Last week's theme around the SB Nation-NBA network was "Disappointment Day" - players who have disappointed their fan bases - and not surprisingly a number of those involved draft prospects who didn't pan out.

And the common theme - when excluding those who turned out to be stars with other teams - was simply that fans and GMs alike believed these prospects to be "talented" but for one reason or another they didn't pan out, including the Swish Appeal entry about the Phoenix Mercury's 2005 WNBA Draft.

The biggest example of draft day disappointment from that list as a Golden State Warriors fan wasn't even the person that Golden State of Mind ultimately chose as the most disappointing in franchise history but a player who the Golden State Warriors eventually decided to give up on: Anthony Randolph, for many of the reasons described by Seth Rosenthal at SB Nation's New York Knicks site Posting and Toasting.

We salivated while he drilled jumpers in practice, held each other and wept quiet tears of mirth when he blocked a few shots in scrimmage, then finally succumbed to the spectacle and thumped on the press table like savages when he dunked on someone in transition. I'm pretty sure Frank Isola made fun of Osborn and me for that, at which point we growled at him for doubting Randolph's potential, then resumed raising the roof in excitement. By the time Randolph finished an off-the-backboard alley-oop from Toney Douglas during preseason in Milan, I was certain he was poised for a breakout season with D'Antoni's fast-paced Knicks.

In the sordid history of Warriors' draft picks, drafting this guy at #14 in the 2008 draft doesn't even rank in the top 10 of individual disappointments - when go to the lottery as often as they do and come up empty, gambling on a guy like Randolph actually exciting because it looks like he can do so much.

Benjamin Hochman of the Denver Post summed up what Knicks, Warriors, and possibly some open-minded Minnesota Timberwolves fans thought about Randolph when they first got him: "He’s a classic case of a young talent who came out of college too soon. His talent is undeniable. But he remains a work-in-progress."

That leaves a question: if so many fans, GMs, and journalists who see this guy play seem to believe that he's "talented", why is it that "he remains a work-in-progress"?

The answer might be that the way we tend to define "talent" is grounded far too much in our notions of what we want a player to become than what they actually are. Or, as Evanz of The City Blog and GSoM alludes to, perhaps we're not very efficient at understanding the principle components of talent. In any event, something isn't quite right.

I just read Michael Lewis' Moneyball after finally seeing the movie recently - both are worth checking out - and though the book is often framed as a book about sports statistics, the unifying theme is actually about better understanding what constitutes "talent", in terms of building through the draft, free agency, trades, or even in through the lens of the failed career of Billy Beane, the protagonist (pg. 55).

With that, he concluded his fruitless argument with his talent. He decided that his talent was beside the point: how could you call it talent if it didn't lead to success? Baseball was a skill, or maybe it was a trick: whatever it was he hadn't played it very well. In his own mind, he ceased to be a guy who should have made it and became a guy upon whom had been heaped a lot of irrational hopes and dreams. He had reason to feel some distaste for baseball's mystical nature. He would soon be handed a weapon to destroy it.

Of course, that "weapon to destroy it" that Lewis referred to was the power of sabermetrics or advanced statistics that evaluate events on the field beyond the boxscore, either by counting different things or counting the traditional things in different ways.

But that also suggests a false dichotomy that distorts the core theme of needing to find a way to rethink talent (and more specifically, rethink how the market values talent in economic terms) - as Houston Rockets GM Daryl Morey wrote in a piece for Grantland, "...information with real power comes in a variety of forms: both in the stereotypical form that the movie will surely play up of databases and spreadsheets and analysts and predictive models, but also in the form of expertise and experience acquired only via a lifetime of playing and coaching the game. The best organizations bring that all together."

But that false dichotomy is exactly why I was drawn to reading Moneyball.

After finally seeing the movie - in a special screening on the field of the Oakland Coliseum where I watched the Oakland A's a number of times as a kid during the glory years of the late '80's mentioned in the book - I was sort of bewildered by how people have latched on to this divisive discussion about statistics vs. perception* and how it has been discussed both within baseball, within sports, and beyond. There are valid points in some of the critiques (e.g. here, here, and here) but they all seem to miss what this story was actually about.

It's oversimplifying to say it's not a movie about statistics - as a Hollywood production, they did take some creative liberties with the narrative in order to make it palatable for a mainstream audience and did so quite well. But it's really not a story about statistics, as it's used in common parlance and framed far too often - it's about a cash-strapped team needing to find a way to exploit market inefficiencies just to field a decent team in a world with a great disparity between rich and poor teams, much less compete for a playoff spot. It would be wrong to say that statistics are incidental to that narrative, but statistics were a tool to overcome a significant obstacle for the A's.

We could express the core principles at work in the A's front office to address that problem as follows:

• Exploiting market inefficiencies
• Maximizing the talent you have
• Minimizing risk

Yet getting back to that bigger theme, the movie is about development, dogma, innovation, routine, the tension between focusing on process vs. outcomes, and the dialetical interaction between the objective and subjective not only within baseball or sports, but even within Beane, who championed a new approach that was essentially the anti-thesis of how his career was mis-evaluated. In essence, this was a book about the human capacity to observe a situation, imagine new possibilities for how we work within it, and either bring that re-imagined situation to fruition or radically change the way it operates. That's actually a concept straight from the annals of Marxist theory, which we don't have to belabor (though relevant to the entire narrative), but suffice it to say that it's both the psychological foundation of a revolutionary act and the essence of creative thought, whether we think of that in terms of artistic ability, navigating a basketball court, or solving economic problems. That all begins with a willingness to question everything we assume to be true about the world.

The power of the book, to a greater extent than the movie, is that Lewis painted a stunningly vivid portrait of the intellectual underpinnings of "Moneyball", regardless of whether you take issue with how he might have been advocating the quantitative over the qualitative. It was as impressive an intellectual history as it was a sports book and for that it deserves all the accolades it received - few books can work so well on so many levels as that one did.

But obviously, having read the critiques and the byproducts of this work for years even prior to watching the movie, I found myself trapped in my head as both an A's fan and a basketball fan while watching the movie and thinking specifically about that challenge of evaluating talent, particularly in the draft.

James Bowman has already described one aspect of that in the past (and has written more here about Moneyball in basketball), but far more intriguing to me was the rather arbitrary way that people think about "talent" (i.e. irrational hopes and dreams, even - at times - in the face of disconfirming evidence).

My own interest in statistics had nothing to do with an interest in math or a background as a statistician - I hated math during the entirety of my school experience and never thought about diving into advanced statistics until I started following the WNBA. The problem, it seemed, was a matter of limited perception of the hierarchy of talent - there are those that conventional wisdom points a novice WNBA fan to and those that, for reasons we don't have to explore now, are ignored by and large. I simply wanted some sort of way to better appreciate the landscape of talent in the league as I watched it and tried to catch up on the years of WNBA basketball I hadn't watched. The players with the most buzz and gaudiest stats always stand out; but what about the less heralded players who seem to make an impact? How do we value players who might not get top billing by traditional standards (e.g. scoring, rebounding, and assist averages)? And the driving question for me: What are the right statistics to look at so that I can better understand the game?

That's an ongoing process of course - I'd be lying if I said I've figured it all out after a few years. But that failure in conventional wisdom seemed to me to be most evident in the draft, particularly in the 2010 WNBA draft which was the first I followed closely - we don't have to rehash what went wrong, but 6 of the 12 first round picks are now out of the league just two years later after largely unproductive stints.

And that's where Moneyball principles, or ways of thinking and seeing, are most relevant to the WNBA - the economic dynamics don't exist in the same way because of the salary structure and relatively miniscule differences between contracts in U.S. professional sports terms. But it's also clear that there's a challenge in the WNBA - with only 11 spots and a salary cap, how can you best maximize those roster spots? In a game that demands one of a few elite stars to win a title, how can we better understand what constitutes a "talented" prospect, which fills a roster spot cheaply and thus leaves cap room available to pursue a top-tier star?

As we've described multiple times on this site, to varying degrees of success, statistics sort of illuminate the landscape but even with the numbers there has to be an associated understanding of what's useful, what's not, and how we use it; they give us tools to help us rethink how we do and think about things, as opposed to giving us cold and fast answers to problems as we're taught to do in school.

Going back to the point above about Anthony Randolph, he was enticing as a prospect because it seemed like he could do it all - watching footage of his college games, it's a wonder he didn't go higher. Everything Rosenthal mentioned was on display and the Warriors were overjoyed to find him with the 14th pick.

But Randolph's statistics, in college and literally everywhere he's been since professionally, told a different story: an inefficient scorer and a turnover prone ball handler who didn't rebound all that well despite a significant height advantage in college at 6'11". Randolph, it seemed from his statistical profile as a collegian, hadn't figured out how to successfully take all those abilities he had and contribute to the success of a team; neither LSU nor the Warriors nor the Knicks nor the Timberwolves have been terribly successful teams with Randolph on the floor.

Randolph definitely possessed some elements of talent that Evan described: he obviously had height, length, some raw athletic ability (quickness/speed, leaping ability, hand eye coordination for his size), and he gave off the impression that he had some basic skill. But one thing he didn't seem to have was an "efficient perception of reality," as described well by Matt Moore of Pro Basketball Talk:

A key element in actualization is an "efficient perception of reality." And on the singular level, this is difficult to translate to team success. This is manifested, essentially, as confidence. The "you want guys who aren’t afraid to take that shot?" is built out of their own knowledge that they can make that shot. They may not have an efficient perception of reality, but in that sense, those players are not self-actualized. This is essentially the difference between J.R. Smith and Kobe Bryant. Smith and Bryant both feel they can hit that shot. The difference is that Bryant has been able to.

Statistics don't tell us whether a player is self actualized - though that would be really, really cool to have - but as Moore alludes to, a player who shoots a lot but doesn't make a lot might not be using their abilities most successfully. A 6'11" player who spends a lot of time on the court but doesn't rebound at a very high rate probably isn't maximizing their potential for success. A player who seems to wow you but can't seem to contribute to his team when he's not wowing you might not be as good as everyone thinks.

Could the Warriors have predicted that Randolph was headed for an unproductive career? Not necessarily. First, statistics, particularly in projecting the future of collegians, are still not perfect. Second, it's hard to know - as was another core theme in Moneyball - where players are on the path to self-actualization that Moore described well. Third, the Warriors are the Warriors. But the bigger question is could they have made a better decision with different information and minimized their risk of drafting an unproductive player? And that, it seems, is possible.

And that's sort of the interest and fun in the statistics: how can we, even as fans, think about the game in more nuanced ways? How can we pick out what we should or should not be watching as we follow our favorite teams? For the die hards, how can we head into the college basketball season with a better grasp of which college players we should follow as future contributors to pro rosters?

In other words, how can we be more creative about consuming and enjoying professional sports than we typically were when so much less information was available to us? I know there are plenty of people who simply respond, "I don't care - I don't want to think when I'm watching sports." And that's fine. But sometimes more information simply helps us appreciate what's actually occurring on the floor - qualitatively and quantitatively - a bit more deeply.

* "Statistical metrics" are a type of "observation", which is why nothing annoys me more than people saying, "I don't care what the numbers say - I KNOW WHAT I SEE." Sadly, a lot of people have created that dichotomy in their minds and this book does spend considerable time mocking that, with Joe Morgan as the human embodiment of the anti-stats straw man (though sadly not an uncommon straw man). So on second thought, if you find yourself given to saying "I KNOW WHAT I SEE" or siding with Joe Morgan about the 2002 Oakland Athletics, you might not want to see this movie or read the book.