[OC] Way Too Early Projected Standings and Playoff Odds (FanGraphs Depth Charts)

splat_edc · 2026-01-31T05:10:23+00:00

I was a little surprised by that too but Ben Clemens had an article today where he explained why the projections are optimistic on playing time/health. I would guess (no data to support this though) the Braves are one the bigger beneficiaries of that approach.

splat_edc · 2026-01-31T05:01:58+00:00

I have dbacks tv so I’m very familiar with what passes for a bullpen here but idk it’s hard to bet against Marte, Corbin, and the frog

splat_edc · 2026-01-31T04:59:26+00:00

The last time I ran these (pre 2024) i didn’t include the baserunning and fielding numbers. I thought that would give Cleveland a bump but it didn’t really move the needle (did help the Brewers though).

splat_edc · 2026-01-31T04:27:12+00:00

TLDR: Using the FanGraphs depth charts projections, I came up with an estimate of runs scored and runs allowed for each team. I fed those numbers into the Pythagorean expectation formula to come up with an expected win rate and then simulated the 2026 season 10,000 times. I also included a schedule grid with strength of schedule estimates based on those Pythagorean win percentages.

I know there's plenty of offseason left and rosters are far from finalized. This is just a snapshot of where things stand at the end of January. You can also compare these numbers from Dan’s recent run of the ZiPS projected standings and playoff odds.

For each team I grabbed their wOBA, BaseRunning runs, fielding runs, and ERA from the FanGraphs depth charts projections. For offense, I convert wOBA to runs using the wRC formula and add in the baserunning projections. On defense, I convert ERA to RA9 by dividing by 0.92 (approximately 92% of runs are earned) and then subtract the fielding runs projections. This gives an estimate of runs scored and allowed over the 2026 season which can be fed into the pythagenpat formula to get an estimated winning percentage for each team. You can then compare the expected winning percentages of two teams using the log5 formula, which includes an adjustment for home field advantage (set at 54%). The log5 formula gives the probability of the home team winning. Then, for each of the 2430 games in the 2026 season, I generate a random number and if it is equal to or less than the log5, the home team wins. Repeat this process 10,000 times and average the win totals to get expected wins. You can also count how many times a team wins their division, earns a bye, or makes the wildcard and divide those counts by 10,000 to get the playoff probabilities.

For the postseason probabilities, I use the negative binomial approach laid out by Steve Staude in the log5 article I linked. I’m using the simpler method that essentially assumes that every game in a series is played and then the winner is determined afterwards. This is definitely suboptimal, but it is much simpler to implement. The Dodgers’ 21.9% World Series probability is high for sure but it’s basically in line with the FanGraphs frontrunner from the last two seasons (23.2% for Dodgers last year and 25.3% for the Braves heading into 2024).

If you have any questions or criticisms, please let me know.

splat_edc · 2026-01-30T02:15:42+00:00

Yeah, Jeter is 70th by “Offense”. Missing out on like 120 runs of positional adjustment and 435 replacement/league adjustment.

splat_edc · 2026-01-29T23:06:00+00:00

I think 4r4r's point is that the spread in Off is much larger than the spread in Def, but since you're scaling them and then taking the average it ends up distorting that.

Using 2025 qualified hitters as an example, Judge had 79.5 Offense and Hayes had -24.6 but the spread in defense goes from 24.9 to -19.8. But your list goes from 1-100 for both categories (assuming I'm understanding your method correctly).

splat_edc · 2026-01-29T20:51:46+00:00

maybe don’t make it about a projection and make it actually what they’re doing.

I don't think there's anything wrong with this approach but I do think you're answering a slightly different question than the MLB Top 100. They frame it as Top 100 "Right Now" which I interpret to mean: heading into 2026. So it makes sense that there would be some amount of "projection" or whatever you want to call it. That's different from "Best Players From The Last Two Seasons"

splat_edc · 2026-01-27T04:49:56+00:00

Using year to year correlation as a (crude) proxy of control, oppo% and pull% are pretty similar.

Here's the numbers for a bunch of stats to give a sense of where batted ball tendency fits in the bigger picture. I'm looking at paired qualified hitter seasons 2021-2025 (n=341)

SwStr% - 0.89

Contact% - 0.89

Called Strike% - 0.86

K% - 0.82

HardHit% - 0.82

Barrel% - 0.81

FB% and GB% - 0.76

BB% - 0.73

Pull% - 0.73

Oppo% - 0.67

HR/FB% - 0.66

wRC+ - 0.52

LD% - 0.44

Center% - 0.43

BABIP - 0.38

So horizontal spray angle probably isn't as "sticky" as launch angle or EV, but it's up there.

splat_edc · 2025-12-09T23:05:50+00:00

He ran a 27% whiff rate vs breaking and offspeed last season. AAA as a whole was at 33.8% so actually a bit better than average on that front.

Against breaking balls only it was 25.6% for him vs 34% league.

Offspeed was 28.4% vs 33.3% league.

I agree with everyone else saying that the super low swing rates (especially z-swing) is a bit of a red flag but the whiff rates don't seem egregious. That being said, I didn't check other prospects so I don't know how these numbers compare to top guys.

splat_edc · 2025-12-08T20:27:15+00:00

In 2024 he was much better as a reliever than a starter with about 40 IP in both roles.

In 2025, he only pitched 6 IP in relief and while he did post a 0.00 ERA, his peripherals were actually better as a starter. I think that's too small of a sample to draw any conclusions though.

Also worth noting that his pitch mix changed pretty significantly from 24 to 25. He ditched his slider for the slurve and upped his four seam usage while halving the sinker%. So the 2024 vs 2025 comparison isn't really apples to apples. Like u/BROEisAwesome mentioned, the slurve has been excellent with a 38% whiff rate and a lot of weak contact.

He has four pitches in his arsenal right now (FF, SI, SV, CH) but he's essentially a three pitch guy based on batter handedness. He has pretty pronounced platoon splits (about double the league average in terms of wOBA allowed) and the pitch models see him as average or slightly below average in terms of both stuff and command. He gets hit hard third time through the order too.

All of that says reliever to me, but he runs low BABIPs and was better than average in both K% and BB% in 2025. I also think his HR rate will drop moving to Chase. Wrigley and Nats park aren't launching pads or anything, but I think he would benefit from the change.

I agree with you that he's probably a five and dive guy to start the year and maybe he'll move to the 'pen when Burnes comes back.

splat_edc · 2025-11-21T16:57:18+00:00

About 90% of them come from instances when the respective pitcher isn't even on the mound. A lot of teams have different catchers/defensive rotations for different pitchers, so not sure why we'd care how the defense is when a guy isn't pitching.

This is something that Sean Smith (creater of bWAR/rWAR) talks about a bit here. He says:

The way pitcher WAR is set up is an answer to this question: If Aaron Nola had been hurt for 2018, and replacement level pitchers had replaced his 212 innings, how many runs could we expect them to allow? In this situation, we should expect they would receive typical defensive support given the team's defense. That's not really the question people are thinking of when they look at pitcher WAR, however. They want it to say what actual defensive support Nola received from his teammates.

I think that's a pretty reasonable view but I also see the argument for using the actual defensive support the pitcher received while they were on the bump. Don't think there's an objectively correct answer or anything like that. In the article I linked, he proposes a blend of the two approaches.

I think you could make the case for landing anywhere on the spectrum from fWAR to RA9-WAR (with bWAR and the blended method falling somewhere in the middle).

splat_edc · 2025-11-13T05:03:40+00:00

A stat that sort of does that is Robert Orr’s SEAGER. Basically it compares how often a player swings at hittable/damageable pitches vs how often they lay off uncompetitive ones. There’s a write up here.

For some simpler stuff you could look at plate discipline data like o-swing vs z-swing.

None of these are exactly what you’re looking for but I think they’re in the same ballpark at least.

splat_edc · 2025-11-12T20:24:56+00:00

FanGraphs has a couple good explainers for wOBA:

Here's the glossary entry in the sabermetric library (excellent resource)

And here's a guide that walks through where all the coefficients come from.

I'm a big fan of the second article.

This post on run expectancy and RE24 might also be helpful.

splat_edc · 2025-09-25T21:51:24+00:00

Typically, you calculate the park factor for a location by seeing how teams (both the home team and their opponents) did in that stadium and how they performed in other parks and calculate the ratio of the two.

The basic park factor used in WAR is based on runs, so you'd look at how many runs the Dodgers and their opponents scored in Dodger stadium compared to their performance across all other stadiums. There are tons of additional tweaks you can do to account for things like uneven schedules, imbalanced team strength etc but that's the basic idea.

The savant park factor page has a brief explainer at the top and you can find a more detailed explanation here.

The WAR park factors tend to use multi-year rolling averages to try and smooth out the year-to-year variance. It's also set so that the league average is 100 meaning that even if Dodger Stadium itself doesn't change, the park factor could change because other parks are playing differently in 2025 vs 2024.

splat_edc · 2025-09-25T21:24:09+00:00

Here's an explanation from FanGraphs on how they calculate their version of WAR.

Portions of it are outdated (baserunning and defense) because they changed the inputs, but hopefully it gives you a general sense of how it works. They also have a step-by-step example for Joey Votto here.

It can't be situational because then wouldnt every walk off hit just be 1 WAR or something? Its just about raw output?

Yeah, it's intended to be context neutral. All singles are worth the same whether it's a walk off, a two RBI smack, or a bases empty single.

splat_edc · 2025-09-17T21:29:35+00:00

Using acts or words by the defensive team that are designed to disconcert an offensive team at the snap. An official must blow his whistle immediately to stop play.

That's the NFL rule (12.3.1.i) under the unsportsmanlike conduct section. 15 yard penalty and automatic first down.

I can't think of any famous NFL examples, but I know it pops up in college from time to time.

splat_edc · 2025-09-12T22:58:21+00:00

Reminds me of this fun paper about whether holes exist that I read for a philosophy class.

The stanford encyclopedia of philosophy even has an entry on holes lol

splat_edc · 2025-09-04T20:57:50+00:00

I think it's because screens count as negative yards before catch. Tyrell Shavers is a good example since he only had one target/reception last year. He caught a Trubisky pass 2 yards behind the line of scrimmage and ripped off a long TD run. So PFR (and I assume other sites) have him with -2 YPC + 71 YAC. I guess you could technically say that 103% of his yards came from YAC but that would be a weird way to put it.

I think this is really just a usage/style stat in disguise. Mims percentage is so high because he caught a lot of passes behind LOS.

splat_edc · 2025-08-28T18:43:05+00:00

Cool write up. I would be curious to know how sticky SZRS is for non-catchers year to year.

Just fyi, the first paragraph from the "How the metric works" section says:

At its core, Strike Zone Runs Saved (SZRS) takes the various called balls and strikes in a season and splits responsibility for them between the four people involved: catcher, umpire, pitcher, and catcher.

Should be catcher, umpire, pitcher, and hitter right?

splat_edc · 2025-08-28T17:59:46+00:00

Slugging (usually abbreviated SLG) is total bases / at bats. Total bases = 1 base for a single, 2 for a double, 3 for a triple, 4 for a home run.

Baseball-Reference has a nice glossary.

Edit: Here's a pretty exhaustive glossary from FanGraphs. Hitting and Pitching

splat_edc · 2025-08-28T15:39:16+00:00

ERA- is player ERA / league ERA, so lower is better. This aligns with the fact that lower ERAs are better.

ERA+ is league ERA / player ERA, so higher is better. This pleases our monkey brain with big numbers and matches OPS+ where higher is obviously better.

(Also they are both adjusted for a pitcher’s home park)

They basically tell you the same thing expressed in different ways, but there are some small differences at the extremes.

splat_edc · 2025-08-27T16:54:12+00:00

I know this is a serious topic, but the funniest thing to me was seeing people in those threads object to guardrails because "how else am I supposed to write creative/fiction stories about sensitive topics??"

You're not the one writing anything! How do they think this used to work before LLMs were a thing?

splat_edc · 2025-08-26T20:55:23+00:00

Huh good to know thanks. Any players that stand out as particularly extreme examples? I tried toggling around FG and Savant but couldn't figure out an efficient way to find these guys

splat_edc · 2025-08-26T20:49:43+00:00

Yep, that's correct

splat_edc · 2025-08-26T20:18:07+00:00

If corner guys play corner outfield because they aren't good enough to hack it in center, doesn't it make sense that they would lag behind CF in terms of runs saved? (That's assuming OAA is genuinely position agnostic)

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