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Punditry was something quite removed from my work life and home life. I avoid the detritus that passes for political analysis in the United States, choosing instead to focus on long form articles in The Atlantic, the NYT Magazine, and The New Yorker. I am surprised at one “holy war” (*nix vs. Win vs. Mac style) that has cropped up regarding ereaders.

Emma Silver is one of the latest to defend paper books against in silico texts. My acquaintance, Chris Meadows, has written a response to it; these two provide a snapshot of the types of arguments slung by both sides.

Generally, most partisans talk up the virtues of either paper or e-books. That is, they defend the form used by readers to engage authors.

My problem with these arguments is that neither side focus on the real issue. Reading is not a competition between old-school curmudgeons and bleeding-edge tech heads. Reading is being assaulted by demands on our attention by video games, movies, television, music, and time spent with friends and families. Whether one goes to a concert, a theater, sits on a couch, in a bar, or use the Internet is besides the point. Again, it is not the how one obtains entertainment that matters, only that, with the limited time we have, we seek other types of entertainment.

In this context, I do not see e-readers (whether it be Kindle, Nook, or a software reader on an iPhone/Android phone/netbook/PDA) competing against paper. The e-readers are competing against the devices people use to listen to music and watch movies on the go. That is why I think it is in every book lovers interest to promote long-form reading, and to defend this form from subversion.

No one can predict how devices like the Kindle will affect the novel and historical scholarship, two types of writing I would classify as most endangered. There will always be a demand for light fiction. There will always be people who seek out information and political interpretation from sources with whom they already agree with. There will always be a demand for hack and slash biographies providing salacious drug and sex habits of the rich and famous.

Novels and histories require an immense amount of attention. I can see that histories will become more “multimedia” in the future. Histories already are: photographic plates and maps are generally included, along with charts, even in paper versions. As the recent future recedes, we will be able to include more news and sounds. And why is this a bad thing? For instance, why wouldn’t we want to hear Churchill speak? He was a brilliant writer and a speaker; how wonderful would it be if a discussion of his service during World War II also provided aural examples of his rousing speeches to raise British morale?

The problem with anti-technology screeds is that they ignore the proscriptive phase of the argument. The solution will never be, let us ignore the device. It is already too late: the devices are too popular. I see the Kindle becoming the paperbacks of the ebook world: it cannot not yet do video. The iPad and Android tablets will drive ebook development, not the Nook or the Kindle. These will provide the basic platform for how texts are presented to the public.

And there is a real fear here that long-form reading will be lost, since it is so attention-intensive. The defense of reading will be successful only if we can persuade youth to turn to long form (paper and electronic) books when they desire knowledge and thoughtful analysis. That is where we all need to focus our efforts; to teach the young that, for some things, they need to sit, read, and think. We need to increase exposure of historians who write with brio and panache. We need to convince future readers that long form books are still relevant, providing the best method of compressing knowledge and complex ideas (as opposed to the fact and information based content stored in databases and across the Internet.) If an e-reader is how the youth today will engage with long texts, then we need to do more to insert ourselves into the processes by which books and their presentation is brought to the public. The paper versus electronic format as a diversion. We cannot afford to lose to the perception that long books belong with the dinosaurs.

Although this blog is ostensibly about books, I’ve written a lot about sports, mostly dealing with how non-scientist readers perceive statistical analysis of athlete productivity. This issue fascinates me; I think how people think about sports statistics provides a microcosm in how they may respond to similar treatments in the scientific realm. Economists, mathematicians, engineers and physicists will provide a better explanation of the analysis than I can. Instead, I want to focus on the people who draw (shall we say) interesting conclusions about research.

In a recent podcast, Bill Simmons interviewed Buzz Bissinger on the BS Report (July 28, 2010). Bissinger gained some negative exposure as he had railed against the blogosphere and sports analysis. In this podcast, Bissinger was given some time to elaborate on his thoughts. He most certainly is not a raving lunatic, but he did say a few things that I find representative of how statistical analyses are often misinterpreted by non-scientists (and  even scientists.)

Bissinger took the opportunity to trash Michael Lewis’s Moneyball, mostly by pointing out how Billy Beane isn’t so smart, and that all in the end, the statistical techniques didn’t work – only Kevin Youkilis – mentioned in the book, had proven to be a success. I think that misses the point. Yes, the book documents the tension between the scouts and the stat-heads. I think Lewis chose this approach to make the book more appealing, by taking the human interest angle, than simply writing a technical description of Beane’s “new” approach. Perhaps Lewis overstates the case in showing how entrenched baseball GMs were in relying on eyeball and qualitative skill assessments, but the point I got from the book was that: Beane worked under money constraints. He needed a competitive edge. Most baseball organizations relied on scouts. Beane thought that to be successful, he needed to do something different (but presumably had some relevance) to provide baseball success.

Beane could have used fortune tellers; I think the technique in Moneyball (i.e. statistical analysis) is besides the point. Beane found something that was different and based more of his decisions on this new evaluation method. This is a separate issue from how well the new techniques performed. the first issue is whether the new technique told him something different. As it happens (as documented in Moneyball,  Bill James’s Baseball Abstracts, and by many sports writers and analysts), it did. The result is that Beane was able to leverage that difference – in this case, he valued some abilities that others did not – and signed those players to his roster. The assumption is that if his techniques couldn’t give him anything different from previous methods of evaluation, than he would have had nothing to exploit.

The second point is whether the techniques told him something that was correct. And again, the stats did provide him with a metric that has a high correlation with winning baseball games – the on-base percentage. So one thing he was able to exploit was the perception in value of batting average (BA) versus on-base percentage (OBP). He couldn’t sign power hitters: GMs – and fans – like home runs. He avoided signing hitters with high BA and instead signed those with high OBP.

This led to a third point: Beane can only leverage OBP to find cheap players (and still win) so long as there were few GMs doing the same. Of course the cost of OBP will increase if others come onboard and have deep pockets (like the Yankees and the Red Sox.) So Beane – and other GMs – would have to become more sophisticated in how they draft and sign players. Especially if they work under financial constraints. As my undergraduate advisor said, “You have to squeeze the data.”

One valid point point Bissinger made was that the success of the Oakland A’s coincided with the Big Three pitchers. So clearly, Bissinger wrote off a significant amount of  Oakland success to the three. That’s fine, as the question can be settled by looking at data. What annoyed me is when readers do not pay attention to the argument. I just felt that Moneyball was more about how one can find success by examining what everyone else is doing, and then doing something different. The only constraint is whether  something different would bring success.

I felt that Bissinger is projecting when he assumes that using stats means the rejection of visual experience. The importance of Moneyball is in demonstrating that one can find success by simply finding out what people have overlooked. Once the herd follows, it makes sense to seek out alternative measures, or, more likely, to find out what others are ignoring. If the current trend is on high OBP and ignoring pitchers with a high win-count, then a smart GM needs to exploit what is currently undervalued. Statistics happens to be one such tool – but it isn’t the only tool.

And part of the reason I write this is, again, to highlight the fact that people usually have unvoiced assumptions about the metrics they use. The frame of reference is important. In science, we explicitly create yardsticks for every experiment we perform. We assess things as whether they differ from control. It is a powerful concept. And even if the yardstick is simply another yardstick, we can still draw conclusions based on differences (or even similarities, if one derives the same answer by independent means.)

This brings me to recent Joe Posnanski and David Berri posts. The three posts I selected all demonstrate  the internal yardsticks (hidden or otherwise) that people use when they make comparisons. I am a fan of these writers. I think Posnanski has provided a valuable service in bridging the gap between analysis and understanding, facts and knowledge. Whether one agrees or disagrees with his posts, I think Posnanski is extremely thoughtful and clear about his assumptions and conclusions, which facilicates discussion.  The post has a simple point: Posnanski wrote about “seasons for the ages.” A number of readers immediately wrote to him, complaining about how just about anyone who hits 50 home runs in a season would qualify. To which Posnanski coined a new term (kind of like a sniglet) – obviopiphany.He realized that most people simply associate home runs with a fantastic season for a hitter. That isn’t what Posnanski meant, and in the post he offers some correction.

The Posnanski post has a simple theme and an interesting suggestion: the outrage over steroids may be due to the fact that people assume that home run hitters are good hitters. Since steroids help power, the assumption is that steroids make hitters good – which in most cases simply means more home runs. But Posnanski – and others sabermetricians – propose that one must hit home runs in the context of getting fewer strikeouts and more walks. The liability involved in striking out more, and not walking, is too much and washes out the gains made from hitting the ball far. Thus Posnanski posts names a 5 players who are not in the Hall of Fame, and aren’t home run hitters, but who nevertheless produced at the plate – according to some advanced hitting metrics. I won’t go into this more, except to say that here, Posnanski makes his assumptions clear. He uses OBP+, wins above replacement player, and other advanced metrics to make his point. But it is telling that Posnanski had to stitch together the assumptions his readers had – that the yardstick for good hitting simply boils down to home runs.

The Berri posts describe something similar. One of them is from a guest contributor, Ben Gulker, writing about how Rajon Rondo was not going to be selected for Team USA in the world championship because he doesn’t gather enough points. The other highlights how the perception of Bob McAdoo  changed as a function of the fortunes of his team. Interestingly enough, McAdoo became a greater point getter while becoming a less efficient shooter and turning the ball over more; at the same time, his reputation was burnished by the championships his teams won.

The story has been told many times by Berri. It seems that in general, basketball writers and analysts associate good players as those who score points (in the literal sense, regardless of shooting percentage) and who played on championship teams. There are several problems here. Point getting must take place in the context of a high shooting percentage. One must not turn the ball over, one must rebound, one must not commit an above average number of fouls, and hopefully get a few steals and blocks. I don’t think anyone would disagree that such a player is a complete player and ought to be quite desirable, regardless of how many championship rings he has or if he scores only 12 points a game. Berri has examined this issue of yardsticks, and he has found that what sports writers, coaches, and GMs think of players has an extremely high correlation with, simply, how many points they get (this is shown by what the writers write and how they vote for player awards, how often coaches play someone, and how much GMs pay players.)  The verbiage writing up about the defensive prowess and the “little things” are ignored when the awards are given and fat contracts handed out. Point getters get the most accolades and the most money.

And the other point is how easily point getters reflect the luster of championships. Nevermind that no player can win alone, but this again is an example of how people end up with not only unspoken yardsticks, but also choose a frame of reference without analyzing if it is the correct one. The reference point is a championship ring. As has been documented, championships are not good indicators of good teams. The regular season is. This is simply due to sample sizes. More games are played in the regular season. Teams are more likely to arrive at their “true” performance level than in a championship tourney with a variable number of games – and frankly where streaks matter. A good team might lose four games in a row, in the regular season, but they may lose only 10 for the year. In a tournament, they would be bounced out if they lose four in a series.

In this context, the Premier League system in soccer makes sense. The best teams compete in a regular season; the team with the best record is the champion. So people who assume that a point-getter who plays on a championship is better than a player who shoots efficiently (but with fewer points) and rebounds/steals/blocks/does not turnover above average, and on a non-champion team, make two errors. They selected the wrong metric twice over.

With that said, I could only have made that point because of newer metrics that provide another frame of reference. Moreover, the new metrics tend to have improved predictive abilities over simply looking at point-getting totals. Among the new metrics, there are some that show a higher correlation with the scoring difference (and thus win/loss record) of teams. It doesn’t matter what they are, but an important point is that one can derive these conclusions about which metric is better or worse.

This is the main difference in scientific  (of which I include athlete productivity analysis) and lay discourse. In the former, the assumptions are made bare and frames discussion. A good scientific paper (and trust me, there are bad ones) makes excruciatingly detailed descriptions of controls, the points of comparisons, any algorithms/formulae, and how things are compared. In the lay discourse, this isn’t the standard one would use, because communicating scientific findings to other scientists use a stylized convention. Using such a mode of communication with friends would make one a bore and a pedant – not to mention one would become lonely real quick.

One recent meme making the rounds on the Internet is the site “I write like…” I haven’t looked into the algorithm yet, but I’m not sure if I can. It isn’t obvious on the website what the statistical analysis entails. But of course, I was curious about my writing style. Some preliminary findings:

1) Repeated submissions with the same text results in the same author

2) Of the 12 samples I submitted (all from this blog), I got the following results:

The Arthur Conan Doyle hit is an interesting one. It came from my post on James Patterson’s King Tut book. Part of the algorithm must account for theme/genre, probably based upon a concordance. There’s no reason to think that I changed my style so much when I wrote about crime. The algorithm might have narrowed the field down using certain keywords, and then selected an author.

What I write here are essays. I’m not sure what it means to bear similarities to (mostly) fiction authors.

I am mildly insulted by the HP Lovecraft: perhaps that post rambled and didn’t come to a point?

All in all, a nice bit of fun.

Update: It turns out I was on the right track regarding “keywords”. Here’s a report from the Huffington Post, which contains a few words from the author of I Write Like.

I got to thinking about a difference between writers and commenters. One crucial difference is skill, naturally. However, I am thinking about some of the emails sportswriters such as Joe Posnanski, Dave Berri, Peter King, and Bill Simmons get. The best correspondence they publish tends to follow up on a thought, often giving an example about some tragedy the pundits had written about.

Considering this small and selective sample, I concluded that the main difference beween lay writers and the professional is context. Professionals establish context in which lay writers tend to work. That is, professional writers organize examples by their themes, while the lay writers (i.e. commenters) write single examples. This leads, firstly, to the difference in length. The commenters provide an example or a vignette that refers to the established idea. I suppose one-graf bloggers tend to fall into this category, no matter how good the actual prose is. The professional writer would have developed the context for his main argument before using examples to emphasize his own point. While longer is not always better, of course developing ideas take up space. This leads to longer pieces. It takes a bit of skill to compress ideas into a paragraph (try reading abstracts from science papers and see if it makes sense to someone outside of the field you work in. The good ones will make sense to someone who doesn’t work in your field.)

For now, I want to focus on the difference between a professional writer’s and a scientist’s mode of writing. At the level of sports pundits and analysis, there are the Joe Posnanskis and Bill Simmons of the world, and there are popularizer of research, like Dave Berri. All three are wonderful writers for their fields, but I would rather read Posnanski and Simmons before Berri, if considering only the literary aspects of their writing. Nevertheless, the main difference between the two is not in the scope but in the details that provide context for their pieces.

Recently, Posnanski wrote about his desire to adopt a baseball stat for his blog. He hinted at reasons for disliking OPS (simply, on-base percentage + slugging avg), and presented an argument for his “hitting average.” That’s all fine and good; readers of Dave Berri’s blog and book Wages of Wins will note that finding Berri in fact tries to find statistical measures of athlete “productivity” that relates to point production and thus, wins. Now, here’s the difference between Posnanski’s and Berri’s approaches. It certainly isn’t scope, since both are ostensibly doing the same thing. However, Berri’s approach is scientifically sound where Posnanski’s isn’t, despite Posnanski dealing with objective mathemetical measures.

A caveat: I am not saying that Posnanski’s stat or approach is wrong. Posnanski has made every attempt to say that what he is doing is more for aesthetic reasons and than to find THE stat, the single model that explains MOST aspects of baseball. Again, I am merely considering their styles of presentation, which are partially limited by the scope and how they approach the details.

In any case, Posnanski details how stat-geek readers of his blog, led by Tom Tango, generated a new stat called “linear weights ratio.” Posnanski tests this stat out by checking the rankings of a number of players; of course, there is some alignment with more traditional advanced baseball stats. He also presents the formula for his hitting average, for readers to play with. Again, there’s nothing intrinsically wrong with this; Posnanski isn’t doing econometrics. If anything, he is doing a great service by getting various reads to think mathematically. But Posnanski doesn’t provide a context to evaluate that new metric. Mainly, he doesn’t compare this metric to established metrics. In contrast, Berri’s approach is, in essence scientific, since his arguments are constrained by the context of describing and comparing these metrics.

This context is the difference between a layman’s approach and a scientist’s approach. Berri did much the same thing as Posnanski suggests in researching basketball players’ productivity. Berri looked at the linear regression of things like points score, shooting percentage, rebounds, turnovers, and so forth, on the amount of points scored. Based on these stats and the weights identified from the regression analysis, he generated a linear model. He placed this stat, Wins Produced, into context by first applying it to all NBA players through all years for which stats are available, he compared its correlation to points scored for and against to existing NBA statistical models, and he generated points of comparisons for each NBA player to the “mean” player at his position. In this way, he is able to actually determine that his measure has a higher correlation to the efficiency differential (points scored – points given up) than the other stats. He was also able to identify the main difference between his and other models, in that the other models tend to use points scored as opposed to the ratio of points scored and shots attempted.

The weights Berri used are not arbitrary in the sense that he simply pulled them out in order to emphasize some difference between NBA players that he thought should exist. Naturally, he might have removed some measures from his model because the weight isn’t high enough, but that’s a different matter from “fine tuning” the weight. Regardless, the most important point is that generally, he made a model from the aggregates that significantly correlated with efficiency differential before applying the model to the players. In this way, he has created rankings of NBA player productivity that has generated some arguments in the sport pundit community (for an example, see here, here, here and here.)
While the particulars aren’t important, the conflict is illustrative of a scientific versus a more laid-back  (although it could still be rigorous) analytical approach. For Berri, he simply sets up a model, cranks out the numbers, and then organizes his views of the players by examining the stats. For the laid-back approach, one sees if the stat is properly associated with a player. Again, this latter approach is fine, within its domain. Sports writers are not scientists, nor do they control the purse strings for a sports team. Even within a sports franchise, one does not need to rely on statistics, if they so desire. As Berri notes, the stats comprise merely one component of NBA evaluation. It’s a shortcut to organizing player’s performance. In no case does it substitute ways of identifying why certain players are not rebounding, or generating enough assists, or reducing their turnovers.

In the Posnanski example, he presented a stat which is correlated with runs scored in baseball. He didn’t say whether this correlation is necessarily higher than other measures (such as OPS). This is a subtle point that is often missed. If the correlations between both measures are similar, than there really is no difference. Of course, there may be a lot more numbers involved in one over the other, but most scientists would simpler choose with one with fewer values. It’s probably also easier to calculate. Using the other numbers do not give you added value. I have seen people talk about complex stats as if complexity (lots of math squigglies) is somehow better or is more correct. That is not the case.

So, how does this relate to writing styles? Well, if the laymen write in examples, and professional writers extract themes and trends from examples, then scientists try to extract ideas/themes/trends that apply to all examples (well, ideally, all, but in generally they try to capture data from a meaningful sample that is indicative of the whole population.)

However, there is a limitation in the presentation of a scientific finding: the conclusions are bound by the premise of the hypothesis and the methods and measures that are used. Thus, in Berri’s case, he presents arguments for NBA player’s productivity in terms of his measure (or other measures, if he’s interested in comparing the different metrics.) But he is constrained by that, less so in his blog, but certainly in his peer-reviewed papers. As a matter of fact, Berri’s blog tends to be a bit dry, breaking down a player’s deficiencies by examining the particulars of how low his shooting percentage, rebounds, assists, etc are relative to the league or position average. Just as importantly, Berri suggests that the metric is best used as an entry point into proper player evaluation and development. It’s a short hand for identify players who might be improved. Despite Berri suggesting players don’t change much from year to year, from team to team, from coach to coach, it may be because no one has tailored a practice program for players based on this simple evaluation. Or it may reflect the ceiling offered by a player’s talent. Aside from these straightfoward analysis of why players have below, above, or near average productivity, Berri doesn’t write about how he might enjoy watching certain NBA players. I think it gives an unfair impression that he is a bloodless machine who doesn’t know what a basketball looks like. His model does not account for flair, style, or aesthetics that is probably the raison d’etre for watching sports in the first place.

For sports writers like Simmons and Posnanski, they approach it from the aesthetic domain first. The assumption is that they have an eye for talent and style, and that this is applicable to how everyone else enjoys watching that player or game. I don’t mean that they are interested in a so-called objective way to rank the entertainment or productive value of these players. I mean that they want, but are frustrated by the fact that they can’t always, to identify an essence of a player that can be applied without qualification or exception and can be easily demonstrable. The clearest example is in the way some describe and compare Kobe Bryant to Michael Jordan. Dave Berri can rank the two, not only in absolute terms but as some standard deviation above the league average for their eras. In that comparison, not only is Jordan more “productive” than Kobe, he is a nearly twice so. Simmons would argue that Kobe is the best there is now. He might be a cut below Jordan, but there is no player closer.

One solution here is to recognize that there is a difference between the professional and the scientific presentation of ideas. Berri started from the metrics first, despite whatever he might think about the players. Simmons cannot, or would not, separate the aesthetics and productivity of the players he enjoys watching. There is nothing wrong with either approach. The only difference is that Berri’s work easily translates into a scientific publication format. Its details all concern finding some measure, defending that measure, identifying advantages of using that measure, and discussing how this measure may be insufficient. In other words, Berri and other scientists are biased into finding “measurables”. For better or for worse, because in the end, the basic scientific hypothesis is “how much.” How much did this drug improve patient outcome? How much did the tumor reduce? How much is a photon deflected from its true path by a massive body? Can we identify how many molecules of this do we have?

This isn’t necessarily a reductionist approach; at its best, finding quantifables is a way of creating a reference point so we can start to discuss things. Thus, the proper angle to take against a scientist (i.e. Berri) is to identify and improve on his assumptions, find a different metric that gives a higher correlation, or improve on his metric by finding more terms that add value to enhance correlation. In other words, scientific discussion is limited by the context of the methods, which acts as a framework for subsequent arguments.

The sports writers do not have this limitation. They can seque between stats and aesthetics. Like Simmons, they can also sprinkle pop-culture references that actually advance their argument. However, I think because they do approach things from an aesthetic angle first, they tend to provide contexts based on motifs and not on metrics. In other words, it allows Simmons to focus on the literary spin of his piece, relating the NBA offseason to lines from  the movie Almost Famous. It allows Posnanski to say that he wants a new stat, because he doesn’t like how OPS is pronounce “ops” and not “Oh-Pee-Ess”. There is a lot of room for literary flourish, which shouldn’t make the argument any more objective, but it becomes much more enjoyable.

Interestingly enough, and, ironically, I haven’t looked at this for all cases, I think for the most part, Simmons and Berri emphasizes the same attributes they want from their ideal basketball player. They want someone who can shoot well (i.e. high shooting percentage), score a lot of points, make passes for assists, don’t cough the ball up, and make rebounds. Where they differ is in how they rank the so called “top players”.  Berri has noted that most conventional players evaluation centers on points scored (without regard to the number of misses the player made.) He has noted that player rankings and player salaries have a correlation of 0.99 compared to points scored. And strangely enough, Berri’s work showed that scoring points, by itself, does not lead to higher efficiency differentials. Despite what writers and general managers profess about finding complete basketball players, they put their money on the point-getters. In other words, all the verbiage devoted to arguing how smooth and graceful players are, how much one should enjoy their talent before they fade into old age, the idea of “aesthetics” and “points” are no different. It’s interesting that Berri noted that in fact there may be an implicit metric being used to evaluate players based on the so called explicit measure of a player’s style/gracefulness/aethetics.

Note: Not cool. I have had part of my review for Little Children and The Abstinence Teacher in draft for a couple of weeks. My blog is titled “No Time to Read”, I have less time writing reviews. I wanted to avoid writing short posts, as I prefer reading and writing longer, more thoughtful pieces. One thing I want to do is to combine and find links among multiple works (and I try doing that, somewhat clumsily, in this review.) To hell with that, I suppose. I’ll post the review as written, and I will follow up with the review of TAT “shortly”. I’ve also linked the books to Porter Square Bookstore, rather than Amazon, as Amazon doesn’t need my help.

Little Children and The Abstinence Teacher are two complex, sympathetic works. These are the only two Perrotta books I have read, but it is clear to me that he is a generous author, who is able to detail the complex thought chains lying below each of his characters’ surfaces. This generosity turns symbols into living, breathing people, enabling them to transcend simple, thematic opposition and actually interact with one another. The key point is that he does not treat the opposition as punching bags.

Little Children is the lesser work of the two, if only because the plot seems stilted next to the personalities. The inclusion of a child-molester in this story seems to serve no purpose other than to enable some opportunities for Brad to get out of the house (as part of a neighborhood watch group) and to provide some dramatic tension near the end of novel.

There is one misstep in characterization that occurs on the first page, when the women are introduced – except for our protagonist Sarah – as the mother of so-and-so child. It isn’t symbolism: it is a neon  sign that states Sarah is the contrarian of the bunch, a lapsed feminist who longs to be defined by anything other than motherhood. For the most part, the other women, who serve more as the Harpies than a Greek chorus, are not fleshed out. There is one little vignette where the shrew’s (Mary Ann’s) unhappy home life is laid bare, but for the rest of the story they serve to remind Sarah of the destiny awaiting her. No conversation is more meaningful than where the offspring is going to preschool, what toys are being recalled, what TV shows one had watched through heavy-lidded eyes.

That alone would drive one to drink, but Sarah chooses adultery instead. She was and is a mousy girl, who wanted to but couldn’t date the popular jock in high-school or college; she achieves this juvenile ambition by eventually sleep with Brad, a househusband who should be studying for his third attempt at passing the bar exam. The affair has great power within the context of the trapped lives both Sarah and Brad feel they lead. The excitement isn’t so much in the illicit nature of sneaking behind their spouses but rather in the fact that they share a common appreciation of one another. Therein lies the trick in Perrotta humanizing the two; certainly, I felt badly for Richard and Kathy, the spurned spouses. But I felt more sadness than anger in Sarah and Brad finding their escape in each other.

The humanization comes because one can identify with the cause of the affair: the perception that one’s spouse doesn’t fully appreciate him as a partner. It is not a matter of reality; it is that one spouse feels put upon and felt the need to seek that appreciation elsewhere. Brad is the simple case: he is going through his mid-life crisis early. He has failed the bar exam twice, but he states he entered law school on a whim. He watches teenage boys skateboarding and longs to join; instead, he winds up with a bunch of cops and ex-cops in a football league. He is satisfied being a house husband, but of course his wife is expecting him to contribute financially. Her moral support of his attempting the bar exam has crossed from wishing him well into an expectation that he will fail and not pull his financial weight. Sarah’s case is just as simple: her husband isn’t interested in her. She wants to be significant. She is intelligent, but decides that the only way to distinguish herself from the pack of mothers is to flirt with Brad. The two hit it off.

It would have been  cheap for Perrotta to distance the reader from Richard and Kathy. Instead, Perrotta turns them into people, each with flaws. Kathy is a harried woman, one reaching the limit of her patience with her husband. Fairly or not, she feels too put upon. She works and so doesn’t spend enough time with her son. Although she is following her dream of directing documentaries, it doesn’t pay well. She has been understanding and a cheerleader for her husband – despite his repeated failure. She is tired. Richard is more difficult to describe; he appreciated Sarah’s intelligence when they first met and now provides financial stability for their family. But in the end, he too is tired and desires something less ordinary.

That is what I like about Perrotta’s writing. Sure, he slings barbs at suburban life, but his characters are people like you or me. Under any number of circumstances, we could be Sarah, Richard, Kathy or Brad. Perrotta’s characters in an understandable manner, despite our disapproval. Recently, I had read Pinker’s The Blank Slate, which helped crystallized some ideas about human emotional and cultural baggage for me. Perrotta’s characters strike me as real because he describes the dissonance between basic desires driving action (i.e. nature) and  professed desires (the sum of education, environment, and upbringing) so well.

One scene that illustrates this is when Brad notices that his son flat out ignores him as soon as Mom (Kathy) comes home. That scene bundles the flash of Brad’s jealousy of the bond between son and mother, the fact that the boy and mother essentially enter their own world and exclude him, and the fact that he might be feeling both unmanly (for being a house husband) and his efforts not being recognized by his son or appreciated by his wife. Everything about this scene rings of authenticity. Again, without declaring whether there is validity in the perception (although one will be either sympathetic to Brad or not), the sum of all these minor events build up the case that Perrotta is interested in explaining (and thus looking past one’s view of the adulterers), but not excusing , Brad’s and Sarah’s behaviors.

I would guess the moral of the story is that communication only goes so far. Perhaps that is what love means: that a partner thinks enough of the other person to continue talking. If so, then Perrotta must think the world a loveless place.

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