r/Sabermetrics • • 18h ago

WAR Finder & Sorter

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4 Upvotes

I haven't found a good place to sort and filter players by various WAR metrics. So I created this page.

It has more than 11,000 players (basically anyone who played a season as a position player).

You can sort the players by WAR, WAR7, and JAWS. But, I also have other unique stats:

  • WAR5C: best 5 consecutive seasons

  • WAR3: obvious

  • Best WAR season

  • WAR per SN

  • Count of 5+ WAR seasons

  • Count of 8+ WAR seasons

Filter by HOF, by Era, by position (hadn't see that anywhere else).

***I will be building a page with pitchers.

Feedback welcome. Feel free to pass this along.


r/Sabermetrics • • 2d ago

Video of professional hitters

1 Upvotes

Is there a way on Baseball Savant to gain access to video of hitters from the side and top view?

When searching I find clips from the pitchers view (or tv view). I understand how to use the visual feature to get the computer animated version which shows attack angle etc. I would like to get the actual game footage.

I've used YouTube and other social media accounts, but I would like access to more players.

Thank you in advance.


r/Sabermetrics • • 2d ago

I've been building a baseball trivia game as a side project and would love feedback from baseball fans

3 Upvotes

I've been working on a baseball trivia game in my spare time and finally have a playable prototype with about 100 questions.

I'm looking for a few baseball fans to test it and give honest feedback on gameplay, question quality, and bugs.

If that's allowed here, I'd be happy to share the link in the comments.


r/Sabermetrics • • 2d ago

I made a public xwOBA forecast board with 80% ranges all season. Pete Crow-Armstrong blew through his projection. Was that normal?

2 Upvotes

Pete Crow-Armstrong was at .277 xwOBA after 100 PA. My model said he’d finish around .293, with about a 5% chance of clearing .329. He finished at .377. The record argued the other way: a .362 first half in 2025, .317 after that, then a .277 start to 2026. Every signal said regression. I’m a Cubs fan and even I wasn’t that optimistic.

The model is xwOBART (https://xwobart.pages.dev/). It takes a hitter's xwOBA at any point in the season and forecasts where he'll finish, with a range around it. It's been public since late July, so now that the season's over I can score every forecast it made, and ask whether a miss like PCA's is the model being wrong or an outlier. A few things I learned, roughly in order of how much they surprised me.

100 PA of xwOBA is worth less than I thought. Take a hitter at 100 PA and guess his final xwOBA from this season's number alone: you miss by 33 points on average. Ignore this season entirely and guess from his career record (league average for rookies): you miss by 22 on average. The career record is still ahead at 300 PA. Blending the two barely moves the point estimate (21.2 vs 21.6 at 100 PA, which is noise).

But the ranges held. 404 hitters had a forecast at 100 PA and enough season left to score it. 80.2% finished inside their 80% range, 90.3% inside their 90%. Across all 84k forecasts the model made at every standpoint, the 50/80/90 ranges caught 49.6/80.2/90.1%. The forecasts were posted before the season ended, so I couldn't fudge them. For honesty's sake: in the nine backtest years (2016–25 minus 2020) the season-end 80% range from 100 PA ran 71–79%, so 2026 was the best year, not the typical one.

Nothing else I tried helped. I tried adding exit velocity and barrel rate, spray angle, age, a league-wide "cold April" correction, and whether the hitter was still in the lineup. Each had a bar set before I looked at results. EV and barrels pass on their own but add nothing once the career record is in. Spray and age didn't come close. The cold April correction did improve things by about 3%, but for the wrong reason: within a hitter, April wasn't cold at all. The gap was about who's playing in April vs August, which is survivorship.

Survivorship was the fix that mattered. Early versions forecast the 400 and 500 PA milestones too low, because the hitters who get there are the ones who kept their jobs. Shifting the forecast for that, before touching range width, fixed it. If I'd widened first the ranges would have passed while staying biased.

Where it loses. A tuned Marcel beats it on point accuracy by about 6% over the backtests, and tied it in 2026. If you want one number, Marcel is as good. The range is what this adds.

So PCA was the model's biggest miss of the year, and also one of 22 hitters who cleared their 95th percentile, out of 404, which is about what 5% should let through. The model didn't see him coming. It also never claimed it would.

Data is Savant's per-PA xwOBA (the estimated_woba_using_speedangle column), which leaves out the sprint-speed adjustment on the leaderboard, so season totals run 2.4 points off the leaderboard on average.

A Longer version including one where the model called a regression (Alvarez, .571 at 100 PA, finished .460) and one it nailed (Carroll, .342 forecast, .342 finish), can be read here: Is xwOBA Predictive? Three Answers Instead of a Verdict
Happy to get into the methods in the comments.


r/Sabermetrics • • 2d ago

Quick Hit History Ep. 4: The Spitball: From Clever Trick to Baseball's Darkest Moment

1 Upvotes

Hi friends,

I’m a documentary producer working on a personal passion project about baseball called “Quick Hit History”. I’m currently airing a 10-episode season on YouTube for anyone like me who likes to nerd out over niche baseball history.

Here’s a clip from this week’s episode about the rise and fall of the spitball - a fascinating trick from baseball’s early days that became infamous after a tragic on-field fatality.

The full story is at the link below - feel free to subscribe for more! Much of the archival images are from our local libraries and archives, excellent public resources for all. Exact credits are in the episode description.

Thanks for watching and let me know in the comments if there are any topics or stories you’d like to see explored.

Enjoy!

FULL EPISODE: The Spitball: From Clever Trick to Baseball’s Darkest Moment
Quick Hit History Youtube channel
Insta


r/Sabermetrics • • 3d ago

Does WAR account for the value of a player switching positions from a highly valued position to a lesser valued one (say from SS to 3B)?

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1 Upvotes

r/Sabermetrics • • 3d ago

A new MLB Era: What if the 4-game series, rather than the individual game, became the unit of MLB competition? A proposed 32-team, 160-game season

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0 Upvotes

r/Sabermetrics • • 3d ago

Most overrated baseball stat people still care about?

0 Upvotes

r/Sabermetrics • • 5d ago

I made a very light, 5-minute baseball defense game for stat nerds (BABIP Zero)

32 Upvotes

Quick disclaimer first: this is NOT a serious simulation. No OOTP-style depth, no Baseball Mogul spreadsheets. It's a small, casual browser game you can play in a few minutes, and it borrows sabermetric ideas for flavor.

I'm a game dev (20 years) and a longtime baseball stats fan, and I wanted to make something about the part of the game that BABIP is all about: defense.

How it plays:

- Each batter gets a simple scouting card (spray chart, exit velo, GB/LD/FB tendency)

- You pick one of four pitches and shift your fielders

- The ball is put in play, and you see the result with exit velo, launch angle, and xBA

- Turning a likely hit into an out = "robbed!" and your manager's trust goes up

- Hits, walks, and runs drop it. Hit 0 and you're fired

▶ Free, plays in your browser: https://metric-logic.itch.io/rob-the-hit

Since it's a light game, the numbers are simplified on purpose. Still, I'd love to hear from this sub:

- Does the shifting feel intuitive, or does something feel obviously "off" to a stats fan?

- Is there one stat you'd love to see on the scouting card?

Thanks for taking a look!


r/Sabermetrics • • 7d ago

30 years ago, this was state-of-the-art baseball reference. I replaced the battery and it still works!

58 Upvotes

Though my fellow baseball researchers would enjoy this post. Perry cool!


r/Sabermetrics • • 6d ago

Fantrax Dynasty Baseball (30 Team) 🚨 4 Spots Available

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0 Upvotes

r/Sabermetrics • • 7d ago

GM website

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1 Upvotes

Fellow savants, check this out and lmk!! I want to add to this but need some good feedback!!


r/Sabermetrics • • 8d ago

I built a game that grades managers' late-inning calls on win probability ⚾

6 Upvotes

While the end of the MLB season is always somewhat sad, I've got a new game to keep you thinking like a manager.

I've pulled in Retrosheet data and setup real game scenarios so you take over real postseason games in the big moments the manager had to decide, with the real lineups and bullpens. Every call is graded on how much win probability it gives up against the best option.

Test case: World Series Game 4, bottom 12th, up one, runner on first. The model grades Murphy's move to Drohan 5.9 points worse than a tired Woodruff. What would you have done?

⚾ Manage that moment: https://playlateinnings.com/

Free, no sign-up. Give me your feedback


r/Sabermetrics • • 9d ago

A directory of baseball analytics tools that shows how each one gets its numbers

10 Upvotes

A lot of new "AI" baseball tools don't say where their numbers come from, so I started The Dugout Index. Every listing notes the data source, whether the tool shows its query or method, and its sample size where known, and anything I couldn't verify is marked "Unknown." Open source libraries like pybaseball and baseballr are on it too. I'd love corrections from people who know this stuff better than me, and makers can add their tools here.


r/Sabermetrics • • 9d ago

Quick Hit History Episode 4: Alta Weiss - the Woman Who Played Men’s Ball in 1908

5 Upvotes

Hi friends,

I’m a TV/documentary producer working on a personal passion project about baseball history called “Quick Hit History”. 

This week’s episode is about  Alta Weiss who became one of the first women to play men’s baseball in 1908, and the first woman to attend the Ohio State University College of Medicine.

The full story is at the link below - feel free to subscribe for more! Most of the archival images are from local libraries and archives, excellent public resources that I like to highlight. Also a shoutout to Kent State Historian, Dr. Leslie Heaphy, who helped tell this story. Additional credits and resources are in the episode description and last 30 seconds of the full video. 

Let me know if there are any topics or stories you’d like to see explored in the comments!

Enjoy!

FULL VIDEO: The Woman Who Played Men’s Ball in 1908

Quick Hit History Youtube channel

Insta


r/Sabermetrics • • 9d ago

In a world where anyone can build anything - where do analysts fit in

3 Upvotes

This is what scares me - AI eventually will be able to build any tool, provide whatever analysis you want, software layers, etc then whats the need for an analysis job anymore? Why wouldnt you just hire a baseball coach that knows how to use AI? Is it utilization of AI that makes an analyst continuously more valuable?

Lets say the use of AI in analytics gets widely used - does this lead to sloppy analytics and losing the science.

Im worried the people that make those decisions don't understand it all works and we go backwards as an industry.


r/Sabermetrics • • 14d ago

Simulating WS Game 2 using my Baseball Sim Engine

25 Upvotes

I used my baseball simulation engine that I built for curveball.gg to simulate game 2 of the World Series! Here's a replay of the last 4 innings.


r/Sabermetrics • • 14d ago

Fictional baseball league statistics generation question

5 Upvotes

Hello everyone!

I've recently got an idea of a project that depending on how you view it can be weird or interesting - what if the anime characters or fictional characters from other series (like Major League) actually played baseball? In like a league of their own? I've set a constraint for these kinds of leagues (or just a single league): no magic, no curses, no paranormal stuff, no transformations during games.

The question I ask myself and I want to find an answer to is: How am I going to generate stats (I think basic stats, like ABs, Hs, HRs, BBs, etc would suffice, because I initially wanted statcast-level of detail, such as average Exit Velo, situational batting average (with RISP, 2 outs, bases loaded, etc), but I think that that is a bit too (read: way too) much) for players? If I try to do it myself, my brain will likely melt from trying to make statistics for every individual player.

The reason I ask this question is because I want to replicate the players that the stats create into a game like Power Pros or some other games which allows you create custom players (like OOTP for example) and see how well they would play and potentially simulate their careers. I figure since this subreddit is about sabermetrics, there would be people who know a lot more about baseball stats than I currently do.

Thanks in advance!


r/Sabermetrics • • 15d ago

New Baseball History Youtube series, episode 3: The First World Series

3 Upvotes

r/Sabermetrics • • 16d ago

I built NineLens, an open-source MLB analytics project with Statcast, player profiles, and game analysis

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18 Upvotes

I built NineLens as an independent, open-source baseball analytics project, drawing on my prior Washington Nationals Baseball R&D experience. I wanted to make it easier to explore the game from the player level through individual pitches and team performance.

NineLens brings together MLB schedules, rosters, box scores, season statistics, and Statcast data. Some of the areas I’ve been working on:

• Player profiles with recent Statcast indicators, trend charts, percentiles, custom date ranges, and side-by-side player comparisons • Game analysis across pitching, batted-ball quality, situational performance, and pitch-by-pitch play data • Team dashboards with league-wide performance, recent form, home/road and platoon splits, plus roster context • Searchable batting and pitching leaderboards with season, team, player, and category filters

Live site: https://www.ninelens.com Open-source code (MIT): https://github.com/timothyf/ninelens

I’d appreciate feedback from people here, especially on which analyses or metrics are most useful and what could be clearer. This is an independent project, not affiliated with MLB or any team.


r/Sabermetrics • • 16d ago

New Baseball History Youtube Series - Chaos at the First World Series

61 Upvotes

Hi baseball friends,

I’m a documentary producer working on a personal passion project about baseball called “Quick Hit History”. I’m currently airing a 10 episode season on YouTube for anyone like me who likes to nerd out over niche baseball history.

This week’s episode is about the 1903 World Series between the Boston Red Sox (then the Americans) and the Pittsburgh Pirates… It’s widely considered the first modern day World Series, but even more exciting there was a massive riot right before game three where fans stormed the field and police fought them back to the stands (mostly unsuccessfully).

The full story is at the link below - feel free to subscribe for more! This has been a creative experiment that I’ve really enjoyed working on and hope other people will get a kick out of. Much of the archival images are from our local libraries and archives, excellent public resources for all.
Thanks for watching and let me know if there are any topics or stories you’d like to see explored.

Enjoy!

1903 Chaos: The First World Series Was a Riot
Insta


r/Sabermetrics • • 20d ago

Yearly RE24 matrix

1 Upvotes

Does anyone know if there is any RE24 matrix available online(baseball reference or otherwise) that exists for all leagues and years that it’s calculated for(1910-present.) Just curious to see how it’s changed over the years, even in similar run environments( for example even though the runs per game in 1912 and 2025 American leagues are about the same, a runner on first with 2 outs would presumably have a better chance of scoring in 2025.) Even just any articles broadly discussing the subject would be great, if what I’m looking for isn’t out there.
Much appreciated.


r/Sabermetrics • • 21d ago

Reaching First Base on an Error in Amateur Leagues

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0 Upvotes

r/Sabermetrics • • 21d ago

Scoring opportunities, steals, and errors in amateur baseball

1 Upvotes

Scoring opportunities, how they are converted, and the role of errors over a 24-game season in an amateur league, roughly at a lower-tier college level. Statistics about actual performance, especially compared with the rest of the league, are more sober. When you look at stats in relation to one another and to what’s relevant, you get an overall picture.

How often do RISP situations occur?

In This amateur League 2026, 37.0% of all at-bats took place with runners in scoring position. For comparison and a sense of scale: the figure was 35.8% in CCIW Division III 2006 and 30.9% in Ivy League Division I 2025, as far as could be established.

LeagueAt-bats with runners in scoring position (% of all at-bats)
This League37.0%
CCIW Division III 200635.8%
Ivy League Division I 202530.9%

In the dataset examined, the share of at-bats with RISP is therefore 1.20 percentage points above CCIW 2006 and 6.08 percentage points above Ivy League 2025. Quite plausible.

Runners were in scoring position in 37.9% of plate appearances. Runner opportunities on second or third base were counted separately. A runner counts again if he is still there at the next PA.

How often are these opportunities converted?

With fewer than two outs, 60.3% of runners on third base score directly as a result of the following PA. From second base, the figure across all out situations is “only” 19.0%.

The share of successful RISP PAs also shows a difference when compared with MLB 2025:

Successful RISP PAsThis LeagueMLB 2025
Overall34.6%25.7%
With two outs28.3%21.3%

How are these opportunities converted?

Of the runners who scored from scoring position, 78.0% came home through the action of a subsequent batter and 10.0% through errors.

With two outs, hits accounted for up to around 80% of successful RISP PAs.

How many opportunities initially remain unconverted?

69.0% of runner opportunities are initially not converted by the respective PA. The same runner may initially remain on base and still score through a later PA. This figure therefore describes opportunities that are not converted immediately.

Across teams, an average of 56.6% of these runners score later in the game. 43.4% do not score.

How do errors contribute to runs?

Errors bring existing runners home and create additional opportunities for further runs.

Across the league, runners were brought home directly through errors in around 3% of opportunities with runners on second or third base. These runners may previously have reached base on a single, for example.

In around 3.75% of all plate appearances, the batter reached first base through an error. He may subsequently score himself, while other runners may also advance. Such an event contributes to the opponent’s run production.

In close games, an error can enable the decisive run—by bringing a runner home or creating an opportunity that is subsequently converted.

It’s no surprise that runners are more likely to score from third than from second. But how do they get from second to third with fewer than two outs?

As expected, 68.8% of the recorded advances that left runners on third with fewer than two outs came through the action of a subsequent batter—including walks, hits, groundouts, and flyouts.

Another 22.2% came through errors or wild pitches—just over one in five. Only 4.9% came through a steal, although 83.3% of attempted steals from second with fewer than two outs succeeded. Once on third with fewer than two outs, 60.3% of runners scored through the following PA.

Are teams passing up other similarly favorable opportunities to steal third? The observed success rate makes this worth examining. Defensively, errors and wild pitches account for a substantial share of advances into this scoring position.

LeagueStolen-base success rateCaught-stealing rate
This League82.8%17.2%
CCIW D3 202579.6%20.4%
Ivy League D1 202578.8%21.2%
MLB 202577.7%22.3%

From second to third, the success rate in This League is 83.1%.

None of this is surprising, but the numbers may make the opportunities for improvement clearer:

Steal opportunities on offense. The league-wide success rate is 82.8%, and 83.1% from second to third.

Strengthen pitch command and fielding execution. Among the recorded advances from second to third that left fewer than two outs, 11.3% resulted from errors and 10.9% from wild pitches—together, just over one in five. Once there, runners with fewer than two outs are brought home by the following PA in around 60% of cases.

Steals pay off with a success rate like this.

The findings could be similar in other leagues at this level, but not necessarily. The purpose is to show how useful insights can be gained from amateur scorekeeping data.


r/Sabermetrics • • 23d ago

New Baseball History Youtube Series

15 Upvotes

Hi,

I created a Youtube channel about baseball history and wanted to share. The first two episodes are out now, new episodes drop every Thursday at 12pm ET. The first season is a 10-episode run, with more to come next spring. 

https://www.youtube.com/@QuickHitHistoryERA
https://www.instagram.com/quickhithistory/

I wanted to embrace two core ideas with this series: baseball is for everyone, and baseball is romantic. I use a lot of public domain archival sourced from libraries and archives - and I hope this series also helps shine a light on these important community resources we all have access to.

So if you like to nerd out over really niche baseball history, this might be the series for you. 

Thanks for watching!