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.
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.
I ran Harper’s 3-year averages through the Park Factors “by handedness” in the Fangraphs Guts! section. Here are the results.
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.
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!
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?
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)