One of the most prolonged, deep, and I'd add enjoyable, theoretical debates on the Sportsbubbler board is about how to measure runs production, and what traits are valuable to producing runs. The tone of the debate usually centers around something like RBI vs. something like OBP, with the efficacy of both stats as hitting stats questioned.

RBI proponents often argue that OBP does not reflect a hitter's ability simply to drive the ball in a situation where a runner is in scoring position, and that extending the hitting zone rather than drawing a walk is a very valuable hitting trait. OBP proponents often argue that OBP is a more comprehensive hitting stat conducive to measuring run production because it measures the rate at which a player is likely to make an out, taking the "negative" position that not making outs is what contributes to successfully scoring runs.

Between the two, we can find all sorts of intermediate positions, positions which question what the stats actually measure, what the stats are contingent upon, and ultimately, what types of values rest at the core of a successful offense. It should not be surprising then, that RBI camps often dwell on the amount of contact an offense makes, while OBP camps are not as off-put by striking out. These are obviously over-simplified for the purpose of presentation on my part, but the basic gist of the positions is captured, and there are many points in between.

In the meanwhile, even if an RBI is contingent upon how many runners are actually on base, where a batter finds himself in the order, and other circumstances a batter might not necessarily control, there are other ways to measure the salience of RBI -- for instance, but calculating the % of RBI against baserunners, a stat kept at the Top of Individual Player GameLogs at www.baseballreference.com.

With a nod to the "stat geeks," BR also includes the number of PA a batter faces with runners on base. Even better, each individual player can be compared to an MLB average player that has the same number of PA with runners on base. This provides an average RBI number and an average number of baserunners per PA that can be used to measure the success and efficiency of a player's RBI.

So, for an example, Russell Branyan's RBI Opportunities reads the following way:

RBI Opportunities
	Russell Branyan -- PA: 141	RBI: 20	Actual Runners on Base: 72 (33-25-14),
	ML Avg. Player with PA: 141	RBI: 16	Avg. Runners on Base: 88 (43-29-15)

From this snapshot, we see Branyan's RBI total (20), Branyan's PA with runners on base (141), the actual runners that were on bae (72), their positions on base (1-2-3), and then by comparison, the ML Average Player with the same number of PA with runners on base, their Average RBI (16), and their Average Runners on Base (88).

From these numbers, we can roughly conclude that although Branyan only has 4 more RBI than the league average for a player with his runners on base PA, he has actually had 16 less runners on base; so while he drives in 27.78% of his runners on base (20/72), we can see that that's actually an above-average clip compared to the Average (16/88 = 18.18%).

I used these average RBI and average Runners on Base as an Olive Branch -- it's not my contention that driving in runs itself is useless, but that the simple, lone RBI number does not actually capture a player's ability to RBI.

So, here are your 2008 Milwaukee Brewers' RBI Leaders, according to their +/- % over Average lgRBI and Average lgRunners. Read it like...

Name: PA; RBI / Runners on Base (League Average RBI / League Average Runners on Base); RBI%; +/- Average RBI%

Underlined Runners on Base means that that player has seen fewer than average baserunners per PA.

This should help us conclude the most efficient and valuable RBI players on the Brewers, and hopefully serve as a bridge between RBI and other rate stats.

RBI%

Braun: 474 PA; 82 RBI / 281 Runners (53 lgRBI / 297 lgRunners); 29.18%, +11.34%

Branyan: 141 PA; 20 RBI / 72 Runners (16 lgRBI / 88 lgRunners); 27.78%, +9.60%

Cameron: 310 PA; 44 RBI / 161 Runners (35 lgRBI / 194 lgRunners); 27.33%, +9.29%

Fielder: 473 PA; 65 RBI / 283 Runners (53 lgRBI / 296 lgRunners); 22.97%, +5.06%

Hart: 445 PA; 62 RBI / 278 Runners (50 lgRBI / 279 lgRunners); 22.30%, +4.38%

Hall: 344 PA; 44 RBI / 206 Runners (39 lgRBI / 215 lgRunners); 21.36%, +3.23%

Kapler: 177 PA; 28 RBI / 131 Runners (20 lgRBI / 110 lgRunners); 21.37%, +3.19%

Hardy: 413 PA; 49 RBI / 242 Runners (46 lgRBI / 259 lgRunners); 20.25%, +2.49%

Weeks: 411 PA; 33 RBI / 206 Runners (46 lgRBI / 257 lgRunners); 16.02%, -1.87%

Kendall: 394 PA; 29 RBI / 228 Runners (44 lgRBI / 247 lgRunners); 12.72%, -5.09%

Counsell: 170 PA; 9 RBI / 101 Runners (19 lgRBI / 106 lgRunners); 8.91%, -9.01%