Baseball's Billion Dollar Equation

In 1999 Voros McCracken infamously speculated about the amount of control the pitcher had over balls put in play. Not so much, as it turned out, and DIPS was born. It's tough to put a value on something like DIPS, but if an MLB team had developed and exploited it for several years, it could potentially have been worth hundreds of millions of dollars. Likewise, catcher framing could easily have been worth hundreds of millions.

How about a billion dollar equation? Sure, look at the baseball draft. An 8th round draft pick like Paul Goldschmidt could net you a $200M surplus. And then there's Chase Headley, Matt Carpenter, Brandon Belt, Jason Kipnis and Matt Adams. The commonality? All college position players easily identified as likely major leaguers purely through statistical analysis. You can also do statistical analysis for college pitchers, of course, but ideally you'd also want velocities. These are frequently available through public sources, but you may have to put them together manually. We'll also find that GB/FB ratios are important.

There's plenty of public data available. I've made yearly NCAA college baseball data available in my public baseball GitHub account; it covers 2002-2014, which is plenty of data for analysis. Older years are also available, but only in PDF format. So you'll either have to enter the data manually, use a service or do some high-quality automated OCR. My repository also includes NCAA play-by-play data from several sources, which among other things is useful for building catcher framing and defensive estimates.

Also publicly available, and will be available in my GitHub over the next several days:

1. NAIA - roughly NCAA D2 level
2. NJCAA - junior college, same rules as NCAA
3. CCCAA - junior college
4. NWAACC - junior college

Prospects come out of the NAIA and NCAA D2/D3 divisions every year, and with the free agent market valuing a single win at around $7M you want to make sure you don't overlook any player with talent. With JUCO players you'd like to identify that sleeper before he transfers to an NCAA D1 and has a huge year. Later you'll also want to analyze:

1. Summer leagues
2. Independent leagues

We'll start by looking at what data is available and how to combine the data sets. There are always player transfers to identify, and NCAA teams frequently play interdivision games as well as NAIA teams. We'll want to build a predictive model that identifies the most talented players uniformly across all leagues, so this will be a boring but necessary step.