This chapter is a guide to some popular formulas for understanding baseball: common statistics that you remember from childhood and see displayed on the scoreboard during every game, more complicated statistics that illustrate the complexity of the game more clearly, and some advanced statistics developed by sabermetricians over the past 30 years.
The hacks in this chapter are a little different from the others in the book: each hack presents a single formula (or a set of formulas) as an equation (or a set of equations). I explain what each formula is designed to measure, and I present summary statistics for each formula that show the distribution of values by team and player over the past decade. I also give samples of a few exceptional seasons (by teams and/or players).
I selected the hacks in this chapter by picking the simplest, cleverest, and most useful formulas I could find. A few of the best systems for evaluating players, such as Baseball Prospectus’s PECOTA, Bill James’s Win Shares, and TangoTiger’s Base Runs, work well in practice but are too difficult to be called hacks. I was looking for simple, clever tricks that would be easy to understand.
In this chapter, each hack gives you summary statistics that provide you with some idea of what good, bad, and average values are for each statistic. This is a trick that I learned at work while analyzing data on network security, advertising effectiveness, ...