One thing you should know about me is that I love a good plot twist. Seeing a bunch of circles designed along carefully chosen steps representing the x-axis and y-axis, with oddly placed circles indicating some kind of miracle or high achievement, and a set of circles showing certain similarly arranged players or teams, has gotten my humor for years. If you think I haven’t done a baseball stat post with a scatterplot over the years…
2022
2023
2024
2025
My go-to tool was baseball savant custom leaderboard tool. A flexible page that allows you to easily pull up a leaderboard based on all the common metrics and statistics you can find on the website, sort by minimum PA (or BF), then create a scatter plot and highlight certain points in red when you click on them. For years, I relied on this basic tool to find information, and in 2024, Baseball Savant came out with bat tracking metrics (which I wrote about at the time. here). Part of that deliverance came to them Leaderboard to follow the Batwhich not only organized the circles (a good thing) but also included a picture of the player in each circle. The most disappointing part of this new feature is that the Statcast team didn’t develop the first scatter tool to sort out every person’s face. everything in these charts.
Fast forward to 2026, with the introduction of tools like Claude Code, we now have the ability for jamokes like me to develop online tools without a dream and a github account. This leads us to the purpose of this article, which introduces the Structure Breakdown Tool that will allow you to take any hitting or pitching statistics (or aggregated at the team level) found on Fangraphs, and create a nicely formatted scatter plot. To do something like this before, you had to extract data from a website and manage it using Excel, but with this tool, anyone can quickly create their own scatter plots at home using the latest data and all the great metrics you can find online.
Let’s go through some examples of questions available in the scatter plot tool:
Tip #1: xwOBA vs wOBA for professional hitters
This is the standard gold piece of disintegration, the reason why we started planning. xwOBA should give us an estimate of what a player’s wOBA should be, and of course, wOBA (for those who don’t know) is a more calculated version of OPS (since OPS gives more weight to SLG). In theory, since xwOBA was literally created to measure wOBA, in a perfect world, the line of players would be lined up in a diagonal line from bottom left to top right (basically xwOBA = wOBA), but of course, we see above where other factors come into play.
If you look at the bottom right, you can see “unlucky” players like Michael Harris II and Ronald Acuna Jr., on the right, more reasons to buy the breakouts of Francisco Alvarez, Dillon Dingler, and Jordan Walker. At the top left are “overachieving” names like Mauricio Dubon, Christian Yelich and Chandler Simpson (although Simpson’s may be related to speed), at the very bottom Josh Naylor is showing signs of being better than what we’ve seen, and finally in the dunce’s corner in the bottom left corner are Cedric Mullins, Jacob Wilson, Joseb bad, badler and Jose Cabellero. It’s a great way to quickly gauge when a player you’re worried about is on the hitting spectrum.
Episode #2: SIERA vs ERA starting pitchers (min. 10 IP)
This is similar to Plot #1, but for pitchers, and while we have the xERA metric, SIERA was found to be more predictive than its statcast cousin xwOBA. Normally, this build will feature professional mechanics, but since it’s early in the year, we’re going with a 10 IP minimum to include #3 to #5 starters who haven’t started yet for the third time. Just like we did with the first plot, if SIERA was forecast correctly, we should see this graph plot a diagonal line from bottom left to top right (SIERA = ERA), very close to that 2025 full season version this graph looks like:
Looking at the same corners as we did the first time, it’s clear that Cam Schlittler has the lowest SIERA in the league, supporting his outstanding start. It’s also interesting to see Ohtani hanging around in the cornerback (above SIERA average, lowest ERA), and finally, I’m not sure why Bryce Elder’s face appears next to these beautiful bowls:
Episode #3: Barrel% vs Slugging Percentage for professional hitters
This is where we begin to break down the individual characteristics of a player’s stat line versus basic ability. We know that hitting the ball is associated with a higher slugging percentage (more base hits). Therefore, creating a scatter plot like this allows us to see if there are players with high rebounding percentages that we think should be covering the ball, but are actually not covering the ball at all. If you look to the upper left you’ll see Taylor Ward leading the way with an impressive .574 entering Friday (4/10), but it turns out he’s yet to hit the one barrel. This doesn’t go well with guys like Nico Hoerner or Xavier Edwards around Ward, who always excel in their communication skills.
Episode #4: Stuff+ vs Innings Pitched (Radiators only, Min. 1 IP)
Now we enter the higher level, the dream state. Here’s a breakdown of the Rays shortstops that shows two things: A) Who pitched the most, and B) Which pitchers have the best stuff+numbers. The reason I want to show this in the form of a scatter plot is that you can see the clusters here. The two guys with the most innings (Cole Sulser and Yoendrys Gomez) are the pitchers with the most innings, Jesse Schlotens and Mason Englert have been used but they are low+, Griffin Jax, Ian Seymour and Kevin Kelly are high+ and finally there is Bryan Baker who seems to be the king of things, previously written it is a good sign of being an imminent heir.
Episode #5: wRC+ (hitting) vs. ERA (pitching) for all teams
This last one is very interesting as I have never seen another website that posts a tool that tries to visualize where a team ranks offensively and defensively in one chart. It’s amazing how similar the Nationals and Astros are until they both have great offense but terrible pitching to start 2026.
