The Padres Improved More than We Realize

Even though we all knew about it a few days ago, the San Diego Padres made the signing of Manny Machado official yesterday.  The Padres were considered a surprise suitor for arguably the most talented free agent in the pre-2019 class (no offense to Bryce Harper), but when you objectively look at the organization, the move made all the sense in the world.  I reposted an article I wrote back in December just to emphasize this idea, and today it gives us the opportunity to look at how drastically the move has changed things in San Diego.

We’ve seen big-name free agents land in new places for lots and lots money before, but a Machado-caliber free agent only comes around a couple times every decade when you factor in age – think Alex Rodriguez signing with the Rangers entering his age 25 season.  Machado’s just 26 years old, and his expected output coupled with his age made him a supreme acquisition for anyone willing to fork out the money.  Now, we’re not considering some makeup issues that have been made evident through a combination of lacking hustle, throwing a bat at an opposing player, and spiking multiple defenders on the basebaths – those are unquantifiable attributes (for now).  When we look him from a purely analytical standpoint, there’s very little to dislike for the Padres.  Even the $300 million he’s due over the next ten seasons doesn’t seem so bad, given that his projected WAR over that period is actually worth more according to ZiPs. 

In my post from December, I looked at organizational strength based on both prospect Future Value (FV) and the 2018 WAR of the players on each team’s roster as of 12/10/2018.  Overall, the Padres ranked right around the middle of the pack, carried almost exclusively by their powerhouse farm system.  At the time, the Padres found themselves in the bottom half of MLB at 3 of the 9 positions; SS (18th), 3B (25th), and RF(20th). 

Then they signed Ian Kinsler.

Kinsler seemingly strengthened an already great strength of the Padres – they were ranked 6th at 2B before signing Kinsler.  But it’s since come out that Luis Urias, who was projected as their 2nd baseman on Opening Day 2019, will likely be moving over to shortstop while Kinsler mans 2nd.  That’s how we expect it to play out until their top prospect, Fernando Tatis Jr, is ready to make his debut at shortstop.  While Kinsler essentially becomes a space-filler until Tatis is called up, his signing alone improved the Padres rank at two positions:


After signing Kinsler, the Padres moved into the upper half of MLB at the shortstop position – entirely on the shoulders of prospects.  Kinsler and the positional reshuffling that followed him effectively left the Padres with 2 weak positions while strengthening 2 others.  The Padres became undeniably better than they were before Kinsler, but they still hadn’t addressed their greatest positional weakness…until this week.

The Padres ranked 25th out of the 30 MLB clubs at 3rd base when the month of February started.  When the month of February ends, they’ll rank 8th.  If that’s not as high as you’d expect given the talent they just acquired, keep in mind that we’re looking at talent throughout the organization AND 2018 production, neither of which they had much to speak of until a few days ago.  Let’s visualize where the new Padres find themselves at 3rd base:


The team that ranked 25th (where the Mariners currently rank) at 3B is now outranked by teams whose 3rd basemen include Justin Turner, Nolan Arenado, Jose Ramirez, Matt Chapman, Alex Bregman, Matt Carpenter, and Anthony Rendon.  Machado is obviously better than 1 or 2 of those guys, and arguably better than most of them.  But the point isn’t the number of teams in front of them or behind them; it’s the number of teams they leapfrogged.

With one gigantic paycheck, the Padres turned their most glaring long-term weakness into one of their greatest strengths for what should be most of the next decade.  Using the methodology I used in my December article, there’s only one weakness left for the Padres to address.  Given their crowded outfield that features a combination of young former prospects who were either unproductive or injured in 2018, RF probably isn’t as urgent a need to address as it may have appeared to be on paper (or on-screen) when I ranked every team by position 2 months ago.

They probably won’t be good in 2019, and they might not even improve their win total by all that much.  But given the needs they addressed, it would seem as if the Padres are a good pick for the most improved organization this offseason, even more so than the Reds (who got better on the 25-man roster level at least).  If I had to guess, the Reds will likely improve on their 2018-win total more than the Padres will in 2019.  But the Padres likely improved their win total for the next few years by a whole lot more.  And it wasn’t all Manny Machado…but most of it was.

Patrick Ryan
Repost from 12/10/18: Ranking MLB Organizational Strength by Position

DISCLAIMER: I’m purposefully posting this article I wrote back in December because I’d like to revisit it in an upcoming article I’ll have on Manny Machado and his deal with the Padres. Stay tuned!

The charts that follow combine each team's share of 2018 WAR at a given position, with their share of Future Value (FV) at that same position (as determined by the Fangraphs BOARD prospect ratings). The sum of these two shares calculate the relative positional strength at each position, which is how the charts are ordered. So, for example, the Indians have Francisco Lindor and a whole bunch of shortstop prospects. Lindor and friends combined for about 9% of the WAR at shortstop in 2018, and Cleveland's plethora of SS prospects make up 8% of the future value at SS as well. When we combine the two shares (9%+8%) we get 17% - far and away atop the shortstop position, and well in front of the runner-up Nationals. This iteration reflects rosters as of 12/10/2018 – so Paul Goldschmidt's share of WAR is embedded in the Cardinals' portion of the 1B chart, and Patrick Corbin's with the Nats. As you can see, a considerable share of 2018 Catcher and 2B WAR is still available on the FA market. I'll have a longer post where I walk through each of these at some point, but it's a little difficult at this juncture since they're changing almost every day – with the M's and Dbacks both in some kind of tear-down mode, and the bulk of free agents yet to find a team, these charts are far from what they'll look like on Opening Day.

Patrick Ryan
The 167 Baseball-Nerdiest Cities in the US
Here we'll use data from Google Trends to determine the nerdiest baseball cities in the US. About 90% of the words in this post are on the basis and methodology for the analysis. If you just want to see the rankings, skip to the end.

Whenever I discover that someone I know is a baseball fan, I try throwing a few advanced metrics into the conversation just to gauge familiarity. I should preface this by mentioning I don't think any level of familiarity with advanced metrics changes a person's value as a baseball fan – whether we're exporting Fangraphs data or we're listening to sports talk radio, passion is passion regardless of how we waste our time with something as pointless as baseball fandom. But while I enjoy baseball conversations with fans of all types simply because its baseball, I love getting the insight from fellow stat geeks because I want to know what others value. There's so much to learn from the massive collection of data generated by baseball that it's impossible for one person to know everything on their own. The biggest problem I have throwing in the statistical jargon is simply the lack of bites on the other side of the conversation; I never get the long-awaited bWAR versus fWAR debate that I truly long for in casual chitchat. I see it on the message boards in droves. Fangraphs, Beyond the Box Score, and even Reddit all seem packed with geeks, so why isn't the bar by Angel Stadium flooded with a few of the same people an hour before first pitch? This had me thinking about something that's probably kind of dumb, but to me at least, is still very interesting…

Geographically speaking, where do I find all the baseball stat geeks? The nearest brewery to which stadium am I most likely to find someone equally as annoyed as I am about being unable to split half-seasons and years in the same export at Fangraphs?

Thanks to Google's dominance in both search engine quality and creepy monitoring of our each and every move, Google Trends was my go-to resource for the data I collected. For the uninitiated, Google Trends is a way to measure the search interest in a particular term over time or space (geography), and also compare the interest of different terms to each other over those same dimensions. "Search interest" is probably better defined by the less-marketable term "search volume", though the data produced by Google Trends isn't a direct measure of volume like total searches or search percentage – it's a 0-100 scale that controls for general search activity in a given area (same as controlling for population). Now I probably could've simply looked up Fangraphs on Google Trends (which I did) and called it a day, but the lack of rigor made it seem shallow.

It's obvious from the Google Trends graphic when and where Fangraphs garners the most interest; baseball season, the Pacific Northwest, the area between Chicago and St Louis (I looked this up and it's apparently called the "North Central Midwest") and the Pittsburgh area. But we're still not done. For US-only searches, Google Trends usually returns more data points using the "Metro" subregion, which is actually the Designated Market Area (DMA) used by Nielsen (the TV ratings people) rather than the Metropolitan Statistical Area as I'd first assumed. The exported data from Google Trends for Fangraphs revealed a handful of DMAs with search volume too low to register any quantifiable level of interest. I'd wondered if the same places would garner similar results for Baseball-Reference, and much to my delight, I found a correlation coefficient of r=0.85 (R2 is shown on the chart). I also pulled Google Trends data for the phrase "Happy Thanksgiving" (it was trending at the time) as a control set to reassure myself the correlation between Fangraphs and Baseball-Reference wasn't a probable outcome for any random Google search; this yielded a correlation coefficient of r=-0.10 with Fangraphs…hooray!

This means it's very likely certain regions are more baseball-nerdy than others – not that searching for Fangraphs or Baseball-Reference makes a person a baseball nerd, but the aggregated data certainly represents a solid proxy. I wanted to collect more Google Trends data on search terms that are similarly stat-geeky, so I tried "sabermetrics", "Bill James", and "Moneyball", but neither sabermetrics nor Bill James yielded enough data points due to lack of volume, and the very vast majority of Moneyball's search volume was generated when the movie came out – not a desirable trait. Still, with more search terms, we have more data, and we'll be a lot more confident in the results while mitigating bias; similar to diversifying your portfolio to mitigate risk. So I eventually ran the following through Google Trends, both individually and combined (for volume comparison between terms):

My goal, if you couldn't tell, was to use the aggregated data to determine the best (and worst) baseball-nerd cities and regions by summing the total interest generated for the five Google searches by location. To accurately reflect their search proportions, each Google Search was weighted by its individual search volume relative to the combined volume of all five (visualized in the appropriately titled donut chart).

I added a finishing touch for a sixth and final Google search-related measurement that doesn't fall completely in-line with the other 5:

  • The search volume of MLB compared to the search volume of NFL
    • Each metro has a combined MLB-NFL search score of 100
      • One score for MLB
      • One score for NFL
      • They sum to 100
    • So the most baseball-ish city possible would have a MLB score of 100 and an NFL score of 0 (this place doesn't exist…or else it's way too small to be a blip on Google's radar)
      • A sad (though not too relevant) side note – no metros returned an MLB score above 42…but way to go Peoria-Bloomington, IL!
    • Since it isn't measured in terms of volume, I weighted the MLB-NFL score at 1/6th (~16.7%) of the combined score and re-weighted the other five accordingly

The final weighting of the combined score is shown in the next chart:


The very last table in this post reflect the complete rankings, which I can't say are too surprising. The map generated by the initial Fangraphs search made them a little more predictable, but there's certainly more clarity after we combine all the data. I also put together a heat map in Tableau that visualizes nerdiness in the 48 contiguous states (sorry Hawaii, Honolulu [112th] did register some data…nowhere in Alaska did). Let me also mention that Tableau doesn't recognize Designated Market Areas as a geographic variable, so I had to map them out by ZIP code…which was the greatest pain in this entire post before I learned it would've been much easier had I mapped them out by county instead of ZIP. Well…the numbers basically speak for themselves. As much as I cringe hearing Cardinals fans claim the "Best Fans in Baseball" designation, they're easily the nerdiest. They're also the most engaged on social media, which makes their nerdiness pretty understandable. So congratulations St. Louis and surrounding area, you're a bunch of nerds – which makes me reeeeeeally want to visit Busch Stadium when the A's come to town in 2019. The Columbia-Jefferson City area is the market directly west of St. Louis, and directly east of Kansas City, though the Cardinals generate about twice the search volume the Royals do in the area. The third result, Champaign-Springfield-Decatur, IL, generates more search volume for the Cubs than the Cardinals, however – SO YOU AREN'T THAT GREAT CARDINALS FANS! The other strong areas include Pittsburgh, Chicago, and New England, each one home to notably loyal and passionate fanbases – though I have to admit Pittsburgh ended up higher than I might've guessed. I'm also guessing Meg Rowley and Patrick Dubuque are solely responsible for Wisconsin appearing twice in the top 15…and probably a little for Seattle not being as sad as the rest of the west coast. The west coast is basically inept when it comes to nerding out on baseball, which is sad news for me. 23rd-ranked Seattle-Tacoma is the only west coast area in the first 38, and that's when the Bay Area finally joins in at 39th. I find the very bottom of the rankings interesting – maybe even more so than the top. These areas could easily be the places where the most blue chip high school football prospects come from in any given year – 12 of the bottom 15 are from deeeeeep football country – Texas, Oklahoma, Florida, Mississippi, and Georgia. Compared to the top of the list though, the bottom is also generally much further in proximity from any MLB team. What's all this mean? Probably not a whole lot. But I've been to the bars near Fenway, and they were definitely enthusiastic about baseball in a way I don't ever expect to witness in Anaheim. If that same enthusiasm is topped by the nerdiness engulfing the area between St. Louis and Chicago, I actually look forward to visiting the Midwest – something I've never felt before in my life. At the very least, I'm guessing it beats trying to talk about run differential in El Paso.

Patrick Ryan
Identifying 2019 Bounceback Pitchers
Okay, a lot of people think ERA sucks. Sure, I don't really disagree in the sense that it's luck-laden and a poor predictor of future performance. It's a shallow measure, but it still seems to get the best of those even at the highest levels; Jon Gray was left off the Rockies' playoff roster after posting a 5.12 ERA that wasn't really compatible with his 9.6 K/9 and 2.72 BB/9. Domingo German couldn't stay in the Majors with his 5.57 ERA in spite of striking out nearly 11 per 9 and walking 3.5/9. This isn't a defense of ERA by any means – its not. This is a guide to find out who's 2019 ERA is (probably) going to be better than their 2018 ERA, and it's pretty simple. Fangraphs features a metric called "E-F", which is simply a pitcher's ERA minus FIP. This can give us some idea of how representative the pitcher's ERA actually is – grossly oversimplified, it gives us a measure of luck. The following facts have been fairly well-documented, but just for a refresh, I want to reiterate the following: ERA is a relatively poor predictor of future ERA FIP is a better predictor of future ERA but still not great xFIP is a better predictor of future ERA and future FIP than both ERA and FIP Results-based analysis is tricky business, but not totally unreliable when done correctly. ERA is far from the ideal indicator of a pitcher's ability, which has been addressed through FIP, which also includes a lot of noise that's washed away in xFIP. Things that show little or no year-to-year correlation, such as HR/FB% or BABIP, are controlled for by applying constants in the calculation of xFIP, which is why it's probably the best metric we use to evaluate how good a pitcher's been, at least in the same context of ERA. Unfortunately, fans, fantasy leagues, and the general consumption of baseball continue to emphasize ERA in spite of it's obvious shortcomings, probably due to a fear of adaptation. So even though it would be more practical and easier to predict future xFIP, we're going to predict future ERA with xFIP, since it's still the best we've got.

Let's check out the correlation matrix of ERA predictors I put together. This uses all big-league pitchers from 2010-2017 with at least 30 IP in a given half-season who also threw at least 30 IP in the subsequent half-season. I did notice that the within-period correlations aren't identical in both time periods (ERA's respective correlation to FIP and xFIP is .67 and .49 in t=0, but .70 and .55 in t+1…this still occurs even when ERA-/FIP-/xFIP- are used instead, so I'm theorizing that it's just a matter of a pitcher gaining consistency with an additional year of experience, but that's another post for another day.) We can see that each of the bullet points above are reflected in the matrix, and that xFIP does a much better job of predicting the future than any other metric. So what am I trying to prove here? That xFIP is a super useful metric that isn't used enough for predictive analysis! And unlike ERA, xFIP is a superb predictor of itself, which is why I highlighted that particular part of the matrix, and added the chart on xFIP predictability. Worth noting is that the full-season correlation between ERA and xFIP is a much better-looking 0.64, compared to the half-season correlations shown in the matrix, so being able to predict xFIP from one period to the next is pretty valuable. 


So now that I've emphasized the value of xFIP versus the other metrics as predictors with some visual overkill, I'm going to rework the Fangraphs' metric I mentioned earlier: instead of E-F (ERA-FIP), we'll be using E-X (ERA-xFIP).

Let's set up some definitions that will apply to the remainder of this post:

  1. Overachiever – A pitcher who's xFIP exceeds his ERA. In this case the E-X is negative.

    2018 Example: Wade Miley; 2.57 ERA/ 4.3 xFIP/ -1.73 E-X with MIL

  2. Underachiever – A pitcher who's xFIP is less than his ERA. In this case the E-X is positive.

    2018 Example: Marcus Stroman; 5.54 ERA/ 3.84 xFIP/ 1.7 E-X with TOR

The intuition here is simple enough – overachievers are due for positive regression (remember that "positive" is bad when it comes to ERA/FIP/xFIP) and underachievers are due for negative regression. In other words, pitchers with a negative E-X should see their ERAs increase, while pitchers with a positive E-X should see their ERAs decrease. I said "should", but I really mean "do", because the effect is quite robust when we use aggregated data. The first chart looking at ERA changes from 2017 to 2018 suggests that, while E-X is a good indicator of the direction a pitcher's ERA is headed, underachievers appear to be more predictable than overachievers – at least using non-normalized metrics.



Now since ERA is known to fluctuate over time and we need normalized metrics to compare across eras, I wanted to see how predictability changes (if it does at all) when we use ERA- and xFIP- instead of standard ERA and xFIP. Here, the effect is consistent across both groups (both overachievers and underachievers). Take a look at the chart below:

ERA & xFIP (ERA- & xFIP-)


This tells us that roughly 73% of overachieving pitchers in 2017 saw a rise in their 2018 ERA, while almost an identical portion of 2017 underachievers (72%) saw a decline in their 2018 ERA. That means, with respect to this sample, nearly three-quarters of the time we accurately predicted the direction of future ERA by subtracting xFIP- from ERA-. This is pretty powerful, but it's limited in the sense that we're looking at a binary prediction – its yes or no; while we can reasonably expect the ERA to increase or decrease, we don't know by how much. And we all know to be skeptical when sample sizes are small; just 169 pitchers threw at least 40 IP in both 2018 and 2017, so let's see what happens when we have a sample 8.5 larger than what's reflected in the 2017/2018 chart…

ERA & xFIP (ERA- & xFIP-)


And there you go; 71% of overachievers saw their ERA go up in the subsequent half-season, and 72% of underachievers saw their ERA go down – basically unchanged from the previous chart. Here, time is grouped into half seasons rather than full seasons, which gives us an even greater sample to look at. So E-X is legit when it comes to predicting improvement or decline, but why not build on that if we can? If we're trying to identify bounceback candidates, wouldn't it be nice if we could know exactly how likely it is that a pitcher's ERA will be lower next season (or next half-season) than it was in the most recent one?

Obviously the answer is 'yes', so I modeled the probability of ERA improvement using E-X as the singular dependent variable and ran a logistic regression on the binary outcome of whether or not ERA improved in the in half-season t+1. The summary statistics are shown below, as well as how to calculate the probability.



Calculating the probability estimate of this model isn't like a typical linear regression, so if you wanted to apply it to a particular pitcher on your own, here's how it works:


So rather than going through too much more math, lets move on to what the model tells us by using the probability of ERA Improvement chart:


This shows us the estimated probability of a given pitcher improving his ERA in the next time period (in this case, half of a season), based on the E-X in the most recent period. While the model is built off half-season samples, we can reasonably apply it different time groups that occur consecutively, like a full season (we don't want to stray too far from the half-season though, because we'd fail to account for a lot player-specific changes that might occur in the two time periods. For example, we wouldn't want t=0 to be the last 5 years, where we're trying to predict improvement in the next 5 years, because a lot of changes could occur with the pitcher we're looking at; his mix might change, his velocity almost certainly will, perhaps Tommy John surgery, etc.) So, at an E-X of 0, we see the probability of improving ERA is 50%, which is right where we'd expect it to be (actually it's 49.8% if we take it out to the thousandths place…the absolute probability difference in an E-X of 0 and -10 is actually almost the same as the difference between 0 and +10, but I kept the probability estimates to two decimal places for the sake of simplicity). The greater the E-X in the most recent (half) season, the more likely it is the pitcher's ERA will drop in the next (half) season; even though only 18% of pitchers post E-Xs of at least 20, it's certainly worth noting their probability of improvement is better than three-quarters. Even more rare is an E-X of 40 or greater, which occurs just 4% of the time, but is practically a guarantee of improvement at 91%.

So just for fun, let's apply the model to a pitcher using his 2018 E-X, and determine the probability that his ERA will improve. One guy a lot of people might be curious about is Sonny Gray; are greener pastures ahead for Sonny in 2019? Or was all that chaos in New York City the catalyst to an irreversible downward trend? Well…let's find out!


2018 Sonny Gray – NYY

ERA: 4.90    xFIP: 4.10

ERA-: 113    xFIP-: 97

E-X = 113-97 = 16    

Now we'll apply the model…

1/(1+e^-[-0.06+{0.059*16}]) = 0.718

Estimated probability of improvement is 71.8%! So Sonny Gray's got a pretty good shot at being a better pitcher in 2019 than he was in 2018.

Let's do another…how about NL Cy Young Award winner Jacob deGrom? deGrom had an absolutely insane year that a bunch of morons tried discrediting at various stages, but most of the people reading this are probably aware of how special it actually was. So how likely is it that deGrom could be even better next year?

2018 Jacob deGrom – NYM

ERA: 1.70    xFIP: 2.60

ERA-: 45    xFIP-: 64

E-X = 45-64 = -19

1/(1+e^-[-0.06+{0.059*-19}]) = 0.245

So the model gives deGrom a 24.5% shot at improving his ERA in 2019, which isn't that bad considering there's not much room for improvement when your ERA is 1.7…the closer you get to 0, the more improbable improvement becomes!

Instead of continuing with random case-by-case examples, I added a few names to the probability chart to go along with Sonny Gray and Jacob DeGrom. I also built a table of 25 semi-randomly selected pitchers alongside their 2018 numbers and their respective 2019 ERA improvement probabilities. One thing that's fairly clear, though also quite intuitive, is that it's difficult to improve upon good performances; DeGrom, Max Scherzer, and Justin Verlander are unlikely to be better in 2019 than they were in 2018, largely because they were just so good. Applying that same intuition to the other end of the spectrum, it's pretty easy to improve on bad performances – Clayton Richard is almost certainly going to be better in 2019 because he set the bar so low. Those are the predictable cases – the ones in which the probability model does nothing but reaffirm what we'd basically known. Among those shown in the table, the more interesting cases are those of Josh Hader and Carlos Carrasco, both of whom enjoyed incredible 2018 seasons, and are actually more likely than not to improve in 2019. There's also a few names not shown in the table who are in the same boat as Hader and Carrasco, such as Patrick Corbin, Dellin Betances, Ross Stripling, and Edwin Diaz – all of them are likely to improve in 2019 after being phenomenal in 2018.
Patrick Ryan