# Baseball by the Numbers—Batting Average (BA) and Slugging Percentage (SLG)

This is the first post in a series I am starting which will discuss baseball statistics, both traditional and new, using real life Cardinal players numbers. The purpose is to both explain the statistic and give it a real life application. I hope it becomes both a learning tool and a catalyst for discussion. I want to make it clear that I am not putting myself out there as an expert, far from it. I will be doing research and learning along the way myself, and when I make a mistake (and I will), I encourage my readers to let me know. However, if you do let me know, please do so politely (preferably with a source) and not by telling me I’m too stupid to live. That kind of dialogue is not helpful to anyone. And also be sure it is a mistake before you light into me. Pay attention to context and source.

For most stats I will be using Fangraphs. com. I will occasionally use Baseball-Reference.com, especially in an area where the two sites differ. When I do use BR I will specifically say so, if I do not, assume the stat comes from Fangraphs. My hope is that we all become more knowledgeable along the way.

St. Louis Cardinals right fielder Carlos Beltran (3) at bat against the Washington Nationals during the eighth inning of game four of the 2012 NLDS at Nationals Park. Mandatory Credit: Brad Mills-USA TODAY Sports

This post will look at batting average and slugging percentage. Batting Average (BA) is calculated by dividing the number of hits a player gets by the number of his at bats. It is a simple and straightforward statistic, but it has flaws. The biggest problem with BA is that it measures only the quantity of hits and not the quality. A swinging bunt infield single counts the same as a home run. This is important in the sense that the ultimate goal of baseball is to score runs and by the end of 9 innings score more runs than your opponent. Therefore the quality of a hit counts more in that regard than the quantity. Over time, a player who hits home runs is going to generate more runs than a player who hits singles.

Slugging Percentage (SLG), unlike batting average, measures the quality of hits. Slugging percentage is calculated by dividing the total number of bases by the number of at bats. A single is (1) base, a double is (2) bases, and so on. The equation is (1B) +( 2 x 2B) + ( 3 x 3B) + ( 4 x HR) / AB. SLG tells you what BA does not, that is, the type of hits, not just the number. When evaluating the offensive production of a player in terms of hits, one should never rely on BA alone; to do so would give an extremely skewed and inaccurate assessment of a player’s offensive skills.

For my Cardinal example I will use **Carlos Beltran** and **Skip Schumaker**. In 2012, Carlos Beltran had a BA of .269 and a SLG of .495; Skip Schumaker had a BA of .276 and a SLG of .368. Over their entire careers, Beltran has a BA of .282 and a SLG of .496; Schumaker has a BA of .288 and a SLG of .377. Schumaker has a BA that is close to that of Beltran but his SLG is much lower. Schumaker’s quantity of hits per AB is similar to that of Beltran but the quality of his hits is significantly lower. The difference in these two statistics in this example demonstrates why BA alone does not give a complete picture of the offensive skills of any given hitter. BA does not differentiate between a power hitting outfielder and a singles hitting utility player. That difference is significant in terms of how many runs that player can generate, which is the difference between winning and losing.

There are other stats which give a deeper and broader picture of offensive production. Those stats are for another post. My purpose here was to demonstrate that a hitter is not measured by batting average alone. In order to understand the complex nature of major league baseball hitting, one must look beyond the surface and dig deeper, much deeper. In this series I hope to give you the tools to do that.

And one more thing. The real life examples I use in this series are not meant to be a debate about the relative merits of the individual players I use. This series is about statistics, what those statistics mean and how they can be applied. It is not about whether one player is tougher or nicer or has better facial hair. We can debate that somewhere else. I hope you like this series and I welcome your input.