Farming 101

Below are a number of explainers and definitions that will come in handy when looking at the statistics, numbers, and rankings on Baseball Farm (and throughout the baseball prospect universe)

 

General framework

The general methodological framework for our proprietary statistical rankings considers the following elements:

  • Season-long ranking of statistical categories relative to performance of other players at the same position
  • Age of prospect relative to level.
  • Consistency of performance across multiple levels.
  • In-season and end season awards from multiple publications, including our own weekly Prospects of the Week winners.

Tinkering

Bear in mind that we are constantly tinkering with our proprietary statistical formulas. So we tend to give a general overview of the statistical framework we are using, as opposed to the exact formulas.

‘Blueberry’ Method

Our ‘Blueberry’ ranking method rates an individual prospect’s performance relative to all other MiLB performances last season. Blueberry rankings are on a 0-9 scale, with each number representing the percentile rank a prospect achieved for each category last season.

The ‘Blueberry’ hitting categories (reading left to right) are Batting Average – On Base Percentage – Slugging Percentage – Stolen Bases – At Bats.

At Bats are included as a rudimentary measure of 1) a player’s durability and 2) the reliability of a player’s Blueberry line.

Example:

Heliot Ramos’s (SF) 2017 Blueberry line is 9 – 9 – 9 – 8 – 5

Therefore, he finished in the 90th percentile or above relative to his peers in Batting Average, On Base Percentage and Slugging Percentage. He finished at the 80th percentile or above relative to his peers in Stolen Bases. And he finished at the 50th percentile or above relative to his peers in terms of At Bats. Therefore, Ramos put up better numbers than at least 90% of all MiLB hitters last season, but the final ‘5’ in his Blueberry line suggests we use some caution in projecting the same results going forward given the small sample size of ABs we are working with.

Organizational Depth Charts

The organizational depth charts, found on the carousel on the home page, are backward looking. They recap and rank the players in each organization at each position based on the previous year’s statistical performances.

The organizational hitters rankings are weighted rankings comprised of: runs created, batting average, on-base percentage, runs scored, home runs, stolen bases, and strikeouts.

The organizational pitchers rankings are weighted rankings comprised of: earned run average, walks and hits divided by innings pitched, total seasonal strikeouts, home runs allowed, and strikeout to walk ratio.

wRC+

Baseball stats guru Bill James created the ‘Runs Created’ statistic in order to give a hitter credit for the runs he created both hitting and running the bases. Runs created basically takes all of a hitter’s batting and base-running events and weighs them relative to their contribution in creating an individual run.

wRC+ is Fangraph’s verison of Bill James’s Runs Created metric, is league-adjusted, park-adjusted, playing-time neutral, and pegged to 100 for its scale. It allows us to compare hitters across leagues on the same scale using a single number. The ‘average’ hitter always posts a wRC+ of 100. Anything above 100 is better than average, anything below 100 is below average.

wOBA

Baseball stats guru Tom Tango created the ‘Weighted On Base Average’ (wOBA for short) statistic in order to remedy the deficiencies in simply adding OBP to SLG to create OPS. wOBA is derived by giving each hitting event its equivalent run value, and then scaling all of a hitters events similar to the OBP scale. As Tango notes, “In other words, an average hitter is around 0.340 or so, a great hitter is 0.400 or higher, and a poor hitter would be under 0.300.”

In my experience, most Strat-o-Matic players rely on wOBA over OPS when evaluating hitter’s cards. I rest my case.

Age Relative to Level

An important factor to keep in mind when evaluating prospects is their age relative to the average age of the level they are competing in. A player who is far older than his competition deserves a statistical downgrade. A player who is far younger than his competition can be cut a little slack. This is especially true if it is a player’s first time playing at a particular level.

Here is a table Fangraphs put together in 2o12:

Level Average Age
AAA 28.2
AA 23.8
A+ 22.4
A 21.2
A- 20.9
R 19.4

Part of the reason why Ronald Acuna’s 2017 success was so impressive was that he was a leading hitter as a 19 year-old in AAA. Conversely, we have to temper our expectations when we see a 23-year old college grad beating up on a bunch of kids in rookie ball. Baseball Farm accounts for age differential as part of the performance weights in our rankings systems.

Repeating levels

Repeating levels is not always a bad thing. Frequently, players need a second shot at a particular level of competition in order to work through a certain shortcoming. However, if a player repeats a level and shows no statistical improvement, it is probably time to show that prospect the door.

White Sox prospect Courtney Hawkins is a good example. Believe it or not, Hawkins was once considered one of the top prospects in the White Sox system. But once he hit AA, he struggled:

2015 (AA) – .243/.300/.410, .329 wOBA, 99 wRC+

Instead of forcing him up the ladder, the White Sox left Hawkins at AA Birmingham and hoped to coach some improvement into him:

2016 (AA) – .203/.259/.349, .279 wOBA, 72 wRC+

At this point, Hawkins should be jettisoned. But he’s a good, hard working kid and the White Sox are a good organization, so they gave him another shot:

2017 (AA) – .190/.252/.325, .267 wOBA, 64 wRC+

Just because a big league team hangs onto a guy, doesn’t mean that you have to as well.

Pitcher’s Height Doesn’t Matter

Many prospect writers fall into the trope about a ‘towering’ pitcher, suggesting that a pitching prospect’s height should be factored into our evaluation of their potential. I am as guilty of this as anyone. When I saw White Sox prospect Alec Hansen walking around Camelback Ranch like Dr. Frankenstein’s monster, I got irrationally excited.

In reality, however, there is no statistically significant correlation between a pitcher’s height and either his effectiveness or his durability. This presents dynasty owners with a buying opportunity. Mine those short pitchers to find the next Pedro Martinez while your competitor’s fumble around with the next Mike Pelfrey.

Green Bean Ratio

The ‘Green Bean’ Ratio is a hitter’s groundball rate divided by his line-drive rate and his flyball rate (GB/(LD+FB)). A good Green Bean ratio is somewhere under 1.0, while a great Green Bean ratio is in the 0.50 range. Our research has shown that a hitter’s Green Bean ratio is correlated to a hitter’s ability to hit for Slugging Percentage. In other words, as a batter hits more line-drives and flyballs, he is also more likely to score extra base hits.

For your research, think of the Green Bean ratio as a leading indicator for Slugging Percentage improvement. If you see a prospect make strides in his Green Bean ratio, a power breakout might be on the horizon.