Diamond Plots and Diamonds In The Rough, Part 2

Last week, we introduced diamond plots and very quickly looked at applying the visual to MLB farm system composition.

From that post:

"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 what we got: 

Hard to quibble with it from a visual perspective - nice even spread, some clear winners and losers, and even some high-level trends to parse out. Be sure to check out my post from last week for some more thoughts there. 

Which brings us...to this week.  As noted, last week was just a number's game. To really get to the crux of any farm system - not just how wide it is, but how deep it is too - we need to examine the value of the system.

Luckily, we have a lot of great research to lean on here, notably HERE and HERE from Craig Edwards. 

I'd encourage a deep read of Craig's work regardless, but the key takeaway for us is that empirical analysis of prospect outcomes (WAR) based on their evaluation / rating allowed Craig to assign a present value to each player (in dollars terms, using $/WAR and a discount rate). 

Craig's table looks like: 

Tallying up each prospect's PV at any given time for each organization gets us to the total Farm System Rankings from Eric Longenhagen's 2024 Prospect Report. So, for instance, summing up the 26 ranked players in the Angels farm system from the 2024 Report based on their ranking and respective PV gets us to their (last-ranked) total value of $76M. 

What's not intuitively clear from the linked report from Eric, however, is how each organization arrives at that total value. In other words, we need to break out each organization's split between total hitter and total pitcher value to get a sense of the stylistic differences between organizations.

As fans of baseball will know, pitchers are just much more difficult to actualize than hitters. There will just be fewer "stud" pitching prospects compared to stud hitting prospects, as the risk bars associated with the entire pitcher pool push grades lower as a whole. With the modern game moving away from true ace starting pitcher archetype in general too, those stud pitching prospects end up making less of an impact than stud hitting prospects anyway.

More critically, the way Eric and the Fangraphs team assign values, the whole 40 and 35+ tiers are essentially reserved for pitchers with question marks who could serve a narrow role in MLB. These lower tiers are more or less a clearinghouse for future relievers, and as such there are just many more lower ranked pitchers than hitters. 

All this means that total pitcher value for the top ranked team (Pittsburgh, ~$155M) is basically half that for the total hitter value top team (BAL, ~$310M). Displaying both hitting and pitching value on the same symmetrical chart is unpleasant on the eye, as most teams get jammed up towards the middle and the separation in teams is hard to see. 

So to get that beautiful symmetrical plot, let's convert each value to a z-score to normalize the values.

Making each component value into a z-score will show us how many standard deviations from the mean that respective value is. The larger the z-score, the more "impressive" it is considering the underlying scaling of the rankings. 

Here is the table by dollar values: 

And here is the table by z-score: 

And so while before we might struggle to weigh in our minds the Orioles hitting and the Pirates pitching, now we can see: the Pirates having more than $150M in pitching value at the time of the report is BONKERS, a full 3 standard deviations and change above the mean value. While the Orioles hitting crop is still impressive - 2.5 standard deviations is nothing to sneeze at - the Pirates are on another level. At the time, the team boasted 3 Top 100 pitchers, including now-big leaguers Paul Skenes (65 FV) and Jared Jones (55 FV). 

Are there any other interesting orgs when it comes to z-scores? Only 13 organizations were above the average in total value. The most balanced of those orgs is (unsurprisingly) the Dodgers, 1.5 standard deviations or so above the mean in both hitting and pitching. The most unbalanced were the Red Sox (heavily levered to hitting prospects) and the White Sox (heavily levered to pitching prospects). 

Huh...I wonder if those two orgs recognized this dynamic and made a trade recently... :)

We came here to visualize this, though, not just talk it into submission! 

So without further ado, here's the diamond plot of 2024 Fangraphs System Values, broken out by z-scores of total hitter and total pitcher value: 

I'm careful not to say that this plot neatly proxies organizational philosophy, because one of the key vectors of any organization is how well they "over"develop their prospects - driving overachievement from what an outside ranking might otherwise suggest is a major marker of success for any player development group. So just because an organization doesn't have a lot of projected PV from their hitters, for instance, doesn't mean that group is destined to under-produce . These PVs are in no way deterministic. 

However, this plot does a fine enough job helping us to see the snapshot of where organizations find themselves, as seen from the outside. Here are some quick hitters: 

1. While the Angels farm system may simply be not very valuable, there are a few other similarly valued farm systems who are  in some ways very interesting. For instance, the  Marlins 3rd to last farm system is actually above average in pitcher value, fitting with their perception as spitting out big dudes with big stuff. 
2. The quickest way to a poorly ranked farm system is to fail to develop hitters. All bottom 10 organizations are at least a half standard deviation below the mean hitting value, and most are at least 1 full standard deviation below.
1. This reflects the dynamic we discussed above - hitting prospects can be evaluated much more confidently than pitching prospects, so when you whiff on hitting development, it's clear you really whiffed...the margin for error is just so much smaller when you're talking placement league-wide, and the organizations that get it wrong really bury themselves. 
3. As Eric notes in various places, Craig's values do strongly favor organizations with a few potential stars rather than deeper organizations. We see this dynamic help the Pirates (very top-heavy)  and hurt the Diamondbacks (a deep organization without a true stud). 
4. Finally, my eyes keep coming back to the middle third vertical portion of the plot where so many teams find themselves. Knowing most of the market is "generalist" focused (i.e., a pretty balanced system between value of hitters and pitchers), is there a strategic pathway to lean in on specializing in one type of prospect vs. another? Could an organization that's known as the hitting factory find some alpha by pushing themselves farther to the right on the chart than anyone else? 
1. That strategy might actually work even better being the "left-most" team (i.e., most pitching-focused) considering the sheer attrition of pitchers league-wide. 

Anything stand out to you? Any other places you'd like to see me take the analysis? Please drop me a note in the comments! 

Next week's post is TBD - possibly similar analysis in past years (separate or combined scope), or maybe something new...

Thanks for reading! 

Code is HERE on my GitHub. 

Notes: 

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

From: 

https://thef5.substack.com/p/how-to-diamond-plots-in-r

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