Perfectly Cromulent Musings

Happy holidays to all! Let's celebrate with some data visualizations.  Last week we took a step further into the world of z scores and prospect values. Examining the 2019 - 2024 timeframe with data from Fangraphs, we looked at the relative rank of each organization's class of prospects by year via z scores. Normalizing prospect values this way then helped us make comparisons across years, adding a new angle of analysis and helping us celebrate the very best of the best (and, at times, the worst of the worst...). That full table can be found  HERE  on my GitHub.  Let's take that csv into the shop for an upgrade today. First, we'll sum up each of the z scores by organization, to provide a sort of "total prospect value" across the timeframe. These "Total Prospect Z Scores" give us a window into which teams were the most successful at developing prospects from 2019 - 2024. The higher an organization's cumulative z score, the more they outperformed t...

Building off of last week's post - where we moved the lens towards MLB farm system value, not just prospect count - let's take a look at z scores by organization and by year. Here, we'll run the same exercise as we did last week, creating a z score value (number of means above the standard deviation) but for each year, getting us 30 teams x 6 years = 180 observations of z scores.  Critically, each z score is anchored to the season in question. That is to say, the value of farm system's hitting, pitching and total prospect count is benchmarked to the league-wide values from that year alone. I think measuring z scores within years like this would more accurately help us track population-level changes in prospecting. Normalizing within each year ultimately helps us make comparisons across years that reflect newfound understanding of valuation at a macro level.  For instance, comparing the nominal values of SFG's 2020 hitting prospects ($191.5M) and Milwaukee's 202...

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 brin...

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 ...