Exploring Mock Trades with “Baseball Trade Values”

There’s a fairly new & very interesting website I ran into called BaseballTradevalues.com. The creators have been analyzing and recording real-life MLB trades for years, and have used that information (and a background in finance) to build a model for player trade value. It’s very complex, and totally worth your time to read up on their methodology. You won’t find every last professional player here, but you will find every MLB player & every noteworthy prospect.

Doing mock trades is really something else, as I’m convinced that there isn’t one single proposal out there that fans from both sides won’t whine about (WAY TOO MUCH!; OVERPAY!; NOT ENOUGH!; HARD PASS!). Though the complaints usually come from people who are either uninformed or too lazy to do their own research, it’s still exhausting. That’s why I really like this site, which allows you to simulate your own trades while taking 99% of the bias out of the equation.

I spent some time playing with the trade simulator this morning, and wanted to share some of the “deals” I came up with. While nobody seems to have a stronghold on Farhan Zaidi’s plans for the deadline, it’s still fun to see how the Giants match up with other organizations.

Most of these deals are of the selling variety, but I did include a couple low-grade “buyer”  deals in there too. Hey, you never know…

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The Rays, like most teams, would love Will Smith. Their system is loaded, and the Giants probably aren’t touching their top 5. By himself, Smith only nets $9M in value. That’s not near enough to get Vidal Brujan (31M) or Shane Baz (19M). It does put them in the ballpark for Lucius Fox, but I opted for McClanahan, who’s having a great first full season, instead. I would have loved the Giants to get Nick Solak, who went to Texas, but Colin Poche is an MLB-ready lefty who I’ve had my eye on for a while. There’s probably room for a low-level player add-on here as well.

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Farhan has given some hints that he would like to both buy & sell this year, if possible. With that in mind, one question I had was, “What would it take to get Tyler O’ Neill?” Turns out it would take quite a bit, according to the simulator. Gott’s controllability is worth decent value, and helps get the Giants in the ballpark. Oviedo is a tall righty in AA, the #18 prospect according to MLB Pipeline. Gotta think Cards fans would feel like they won this deal if it went down…

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Would any of us be surprised if the Giants and Twins hooked up on a Smith & Bum blockbuster? Probably not. But just how much would those two net together? According to these projections, it’s about $20M in trade value. That’s about even value for Trevor Larnach ($19.5M), and just enough to package beast right-hander Jhoan Duran (#8 MLB Pipeline) with Brent Rooker (#7). I decided to spread the value out a bit, adding a couple of former high draft picks in Nick Gordon & Travis Blankenhorn. I considered rising righty Jordan Balazovic & SS Wander Javier as well. Boy, the Twins are loaded!

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Speaking of teams who have what it takes to load up for Smith & Bum… I know a lot of us are dreaming on names like Drew Waters & Kyle Wright, but those guys appear to be too rich for what the Giants can offer (imagine that!). I’ve liked Bryson Wilson for a while, and frankly, I think if the deal went down like this, the Giants would end up with 3 arms who would see a good chunk of time in their future rotations. All three are 21 years old. Crazy thing is, this deal doesn’t even dent Atlanta’s prospect depth (though Allard & Wentz are both considered top 10 in their system). This one feels like a win-win for me.

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The Giants should have made an offer for Santana when he was still with Milwaukee. They have another chance now, and there’s decent 2nd & 3rd tier depth in the farm system to move a few of these guys if need be. Santana will swing and miss like Tyler Austin, but he’s also proving to be a guy who you can pencil in every day (at least offensively). He’s controllable for 4 more years to boot…

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See my rationale for Tyler O’ Neill above. I know the Yankees want to act like Frazier is their golden boy, but I don’t think anybody’s really buying that. If it came down to getting a Bumgarner/Moronta package from the Giants, I think they’d be more willing to part with Frazier & Albert Abreu than some of their rising studs like Deivi Garcia & Luis Gil. I don’t know why, but I just have this hunch that Frazier is the type of player Zaidi is going to bother Cashman about.

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Oh boy, Brewers fans aren’t going to like this… but here’s the deal. Outside of Hiura & Brice Turang, their farm system isn’t very good. If the rumor is the Giants like Dubon (#5 in the org), we’ll start there. Aaron Ashby (#9) is a lefty with a 65 curveball who’s having a nice year in A ball. Jake Gatewood is a former top 50 pick who just hasn’t put it all together.

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If the Giants want to tighten their lineup a little bit, Panik’s spot is a pretty obvious place to upgrade. Farhan seems to like dealing with Baltimore. I really wanted to find a package for Trey Mancini, but his value is just too high right now (near $30M). Villar’s having a nice year, as is Shaw… Would the Giants do something like this?


Updating Game Score Metrics

A few short months ago, on Christmas night no less, I had a revelation that I might be able to use Game Score to create a WAR total. Here’s the post I wrote then, explaining my methodology and giving player/team/league examples.

As excited as I was to share my shiny, new Game Score WAR – “gsWAR” – metric, I was also pretty clear about my feelings on its limitations. Here’s what I wrote then…

As I said earlier, I’m not going to pretend that Game Score WAR is anything to be held in the high regard that the Fangraphs and Baseball-Reference models are. It’s a simple formula that happens to line up pretty well, but there are certainly some limitations.

One major drawback is that Game Score isn’t park-adjusted…This is a significant issue, and one I have tried to address. It’s not an easy fix, and I don’t know that I’m intelligent enough to make the necessary corrections. I might need to reach out to Mr. Tango for some help!

Another limitation of Game Score is it really depresses the scores for the best pitchers… deGrom earns the highest gsWAR in all the land, but still loses an average of 3 wins compared to the other models. That’s frustrating, but it does make some sense considering the adjustments I had to make.

Wow. Harsh!

Well, I’m learning that like player development, my brain & its thoughts don’t always connect in linear fashion. Rarely do they, actually. But I’ve been hard at work, and I’ve got a few major updates to share with you.

Continue reading “Updating Game Score Metrics”

“Statcasting” the Giants Outfield Defense

Statcast is probably the greatest advancement in MLB during my lifetime, and quite possibly ever. The fact that literally everything that moves between the lines of a baseball field can now be simultaneously measured, tracked, and timed, is pretty unbelievable. It’s also difficult for me to wrap my head around.

While the public has fallen in love with certain aspects of Statcast data more than others (I’m thinking exit velocity for hitters and spin-rate for pitchers are the current darlings), there’s a couple of overlooked areas I really enjoy scouring on the ever-growing Baseball Savant website.

Expected stats for hitters is a newer phenomenon, but one that really intrigues me. Basically, the system can now predict a player’s average, slugging, and weighted on base average (wOBA) just by tracking his exit velocity and launch angle. That’s pretty incredible, and it can help tell a lot more about a player’s season than by just looking at his BABIP.

According to xStats, Joe Panik was one of the most unlucky hitters in MLB last year, with an expected avg of .285 & an xwOBA of .317. Both of those numbers were over 30 points higher than his actual production, indicating he was actually much closer to a league-average hitter than we all realized. On the flip-side, Alen Hanson’s xwOBA of .236 was 60 points lower than his actual numbers, and among the very worst in the game. What does that mean? Well, if Hanson doesn’t make better quality of contact this season, we might expect his numbers to plummet.

Continue reading ““Statcasting” the Giants Outfield Defense”

Twitter Community Projections Part 1: Rotation

This is our first installment of our Twitter Community Projections, where we check out how the numbers line up for the 2019 rotation.

For those unfamiliar with the task, I made a public Google form that required IP, ERA, HR, H, BB, and K for pitchers. I used those numbers (& career averages for HBP) to estimate the Fielding Independent Pitching (FIP) for each entry.

From there, I ran the data through my WAR calculator, which spits out 3 separate scores. The first two are “quick WAR,” as described by Tom Tango. There’s a quick WAR for IP & ERA, and for IP & FIP. The third score is my own creation, Game Score WAR. I average the three scores, and multiply them by a park factor (which for the Giants, in any one season, is 0.93, according to Fangraphs). So, yeah, I tried to pull out all the stops in getting these figures as accurate as possible.

Here are the results!

Continue reading “Twitter Community Projections Part 1: Rotation”

Giants 2019 Preseason Top Prospects

Wouldn’t be spring if I didn’t put out a top prospects list. These are very basic reports, but most of you already know the names. I just wanted to give an idea of how I see these guys stacking up. There are links for further (and more detailed) reading at the end of this list.

So here goes!

#1: Joey Bart | C = The Giants haven’t had a consensus top-40 prospect in many years (Belt?), and Bart is the total package. Keep an eye on his strikeouts, but he needs reps more than anything else at this point.

#2: Heliot Ramos | CF = He survived the big, bad Sally League, but expectations are appropriately higher for him this season in San Jose. Can he elevate from surviving to thriving?

#3: Marco Luciano | SS = Luciano hasn’t had a professional AB, and yet he might be the most hyped prospect in the system this winter. That swing!

#4: Logan Webb | RHP = The velocity is up, the post-rehab gloves are coming off, and he looks be primed for a big summer in Richmond.

#5: Shaun Anderson | RHP = Anderson is Steady-Eddie, but his presence in the Future’s Game shows there’s some internal love happening for him. Big league cameo coming his way… maybe more?

Continue reading “Giants 2019 Preseason Top Prospects”

Finding 40 Wins: The 2019 Giants

It’s FanFest Saturday, so I’m sure you’ll forgive the optimistic & potentially dreamy tone of another post that relies on the Giants signing Bryce Harper. Just hear me out…

Early in the offseason I created a Net Value spreadsheet for MLB last season. While WAR Dollar values are all the rage, it’s actually another facet of the spreadsheet that has stayed with me. Here’s a breakdown of the WAR totals for postseason vs. non-postseason teams in 2018. Keep in mind these figures don’t include every last player who appeared on a roster last year, but they’re pretty darn close. I also rounded them for convenience sake. Oh, and I left out the bottom-feeders, because I felt like it…

Playoff Teams fWAR
  • Yankees = 57
  • Astros = 54
  • Dodgers = 53
  • Red Sox = 51
  • Indians = 50
  • A’s = 45
  • Braves = 42
  • Brewers = 42
  • Cubs = 40
  • Rockies = 34
Non-Playoff Teams fWAR
  • Nationals = 42
  • Cardinals = 40
  • Rays = 39
  • Angels = 37
  • Mets = 37
  • Mariners = 36
  • Pirates = 35
  • Diamondbacks = 35
  • Giants = 22 (23rd in MLB)

Continue reading “Finding 40 Wins: The 2019 Giants”