Sometimes I look at a hitter's wOBA, or any metric for that matter, and wonder the quality of pitching he faced. Other times I think, "What if Player X played in the AL West instead of the AL Central?". Is this guy succeeding because he regularly faces teams in his division with quality of pitching that is better than most of the league? Is Player X faltering because of the same reason?

There is a story behind every wOBA total. Maybe Player X faced Justin Verlander 20 PA's too many, leading to a skewed wOBA in comparison to the league and Player X's true skill.

Most player's are victims of circumstance. They play in the AL West and they have to face Yu Darvish and Felix Hernandez a handful more times every year than the given AL player. Other lucky players face a 2013 version of Clayton Richard or Dan Haren, more than the usual player. The matter of the fact is, some guys have it better than others, and others have it worse than the rest.

To solve for this problem, or simply make a composite tiered wOBA based on the quality of opposition, we first have to quantify the quality of opposition.

We are going to group pitchers into four groups, the top 25%, top 50%, top 75% and top 100%, based on their wOBA against.

The requirements for each quantile are as follows for the 2012 season:

Top 25% Top 50% Top 75% Top 100% < = 0.284 > 0.284 and < = 0.313 > 0.313 and < = 0.354 > 0.354 and < =1.374

Now, before we get into adjusting, let's look at some quality distributions amongst pitchers:

Quality Distributions -- Pitchers

Now that we have the requirements for each quantile, lets look at which percentages of the league falls under each category:

Top 25% Top 50% Top 75% Top 100% 24.75% 27.57% 25.75% 21.93%

So according to the distribution, there are more pitchers of quality in terms of wOBA against than those of terrible quality. Now this is probably because we are just looking at large percentage gaps and wOBA against, but this distribution looks good for what we want to do -- adjusting a hitter's wOBA based on the pitchers they faced, and will come in handy later.

Now that we have seen the league-wide distribution of wOBA, let's break down the analysis into each division. This will give us a good look into which divisions had a abnormal distribution of pitchers for each quantile:

TOP 25% -- Percentage of top 25% pitchers in each division compared to the total number of pitchers.

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 4.43% 2.62% 4.43% 4.23% 3.42% 5.63%

The AL East has the highest density of top 25% pitchers, with the NL West and NL East tying for second. The NL Central, comes in last by far.

TOP 50%

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 3.62% 6.64% 6.44% 3.22% 3.82% 3.82%

Meanwhile, the NL Central, despite having the smallest amount of top 25% pitchers, has the majority of the league's share in top 50% -- the NL East coming in close second.

TOP 75%

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 4.63% 4.02% 2.62% 3.02% 7.04% 4.43%

Now, the AL Central has the highest density of top 75% pitchers, which is quite auspicious for the hitters that play in that division. Meanwhile, the NL West comes in second with the AL East not far behind.

TOP 100%

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 3.82% 3.62% 2.82% 2.21% 4.02% 5.43%

Lastly, 5% of the league's worst pitchers reside in the AL East. Combine that with the previous quantile and the AL East has 9.86% of the league's bottom half of pitchers. The AL East, however, comes in first with the most pitchers in the 75 and 100 quantiles at 11.06%.

Now I split up the percentages based on the top half and bottom half quantiles:

TOP HALF:

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 8.05% 9.26% 10.87% 7.45% 7.24% 9.45%

The NL East is the most top heavy division in terms of quality pitching, with the AL East coming in close behind.

BOTTOM HALF:

NL WEST NL CENTRAL NL EAST AL WEST AL CENTRAL AL EAST 8.45% 7.64% 5.44% 5.23% 11.06% 9.86%

The AL Central is the most bottom heavy in terms of pitching quality, with the AL East coming into second again.

Given what we see here, it only makes sense to adjust a player's wOBA based on the quality of pitching he faces. So let's dive in:

ADJUSTING FOR QUALITY OF OPPOSITION -- INTRODUCING awOBA

Essentially we want to be able to adjust a player's wOBA in two ways:

Adjusting performance in each quantile by comparing league average to total PA. Weighting opportunities by comparing PA in each quantile by league average PA.

In order to fulfill number one we will use the following formula:

1) (wOBA_quantile - lg_wOBA_quantile) * (PA_quantile/ PA)

This will give us a number of a player's performance against a certain tier of pitching relative to his opportunities as a whole against that group.

The next formula will allow us to weight the product of the first formula, so as to adjust for quality of opposition:

2) PA_quantile / lg_PA_quantile

This should give us a number that we will use as a weight in our final calculations of awOBA.

For the final calculations, we will add each number produced from the first formula and multiply it with the weight. Next we will add the products to a league average wOBA constant.

For Joey Votto, the calculation will look like this:

Joey Votto's awOBA calculation

dwOBAt25 dwOBAt50 dwOBAt75 dwOBAt100 dwOBAt25 PAwt50 PAwt75 PAwt100 awOBA 0.0148 0.0480 0.0371 0.0148 0.999 1.18 0.815 0.631 0.442

The formula is as follows, using the headings from above:

awOBA = (dwOBAt25 * dwOBAt25) + (dwOBAt50 * PAwt50) + (dwOBAt75 * PAwt75)+ (dwOBA100 * PAwt100) + (lg_wOBA)

In the end it will look like this:

0.442 = (0.0148 * 0.999) + (0.0480 * 1.18) + (0.0371 * 0.815) + (0.0148 * 0.631) + 0.331

2012 Leaderboards

Unfortunately, Retrosheet does not release play by play data for 2013 until the end of the season. For this reason, we will have to look back at this retrospectively on the 2012 season:

Top 25 in awOBA:

Bottom 25 in awOBA:

BIGGEST WINNERS (HIGH DIFF):

BIGGEST LOSERS (LOWEST DIFF):

Full leader boards are located here: FULL LEADERBOARDS FOR 2012 awOBA

. . .

Big thanks to James Gentile for helping pull data for this piece and Stephen Loftus for help with the formula. You can follow James on twitter @JDGentile. Stephen does not have a twitter, yet.

Max Weinstein is a staff writer for Beyond the Box Score.

You can follow him on twitter @MaxWeinstein21.