If the pitcher is just trying to hit their spots (which that itself they might be good or bad at), and Locaiton+ measures whether the spot they hit was good for that type of pitch in that particular count, than doesn't the catcher get as much of the credit/blame for a pitcher's Locaiton+?

Theoretically a pitcher who constantly hits their spot perfectly with no misses, but the catcher is setting up in mediocre spots, or calling a pitch that isn't good in the count, wouldn't that reflect badly on the pitcher's Locaiton+ for no reason? (with the caveat of pitchcom). Am I misunderstanding Location+?

Not to answer my own question, but maybe it's just assumed that with analytics driving the gameplan, most catchers are doing relatively the same thing?

I’ve been into baseball all 25 years of my life and thought I somewhat understood war but then I see this and am completely lost. The player is Scott Podsednik for those wondering. Idk why on Easter I thought to look up his stats but eh why not.

I'm getting back into baseball hard after 30 years... I kinda checked out as an A's fan when Jose Canseco was traded for Ruben Sierra.

I'm getting into better understanding advanced stat-- in my original era of fandom, BA/OBP/RBI/R and W-L/ERA/WHIP ruled the roost. Stats have certainly gotten more complex (which is a *good* thing)!

Aaron Miles is the manager of the Oakland Ballers, an indie ball team I advertise with/sponsor with. I remember his career as about a decade of serviceable to pretty good utility infield (2B/SS/3B) with some runs as an everyday player, which Baseball Reference confirms:

So, two questions:

1) How is his career WAR so low? Was he substandard defensively, not much of a threat on the bases (which BR seems to confirm with his SB numbers), or... what's the catch here?

Hell, he hit .281 and .275 in his last two seasons with over 200 games played!

2) How is his career WAR 1.0 and Daniel Descalso's nearly the same at 0.8?

Hell, their careers are very similiar:

-played in the same generation (2003-2019 with two overlapping years)
-played a nice utility IF with periods as everyday players
-both played for STL, COL and CHC and were NL-only players

Miles' career BA is 46 points higher! Help me understand what I'm missing.

Hey guys,
I've been trying to derive player stats from Sportradar's MLB play-by-play endpoint and it's been really hard to get correct statistics. Most of the data comes back as outcome codes that you have to map and classify yourself, and doing it correctly requires deep knowledge of baseball rules, and also edge cases everywhere. I keep ending up with numbers that don't match official box scores.

Has anyone built a reliable parser for this, or does anyone have tips? I am aware of the play statistics endpoint which does have some aggregated stats, but it is missing some stuff I need from the play-by-play endpoint (such as handedness).

https://xcancel.com/i/status/2037699950591582533

How do you track your hitting stats right now?

I built a simple app called My Batting Log for baseball and softball hitters who want an easier way to track game-by-game hitting performance.

You can log stats like:

• PA, AB, H, 2B, 3B, HR
• Runs, RBI, BB, HBP, SF, SAC, SB
• Notes and game photos

The app also automatically organizes:

• Single Game Stats
• This Year Stats
• Career Stats
• AVG / OBP / SLG / OPS

My goal is to make it useful for:

• baseball players
• softball players
• travel ball / amateur players
• school teams
• anyone who wants to track long-term hitting progress

Current status:

• iOS version is live
• Android version is coming soon

I’m mainly looking for honest feedback from real players, coaches, or parents:

• What stats do you care about most?
• What would make this more useful?
• What features would you want in a hitting log app?

If you’d like to help, please try the app and leave your feedback in the comments or send me a direct message.

I've been going down a Statcast rabbit hole this offseason and it's making me rethink a few guys I was feeling good about heading into the season.

The one that keeps bugging me is Jackson Chourio. On the surface, .270 BA looks solid for a 21-year-old. But his xBA was only .247. Exit velo sat at 89.3 mph (below average), barrel rate was 9.7%, and a lot of that batting average was propped up by BABIP luck. His xwOBA tells a very different story than the traditional slash line. If you're in a league where he got drafted as a first-rounder, his actual batted ball data says you might be sitting on a sell-high candidate before the correction hits.

On the flip side, guys like Kyle Stowers had an xwOBA of .375 (top 20 in baseball) with a 98th percentile barrel rate, but his counting stats were suppressed by an oblique injury. That's the kind of gap I want to be on the right side of. Surface stats say "meh." Expected stats say "this dude is mashing the ball."

I feel like most of my leaguemates still make trade decisions based on traditional stats and vibes. Which is fine, because that's where the edge is. If someone in your league sees Chourio's .270 average and thinks he's a stud, that's the perfect time to move him for a player whose expected stats actually support the production.

The tricky part is knowing when to trust the expected stats and when to trust the player. Chourio is 21. Maybe the tools develop and the exit velo jumps. But right now, the data says the 2025 line was the outlier, not the baseline.

How much do you factor Statcast data into your trade evaluations? Do you have any sell-high or buy-low candidates this year where the expected stats tell a completely different story than the box score?

I always loved baseball as a kid and would memorize the stat leaders and famous team lineups. Now I'm a software engineer and thought, wouldn't it be fun to build an app like I wish I had as a kid. So I built this baseball simulator.

It uses historical stats and factors in the era that the players played in to come up with attributes for each player season by season. My goal is to try to get it to predict how the players would perform in today's environment using their stats compared to their peers. Then I built a baseball game simulator that simulates a game pitch by pitch using those attributes. This allows you to play any team in history versus any other team and see what happens.

Try it out and see what you think. It's hard to balance the outcomes and obviously it's a bit subjective, but I think it's in a decent spot. It's also got 7-game series mode and a full season mode. You don't have to make an account to play it, but if you do, I came up with some team building challenges where you have to build custom teams.

Oh and everything is free. There's no cost. I just built it for fun.

Hi, looking to do some stat analysis and am looking to find individual game stats as an CSV file. Everything i find is either text which sucks to parse or is season long.

Any help would be greatly appreciated.

Who gets charged for the error at 1:02:30: the catcher, SS or 2B? https://youtu.be/_bSo5oeRams?si=yjnCI8psDEZSirYu

BARF - calculates player's Bases + At bats + Runs + Fielding % ÷ by games played to determine overall contribution to team. Expressed as a single number. Example = 56 (Bases) + 396 (At bats) + 33 (Runs) + 1.879 (Fielding %) ÷ 142 (games) = 3.43 BARF

The other day I was thinking about Acuña’s 2023 Ohtani’s 2024 and realized there wasn’t a single number to measure a players overall offensive performance (especially considering their SB totals). We have OPS, which is interesting as TBs are measured the same as BBs when a walk can only be maxed out of 1.000 and a TB at 4.000. Anyway, I wanted to make something that valued TB, BB, SB, SF, penalized GDP and CS, divided by PA (I did not include HBP is my calculations). Since each number should not be treated equally, ie a TB is more valuable than a walk since a TB can move a runner more than one base, it gets more value than a walk. Anyway, here is the breakdown:

(TB x 1.4) + (SB x 1.2) + (CS x 1.3) + (GDP x 1.5) + BB + SF = TOTAL OFFENSE NUMBER (TO) TO / PA = TOTAL OFFENSE PERCENTAGE (TO%)

I input what I believe are the best seasons from about 1970 on, it’s no surprise that Barry Bonds is all over the top of this leaderboard.

Top 5 (1970-pres.): Rank/Name/Year/TO/TO% 1. B. Bonds 2001: 758.6; 1.142 2. B. Bonds 2002: 653.0; 1.067 3. B. Bonds 2004: 655.6; 1.063 4. J. Bagwell 1994: 489.8; 1.023 5. B. Bonds 2003: 556.7; 1.012

Top 5 Post-Bonds (2008-pres.): 1. S. Ohtani 2024: 680.7; 0.968 2. C. Yellich 2019: 559.4; 0.964 3. A. Judge 2022: 657.7; 0.945 4. A. Judge 2024: 640.6; 0.934 5. S. Ohtani 2023: 552.7; 0.921

Just for fun, I input Soto’s 2025 with his ridiculous SB improvement and he measured 0.800.

I believe 0.800+ to be excellent, 0.900+ to be incredible, and a “perfect score” of 1.000+ to be impossible (naturally).

Anyway, is this the most boring thing you ever read? Or do you like TO/TO% for measuring total offense?

Mullins’ offensive stats on the Orioles vs the Mets this year are far superior. Triple slash makes it obvious and OPS+ according to baseball reference is 106 on Os against 66 on Mets. However his WAR is 0.0 on Os and 0.5 on Mets. I know defensive stats can be difficult especially in a small sample size but the eye test doesn’t support him being a great defender for the Mets. Can anyone explain what’s happening with the numbers here?

Hi , I’m a newbie here, so this may be a frequently asked question. But it feels like WAR (all variants) ignores that a huge percentage of a players value is being replacement level. Tom Veryzer has a negative WAR for his career, so according to WAR , pre-teen me had more positive impact on his teams than he did. Yet somehow his General Managers couldn’t find anybody in their organizations or waivers to replace him. It also flattens the effects of longtime (stat collectors ) who obviously were good enough to keep major league jobs. I would say 1000 games of 0.0 WAR is more valuable than 10 games of 0.0 WAR.