So here are the long awaited and never duplicated HVaC ranks. This edition includes all pitchers as well as an overall Top-100 (ok, really 923 or so). I also wanted to elaborate a bit more on the way the scores and the ranks are designed and developed. I’ll admit that it is a bit confusing overall and the questions I have received on it are fantastic. Trust me when I say I appreciate all the feedback and please keep it coming.

Ultimately, the biggest point to keep in mind is that we are looking at players that create the greatest difference from the mean in a given week. The greater the positive standard deviation from the mean, the better that specific skill is for the player and for an owner to grab in a draft. Let’s look at one specific example on this to draw the point.

If we look at the catching position, **Matt Wieters** is placed as the ninth best catcher according to ZiPS but he comes across as a top-three catcher in the HVaC and top-20 overall. Why would this be the case?

Wieters and his 22 home runs tie him for the greatest distance from the mean among all catchers with **Carlos Santana**. He will produce roughly 15 percent better than the average catcher in a given week in this category and nearly 12 percent better in RBI. Both are better than one standard deviation away from the mean. Wieters does this while producing an average that is a difference of less than half a hit per week against that same middle number.

The next question is how and why does it translate this way. First, home runs and RBI are two main categories you can get from catchers. We eliminate steals from any weighting since the position averages the lowest number among projected 12-team fantasy starters with three. These numbers put Wieters solidly in the top-three. It goes to top-20 when we compare the average numbers of a starter at each position across the fantasy universe. While Wieters and Santana have strong numbers for catchers, the average positional production is below the average of an overall fantasy starter at any other spot with the greatest differences coming in runs and, not surprisingly, average. In single catcher leagues, we then increase the score by 17 percent as the numbers fall outside of one deviation (for you statistics fans, 83 percent of all numbers fall within one standard deviation of the mean).

This is just one example. Other positions are not hit as hard in terms of raising scores. Other players can fall two deviations away and lower their score (thus raising their rank) as a result. Now that I have you all completely befuddled, check out the latest edition of the rankings again built off the latest ZiPS projections.