This is a post introducing and explaining NERD scores for position players. I’m including the results first and then the background, methodology — all that junk — second.
Curious as to what NERD is? The short answer is: it’s a number, on a 0-10 scale, designed to express the “watchability” of position players for those of the sabermetric persuasion.
For more information, consult the index right after the results.
Here are the current top-20 position players by NERD:
Here are the bottom 20:
You can find the complete results in this spreadsheet.
Despite at least one convincing argument to the contrary, it is, in fact, not space, but rather the objective measure of a position player’s (i.e. non-pitcher’s) watchability, that is the final frontier.
The third- and second-to-last frontiers, as the attentive reader will already know, were the objective measures of starting pitchers’ and teams’ watchability, respectively. These frontiers have been settled, incorporated, and heavily urbanized (metaphorically speaking) by the introduction and enthusiastic use of NERD — that is, the number, on a 0-10 scale, designed to express the “watchability” of a game or pitcher for the learned fan — available in each and every edition of One Night Only at this site.
This omission of a NERD score for position players has been a glaring one, however, and one that has made for the occasionally awkward interaction with Messrs. Appelman, Cameron, etc. around the work of modern art that is the FanGraphs’ water cooler.
Accordingly, in what follows, I’ve endeavored to right this wrong.
Notes on Choosing the Components for Player NERD
The question with which I started — with which any sensible person would start — was, What makes a position player interesting to the baseball nerd? And, on the heels of that first question, this one, too: Of the things that might make a player interesting, which of them are easily measured?
In fact, while both the pitching- and team-oriented versions of NERD contain at least six variables each, it occurred to me that, for the field player, Wins Above Replacement — especially with the recent introduction of baserunning — is almost entirely sufficient.
Components of Player NERD
• Age (Relative to League Avg)
As has been the case with the pitching and team varieties of NERD, such is also the case for player NERD: younger players are exciting. If we consider two players, identical in terms of WAR and everything else except (one is 21; the other, 29), it’ll be the younger player about whom we care more. With one exception, that is. There were players in 2010 (with which numbers I started) who were maybe more interesting because of their advanced age — Jim Thome and Jim Edmonds, specifically. For that reason, players who are greater than two standard deviations above the league-average age begin to receive a bonus again.
• WAR (WAR per 650 PA)
At this point, because it considers so many variables (park-adjusted batting, fielding, baserunnung, mustaches), WAR for position players addresses the things that are also most appealing to the learned fan. There are few players atop the WAR charts who we might expressly not like (unless, of course, said chart-topper as particularly aggrieved our favorite team). I’ve used WAR per 650 plate appearances so that all players can be put on the same scale.
• Batting Luck (xBABIP-BABIP)
I’m quoting myself and most of the bespectacled readership when I say that baseball nerds like watching regression happen. This is why, for example, Luck (ERA minus xFIP) is one of the components of Pitcher NERD. For batters, the concept of luck is more difficult to isolate. We know, for example, that BABIP generally regresses towards .290 or .295 for pitchers. It follows, obviously, that it does the same for the entire population of hitters, too. However, the individual data points (Chris Snyder, Ichiro Suzuki) are more widely scattered. To address this, a number of xBABIP calculators have been — none more easily utilized with FanGraphs’ batted-ball data than slash12′s version at Beyond the Boxscore.
Calculating Player NERD
To calculate NERD, I found, and added together, the z-scores (standard deviations from the mean) for Age and WAR/650 (for all players with 100+ PAs). To this I added the Luck factor multipled by ten (such that, say, a .033 difference between BABIP and xBABIP becomes 0.33). That done, we now have the Raw score.
Because there are fewer variable in this iteration of NERD, the Raw scores aren’t naturally as spread out as in previous versions. To help translate the Raw scores to the 0-10 scale, I’ve multipled same by 1.5.
After doing that, a constant (presently, around 4.2) is added to create an average NERD of 5.00 among the entire population.
The Final Equation
Looks like this:
[[AGEz + WARz + (Luck * 10)] * 1.5] + Constant
Results for 2010
For reference, here are the results from 2010 (with which I actually started, so’s to get the numbers right):
A full spreadsheet of all qualified (100+ PA) players can be found here.
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