Defining Balanced Lineups

We’re used to hearing about teams having balanced or deep lineups. Other teams are defined as “stars and scrubs”. While I think we all know what these term mean, it’s not something that’s ever been quantified (at least, not to my knowledge). Since the issue of depth is an interesting one to me, I thought it’d be fun to to tackle this using wOBA.

For each team, I calculated wOBA on a team level, then the weighted standard deviation for all players. This produces each teams’ distribution, but since the size of the standard deviation is dependent on the average, (meaning that it’s not standard when comparing teams) I used the coefficient of variation (aka CV, simply standard deviation/average) as the final measure of consistency. The lower the CV, the smaller the spread of wOBA performance.

My first run showed a definite bias against NL teams, since pitchers added a lot of variability. So I ran the numbers without pitchers as well — this isn’t as accurate since we’re looking at the entire offense, but it allows for a better comparison to AL teams. I grabbed runs/game from Baseball Reference so we can get an idea of run production as well. The results, sorted by position player CV:

Team wOBA – ALL CV – ALL wOBA – NP CV – NP R/G
 Mets 0.297 20.04% 0.308 8.74% 3.9
 Royals 0.304 11.20% 0.304 10.38% 4.0
 Rays 0.306 12.11% 0.307 11.19% 3.8
 Phillies 0.294 18.74% 0.304 11.34% 3.9
 Twins 0.316 12.42% 0.317 11.44% 4.4
 Athletics 0.313 13.53% 0.315 11.65% 4.6
 Nationals 0.315 18.94% 0.326 12.05% 4.3
 Orioles 0.323 13.47% 0.324 12.23% 4.4
 Cardinals 0.309 17.22% 0.316 12.71% 3.8
 Yankees 0.305 13.86% 0.306 12.78% 3.9
 Brewers 0.315 21.19% 0.327 12.86% 4.2
 Padres 0.281 18.18% 0.289 12.99% 3.2
 Mariners 0.300 14.56% 0.301 13.47% 4.0
 Astros 0.309 14.49% 0.310 13.54% 3.9
 Rangers 0.305 14.35% 0.305 13.81% 3.9
 Pirates 0.324 21.83% 0.336 13.88% 4.3
 Giants 0.310 20.42% 0.320 13.94% 4.2
 Marlins 0.309 20.68% 0.319 14.14% 4.1
 Tigers 0.333 14.97% 0.334 14.23% 4.7
 Red Sox 0.304 15.34% 0.304 14.38% 3.8
 Angels 0.324 15.54% 0.325 14.46% 4.9
 Indians 0.315 15.45% 0.316 14.60% 4.2
 Rockies 0.333 20.11% 0.343 14.61% 4.5
 Reds 0.294 19.78% 0.302 14.70% 3.7
 Dodgers 0.323 18.42% 0.331 14.93% 4.2
 Diamondbacks 0.299 21.09% 0.309 15.47% 3.8
 Braves 0.298 22.65% 0.310 15.59% 3.6
 White Sox 0.314 17.27% 0.315 16.13% 4.1
 Cubs 0.301 20.18% 0.308 16.45% 3.8
 Blue Jays 0.326 18.32% 0.328 17.19% 4.5

On the opposite end of the spectrum, the Blue Jays have widest spectrum in wOBA, with a true stars and scrubs approach — of players with over 200 PA, Jose Bautista, Edwin Encarnacion, Adam Lind, and Melky Cabrera all have wOBAs over .350, Anthony Gose and Munenori Kawasaki each are under .290, and five (Jose Reyes, Brett Lawrie, Colby Rasmus, Dioner Navarro and Juan Francisco) all are sitting around 0.320. Throw in some part-time players with sub-.300 wOBAs, and you get a team with a .326 line. The Mets win the prize for most balanced lineup, unfortunately for them, this means they have a collection of pretty poor hitters, with only Lucas Duda (0.358 wOBA, 553 PA) and Daniel Murphy (0.330 wOBA, 598 PA) putting up strong numbers.

Of course, the ends can always be extreme, and in this case they just happen to be represented by one poor-hitting team and one good-hitting team. So what about the middle? The Astros, Rangers, and Pirates all hover around the CV median, with .310, .305, and .336 wOBAs respectively. No real trend there. The R values of CV against wOBA and R/G are:

wOBA R/G
All (0.11)  (0.19)
Non-Pitchers 0.23  0.06

Very different results depending on whether or not we include pitchers, but either way the correlation is weak.

What does this tell us? Basically, there are a lot of ways to build a solid offense. I expected teams with more balance to perform better, but that doesn’t appear to hold water. While there are other aspects to consider when building a roster, such as defense, depth in case of injury, etc., team compositions can vary yet yield similar results.



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A data analyst in the Grand Rapids, Michigan area, Mark spends parts of his spare time working with spreadsheets. He vaguely recalls what the sun looks like.

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tz
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tz

Good stuff!

I took a quick look at R/G against wOBA and CV split by league (including pitchers) to see if league differences may be masking any correlation, and I didn’t see any correlation there either.

In theory, you’d expect a lineup like the Blue Jays to score slightly more runs than a team with the same overall offense split equally, IF they load the front of their lineup with their “stars” and don’t mix in any “scrubs” in say the #2 slot.

But the difference is probably small enough that it could get lost behind noise from random variation, or even the differences in base stealing and base running among teams (which isn’t reflected in wOBA).