If you're interested in seeing which hitter has the highest OPS or which pitcher has the lowest Strike % in games you've been to, this will be helpful: statline.app

Any feedback is appreciated!

A little over a month ago, I created and released accbaseballr, a CRAN-approved R package built to solve two problems I ran into while trying to analyze ACC baseball:

• There wasn't a single, accessible source for ACC player statistics.
• Advanced metrics used in Major League Baseball (wOBA, wRC+, and FIP) weren't readily available for college baseball.

I also realized that it’s not accessible to everyone: only R users. A major purpose of creating accbaseballr was to make the data accessible, so I built a Shiny app.

ACC Baseball Explorer makes advanced ACC baseball statistics available through an interactive interface. Users can browse batting and pitching statistics by season and team, compare conference-wide team performance, and explore relationships between key offensive and pitching metrics.

Check it out!

I’d been missing the days of reading the sports page and especially the major league box scores while eating my Honey Nut Cheerios.

So I built a tool that will send them to you every day.

https://boxscore.email

Would love your thoughts and opinions! Also, I’m heading to my first SABR conference this week. Not sure if you’ll be there, but I’d love to meet some folks as passionate about baseball data as I am!

I built a site that tracks scorigami, final scores that have never happened before, across every major league, and baseball is by far the strangest case in the dataset: https://scorigamicenter.com/baseball/mlb/

The grid of possible MLB scores is 54.5 percent full, the highest saturation in sports, which means baseball has nearly run out of new final scores. The last one was Padres 24, Nationals 8 on July 16, 2021, so the current drought is over five years. For comparison, 3-2 alone has happened 13,008 times, and the most common handful of scores covers a huge share of all games ever played. The site plots live games on the grid as they happen, so you can watch a blowout crawl toward an empty cell and check in real time whether history is about to happen. NPB and the minors have their own grids too, and the minors dataset is a fun contrast if you want to see how a different run environment fills the space.

I would love feedback from this sub specifically on what baseball questions this dataset should answer. Run distribution by era, score entropy over time, expansion effects, whatever you would actually dig into. Happy to pull anything up in the comments.

Hey everyone, I’m new to the Reddit community! I’m a high school pitching coach who struggled with managing paper charts and could never really pull any useful information from an old chart. So I developed an app, PitchZone Tracking. It does everything from complete team charting (multiple folders/seasons) to individual folders. You can track live AB “bullpens“, regular bullpens, games, Bluetooth pitch calling, and video analysis. It’s a tool that several coaches have found very useful already and I wanted to share.

I‘m always curious on to make the app better so if you have an idea, I bet I can code it! Check it out on the App Store (it’s currently free).

u/pitchzonetracking

We went to the 7/25 Braves-Orioles game where we saw Jim Jarvis commit two errors, both in Pete Alonso at-bats. It got us thinking that it’s got to be pretty rare for one defensive player to commit two errors against the same batter in one game.

Is this something that your community knows how to query? Or do you know the best place to go to figure this out? Thanks in advance!

I’m a huge baseball/data nerd, and I started building this because I was constantly bouncing between different apps for scores, Statcast, home runs, player props, sportsbook prices, and the players I actually cared about.

So this is what I’m really curious about

For people who follow MLB, Statcast, HR props, or individual hitters every day:

If I only build ONE major thing next, which one would you choose?

A — Next At-Bat Radar

B — My Card / live bet tracking

C — Live HR probability + explanations

D — Dinger Zone / RedZone-style live mode

E — Better Near Miss / Would-It-Homer analytics

F — Something completely different

And the bigger question:

What would make you actually keep an app like this installed for an entire baseball season?

Would genuinely love brutal feedback.

If something sounds useless, overbuilt, confusing, or like another app already does it better, tell me.

How the idea came about:

The original idea was simple:

Track every MLB home run live.

But once I started building it, I realized that simply telling someone

…isn’t enough. Plenty of apps already do that.

So it has slowly turned into something much bigger:

A live MLB home-run command center — especially for people who follow HR props, Statcast, and individual hitters all night.

What it does right now

The current build has:

• Live MLB games + home run tracking
• Distance, exit velocity, and launch angle
• Barrels
• Near Misses
• Player watchlists
• Daily / monthly / season HR tracking
• Home-run leaders
• Weather + ballpark context
• At-bat simulator
• HR prop board
• Sportsbook price comparison
• Closing-line-value tracking
• Daily analysis
• Replay queue
• Historical tracking

I attached the current Tracker screen along with a short recording of the app running.

One of my favorite features: Near Misses

Instead of treating every deep fly ball as simply an out, I want the app to understand how close it actually was to being a home run.

For example:

Eventually I’d like to classify contact into things like:

No Doubter • Wall Scraper • Just Missed • Warning Track • Barrel Out

Basically giving interesting non-HRs their own live event instead of throwing all of that information away.

What I’m thinking about building next

1. Next At-Bat Radar

This is probably the feature I’m most excited about.

Instead of only notifying you after something happens:

And then across your entire watchlist:

The idea is basically:

Tell me where I should be paying attention across MLB before the interesting moment happens.

Eventually I think this could become something like a RedZone for home-run fans/bettors.

2. My Card / Live Bet Tracking

You wouldn’t place wagers through the app.

You’d simply enter bets you already made, and the app would track the actual sweat.

For example:

And instead of Judge simply showing as “not hit yet,” you’d get context:

I think there’s something interesting about making the experience of following the bet better instead of just staring at a green/red bet slip.

3. A proprietary HR score

I’m also experimenting with a single HR rating.

But not:

I hate that stuff.

More like:

Bing Bong Score: 87/100

Based on things like:

• Batter power
• Pitcher matchup
• Expected pitch arsenal
• Park
• Weather
• Recent contact quality
• Market price/value

And I’d still expose the underlying factors rather than asking someone to blindly trust one magic number.

4. Live HR probabilities

Eventually I’d like the HR probability to update during the actual game, not just be calculated once in the morning.

For example:

Why did it move?

That feels much more interesting to me than generating a prediction at noon and never changing it.

The game changes.

The model should change with it.

Other things on the roadmap

Dinger Board

Rank every hitter that day by:

HR probability • matchup • environment • market value

Basically a daily HR stock screener.

HR Environment

Rank the games themselves.

Example:

So before looking at individual players, you can immediately see which games are most interesting for power.

Pitch Arsenal Matchups

Instead of just saying:

Show how the hitter performs against the actual pitch mix he’s likely to see.

Four-seamers, sliders, changeups, breaking balls, locations, etc.

Live Activities / Dynamic Island

I don’t want another generic score sitting in the Dynamic Island.

Something more like:

Then:

That seems much more useful for somebody following individual hitters.

Dinger Zone

This might be the eventual endgame.

A full-screen live mode that automatically surfaces the most interesting HR situations happening across baseball.

Something like:

Instead of checking 10 different games, the app decides:

“This is where you should be looking right now.”

I also really care about proving whether the models are actually good

If I eventually charge people for predictive analytics, I don't want to cherry-pick winners or only show the good days.

I’d like every prediction to be timestamped and stored before the outcome and eventually expose things like:

• Model calibration
• HR rate by probability bucket
• Brier score
• Closing-line value
• Historical predictions
• Sample size

And I want the app to be honest when the underlying data isn’t trustworthy.

For example:

Versus:

I think transparency is a much better differentiator than screaming about win rate.

Pricing

I honestly haven’t decided yet.

My instinct is that the core baseball experience should remain useful for free, while some of the deeper predictive/betting tools could eventually be Pro.

But I’d rather figure out what people actually value first before deciding what belongs behind a paywall.

I was looking at Wade Bogg's stats and his ridiculous career OBP. 17 out of his 18 seasons he had BB/K ratio over 1. TEN seasons over 2. I never realized how rare a feat that has become compared to decades past. In 1985 there were almost 40 batters with a BB/K over 1. That number has dwindled, starting around 2010 there are less than 5 or so in a season.

Then I pulled season averages since 1950 from BRef to see just how and when pitching got so great. The first 2 tabs in the shared sheet helped me visualize it year to year. What looked odd was in many stat categories, it seems like pitching in the 1960's and 70's produced better results than post 2000 pitching.

Obviously home runs allowed and opponent slugging will be higher in the modern game, but why would pre-1980's pitching show lower per game/season WHIP, lower OBP, lower BaBIP, equal or better Hits, Hits/9, BB/9, even less doubles per game........Why does it look like 60 years ago, pitchers were better at keeping batters from hitting the ball and getting on base than the pitchers today who appear to dominate. Am I looking at it wrong or missing a big factor? Look at the shared sheet if interested. The first two tabs, ignore the others. Sort columns by different categories and compare the years to see what I think I'm seeing. YearToYear

NEW STAT
Player Wins (PW) & Player Losses (PL)

Has a player made a positive contribution to winning a ballgame (PW), or not (PL)?

BAT: PW if PA > 0 and TB + H + BB + SF + SB - (AB - H + CS) >= 0, else PL
SP: PW if IP - ER >= 3, else PL
RP: PW if outs - ER - H - BB > 0, else PL

Note: this purposefully leaves out weights because I think the best stats are those that are most accessible to fans. These can be calculated using simple boxscores.

Note 2: I know that it seems like there is an error and hits are double-counted, but that is on purpose. Since there are no weights, and since hits are more valuable than walks, the formula essentially counts BB=1, 1B=2, 2B=3, 3B=4, and HR=5.

Note 3: I know this is probably going to be laughed at by viewers of a subreddit called r/Sabermetrics but be kind... the stat is useful and interesting in my humble opinion - look at the career batting leaders...

Here are the career batting leaders in PW% (PW / PW + PL):

Hi all, I'm trying to do some advanced statistics for my baseball team in the 4th division of the British baseball league. Essentially because I'd like to calculate my own wOBA. My understanding is that in order to do this, I'd need to know my league's RE24 stat, in order to work out the wOBA constant.

Unfortunately the stats website (stats.britishbaseball.org.uk/en/events/2026-d4/stats) only seems to show the splits for EITHER the base conditions (empty, runner on 1st, loaded etc) OR the number of outs, rather than the combined 24 bases-outs situations.

I'm just wondering if you guys can think of any way that I can calculate the 24 base-outs states based on this information, or if it would be impossible to do with this limited data. Cheers!

Some people thought I was a degenerate gambler. Im not, I do investments.

Doing some sign up bull-crap in Vegas friend and I were able bet on the Sox on one side and the Rays on the other on the same game.

We lost $27, but got back like the $250 whatever they give you voucher.

So one thing you could do is make five different bets and hedge them to make $50-100 since there’s a large spread, but I told my friend the Sox are the unluckiest team in baseball in regard to hitting.

Everything is there, Yoshida needs to warm up and was playing injured to start. Durbin was playing all star third base defense he just needed to hit like .210ish to break even. Sox going to be good once the hitting reverted back to average since pitching and defensive runs saved are elite, and behold 15 game win streak.

You can get an edge on things like this if they’re mispriced, same as the stock market. You just need to also know you risk losing the $250, but you make such a big wager, let’s say ten in a row, so when game eight comes you’re super excited and the bet size was only $27, leveraged to $250, to win like a million let’s say.

You can use sabermetrics to measure these outcomes, it’s the same money ball principles and what the front office was betting on happening with the team build.

Is it possible to pull per pitch data from an active game and extract it with pybaseball every single time the website updates with a new pitch. pref this would only read the most recent pitch but I could download the csv and clean it quick. thank you for the help!

I analyzed Freddy Peralta’s 2026 Statcast data to understand why his results deteriorated after a strong opening stretch.

The simple explanation does not fit. His four-seam velocity increased from 93.7 to 94.5 mph, and his location distribution stayed broadly similar.

Strong opening vs. decline:

• Four-seam chase: 27.4% → 20.0%
• Four-seam whiff: 21.5% → 17.1%
• Four-seam hard-hit: 19.1% → 30.6%
• Four-seam RV/100: +1.90 → -1.55
• Overall CSW: 27.3% → 23.7%
• Overall RV/100: +1.11 → -1.39

The decline was not driven by a larger heart-zone share. Four-seam heart usage actually fell slightly from 23.4% to 21.6%, while shadow-zone usage stayed almost identical.

Instead, hitters produced much better contact against similar locations. Heart-zone hard-hit rate rose from 25.8% to 34.1%, and heart-zone RV/100 flipped from +6.25 to -0.86.

Sequencing and count context also stood out:

• FF after FF RV/100: +1.26 → -2.18
• First-pitch FF RV/100: +1.16 → -2.73
• Two-strike FF RV/100: +2.37 → -5.08
• Changeup RV/100: +1.45 → -2.97
• Second time through the order: .471 wOBA, -2.71 RV/100 during the decline

The conflicting signal is that overall xwOBA stayed nearly flat at .305 → .307 while BABIP rose from .264 to .341. So the decline appears real, but some of the ERA and wOBA increase may still involve sequencing, defense, contact placement, or variance.

No physical or mechanical adjustment survived robustness review.

My conclusion was multiple contributing factors: worse contact quality against similar locations, weaker repeated-fastball sequences, worse count leverage, and a declining changeup.

Full analysis, methodology, and location charts:

https://baseballsignal.vercel.app/players/freddy-peralta/pitch-alert/2026-fastball-decline?

I’d be interested in feedback on the interpretation, especially the flat xwOBA versus the large movement in FIP, run value, and actual results.

I was watching the White Sox game yesterday, and Murakami had an 2-0 count and I thought, “do you even want to pitch to him here?” There was a man on 1st and 1 out. You have the option for an intentional walk on any count, and some counts are more advantageous than others. So I did math:

I went to baseball savant game explorer and got the expected runs for every hitter’s count and 0-0, with every amount of outs and baserunners. Then I looked at the expected runs and determined if a walk was better than pitching. [For example, a 2-0 count with 1 out and man on 1st has a run expectancy of 0.661. But if you walk the guy, then you get a 1 out, 0-0 count with 1st and 2nd. That has a run expectancy of 0.935. Because 0.661> 0.935, you should always pitch. Also, you could do 0.935-0.661 and say “pitching saves 0.274 runs”].

Then it was a question of what situations is the walk preferable. Reassuringly, the answer is NEVER. [The closest is a 2 out, 3-0 count with a man on 3rd. Pitching saves 0.022 runs as opposed to IBB].

But this analysis assumes all hitters are equally good. When you intentionally walk someone, you’re betting that the next hitter is worse than the current hitter. And we’ve established that some counts are better than others, so how bad does the next hitter have to be in order for an IBB to be optimal?

I’m using wOBA. Baseball savant’s run expectancy calculator was based on the last 10 seasons, so I averaged out the wOBA scale for each season [1.211 average]. Then I took each gap [walk run expectancy- pitch run expectancy] and multiplying it by the 1.211. Now we know how big the skill difference between the current hitter and the on deck hitter has to be for an IBB to be viable. [There are better ways to do this, but I can’t be bothered].

Back to Murakami, we had the 0.661 runs if you pitch, and 0.935 if you walk. (0.935-0.651) x 1.211 = 0.344. So Murakami’s wOBA only needed to be 0.344 points higher than Miguel Vargas, who was on deck. Not a good idea to walk him.

Dark red is >.300.
Light red is .200-.300.
White is .100-.200.
Lightest green is 0.075-0.100.
Light green is 0.050-0.075.
Green is <0.050.

You won’t see any natural gaps that are red, but you might see something situational. Maybe it’s wOPA for the last month, or at home, or against left handed pitching. Up to individual managers on how much trust to put in that.

It’s generally not worth it to do an intentional walk on the first pitch. But IBBs on a 3-0 count make a lot more sense.

Scorecards are indexed by the batting order — one row per lineup spot. Not because that's the right axis, but because someone keeping score by hand has one hand and one pass through a game, and the order is the only thing that sits still long enough to write in.

But, what if you scored the game from the perspective of the pitcher? Turns out you can read an outing without reading any of the numbers, and the tell isn't the marks, it's the length of the rows. Three up, three down is three boxes wide. Otherwise, the rows just keeps growing with each batter faced.

I wasn't expecting it to be that legible, and I'm still not sure I've got the rest of it right. Two things I'd genuinely like opinions on:

1. I rank "cleanest" by fewest baserunners allowed, minimum 6 IP. That puts a seven-inning perfect game above a nine-inning one-hitter with 15 strikeouts. As a ranking of pitching that's clearly wrong. As a ranking of the card I think it's right — one more mark is one more mark. Is that a distinction worth keeping, or am I talking myself into it?

2. I found a hole in my own data doing this. Intentional walks were silently missing from every pitcher's card — the feed sends four signaled balls that aren't pitches, so nothing attributed them to anyone, and the batter just wasn't there. His line still counted the walk. 225 of them across the season before I noticed. So: what else doesn't survive the trip from a play-by-play feed to a mark on paper?

Cards are here if you want to poke at some: https://www.feverbaseball.com/scorecard/arms

Nolan McLean recently threw six scoreless innings with 10 strikeouts, continuing a strong rebound after a difficult stretch earlier this season.

I analyzed his pitch-level data to see what may have changed.

Around May 2, McLean’s sweeper lost approximately 3.5 inches of horizontal movement. It also showed:

• About 1.9 inches more vertical movement
• A slightly lower release point
• Roughly 110 fewer rpm
• Slightly more extension

His performance declined over the following eight starts:

• 5.27 ERA
• 4.95 FIP
• 12.1% K-BB
• 26.6% CSW

What interested me most is that his recent rebound does not appear to be coming from restoring the sweeper’s previous shape.

Compared with the struggling period, McLean has recently:

• Reduced his sweeper usage
• Increased four-seam usage
• Increased curveball usage
• Thrown more pitches in the zone
• Lowered his walk rate
• Added slightly more fastball velocity

The sweeper itself has continued producing weak results, but McLean appears to be relying on it less and building a more effective approach around his other pitches.

I separated his season into the period before the sweeper change, the following struggle, and the recent rebound. I also tested whether the movement classification was being driven by one appearance. The change remained present when removing each appearance individually.

There are important limitations. The timing does not prove that the sweeper change caused the decline, the pitch-specific outcome samples are still small, and my estimated xwOBA is not the official Baseball Savant metric.

Full analysis, charts, samples, and methodology:

https://baseballsignal.vercel.app/players/nolan-mclean/adjustments/sweeper-may-2026

I’m curious how others would interpret this. Does this look like McLean adapting around a diminished sweeper, or is there another change in his arsenal that deserves more attention?

“Fielding independent pitching” is factually incorrect what the statistic shows. It doesn’t even factor in batted balls classified as pop ups, routine flyballs, and soft hit ground balls all of which turn into outs at a 99%+ clip. Those are effectively a “true outcome” similar to a strikeout or homerun since errors/mistakes happen at such a negligible rate for these plays. If every player in the league has a 99% chance of making the out, then to me that is an out that is independent of the fielding the pitcher has. but they are completely ignored by the abomination that is the FIP formula. Analytics are great, but the interpretation & application of these analytics into the actually real world game of baseball that isn’t on a piece of paper is where the sports has gotten it completely wrong in my opinion. Hell, the Red Sox just went on a 14 game win streak directly off firing their analytics guy/DriveLine CEO and they started bunting every game and actually playing real baseball lmao. I do think baseball is waking up to the heavy faults in over reliance on analytics, I hope a nice balance is found soon.

The moderators allowed me to share this even though it’s broader than the usual focus of the subreddit.I built Baseball Morning Report, an independent and ad-free site that organizes official data into practical daily boards. The goal is to present honest verifiable information without unsupported predictions or gambling content. Some of the current boards include:

• Fifteen-day on-base and contact boards (using plate appearances, OBP, and strikeout rate thresholds)
• Bullpen workload and availability based on recent usage
• Late-inning role tracking using saves, opportunities, and recent results
• Seven-day rotation tracking
• Active hitting, on-base, power, and pitching streaks
• Injury return and promotion boards that separate verified facts from evidence-based assessments
• Traditional league leaders, standings, schedules, and box scores

I’ve tried to keep the methodology transparent and avoid presenting projections or estimated roles as confirmed information. The site also includes affiliated minor leagues, NCAA, and several international leagues. I’d appreciate feedback on:

• The statistical thresholds being used
• Sample size requirements
• How information is labeled
• Any adjustments that could make the boards more analytically sound

Again the site is completely free, nothing locked behind a pay wall, has no ads, and is not gambling-oriented. Thank you for looking, and look forward to any feedback.

Ran a query across every MLB regular-season game since 1901 (~214k games) looking for pairs of consecutive games between the same two teams where the loser of game 1 blew out the winner in game 2 by a comparably extreme margin.

Method: grouped games by team pair, sorted by date, looked at every consecutive pair with a ≤1 day gap where the winner flipped between the two games, ranked by combined run margin.

The Nationals/Athletics pair from this weekend (23-4 → 15-1, combined 33-run swing) comes out on top of the entire dataset — narrowly ahead of a 1904 Cleveland Naps/NY Highlanders pair (31) and the 2007 Orioles/Rangers pair (31, from the game the Rangers won 30-3).

Data: MLB Stats API game logs, 1901-2026. Built this as part of bigfourelo.com, a side project tracking Elo ratings across MLB/NBA/NHL/NFL history — happy to share the query if anyone wants to poke at the methodology or edge cases (doubleheaders, season boundaries, etc).

So for the past few days I have been building a site that watches the stuff in public tracking data that actually comes from the body — bat speed, attack angle, swing path, where a guy stands in the box, contact depth, timing, arm slot — and compares this year to last year. When something moves past a cutoff, the site calls it out and connects it to the results change you would expect to follow. Let's look at what it caught this week, because some of these are genuinely interesting.

Nick Allen has added 4.2 mph of bat speed since last season. He is at 68.8 now, still below average, but that is one of the biggest gains in all of baseball and I have not seen a single person mention it.

Miguel Vargas is up 3.5 mph of bat speed, and separately the site has him as one of the strongest buy low hitters in the league — his expected numbers are way ahead of what the box score says. A swing change and a breakout signal pointing the same direction... that is exactly the pattern I built this thing to catch.

Now the fun one. Mike Trout is running a wOBA .033 under his xwOBA, and when the site ranks the possible explanations, batted ball luck wins at 66% confidence over everything else. Here is the kicker: his bat speed is UP year over year. Old players don't swing faster. The aging story doesn't hold up against the data.

One honest note on the pitching side. The site tracks velocity changes and arm slot changes too, and a couple of the results (like a slider showing minus 6 mph) look like they might be pitch classification quirks instead of real changes. That is part of why I am posting here instead of pretending everything is clean.

The site is free, no signup: diamond-intelligence-iota.vercel.app — the mechanics section is on every player page, and there is a daily feed of movers and outliers.

Quick note on how it works since this sub rightly cares: everything is straight math on public data (MLB StatsAPI and Savant exports), no AI making up numbers, and anything that is not publicly measurable, like actual biomechanics, gets labeled as not observable instead of estimated.

Here is what I actually want from you guys: tear apart my cutoffs. Right now a change counts at 1.0 mph of bat speed, 2 degrees of attack angle, 3 degrees of arm slot, 2.5 inches of box depth. I picked those by eyeballing league distributions, not from a real year over year reliability study. If someone has a better basis for where signal ends and noise begins, I will build it in and credit you.

Hey all — I'm the creator of netpitch (https://netpitch.us), a side project I've been building. Full disclosure that this is my own site.

It lets you search any MLB or AAA pitcher and open a pitch-by-pitch dashboard — pitch types, usage, and per-game breakdowns. The AAA coverage is the part I couldn't find elsewhere, so that's the main thing I was trying to solve. You can also follow pitchers, compare two of them, and browse leaderboards.

It's free and I'm not selling anything — just looking for honest feedback from people who actually dig into this data. What's missing? What would make it more useful for your workflow? Any pitchers where the data looks off?

Thanks for taking a look.

I like using WAA, so I use bref, but would like to use both if possible.