The top pitching prospects as projected by Sparkman can be found at about the halfway point of this article if you’re not interested in the ins and outs of the system.

I evolved from a regular old baseball fan into something well beyond that the same way a lot of other people do nowadays - with fantasy baseball. I’d been playing most of my life, but in 2014 I dove into my first league that involved prospects. I didn’t really know what I was doing, but I ended up doing pretty well, I think, rounding up Corey Seager, Josh Bell, Michael Conforto, and Touki Toussaint in the first 7 rounds, and even managed to scoop a 17-year-old Luiz Gohara in the 22nd. Though any success I had past Seager was blind luck, really.

As the number of available names from the public “Top 100” lists began to slowly creep to 0, I didn’t know who to pick. So I stuck with the numbers. In the 10th round, I picked a 2013 draftee who, in his first full season, pitched 120+ IP at A+, struck out 117, walked just 25, allowed only 21 ER, and gave up just 2 HR all year. This, to me at the time, was a great pick.

His name was Glenn Sparkman, pitcher for the Kansas City Royals.

Of course, there was a lot I didn’t know at the time. I didn’t know that 22 year olds in High-A should [more or less] being pitching exactly that well below AA due to competition that is sometimes underdeveloped. I didn’t know that his 11 appearances out of the bullpen were noteworthy, as they would inflate his K totals while deflating his ERA. I didn’t know that his home park (Wilmington) suppresses HR like crazy and that his measly 1.4% HR/FB could in no way be a skill.

This worthless little anecdote always stuck with me, and that’s how Sparkman, the projection system that I’ve developed for pitching prospects, got both its name and a little bit of inspiration.

What Does Sparkman Do?

Sparkman projects the Major League impact of a pitcher through his 20’s using his age and stats at whatever level he pitched at. The projections come in the form of a total FanGraphs WAR total for these years and are based on historical seasons in the minors from 2007 forward. [1]

This will be the first time of many I will say that Sparkman is not intended in any way to replace or be better than actual scouting. That’s not its purpose. When analyzing a prospect, pitching or hitting, visit the actual scouting reports of Prospects Live, FanGraphs, Baseball America, or wherever else first, as the numbers alone will never come close to tell the whole story. What Sparkman can hopefully do is contextualize certain things such as age/level/risk to pershaps shed some light on some pitching performances that were either underappreciated or not as impressive as they seemed on the surface.

[1] Learning to web scrape is very much on the top of my to-do list, but until then, only the years 2007 and beyond for Minor League Baseball data are [free and] available in one nice, neat location for me to download and analyze. On one hand, that’s disappointing; more data is never a bad thing, and I would love to go into the 90s or beyond. On the other hand, the game has changed so much over the last 10 - 20 years, I’m not sure if this data would be more helpful than what I already have. It could even be making the model worse. I won’t know until I get my hands on it. For now, however, I was happy with the data I had for an initial roll out, which was almost 40,000 individual data points from Low-A to AAA over those years.

Methodology

What Sparkman sets out to do is, for each pitching prospect that pitched in the last year, project the percent chance that said pitcher will reach certain career “milestones” before the age of 30. Then, based on those percentages, calculate an expected WAR for each pitcher over that same time period. To do this, Sparkman takes a players stats and uses various logistic regressions that change depending on level and milestone.

Logistic regressions, for those unfamiliar, have two outputs:

0 (or “False”, “Negative”, “No”, etc.)

1 (or “True”, “Positive”, “Yes”, etc.)

All historical seasons in the Minors have a 0 or a 1 associated with them for each milestone at the Major League level. Here’s James Paxton in his 20’s (13.6 total fWAR), for example: