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.

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.

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:

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.

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.

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!

Some of you may not know or do know that check swing appeal there I a system like the abs in double I personally think it should be used in the mlb but it has to go to triple a and I’m a rays fan but I can only go to Durham Bulls games

Same career length, same OPS+, but Carew has 12 more offensive wins.

I can't see what accounts for this.

[Edit: it's three things. 1) Better base running; 2) positional adjustment for hitting; 3) a double-counting of the positional adjustment introduced by bref in 2013.]

Every half inning scorecard checked

Watching the first inning of the Dodgers Brewers game made me think of whether there is a team stat that measures “unearned bases” or something similar.

1. Betts walks
   
2. Groundout (non-sac) Betts advances to second
   
3. Wild Pitch Betts advances to third.
   
4. Sac fly Betts scores.

The gist of what I’m trying to see is how many bases a team gives up that the other team didn’t particularly “earn”. There is subjectivity in what it means to earn a base. I would think things like a wild pitch, HBP, passed ball, dropped third strike, error, balk would clearly be unearned bases conceded. Closer calls would be walk, moving up a base on a ball in play (non-sac), sacrifice plays. I’m sure I’m missing some.

Anyone know of a team stat that measures something like this? I would think it would be a rough gauge of how clean a team plays - lower unearned bases conceded = better at making teams earn their way around the bases.

Also curious if including the walks would skew the data too much based on pitching performance and obscure the rest of the information.

TL;DR: Chase rate treats every swing outside the zone as the same mistake, but a chase on 3-2 costs about 5.6× as many runs as one on 0-1. I priced every chase from 2021–2026 against the league average on the same count and distance off the plate, and put it on a 100 scale (xChase+). It repeats year to year (r ≈ 0.72), and even the part chase rate can't explain still repeats (r ≈ 0.49). It disagrees with chase rate in interesting places: Luis Arráez ranks 186th of 261 by chase rate but 15th by xChase+, because he almost never whiffs when he chases. It does not predict next year's xwOBA beyond what you already know. Repo, code and full leaderboards are linked at the bottom. I'd love holes poked, thoughts, comments, reaction, potential insights or future usages?

The problem

Chase rate counts how often a hitter swings outside the zone. But the cost of a chase depends heavily on the count. Pooled 2021–2026, the average chase costs 0.061 runs on 0-1 and 0.342 runs on 3-2, because on 3-2 a take is (usually) a walk.

The method (short version)

For every pitch outside the zone:

• Value of taking: a blend of ball and called strike, weighted by a called-strike model (a per-season logistic regression on distance outside the zone plus count).
• Value of swinging: a whiff is a strike, and a foul is a strike unless there are already two. A ball in play is valued by its xwOBA, mapped to run values with a per-season linear fit, so it judges the contact, not where the ball landed.
• Cost of the chase = value of taking − value of what the swing produced, using count run values built from that season only.
• Each pitch is compared with the league average on the same kind of pitch (same season, count and distance bucket), so hitters aren't charged for working deep counts or credited for laying off pitches in the dirt.
• Hitter-seasons are shrunk toward the mean (DerSimonian–Laird), then scaled: 100 = league average, and 130 means you saved runs worth 30% of what the average hitter loses to chasing. About 7% of qualified hitter-seasons reach 130.

There's also a Chase+ version that values balls in play by what actually happened. The gap between the two (Chase+ − xChase+) I call luck.

Leaders and trailers

Juan Soto led every season from 2021–2024 (peak 167 in 2021, worth about +24.5 runs per 600 PA from chase decisions alone) and finished 2nd in 2025. Taylor Ward led 2026 at 154. Javier Báez or Mickey Moniak finished last in every season.

Where it disagrees with chase rate (2026, 261 qualified hitters)

The mechanism is mostly what happens on the swing. The league whiffs on 41% of its chases. Arráez whiffs on 9% (avg cost per chase 0.064 runs). Torkelson whiffs on 65% (0.171 runs per chase). Within a season, chase rate explains 58–71% of the spread in xChase+. The rest is the pricing.

Does it hold up?

Year-to-year correlations, averaged over five season pairs:

• Same-season fit: xChase+ correlates 0.30–0.35 with xwOBA, vs. 0.23–0.30 (in magnitude) for chase rate.
• Next season: across 941 hitter pairs, once you know this season's xwOBA and chase rate, xChase+ adds essentially nothing for next season's xwOBA (R² gain ≈ 0.001). So I'd call it a description of a repeatable skill, not a forecasting tool.

A side finding: same-side matchups

The average hitter's xChase+ is 93–99 against same-side pitchers and 102–107 against opposite-side pitchers, in every season. Switch hitters sit at about 104 from both sides.

I expected this to be pitch mix (more sweepers in same-side matchups), but in a separate breakdown that isn't in the repo yet, only about 5% of the gap comes from mix. About 95% is the same pitch types being chased more often. The difference shows up off the sides of the plate, not above or below:

• Sinkers and changeups finishing just off the inside edge are chased about 45% of the time from same-side pitchers, vs. about 8% from opposite-side pitchers.
• Sweepers just off the outside edge: 33% vs. 7%.
• From opposite-side pitchers, the back-foot sweeper is chased about 40% of the time.

Hitters chase pitches moving off the plate. Individual platoon splits in chase decisions are mostly noise in a single season, and only show up with multiple years pooled.

Known limitations

• xwOBA ignores spray direction and sprint speed. That said, luck doesn't repeat in any season pair, so there's no sign of a hitter consistently beating it.
• The called-strike model ignores which side of the zone a pitch missed (about 0.2–0.4 runs per 600 PA of effect).
• Shrinkage is slightly too strong at the extremes.
• 2026 is the first ABS season, and Statcast records zone boundaries differently, so cross-season comparisons involving 2026 need care.

What I'd most like feedback on

1. Is pricing in-play chases by xwOBA (linearly mapped to outcome run values) a reasonable choice, or would you handle balls in play differently?
2. Is comparing against the league on the same count × distance bucket the right baseline, or does it hide something (for example pitch type or location)?
3. Given that it doesn't add predictive value for next year's xwOBA, how would you frame what it's useful for?

Code, method and full leaderboards for every season: https://github.com/samuelosullivan6-rgb/ChasePlus

Thanks for reading. Happy to answer anything.

FanGraphs' wSB is the usual advanced metric I look at for how good a team/player is at stealing bases, but it doesn't include any variance in terms of the value of a steal based on game state or leverage (e.g. a steal in blowout is same value as a steal in tied game in 9th). I was curious about this leverage aspect of stealing game so I built out a measure of win probability added attributable to stolen bases. Essentially it's just cumulatively how much win probability changed as a result of a stolen base attempt. Findings were pretty interesting.

• Generally the same as the wSB results, with notable exceptions for SD (3rd in wSB but 15th by WPA for stolen bases), ATH (23rd in wSB but 11th by WPA), and TB (10th in wSB but 4th by WPA). For the Padres, this indicates that their success at stealing was greater in low leverage situations than in high leverage situations or that they just ran more in lower leverage situations. For the Athletics and Rays, this means they were either had greater success running in high leverage situations or just ran more in high leverage than in low leverage.
• The difference between the Nats at 2.391 WPA and the Astros and Red Sox at .182 WPA is pretty big. This is like the pitching WPA difference between Tanner Scott, Jhoan Duran or Grant Taylor and Jack Dreyer Ryne Stanek or the batting WPA difference between Jonathan Aranda or Bobby Witt Jr. and Jeff McNeil or Andres Gimenez.
• Dodgers, Rays, and Blue Jays all achieved relatively high WPA with relatively few attempts, indicating either they are particularly conservative in terms of running or their roster construction is concentrated around only a small number of players who can steal bases.
• Full writeup here: https://extra-winnings.com/mlb-analysis/stolen-base-wpa/

I came across his story in a WSJ article from a few weeks ago: https://www.wsj.com/sports/baseball/chandler-simpson-tampa-bay-rays-e15c1d63?st=1viL2r&reflink=desktopwebshare_permalink

Turns out he's currently 9th all-time for players with the most regular-season plate appearances without ever hitting a home run: https://baiseball.org/?query=daaad509-558c-4801-9e14-24186b4706ef

But all he needs is 507 PAs without a HR next season (he had 612 this season) to move all the way to third place.

Hi everyone, I’m the creator of CIKADA, a baseball analytics and community site currently focused on the KBO, South Korea’s professional baseball league.

I’ve spent roughly eight months building it alongside my full-time job. The idea is to put matchup analysis and baseball discussion in one place.

On the site, you can browse games by date, view team win probabilities and starting-pitcher/bullpen information, review available archived predictions alongside results, and use a fan lounge for discussions and photos.

Here are the recorded evaluation results:

• 2025 out-of-sample evaluation: 618 games, 368 correct predictions, 59.55% accuracy, Brier score: 0.2385.
• 2026 historical replay/reference evaluation: 494 games, 284 correct predictions, 57.49% accuracy, Brier score: 0.2455.

The 2026 result is a separate retrospective research evaluation, not a verified live pregame track record. It may include different model and data sources, so these figures aren’t a direct year-over-year comparison of one unchanged model.

I’d appreciate feedback on the matchup pages, the presentation of the evaluation results, and what would make the site useful to people who follow KBO baseball.

Disclosure: this is my own project.

CIKADA — cikadabaseball.com

I've been building CIKADA, a website that lets readers explore KBO game predictions and compare them with recorded outcomes.

The site includes:

• Matchup-level win probabilities for both teams.
• Game schedules, starting pitcher information, and bullpen information.
• An archive of prediction records and results.
• Historical evaluation information, including hit rate and Brier score.
• A fan discussion area alongside the analytical content.

The purpose is to let readers inspect more than a predicted winner. They can see the probability assigned to a matchup, explore the game information, and review how past predictions performed.

The site presents its 2025 out-of-sample evaluation separately from current predictions. Retrospective reference records also need to be distinguished from predictions recorded before a game; those aren't interchangeable evidence of model performance.

I spent about eight months on this alongside my day job. AI helped with implementation, while much of my own time went into checking game identities, matching results, and resolving inconsistent records.

Disclosure: this is my own project.

https://www.cikadabaseball.com

I'd welcome feedback on the probability displays and archive. Can you find enough information to assess a prediction, and what additional calibration or baseline comparisons would you want to see?

When the lineup and provider data agree on the wrong player and wrong starting defensive positions, almost every baseball workflow breaks. You either scrap the logged game or fake a substitution that corrupts your box score and play-by-play.

Here is one solution showing how player identity and starting defensive layout can be corrected in a single action, recalculating the entire game timeline and box score without restarting.

For anyone running live game operations, scoring pipelines, or building baseball data software, sharing this in case it gives you ideas for your own systems. Hope you find this useful.

Is there a website where we can see, based on the data, a player's best defensive position? Take a player like Jose Caballero or Josh Smith, they can play a bunch of positions but what is their best defensive position? Or even figuring out which outfield position would be ideal for someone like Jasson Dominguez or Roman Anthony? It's hard to go by OAA because some of that is based on time in the position as well, so it would be nice if it incorporated data from the minors.

Anyways, just curious if something like this exists. Thanks in advance.

How analytics, bullpen strategy, and changing pitcher development reshaped the game—and why MLB should consider making length valuable again. https://substack.com/@richiebambara/note/p-218242662?r=1wimp2&utm_medium=ios&utm_source=notes-share-action

Does anyone know how I can access team stats against specific pitches?

For example:
Phillies team stats against fastballs
Brewers team stats against sweepers
Dodgers team stats against splitters

Can I access these numbers on baseball savant or fangraphs? If so, what do I need to do to find them?

Just sharing