We compile play by play data since 2002, using the overall run expectancy of teams to calculate the most efficient offenses of the 2000's. Then we look at last year's most efficient offensive attacks.

With the DH not coming to the NL until A.S. (After Selig), what type of hitting could we see from pitchers in the coming years?

How can we quantify the goosebumps we get from deep home runs down the line?

Many writers have been critical of the passive approach now widespread in the major leagues. Is this approach really hurting scoring?

This installment takes WHB and gives it more game context, which leads to the development of a new metric: SMASH

Jordany Valdespin and the umpires intentionally or unintentionally ignored ball 4. How did that affect Valdespin's chances of getting doing anything better on the next pitch.

What is the average height of each home run hit to each field at each park and how is that affected by the park dimensions?

Before I make the big jump and create my advanced WHB metric, we will look into what WHB data shows us that BIP data cannot.

Looking at outlier months in league average runs-per-game.

The Cardinals have a winning record once again. In fact they lead the tough NL Central, but how has Mike Matheny's squad done it?