How Much Value Is Really in the Farm System?

Everyone knows that a strong farm system is key to the long-term success of a major league organization. They make it possible for clubs to field competitive teams at affordable salaries and stay beneath the luxury tax threshold, but how much value can an organization truly expect from their farm system? How much more value do the best farm systems generate compared to the worst ones? I decided to take a closer look.

Methodology

The first thing I did was gather the player information and rankings from the Baseball America’s Prospect Handbooks from 2001-14 and entered them into a database. I then found players’ total fWAR produced over the next six seasons, and I added them together to find the values that each farm system produced. I chose six seasons to ensure that teams wouldn’t get credit for a player’s non-team-controlled years, since the value produced would not be guaranteed for the player’s current organization. This method will reduce the total value produced by players that are further away from the majors, but the purpose of this analysis is to focus on the value of the entire farm system and not an individual player’s value over the course of their career.

Let’s look at the 2014 Minnesota Twins as an example. Below is a list of the thirty players that were ranked and the amount of WAR that each player has produced by season.

In this table, we can see that the 2014 Minnesota Twins farm system produced 70.1 WAR over six seasons, with 26.9 WAR coming in 2019. I repeated this process for every team to create my dataset.

Total WAR Produced by Farm System

The first thing I wanted to examine was how much total WAR an organization could expect from their farm. I calculated the average produced for each system to be 45.83 WAR over six seasons. I also discovered that the maximum WAR for a farm system was produced by the 2003 Cleveland Indians with a total of 136.0 WAR, and the minimum value came from the 2008 Seattle Mariners farm with a total of -1.7.

The next thing I wanted to examine was the distribution of WAR values. Do they follow a standard normal distribution or is something else going on? Below is a density plot of all 30 farm systems’ WAR over six seasons.

There is nothing too crazy going on here, but it looks like the distribution is positively skewed, with the 2003 Cleveland Indians an outlier at 23.6 WAR more than the next-highest organization. I am not surprised that the data is skewed, because if a player is doing well early in his career, he will continue to accumulate more playing time and WAR. However, if a player is not performing well, he is in danger of being sent down. This makes it difficult for the left tail to mirror the right tail distribution.

WAR Produced by Farm System in a Single Season

The amount of WAR generated over six seasons is a good way to show the overall production and general well-being of an organization’s farm system, but I believe it is just as important to see how much value an organization can generate in a single year. Producing 45.0 WAR over six years is the average, but how did you distribute them? If your farm system does not produce any value for the first five years, but it then somehow comes up with 45.0 WAR in year six, is that better than producing 7.5 WAR in six consecutive seasons? If you are the GM of a 100-loss club, you may prefer the boost of 45.0 WAR arriving in a single season, since adding just 7.5 WAR will probably not get you into the playoffs. If you are the GM of a contending team, you may want the slow drip instead since your team is already competing for a playoff spot every year. So how much WAR can you expect from a farm system in a single season?

I again calculated the mean WAR value, but this time I found it for each year. The mean value was 7.64 WAR, with a maximum of 31.7 by the 2003 Cleveland Indians farm system in 2005. The minimum was the 2008 Seattle Mariners farm system, which produced -7.4 WAR in 2010. The density plot is shown below.

Once again, we see a positively skewed distribution, but there is something else that I would like to account for before moving forward. To create this visual, I compiled seasons one through six for each farm system and treated all seasons equally, but that may not be the best way to interpret the data. The next visual shows the distributions for each individual season in relation to the year of the organization’s ranking. In our 2014 Minnesota Twins example, the WAR produced in 2014 would be in the distribution labeled “Same Season.” The 2015 season would be in the distribution labeled “Second Season,” and so on and so forth to the “Sixth Season” distribution.

The visual shows us that we should not treat all seasons as equal. The first season distribution is vastly different from the others, with almost 20% of players hovering around 0.0 WAR produced in a season. The second and third season distributions are not as stark as the same season distribution, but these are not like the distributions for seasons four through six. This makes sense, since many of the prospects appearing in the Prospect Handbook are not perceived to be ready for the majors any time soon. I believe that it would be more beneficial for my individual season analysis to only look at seasons four through six to determine how much WAR a team can expect from its farm system.

WAR Produced by Farm System in a Single Season (Years 4-6)

Without Seasons 1-3, the mean moves up to 10.3 WAR and the distribution appears a little less skewed. This is a decent way to look at the data, but it is only looking at one variable. How can we account for the different quality of farm systems? This is where Baseball America’s team rankings come into play.

Total WAR Produced by Farm System Ranking

Each season, Baseball America ranks the farm systems from 1-30. How much more valuable is a top-tier farm system compared to the bottom, or even an average system? The graphic below is an attempt to answer this question.

This graph was created by looking at the total WAR produced by a farm system in six years according to their team ranking in Baseball America. That means that there are 14 data points for each box plot. I decided to use box plots for each ranking instead of looking at the mean or median, because I wanted to highlight how much variance there is for each ranking.

I also added a blue trend line to show the general relationship between team ranking and WAR. It looks like the trend line for the first 10 spots is steeper than the rest of the rankings. This means that moving from the 10th-ranked system to the first has a much larger impact on WAR than moving from the 20th to the 10th.

Single Season WAR Produced by Farm System Ranking

Once again, I thought it would be interesting to look at single-season WAR produced by a farm system. I excluded seasons one through three for the same reasons mentioned earlier. Below is the same box plot chart, but for individual season WAR instead of WAR produced over six seasons.

The blue line shows the downward trend lower in the rankings, but it also appears that the trend line is more linear than the one in the previous chart. I am not sure why this is happening, but I do find it interesting that a single season of WAR is relatively linear, but six seasons of WAR is not.

Conclusions

  • The most WAR produced by a farm system over six years was the 2003 Indians at 136.0. The second-most was the 2006 Marlins with 112.4 WAR. The Indians were such an outlier that teams and fans should probably not expect more than 110.0 WAR from their club’s farm no matter how good it appears.
  • The average WAR produced by a farm system over six years is 45.83.
  • The most WAR produced in a single season was in 2005, with 31.7 coming from the 2003 Cleveland Indians farm system. This means that teams and fans should not expect much more than 30.0 WAR from their farm system in any given year.
  • Seasons one through three for a farm system have vastly different distributions than seasons four through six. This means that teams and fans should not expect their farm system to be productive right away and that they should not be judged too harshly in the near future.

All the player information was obtained from Baseball America Prospect Handbooks and all the WAR figures were obtained from FanGraphs.com.





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tbhbeary
Member
tbhbeary

Down the line, another post should be something like “Old, larger minor league system vs new, smaller minor league system”. It will be interesting to see how prospect development is different and how it affects it. I think it will be a pretty big change. Newer players will have to immediately adapt to a higher playing level, which will help them in the long run, as they will know that they have to always adapt to a higher level of play; stagnation is bad.

ScottyB
Member
Member

Great analysis and article.
It would be great if you could relabel the box-chart graphs on the X axis- instead of case # 1-30, it would be awesome to see the team names

channelclemente
Member

It would be fascinating to see the WAR distributions bifurcated between pitching and offensive skill sets too.

Francoeurstein
Member
Member
Francoeurstein

2017 Braves look like they can put up some serious numbers: Acuna, Albies, Swanson, Soroka, Newcomb, Ian Anderson, Fried, Riley. Pache, Lucas Sims, Minter, Rio Ruiz, Bryse Wilson, etc