Diamond Plots and Diamonds in the Rough

Well, blog, it's been awhile! Here's a quick one to get back on the board. My hope is to make posts like these much more of a regular occurrence, though, so stay tuned! 

Today we'll be working with diamond plots in R. This post is much more proof of concept than anything earth-shattering, but the reps are needed, so away we go. 

First, thanks to Owen Phillips of the F5 for his "How To: Diamond Plots in R" post which I followed to a significant degree to build this. 

Traditional X-Y plots are not necessarily the most intuitive to compare data points intra-graph. By flipping the plot 45 degrees, we better align the graph's visuals with our own human nature - namely, making quality judgements top to bottom in a descending order. 

Here, let's use diamond plots to get a sense of the varying ways MLB farm systems are built, namely, the number of (ranked) hitters vs. (ranked) pitchers in a given team's system. This will be a quantity exercise to start - the value of those players will be the focus of next week's post. 

We'll use the Farm System Rankings from Fangraphs 2024 Preseason Prospect Report to get the breakdown of each system. Remember, these are essentially the prospects of note in a system (ranked with a 35+ FV or higher), not a comprehensive look at how many players each organization has rostered. 

Here's the big reveal! 






If this was plotted on a traditional X-Y plot, movement horizontally would be some sort of "value" judgement. In other words, going across the plot would signify one team's higher ranking to another on a variable. We read left to right but we're not conditioned to see that as "better" or "worse".

Now, moving your eyes horizontally across the graph is a stylistic reading, with the "value" held constant. For instance, the Mariners and Blue Jays both have ~35 ranked players, and we can confirm this by their roughly equal vertical level on the graph. However, the component parts of their respective systems are slightly different - for the Mariners, it's more skewed towards pitching (they are farther left on the graph), whereas for the Jays it's more skewed towards hitting. This makes comparing teams and styles that much easier! 

So what stands out about the results?  

  1. Many of the teams you might associate with being "pitching labs" actually don't have that many ranked pitchers (see: Guardians, Brewers, etc.). Is this a function of having graduated many players in recent years? Will the value-based version of this chart reflect their ability to develop otherwise underrated prospects? 
  2. Thankfully, the Phillies had a few in-season breakouts that make these rankings a little obsolete, but their placement on this chart from preseason reminds us just how "win-now" they need to be these next few years. With a farm system with the second fewest number of ranked prospects in all of baseball, help is firmly not on the way. 
  3. The Rockies may be a sleeping giant. Ownership has never been afraid to spend, but refining their process (and likely, picking a strategy to begin with) has always been the impediment. Their placement in this chart, however, indicates that with a few smart moves at the big league level, the club could be right back in contention.
As aforementioned, stay tuned for a more informative look at farm system values. 

Code is HERE on my GitHub. Thanks! 

Notes: 

Two-way (batter and pitcher) prospects are split (0.5/0.5) between the batter and pitcher tiers for valuation purposes.

From: 

https://blogs.fangraphs.com/an-update-to-prospect-valuation/

https://blogs.fangraphs.com/putting-a-dollar-value-on-prospects-outside-the-top-100/

https://blogs.fangraphs.com/how-the-draft-and-the-trade-deadline-affected-our-farm-system-rankings/

https://www.fangraphs.com/prospects/farm-system-rankings/2024-prospect-list 

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