asked me: “Beer or tacos?” Both, you fool. Why construct an either-or scenario where none need exist? Heady organizations know they need as much good information as possible before they make critical decisions. Boston under Epstein, for example, is a veritable clearinghouse for disparate ideas and perspectives, and so far it’s working just fine.

Silver himself later suggested the over-ambitiousness of earlier sabermetric studies.

Being willing to admit when you are wrong, or at least when your knowledge is limited, tends to help one’s credibility when pressing the really important points. This is a little piece of psychology that all good politicians (and all good poker players) recognize. There is, in fact, a sort of feedback mechanism at work here: as sabermetrics moves more comfortably toward the orthodoxy, it can acknowledge more freely those places where it performs imperfectly, just as a standing president with a high popularity rating can withstand a scandal that would kill the careers of a thousand lesser-knowns in the party primaries. That admission, in turn, should help to increase the sympathy that traditionalists have for analysis, enhancing dialogue and pushing both sides toward the center.

Baseball Prospectus co-founder Gary Huckaby wrote an almost defeatist understanding of how an objective history may portray Moneyball.

Think about it—what are the real lessons, the ones that can actually be applied, that one can take from baseball analysis? Let’s go through the biggies:

• OBP is good. [...]
• Don’t slag pitchers’ arms. [...]

Really, those are the two big lessons. There are a ton of other lessons, many of them valuable, many of them related to correcting the dysfunctional behaviors created by managing to baseball’s accounting system, rather than to winning games and championships.

And later in the same article,

Another way to look at the issue brings the point home much more directly—the scouts versus stats battle never really existed, and that the scouts won. People making their living in front offices have played Oracle and IBM to the analysis community’s “open source.” Those companies (and others) are happy to let a self-directed, competent, and uncompensated gaggle of fragrant, bearded unix gurus take time out from watching Mystery Science Theatre 3000 to develop a fantastic piece of software for the masses, then adopt it as their own without having to spend a huge amount of their own resources on the project. In terms of baseball analysis, the front office folks have learned the lessons, at least the most important ones, and have already internalized the key points that can make their clubs better. In short, the real cause of death for baseball analysis is that it just isn’t very difficult to do, particularly if what you want is a 20/80 solution—80 percent of the maximum available benefit for only 20 percent of the investment.

There just wasn’t much to the benefits of empirical analysis that wasn’t known already through experience. There were a few inefficiencies that required empirics to fix, but scouts were rightfully skeptical of the broad assertions made by analysts. Players who walk in the low minors or college and do nothing else don’t develop into big league players, normally. It’s very hard for a short right handed pitcher to succeed since it’s hard for him to gain velocity as he matures. The irrational stubbornness of traditional baseball wisdom had absolutely no reason to be right scientifically, but it was.

Moreover, other sabermetric axiomatic pillars posses scarce empirical foundation. The ability to hit in the clutch, clubhouse chemistry, the importance of game calling for catchers, and the importance of big league coaches have been frequently marginalized. After several years of such browbeating the notion that such subjective factors hold importance, empirics are finally beginning to emerge suggesting they may actually exist. David Glassko of The Hardball Times presented intriguing evidence that the right managers can increase the performance of individual players in the Hardball Times Baseball Annual 2008. Nate Silver proposed a statistically significant metric demonstrating certain players’ ability to hit in the clutch. Bill James wrote a heavily criticized article condemning those who suggest we know such subjective factors do not exist. The realization that absence of statistical significance in even multiple regression of subjective factors does not preclude the existence of those factors seriously complicates the answers provided by Sabermetric analysts.

The “Scouts versus Stats” debate and its failure to correctly incorporate the difficulty in teasing truth out of empirics is representative of an age-old aphorism. The absence of evidence is not the evidence of absence. The fact that the data set in hand cannot prove that experiential beliefs have truth does not imply that they are incorrect. In a hypothetical vacuum, or a world where you can assume the irrationality of experience, the null hypothesis should be that any factor has no effect on another variable. In practicality and reality, the null hypothesis must be that the position favored by experience and “common sense” is true. There is no need to hold back evidence simply because it is formed on intuition, experience, and “subjectivity”.

Knowledge is a difficult, diffuse entity willing partially to present itself in the minds of many and completely in none. While some of its quantitative, unknown truths may magically appear in empirics, the truths more difficult to articulate may be hidden indefinitely simply because there is no reasonable way to measure them. It was perfectly rational for Beane’s scouts to question his “science”. The assumption with which we should enter the discussion is that the intuition-based, subjective evaluation is true, not that we know absolutely nothing from them. In our own lives, we temper “statistics” we hear with a dose of common sense and skepticism. Why should we discount and ignore theoretically it simply because don’t have the studies to back it up?

In Blink, Malcolm Gladwell discusses our ability to “think without thinking” and that there exist an amazing array of calculations our minds instantaneously go through when presented with a question. He cites several examples where our conjectural, instantaneous reactions are more accurate than those presented by difficult, time-consuming analysis. In the words of Gladwell, scouts are the experts of “thin slicing”. They know exactly what to look for and know it when they see it without a model to tell them.

In many ways, Sabermetrics may be a classic example of the failures of scientism. The fact that we should use science whenever we can to evaluate a theory does not mean that we can always use science to evaluate that theory. F.A. Hayek spoke against this type of thinking in the scope of economics in his Nobel Prize acceptance speech.

Unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena [such as baseball], the aspects of the events to be accounted for about which we can get quantitative data are necessarily limited and may not include the important ones. While in the physical sciences it is generally assumed, probably with good reason, that any important factor which determines the observed events will itself be directly observable and measurable, in the study of such complex phenomena as the market, which depend on the actions of many individuals, all the circumstances which will determine the outcome of a process, for reasons which I shall explain later, will hardly ever be fully known or measurable. And while in the physical sciences the investigator will be able to measure what, on the basis of a prima facie theory, he thinks important, in the social sciences often that is treated as important which happens to be accessible to measurement. This is sometimes carried to the point where it is demanded that our theories must be formulated in such terms that they refer only to measurable magnitudes.

We know that subjective factors have some effect on the performance of a player. The question is how much. Since Sabermetrics has not been able to disprove empirically the traditional null hypothesis by using any method that does not presume the fallaciousness that “the absence of evidence implies the evidence of absence”, we must imperatively assume that, for example, Jason Varitek’s ability to manage Boston’s pitching staff is an important aspect of its run prevention.

It may well be that the Sabermetric movement only confused itself by attacking scouts. There was nothing intrinsically painful or unintuitive to the informed fan that perhaps it is easier to hit for power if you weigh more. In contrast, the narrative platitudes of announcers and journalists, or most charitable the insiders they selectively quote and interview. The fourth estate has an interest in keeping things interesting for the public, not to identify which players who may help a team win.

It’s not even that surprising how long it took for executives to come to terms with a group of arrogant outsiders who think they know something without ever playing the game. The three primary concepts of Sabermetrics- On-base percentage is important, a young pitcher’s arm is a terrible thing to waste, and college players were undervalued in the 90s- were incorporated relatively quickly into the market once they were made apparent.

The prevailing wisdom was true. It is not for stupidity that people cling to the knowledge of traditions, but because more often than not those who insist that they know more than those who spent their lives studying the issues are what they appear to be, arrogant pricks. There’s nothing wrong with looking at the world empirically. In fact, you’ll know more because you did. However, it’s understanding where those numbers falter and that the truth may be hiding where the numbers can never may gain visibility that quantitative wisdom may begin. Knowledge is a fickle, amorphous notion that strives to invisibly squirm out of any top-down definition, model, or generality that may claim to understand what is really going on. The subjective opinions of the many, incorporating all aspects of the vagueness suggested by the “diffuse nature of knowledge”, is categorically more effective in estimating and predicting than the objective understanding of the few.

Political Art

One legitimate purpose of art is to demonstrate emotionally and narratively truth that one could not effectively communicate through essays and arguments. Personally, I feel like I learned more about myself while reading The Fall than through any other experience in my life. Camus’s elucidation of guilt and duplicity is one that I could never give justice to; any summary invariably sells the meaning of the book short. That is what makes it art and great art. It is the unique utilization of the medium and the psychological and emotional evidence of indefinable truths of human existence that could only ever be know in the realm of intuition.

That is the key to art that communicates, or to be flippant about it, any art that is more meaningful than pretty doodles. It communicates ideas that can only be known intuitively. It’s hard to prove, through science and unassuming scholasticism, the notion that everyone has the right not to be attacked with a kitchen knife, but most people feel perfectly justified in believing it. That point of view is not wrong, just reliant on art or similarly intuition-based forms of persuasion to make its point (setting aside the idea that we should only protect life because no one else wants to be attacked, which in turn makes other intuition-based, consequentialist assumptions). When we are realistically unable to make normative statements on an issue, we can only make positive ones or none at all.

Art runs into problems when it stops trying to answer questions of what values we should hold and instead answers how we should get there. Art can tell us it’s a bad thing people are poor; it cannot tell us whether the government can fix it. Art can tell us to consider divinity; it cannot tell us to support churches with the state. Believing art can have any impact on whether we should end a specific war or support free market capitalism misses this point entirely. We can test, if not scientifically and especially effectively, whether such actions will have the desired effect. The failure of politicized art is a corollary to the narrative fallacy. Just as trying to shoehorn the events of history into a neat story fails to look at evidence, using art to force one’s conjectural view of life into an idealized vehicle is entirely meaningless.

Rand said,

Art is a selective re-creation of reality according to an artist’s metaphysical value-judgments. An artist recreates those aspects of reality which represent his fundamental view of man’s nature.

This is absolutely true and as good of a definition of communicative art as any. Ironically, in her own books, Rand misses out the importance of this, the “value-judgments”. All too often, her villains do not somehow oppose the proposed values, but evilly want to subvert them. She later writes a normative argument in favor of capitalism, but her works of fiction suggest an inevitable failure of planning, which in turn is a positive conjecture we may test. Neither flaw belongs in a true work of communicative art.

Rand has been either vilified or beloved, and very much so by what amount to party lines. The academic community is clear in its distaste for either The Fountainhead or Atlas Shrugged, which is understandable if not for everything else but its gratuitously awkward “romance scenes.” However, her choice of values, an unholy union of Ludwig von Mises and RIchard Dawkins, is intuitionally based and non-falsifiable, and thus impossible to rationally dismiss. What throws off the analyses of the press and academics is the flaws above, which while are prevalent in art favored by left-liberals, is difficult to honestly assess until you are the target yourself. Only then does it become morally repugnant.

The flaws of The Cider House Rules and the works of Green Day are completely analogous, but far more acceptable to the left-liberal perspective. Each tell a story that matches an argument. By themselves and their nature, they cannot make such arguments.

Art cannot reasonably answer political questions. It can sway political questions as to the values one should fight for, such as the what and the why. Through more effective means can we answer the questions of the whether, the how, and the how much. Attempting to answer questions that cannot intrinsically be scientifically answered should not be categorically scorned, but the methods we use in applying those values one may quantitatively scrutinize. The communicative role of art is essential and important in the way we may form values, but it should never grasp pretensions of being better than empirics and logic in defining the feasibility of the means that may emerge from the values.

The Occam’s Razor of Events

In Major League Baseball, Dan Uggla of the Florida Marlins is a Most Valuable Player candidate this season. In 2005, he was a non-prospect for the Arizona Diamondbacks. The Marlins didn’t have a real option at second base the following year, and acquired Uggla for almost literally nothing from the Diamondbacks in the Rule 5 Draft. No one thought much of it, as he was considered a marginal player who posted only an ostensibly impressive performance in the minor leagues despite his advanced age. Sabermetrics think tank Baseball Prospectus published in their 2006 annual that

While on the surface he might look like a hustling, dirtier version of Tony Graffanino, there are a few cautions. He spent three years in Lancaster`s bandbox, and only “broke out” after an extended stay at Double-A. He`ll also be 26 by Opening Day. His history of “eventually getting it” made him one of Florida`s Rule 5 picks. With the Marlins he has a chance to start at second. The bar is low, but considering he`ll move from low minors hitter`s parks in offensive leagues to a major-league pitcher`s park, the results should be predictably Uggla.

Tony Graffanino was an occasionally useful second baseman who spent much of his career as a utility player or super-sub off the bench. His best years were around average defensively and offensively for the position. This was not a flattering comparison.

Uggla then went on to finish third in the league in Rookie of the Year balloting, followed with a year of similar value in 2007, and has had his best year so far in 2008.

Why did Uggla’s performance jump so far ahead of expectations?

Occam’s Razor states,

that the explanation of any phenomenon should make as few assumptions as possible, eliminating those that make no difference in the observable predictions of the explanatory hypothesis or theory.

Practically, it is taken to mean that the simplest explanation is the best one. Instead of strong, convoluted theories, we should understand and start with the basic one. There are millions of hypothetical explanations to any set of circumstances, so we have look somewhere. Although the principle is mostly applied to science, I am interested in its application to history, the true causes of events, and to avoiding Monday Morning Quarterback as much as possible.

The simplest reason why anything happens is no reason. No underlying fact needs to change for reality to be different. What we see now in the living world today may often be pure noise and no signal.
Angel Berroa is another rookie who performed well above expectations. In a hotly debated ballot, he won the 2003 American League Rookie of the Year. Before 2003, Baseball Prospectus said

Let’s see: the Royals’ best prospect entering the season injured his knee and missed two months, hit .215 in Omaha after he returned, showed vastly diminished range, and drew six walks by the end of July. On the plus side, he did celebrate three birthdays last year [due to crackdowns after 9/11, players "got older" when it was revealed that they lied about their birth certificates. It is strongly believed younger players have potential to increase the skills they already have. I looked but cannot find a good citation for this, although you can find references to it by googling the catty neologism, "agegate"]. No, it was not a good year for Berroa. His apologists argue that he continued to favor his knee when he got back, which hurt him both offensively and defensively. In their support, he looked much more comfortable both at bat and in the field during a September call-up. The Royals are determined to let him take over as the starting shortstop this season, and given that he turns 25 in April, they might as well see if he’ll sink or swim. There’s upside here, but right now Billy Beane wouldn’t trade Mark Ellis for him straight up.

No one took Berroa that seriously prior to 2003. After that sparkling rookie season, he went on to play worse until he was demoted in favor of someone who, if nothing else, had reasonable defensive credentials, in Tony Pena, Jr.

The simplest explanation to this set of events is that Berroa was more or less the same player throughout the entire period. Nothing caused him to have above average skills across the board in his peak season except pure randomness. This explanation is far simpler than beginning to suggest that he “broke through” in 2003 and later “lost bat speed” or that “pitchers found holes in his swing.” Announcers exhibit the pretense of “knowing” why certain pitchers choose to throw a certain pitch or why a hitter couldn’t come through in a certain situation, while questions of numbers and averages smoothing out seem invalid over the course of even an entire season. The null hypothesis explaining Berroa’s 2003 should be that there is no reason at all.
If we have evidence that something actually changed and caused a result, it is more likely that a “Confluence of Circumstances” caused it rather than a single event. Although this may seem unintuitive and at odds with the previous presumption that normally nothing changes, it is in the same spirit. It’s far easier for a multitude of minute “real” changes to occur in each underlying variable than catastrophic change in one. For any one of these changes, a biased author may attempt to explain the entire event by pointing to one while then assuming the others. It may be true that the event wouldn’t have occurred without one of the causes, but that does not imply that the one is the only cause.

For the classic example, consider the Roman Empire. Quite a few people have already. Would Rome have fallen were it not for the Germans? Probably not. A continued reliance on slave labor and the Antonine Plague? Perhaps. It is most likely that not one of these causes by themselves, but some combination thereof, caused Rome to fall.

Whenever you hear of a city in riot or a failing company, it is safe to assume that it would not have happened had it not been for several other adverse conditions affecting it, rather than the one or two the reporter happens to mention.

People look for simple answers. Those answers aren’t the truly simplest empirically or logically, but the easiest to understand. Just as unintuitively, those “simple answers” are the absolute opposite of Occam’s Razor. It’s far easier for unpredictable, subtle movements in several variables in data or a slight alteration in several factors causes an event than some narrative event to push history forward.

There are certain counterexamples. When the Federal Reserve in the United States decides to raise interest rates, there’s not a lot we can do about it. There are myriad reasons why it may choose to do so, but when it does, all it needs to do is decrease the supply of money until we reach that point. While the question of why the Fed decided to raise interest rates remains, it’s clear enough, with certain theoretical exceptions, how it increased after the Fed made the decision.

The most unlikely scenario is the conspiracy theory. Not only did someone see whatever happened coming, but planned it and used it to his advantage. The strongest implication to make is that of evil. It requires the assumption that whatever happened did not occur due to randomness, that a single variable can control the result, and that someone is able to take advantage of it. Still, there are different “kinds” of evil. It’s reasonably believable that in a controlled setting, such as the votes of congress, that strings can be pulled by lobbyists and the decisions of the few can cause drastic changes for the many. In an uncontrolled setting, such as that of public opinion or the free market, conspicuous evil is next to impossible. When the public is not coerced physically or monetarily into rejecting conspiracy theories (read: the explanation that someone is doing something evil without fear of retribution), it’s more reasonable to point to the general un-truth of the world than, say, the price of oil is high because the companies are trying to screw you.
To summarize, Occam’s Razor slices several distinct layers.

1. It’s simpler to say that randomness caused something to occur than any set of events.

2. It’s simpler to say that a several events caused something than a single event.

3. It’s simpler to say that a single event caused something than that someone caused that event maliciously.

3. It’s simpler to say that someone caused it maliciously than to say that someone caused something maliciously and no one caught onto it while it was happening.

Where does Dan Uggla fit into all of this? Some may suggest he got discouraged by staying in the minors for long while many talented prospects in the Diamondbacks system shut down his future. Others may point to the workouts he undertook after finding out he was drafted by the Marlins or the quality of Major League Coaching he finally received. At this point, with the empirics we do have, I reject the assertion that his improvement may still be pure randomness, but it seems overwhelmingly likely that his radical increases in performance were caused by dozens of inscrutably slight adjustments in place of something that the casual baseball fan can comprehend in print.