When looking at multiple statistical categories, it can be difficult to eyeball what the better value is. consider the following examples:
Player A: 35HR, 100RBI, 100R, 10SB, .285 AVG
Player B: 20HR, 89RBI, 105R, 5SB, .290AVG
Most people would conclude that Player A in this case is the better choice between these two. Most of the categories are close, and Player A has the better stats for the majority. But what about when we introduce:
Player C: 12HR, 75RBI, 130R, 65SB, .310AVG
Now this is a tougher call. To be honest, I do not know which of these imaginary players is better, I can't say for certain if 55 more stolen bases is worth losing 23 homeruns; or if gaining 25 batting points is worth losing 25 RBI. Does the 30 Runs make up the difference to make Player C the most valuable? I don't have these answers off the top of my head, I could take an educated guess (and I would guess C), but I can know for sure by figuring it out with statistics.
To do this, we need to figure out exactly how much one of 1 stat is with more than another. A good way to do this is by calculating its zscore.
Taken from Wikipedia:
"In statistics, a standard score indicates by how many standard deviations an observation or datum is above or below the mean. It is a dimensionless quantity derived by subtracting the population mean from an individual raw score and then dividing the difference by the population standard deviation. This conversion process is called standardizing or normalizing"
Well, doesn't that clear things up. No? ok maybe this graph will help:
Still confused? Me too. In Layman's terms, A zscore is a method used in data analysis which measures how far a given data point is from the average of all the data points. Ok I feel like I'm not getting anywhere with this  let me reference the above example.
Player A: 35HR, 100RBI, 100R, 10SB, .285 AVG
Player B: 20HR, 89RBI, 105R, 5SB, .290AVG
Player C: 12HR, 75RBI, 130R, 65SB, .310AVGX is the Player Stat of interest. X will change for each player that you calculate this for  and yes, you will calculate this for EACH AND EVERY player for a proper evaluation.
The Mu (the one that looks like a lower case U), represents the Average of all the players we will be evaluating, and the Sigma (lower case o) is the standard deviation for the data set. Standard Deviation is just a measurement of how far a data set varies from the average of the data set. Excel has built in functions for both of these : AVERAGE() AND STDEV(), so calculating this is a breeze.
Let's use 2012 stats as an example. I gathered together all the hitting stats for all batters in both leagues and the average player for ALL batters (i excluded pitchers) that had at least 1 plate appearance was 7.769 Home Runs (4910 total). The Standard Deviation for this data set is 9.196 according to Excel.
From here, it is very easy to calculate  especially in excel.
Player  HR Total  ZScore: Mean = 7.769 ; SD = 9.196 
Player A

35

2.961

Player B

20

1.330

Player C

12

0.460

Player  Runs Scored  Zscore: Mean = 32.82 ; SD = 29.39 
Player A

100

2.286

Player B

105

2.456

Player C

130

3.307

Player  Runs Batted In  Zscore: Mean = 31.25 ; SD = 29.64 
Player A

100

2.320

Player B

89

1.948

Player C

75

1.476

Player  Stolen Bases  Zscore: Mean =5.104 ; SD = 8.332 
Player A

10

0.588

Player B

5

0.012

Player C

65

7.189

Notice the 0.012 that Player B has for Stolen Bases? His total of 5 is below the 5.104 league average, so it makes sense that when compared to the average player that this zscore should be lower. When we sum these zscores, we can get a good comparison of how well each player will contribute to all the stats.
These Totals are:
Player  zscore HR  zscore R  zscore RBI  zscore SB  zscore sum 
Player A

2.961

2.286

2.320

0.588

8.155

Player B

1.330

2.456

1.948

0.012

5.722

Player C

0.460

3.307

1.476

7.189

12.432

Surprising isn't it? Now I was expecting Player C to be the more valuable, mostly because I have calculated so many zscores I can estimate the comparisons in my head. However I would not have expected Player C to be over 50% MORE valuable than Player A, and that is even before we consider the Batting Average stat.
Why didn't I include the batting average stat? Allow me to demonstrate:
Player  AVG  zscore: Mean =0.234 ; SD =0.069 
Player A

.285

0.739

Player B

.290

0.812

Player C

.310

1.101

The Problem with doing a zscore in this traditional sense on a rate stat like Batting Average is it does not account for frequency. let's add Player D to the mix. He played 1 game, and went 4 for 5. This is a good game but obviously does not deserve to be considered drafting right?
Player  AVG  zscore: Mean =0.234 ; SD =0.069 
Player A

.285

0.739

Player B

.290

0.812

Player C

.310

1.101

Player D

.800

8.203

Uhoh. There is no way that a Player that has 4 hits total should be valued 8x more than a player that hit over .300 for an entire season. To fix this problem, we need to change the way we look at this stat.
Rather than thinking about just the batting averages, we need to be thinking about how many more or less hits the player would have in comparison to the average player. To do this, we need to know the sum of all the AB for the league, as well as the number of Hits for the league. This stat I have labeled xBA:
Lets expand on the stats for our 4 imaginary Players. The 2012 total hits = 41408 ; AB = 160187
Player  Hits  At Bats  Average  xBA 
Player A

157

551

.285

14.8665

Player B

158

545

.290

17.1187

Player C 
171

551

.310

28.5665

Player D

4

5

.800

2.7075

This is beginning to make more sense. This data can be read that Player A has hit 14.8665 MORE hits than what the average player would hit given the same number of At Bats. For Player D, the average player would have 2.7075 less hits than what he got in his lone game, but because it was just over 1 game, his score is noteably lower than players that have hit over the course of the entire season. This becomes more useful when comparing stats for the platoon hitters of the leagues who will hit a high average, but over a hundred at bats lower than some of the league's every day players.
Is it more valuable to have 103 hits over 356 at bats (.289) or 168 hits over 602 at bats (.267)?
using this method we can calculate this answer exactly. In fact, here it is:
103 hits over 356 at bats = 10.974
168 hits over 602 at bats = 12.383
In this case, and with using the same league totals as the table above, we conclude that the .267 hitter will be more beneficial to your team than the part time hitter. Now if you were to use two platoon hitters and get the same at bats and compare it to the .267 hitter, you would probably make a strong argument  but that is more strategy based and out of the focus realm of this project. This method is strictly an apples to apples comparison and will let you know how the players themselves stack up with their raw stats.
Take note that when you perform the xBA calculation for the entire league, the sum of those calculations should be exactly 0. This makes sense as xBA is using the league average as the benchmark, so when you use the entire league, the average is met. Then you take the zscore of the xBA stat to get your batting average in a comparable state with the others. To finish the example:
Player  xBA  zscore: Mean = 0 ; SD = 10.634 
Player A

14.8665

1.398

Player B

17.1187

1.610

Player C

28.5665

2.686

And so for our imaginary hitters are valued at in the standard 5x5 format for 2012:
Player  zscore HR  zscore R  zscore RBI  zscore SB  zscore xAVG  Total 
Player A

2.961

2.286

2.320

0.588

1.398

9.553

Player B

1.330

2.456

1.948

0.012

1.610

7.332

Player C

0.460

3.307

1.476

7.189

2.686

15.118

This method of turning a rate stat into a counting stat can be used on all the categories. The basic formula is:
With all the appropriate zscores calculated and added for all the categories selected, the Fantasy Baseball Calculator will give you a custom ranking.
Now with all of that said, Click Here to let me explain to you why using this ranking is completely wrong
Quick Links