This year I am in three leagues–one for work, one for “stat-geeks”, and one for family and friends.  I decided to tweak the way I approached the draft this year and wanted to share a bit of the strategy with readers.

One of the biggest problems with standard pre-draft rankings, particularly those of the big fantasy hosting sites (e.g. ESPN, CBS, etc) is that the rankings are based solely on aggregate measures of performance such as total projected points for the upcoming season.  Now, of course the goal is to assemble a team with players that end up scoring lots of points throughout the season, but total points scored ignores the fact that teams compete head-to-head, week-to-week.  In order to make the playoffs a team has to outscore opponents on a consistent basis in order to accumulate wins, not just points.  That means drafting players that not only score a lot of points, but score a lot of points week in and week out.  When it comes to deciding between which players to draft, managers would be better off selecting consistent scorers versus boom-or-bust players (at least, that is my hypothesis).

Let’s take a look at two hypothetical players:

Over the course of four weeks both players score the same amount of total points. However, Player A is clearly a boom-or-bust player while Player B is more consistent week-to-week.  Player A gives you a great chance to win Weeks 1 and 4, but makes it much hard to win in Weeks 2 and 3.  On the other hand, Player B is the model of consistency, giving you a great chance to win each week.  On most pre-draft rankings, Players A and B will look like equally valuable picks, but this is misleading.

This year, I decided to see whether a player’s penchant for boom-or-bust performances was at all consistent and predictable.  The initial answer seems to be yes.

I developed two metrics; one to evaluate high scoring consistency and one that takes predicted points and combines them with scoring consistency.  The first, ConBoom, measures, weights, and then combines the number of times a player scored >=20 points, >=15 points, and <10 points per game over the course of a season.  This is the foundation of the consistency metric.  ConRank combines the ConBoom score for a player with a weighted measure of that player’s predicted total points for the upcoming season.  (How am I weighting each component of the measures?  Well now I can’t reveal the entire secret sauce, now can I?)

I validated the measures against the past three years of actual player data and found that ConBoom scores from one year were highly correlated with ConBoom scores the next year (.70).

I am going to use the new metric to guide my drafts in all three leagues and essentially test how my teams fair against other teams over the course of the season.  With data and predictions for every player I’ll be able to test the method over three league scoring systems, 32 teams, and 256 games as well as validate the measures predictive attributes over another year.

Draft number one is tonight.  Let the games begin!

Now, there is an entirely separate debate whether Beane and his approach actually work.  Instead, the article focuses on whether publicizing Beane’s approach had a significant effect on the way GM’s built their teams.  The basic hypothesis is that teams should place greater value on hitters with higher On-Base-Percentages than other, traditional offensive statistics, such as Home Runs (HR) and Runs-Batted-In (RBI). If the correlation between player compensation and OBP increased while correlations with traditional benchmark statistics decreased that would point to the potential influence of Lewis’ book on the game.

So what actually happened?

The year 1997 may be an outlier in this case, but the correlation between the two [salary and OBP] was .31 in that year. We see that in the late ’90s and early oh-ohs, the correlation danced between .40 and .50. In 2001, one year prior to Moneyball, it was at .44. By 2004, it was .64. The strength of the correlation (as measured by R-squared) about doubled. Coincidence? Maybe. But maybe, just maybe, the people who actually make the decisions in baseball actually read and accepted the conclusions in Moneyball.

Note that in the years before Moneyball, HR and RBI clearly drive the market much more clearly than does OBP. By 2004, the jump in OBP’s popularity had pulled it even, partly because HR and RBI fell in their correlative power. In 2005, OBP was actually the better correlate of salary. Chicks may dig the long ball, but apparently nerds were running the front office of your favorite MLB team. Look what happens after 2005 though. There’s a general downward trend for all three stats. It’s likely that OBP did have its day in the sun, but why would HR and RBI, so long dominant, also fall?

It is an interesting analysis. My skepticism lies in the fact that while we see some interesting correlations it is hard to parse causation out of this. It is clear that the market began to correct itself and alter how it values certain skills. But was this the direct result of Lewis’ book? Or was Lewis simply providing an anecdote representative of a larger, growing trend around baseball? There does seem to be a general trend upwards (if you smooth out the yearly volatility) in terms of salary and OBP even prior to 2002. (Of course, I would be curious to see the data set–are there a few powerful outliers driving the results? Were there a few, big market teams overspending on these types of players?) Also, a number of Beane acolytes went on to run their own ball clubs while the Red Sox hired a young, Moneyball-minded GM names Theo Epstein in 2002 as well as sabermetric godfather Bill James in 2003 (the two would build the team that would eventually win the World Series in 2004 and again in 2007). Likely it was a bit of both–an already growing trend within the game given additional validation and exposure via Lewis’ brilliant book.

The new breed of statisticians tackle that problem. They use powerful computers and sophisticated mathematical models to hunt for meaningful patterns and insights in vast troves of data. The applications are as diverse as improving Internet search and online advertising, culling gene sequencing information for cancer research and analyzing sensor and location data to optimize the handling of food shipments.

With the rise in data also comes the opportunity to extract profits if one can identify the right insights and patterns.  For example, I.B.M. recently launched a new group that will focus on business analytics and optimization.  They plan to grow the group aggressively.

With this shift towards a data-driven world has come a corresponding shift in the value of certain skills.  In this case, sophisticated statisticians and the analytically-minded find themselves in a position where their skills now command both respect and high salaries.  It has also allowed people to pursue careers that weren’t necessarily available to them even just a few years ago. Two of my favorite examples are the rise of statisticians in professional athletics (e.g. the Moneyball approach to baseball) and Nate Silver who went from pioneering the Sabermetric analysis of baseball to political commentator and analyst.

While I am a fan of analytically-driven approaches I also appreciate the potential pitfalls of relying on statistics.  More than once I have heard the quip,”lies, damn lies, and statistics”.  But it’s incumbent on consumers of data to be sophisticated consumers, such that they can call out the sloppy use or, worse, intentional misrepresentation of data.

In the current world we live in, one remains ignorant of statistics at their own peril.