Used Baseball Savant's bat tracking "Swing Timing" leaderboard (2026, min. 100 swings per pitcher-pitch-type combo) to check something coaches say constantly but that's rarely quantified: is a hitter's swing actually calibrated for the pitch he's *expecting*, not the one that's coming?

Split by pitch type, league-wide:

- Fastball: 1.3% of swings arrive early (when hitters miss timing here, it's almost always late)

- Sinker: 1.8% early

- Cutter: 12.8% early

- Slider: 37.2% early

- Changeup: 43.4% early

- Sweeper: 45.9% early

- Curveball: 49.0% early

The jump from fastball/sinker to everything else is enormous and pretty consistent with a "swing defaults to fastball timing" explanation — the bat gets there too soon when the actual pitch is slower.

Also split by zone height, which adds a wrinkle: at the top of the zone, early-swing rate is *low* (4.1%) and miss distance is small, but whiff rate is still ~2x the middle-zone rate. That doesn't look like a timing problem at all — lines up better with flat/high fastballs beating swing plane specifically, not timing. Low-zone whiffs, on the other hand, come with both worse timing AND worse miss distance simultaneously.

Full writeup, data, and code (Python/pandas) here if anyone wants to dig into the methodology or poke holes in it: https://github.com/ejimenezperformance/ep-pitch-recognition

Curious if anyone's seen a similar zone-height breakdown elsewhere, or has thoughts on what's driving the high-zone whiff pattern specifically if not timing.

Good for 4.5 and should throw six

I’m getting ready to release python-mlb-statsapi 1.1.0.

The big update is async support with AsyncMlb and AsyncMlbDataAdapter, while keeping the existing sync API working the same way for current users.

I also did a pretty big README refactor so the project is easier to understand and get started with.

This ended up being a much bigger project than just swapping requests for HTTPX. A lot of the work was making sure sync and async actually behave the same and don’t introduce weird lifecycle or client ownership issues.

The final PR is up and I’m pretty happy with where it landed. I just need to run a couple live MLB API tests before I merge it.

Repo:
https://github.com/zero-sum-seattle/python-mlb-statsapi

PR:
https://github.com/zero-sum-seattle/python-mlb-statsapi/pull/334

I’m in high school right now and my goal is to eventually work in baseball analytics/front office stuff.

I’ve been building a bunch of baseball projects using Cursor and AI and I’ve learned a lot from doing that, but I’m wondering how important it is that I actually sit down and fully learn something like Python or R.

Like if I can build projects and understand what the code is doing, is that enough? Or should I be able to code most things from scratch without Cursor? And between Python, R, SQL, stats, etc what would you prioritize?

Also curious about actually getting into the baseball industry. How much of it is applying to internships/jobs vs networking and knowing people?

What’s the best way to start networking while I’m still in high school? Are there conferences, events, meetups, competitions or anything like that where you can meet people who work in baseball?

Basically if you were in high school and wanted to eventually work for an MLB front office, what would you focus on learning/building right now?

Any advice would be appreciated.

Like a lot of people, I ran my baseball analysis through R piped into Google Sheets, and it was slow as hell. I finally rebuilt all of it as a real site with the help of all the new tech: https://leftslantsports.com/mlb/scores

Live box scores and gamecasts are all terrible. The only one I think even scratching the surface is Savant's VizCast, and it's hard to find, and even that spends most of its space showing you things you're already watching on TV. So I built the gamecast I wanted:

Features:

• Savant-style player performance charts, pitch locations and movement (handedness-aware)
• Stuff check: in-game velocity and spin vs the pitcher's baseline, per pitch type
• Pitcher fatigue and situational tendencies, batter analysis (situational splits still in progress)
• Matchup Lab: any batter vs any pitcher, percentile profiles side by side
• Baserunner speed on the bases-occupied graphic (small/blue = slow, half-circle/red = fast)
• Bullpen availability, scorebook-style box score, advanced team stats, and a PBP you can slow down to match your broadcast delay

Best experienced while watching a live game

I also have a tickets hub connected to google accounts, you can see a personal scorigami and player stats! Largest WPA plays, hardest hit ball and you can log which team you are cheering for if you're the type to go to a game and vibe)

So I hope you test out things and enjoy it! and would love any feedback

Sportsbooks pricing MLB games are weakest when the bullpen comes in as they can't predict who will enter the game. Pitchers can be fresh or fatigued/ they're coming in during jams.. Managers are squaring up matchups.. Tons of random stuff going on and sportsbooks have a tough time pricing it so they just kind of react. A run scores and the total goes up a run. I'll never beat the book on picking who's going to win the game, but there are gaps in their pricing when the bullpen comes in so I figured I would key in on that.

I'm looking at:

Bullpen

• Away/Home Bullpen Quality, rolling 30 day performance
• Away/Home Bullpen Fatigue, actual innings pitched across each the bullpen's last 3 games.
• Current Pitcher, real closer available or a committee with no clear arm, plus live velocity read against a 30 day baseline
  • Fluctuations in spin and velocity have a huge impact on pitcher effectiveness. We index that against their averages to see if they're struggling or firing on all cylinders

Matchup Factors

• Away/Home Upcoming Batters, expected wOBA for the specific hitters due up next
  • The amount of batters I look at changes depending on how many ABs are predicted to be left
• Park Factor, how much this specific park inflates or suppresses scoring
• Leverage, how much this exact moment matters to both teams' bullpen decisions
  • Pitchers statistically give up more hits when there is pressure

Game Situation

• Base State Threat, expected runs from who's actually on base right now
• Platoon Matchup, hitter/pitcher handedness edge
• Jam Situation, is the pitcher in a high leverage jam right now?
• Extra Innings Risk
• Blowout / Mop Up Risk, is a lower quality reliever about to enter on a lopsided score
  • Or if one team is up by 12 runs, we know the position players come in throw lobs

All of that gets weighed against the live total the books are offering. This it's too much shit to look up at once though so I was able to get a model built that takes all of this into account. I linked up a 0 latency sportsbook api for odds and connected to the mlb api for live game data and statcast advanced metrics.

This tool scans all games every 3 minutes and weights out the odds etc and makes a call (all of that data is at the bottom of the page). There is also a drop down in the tool so you can grab any game you're watching and add the live total and run it. It won't run if the current starter is in because the book has the same data we do (or better) and it only runs innings 6-9.

Give it a test and let me know what you think! I would love some feedback.
app.thebettoredge.com/demo

Let me know what you think! bAIseball.org

I wrote up why I did it and what it does here: https://networked.substack.com/p/introducing-baiseballorg.

the gist is that I wanted to look up some very specific, somewhat esoteric stats I'd read about the Red Sox' 13-inning come-from-behind win against the White Sox on August 6th:

• “The Red Sox are just the fourth team to go 26-3 or better in a 29-game span within a season in the past 70 years, and the first since Cleveland in 2017.”
• “The Sox became the first team in MLB’s modern era to score in five separate innings in the ninth or later in the same game.”

None of the available online tools I'm familiar with (Baseball Reference and even Stathead) would let me easily validate if they were true or not. So I hooked up the Sean Lahman database, Retrosheet.org's datasets, and the MLB.com API to an LLM to auto-write the complex queries for me -- and now anyone else who wants to use it.

Things you can ask include:

• Who are the top players who hit the most all-time doubles on Thursdays?
• List the top 25 team seasons by the number of consecutive games in which they hit a home run
• Who is the heaviest player to ever hit a triple?
• What is the tallest sum of heights of a single game's winning and losing pitchers?
• Give me the top 10 players by combined attendance at their away (road) games
• What is the most number of consecutive games a team has played in which either they or their opponents have hit a home run?
• Which players played in the most consecutive games for their team? Include players who switched teams during their streaks
• For the 2026 season, list every game featuring 3 or fewer runs after 6 innings but 10 or more total runs scored by the end of the game

Have fun and let me know what you think! (And definitely let me know if you spot errors.)

I understand WAR has its limitations, but it was a fun exercise putting this web app together that allows you to view PCA vs. Ohtani’s WAR numbers at their current pacing and allow for adjusting WAR inputs to see how different input stats affect each of their WAR numbers.

Supports both fWAR and bWAR. Open to suggestions to improve it or add a feature.

Full disclosure: I’m one of the founders of Ballista Tracking.

We’re building an iPhone-based baseball tracking app for controlled tee work, and the part I think this community may find interesting is the measurement problem itself.

The current workflow is:

• Record a tee swing from a fixed side-on iPhone position
• Detect contact
• Track the baseball frame by frame
• Estimate launch characteristics from the observed trajectory
• Return exit velocity, launch angle, and projected distance
• Store results so sessions can be compared over time

The main challenge is getting reliable small-object tracking across different cages, lighting conditions, backgrounds, ball blur, and phone setups.

We’re currently working through a new detector dataset with a mix of real footage and synthetic augmentation, and our next validation step is comparing the same swings against dedicated reference systems like HitTrax/radar.

I’d be interested in feedback from people here on the validation side:

• What sample size would you consider meaningful?
• Would you compare MAE, RMSE, bias, correlation, or all of the above?
• How would you stratify results across EV, launch angle, lighting, and camera conditions?
• What failure cases would you explicitly report instead of filtering out?
• What level of error would you consider practically useful for player development?

We’re documenting the technical work, testing, and results at r/BallistaTracking for anyone interested in following the build, I just started it but it will be really helpful to document issues or feedback you may have.

The app allows for unlimited swings for free forever so do not expect a paywall.

Website: ballistatracking.com

I understand WAR has its limitations, but it was a fun exercise putting this web app together that allows you to view PCA vs. Ohtani’s WAR numbers at their current pacing and allow for adjusting WAR inputs to see how different input stats affect each of their WAR numbers.

Supports both fWAR and bWAR. Open to suggestions to improve it or add a feature.

Hey everyone,

Like many of you here, I’ve always been fascinated by deep baseball analytics and how stochastic modeling can give us a clearer picture of game outcomes beyond surface-level stats.

Over the past few months, I’ve been working on an independent project: an app that runs 10,000-iteration Monte Carlo simulations for daily MLB matchups. It evaluates advanced metrics (wRC+, FIP, bullpen usage, park factors, etc.) to project win probabilities and key match scenarios.

I built this tool to speed up my own daily analysis, but I wanted to share it with this community to get real feedback from fellow stat nerds and fantasy managers.

Key features:

• Daily match simulations based on updated lineups and pitch matchups.
• Breakdown of key sabermetric indicators.
• Fast, lightweight interface built for quick pre-game analysis.

The app is free to download and use (it's ad-supported, with optional in-app purchases/subscriptions for advanced features to help cover API and server costs). I’d really appreciate it if you could test it out during today's games and let me know what features or data points you’d like to see added next!

Link to check it out: https://play.google.com/store/apps/details?id=com.app.sabermetricsformlb&pcampaignid=web_share

Thanks for taking a look, and I’m happy to answer any questions about the model or methodology in the comments!

I pulled platoon splits for every pitcher with 40+ IP this season

(341 of them, FanGraphs pulled Aug 25) and one clean pattern fell out.

xFIP is usually the first place people look for a platoon split. It's a

bad first look, for a specific reason: it blends strikeouts, walks and a

normalized HR rate into one number, so the components can cancel.

Of the 169 starters with 10+ starts, 20 have a K% gap of at least 8

points between LHH and RHH. In 15 of them the walk rate moves the

opposite way — more strikeouts AND fewer walks on his better side — so

the effects stack and xFIP shows the split clearly.

In the other 5, the walk rate climbs on the same side the strikeouts do.

Those cancel inside the estimator. Four of the five end up with an xFIP

gap under 0.75, above the 0.66 median for the group:

Payton Tolle (BOS) K% 18.6 / 31.5 BB% 5.0 / 7.6 xFIP 4.01 / 3.50 (0.51)

Jacob Misiorowski (MIL) K% 44.8 / 32.1 BB% 6.5 / 5.4 xFIP 1.83 / 2.47 (0.64)

Logan Gilbert (SEA) K% 29.9 / 21.4 BB% 7.6 / 4.6 xFIP 3.37 / 3.86 (0.49)

Cam Schlittler (NYY) K% 34.0 / 25.6 BB% 6.5 / 5.0 xFIP 2.71 / 3.20 (0.49)

(vs LHH first in each pair.)

Check any of these on xFIP alone and none of them look like platoon

pitchers. Tolle's K rate jumps 13 points against righties while his xFIP

moves half a run.

Not saying xFIP is broken — it's doing exactly what it's built to do.

But if the question is specifically "does he miss bats against this

lineup," a run-prevention estimator is the wrong instrument, and it fails

in a predictable direction: whenever command and stuff move together

across the platoon.

I have spent the season developing a quantitative sports model that isolates weather ballistics, air density carries, and pitcher rest cycles to exploit mispriced totals. We trade these strictly as regulated event contracts on Kalshi.

The model is currently sitting on a documented 51-31 overall record (a 62.2% win rate) over our last 82 plays.

The math is proving itself, but trying to run the daily projections, build out a community, and keep up with social media by myself is becoming a massive bottleneck.
I am looking to connect with a few like-minded, active sports bettors who want to partner up and help build this project out.
Specifically looking for:

Community guys who love talking sports, analyzing boards, and want to help manage a live chat room during the games.

Social/video editors who can help turn our daily spreadsheets and winning Kalshi slips into clean content for TikTok and X.

Research co-pilots who want to work directly with the model to track bullpen fatigue splits and late lineup variables.

If you live and breathe sports betting, understand expected value, and want to collaborate on a high-performing model, drop a comment below or send me a DM and let is get to work!

I’ve been working solo on a KBO prediction model for about five months now.

It started as a curiosity project, but it slowly turned into a pretty serious backtest / calibration study. I’m not doing this for a company or a betting group — just a personal quantitative project that got much deeper than I expected.

Right now, the locked internal model track is a fairly simple pre-game model. The current 2025 out-of-sample test looks like this:

- 629 KBO regular-season games

- Variant B: Brier 0.2427, hit rate 57.6%

- Variant D: Brier 0.2401, hit rate 58.3%

These are not final multi-year numbers. They are only from the locked 2025 OOS common-universe test. The reported Brier may move once I run the same locked protocol across 2024–2026 and once the final Pinnacle joined sample is fixed.

The numbers look decent internally, but I’m trying not to fool myself.

A model can look good against results and still have no edge over the market. So the next step is to buy historical Pinnacle KBO moneyline data and compare my locked model probabilities against Pinnacle closing lines on the exact same games.

Honestly, most of the work has not been “adding more features.” The hard part has been trying to make the comparison clean:

- making sure features are pre-game only

- avoiding post-game or post-start information

- handling doubleheaders properly

- handling ties / voids correctly

- comparing model and market on the same joined sample

- not changing the model after seeing the market data

- checking timestamps so that odds really existed before first pitch

That last point turned out to be more important than I expected. I checked an old KBO odds sample from another source, and many of the Pinnacle rows had been updated after first pitch. If I had used them blindly, the market looked absurdly accurate — something like an 88% hit rate. Obviously that was not a real pre-game market signal. It was a timestamp problem.

That made me slow down and lock the comparison rules before looking at the real Pinnacle data.

So the plan now is:

- seal the model variants and inclusion/exclusion rules

- pull Pinnacle full-game KBO moneyline closing data

- filter only prices that existed before first pitch

- exclude ties/voids from the binary Brier comparison

- compare model Brier vs Pinnacle closing Brier on the same games

- call it inconclusive if the joined sample or timing checks fail

The final result might be no edge. It might be inconclusive. Or maybe both locked variants beat the closing line on paired Brier.

I don’t know yet — that’s the point of doing this properly.

Mostly sharing because I think this is the part of sports modeling that people underestimate. Internal OOS results are useful, but at some point the model has to face the closing market.

Has anyone here done similar closing-line validation for a smaller baseball league or a less-covered market?

Just a heads-up that this is quite long, the most extensive post I have done here. The next six paragraphs all focus on my methodology but if you just want to skip to the results, the last two paragraphs are for you.

I am sure that I am not the only one who finds it a little annoying that FG and BRef do not list postseason WAA/WAR on their sites, though I guess I understand it. Still, that factual circumstance has led me to think about how we might be able to objectively judge the greatest postseason runs. WPA and cWPA are fantastic metrics to use, but they are also contextual. While there is nothing inherently wrong with taking context into account to determine how well a player performed, this can result in situations where the game state, mostly out of the individual player's hands, allows for astronomical swings. For instance, in Game 6 of the 2011 World Series (which absolutely kills me to have to revisit as a Rangers fan), Lance Berkman singled on a 2-2 down 9-8 with 2 outs in the bottom of the 10th. This hit increased the Cardinals' odds of winning Game 6 by 26% and the championship by 25.42%. It is one of the most consequential at bats in World Series history, 36th all-time in cWPA according to Baseball-Reference. While Berkman of course deserves credit for coming through in the clutch, he would not have been in this spot if the batters in front of him did not do their job. Similarly, Mariano Rivera had a 22.96% cWPA in the 2000 World Series. His 3.00 ERA was definitely good for the era, but is it worth over 1/5th of a chip in isolation? Of course not, and it's why we use WAR and WAA for MVP discussion and all-time rankings and not regular season WPA or cWPA. With that in mind, my goal was to derive WAR and WAA (mostly WAA) to determine which player on purely his own merit (or as close to as possible) helped his team the most in the postseason.

The African Bull Elephant in the room is that I have nothing to which I can compare my derived figures. FG and BRef do not calculate postseason WAR/WAA (or at least they don't publicize it), so while I can at least do sanity checks on my derived regular season win calcs, I do not have that luxury with the postseason. Still, I think my derivations are mostly trustworthy seeing that I use the same methodology except some slight tweaks. For one, I adjusted the PFs to take out the home/road adjustment that both BRef and FG utilize for full-season PFs. This is because due to the very small sample size, while on average a player will participate in roughly the same number of home and away games, that may not be the case in the postseason. For instance, Randy Johnson pitched 6 games in the 2001 postseason, but only 1 away from home, so he pitched 5 games in super hitter-friendly Bank One Ballpark, which according to Baseball-Reference, had a one-year PF of 120 unadjusted for the home/road splits, which is absolutely wild. I think Johnson should get credit for that rather than just using the reduced PF, and likewise, if a pitcher or hitter played in a super-friendly pitching park, their value-added should accordingly reflect that. That is what I have done and I think it adds value to the calc.

The other major adjustment I made was for how to treat the oftentimes extreme divergences in the postseason vs. regular season scoring environments. Fortunately, the get the data, it was not nearly as onerous as I originally thought, where I went to FanGraphs and downloaded the postseason statistics for the AL and NL since 1903, concentrating on only two data points: runs scored and games played. When comparing to the regular season for that same year, the postseason has seen about 0.59 fewer runs per game than the regular season. Since it is such a small sample size, I went ahead and took an 7-yr. rolling difference, where the seven prior year deltas, current year delta, and seven subsequent year deltas were evenly weighted, summed together, and then that average delta was halved by 50% so as to further reduce noise. This is important because, for instance, had I not halved it, the 1925 World Series would have an 11-year rolling difference of a whopping -1.24 runs (postseason runs per game - regular season runs per game). I think reducing the effect of this variation based on admittedly small sample sizes is the best way to proceed. Still, for the purposes of my calculation, which is solely concerned with the postseason, I think it is helpful to have his adjustment in order to see how much more impactful player A was to the postseason field rather than the regular season field. I am not using this analysis to add or detract from my own personal all-time rankings, but moreso for comparing which player had the better postseason run or the "best/most impactful" postseason ever. All right, with that out of the way, let's talk about some noteworthy postseason runs.

When I think of all-time great postseason performances, not just for a moment (Hal Smith in Game 7 of the 1960 WS), not just for a series (Reggie Jackson during the 1977 WS), but extended runs, four come immediately to mind: Curt Schilling in 2001, Barry Bonds in 2002, Carlos Beltran in 2004, and Madison Bumgarner in 2014. Albeit, all of these occurred in either the LDS or expanded Wild Card era, and all are fairly recent (so feel free to add your own in the comments), but it is undeniable that these guys were absolutely phenomenal in the postseason. Their surface level stats are incredible enough: Schilling and MadBum pitching to 1.12 and 1.03 ERAs while Bonds and Beltran accruing over a 1.550 OPS in 17 and 12 games. But which one was best in a vacuum? We know that Bumgarner had the highest cWPA by far of any of the four, an impossible-to-believe 96.29% over his 52.2 innings, which, just, wow. But again, while his ERA was stupendous, he was also put in situations by his offense not scoring a ton that made the cWPA figure so prone to wild swings, in his case, all positive through his great pitching. But what about his performance void of the game state context and adding in the offensive environment? What about his FIP and what FG would say he "earned"? These were some of the Qs I tried to answer in the analysis. Let's dive into the details.

Bumgarner's FIP was 2.62 in the 2014 postseason, which while really good, pales in comparison to his 1.03 ERA. I got a 337.8 ERA+ and 131.2 FIP+ for MadBum that year while FG gives him a 30 ERA- and 75 FIP-, so roughly the same inverted. FanGraphs converts its FIP values to a RA equivalent by taking the delta between ERA and RA and adding it to FIP, which meaning that league-average FIP is always set to league average ERA, means for the purposes of its WAR calc, FIPr9=RA. Bumgarner had a 2.62 FIP, but because we have to adjust for the RA-ERA delta, since FIP is defense-neutral, his FIPr9 was 2.98 compared to the league average 3.81 mark (which as enumerated in the fourth paragraph, is adjusted for the post-reg. season scoring rolling average delta). This means that MadBum was "worth" about 0.84 fRAA per 9 innings. The post-adjusted Runs per out for the 2014 postseason was 0.141 runs/out, so if you take Bumgarner's 2.98 FIPr9 and divide it by 27, or the # of outs in a CG, we see that he allowed 0.110 fRuns per out (FanGraphs runs per out). Per both BRef and FG, you get the delta of Lg runs per out (0.141) and pitcher runs per out (FIP-based value of 0.110) and multiply by 26.8. This results in a value of 0.771 fRAA per game for Bumgarner. Note that Bumgarner's value already takes into account park factors and his role as a starter vs. reliever. If we multiply 0.771 * 7, the # of games Bumgarner participated in during the 2014 postseason, we will arrive at a value of 5.4 fRAA, or FIP-based Runs Above Average. Using the PythagenPat method gives us a waaWL% of .596, so subtracting .500 (an average pitcher) and multiplying by the # of games at 7 gives him an fWAA of 0.67. Of course, his 1.20 RA fares much better in this analysis, as undergoing the same adjustments and derivations results in his garnering 2.26 bWAA per Baseball-Reference's methodology off the back of an incredible 2.349 bRAA per game. BRef methodology also credits/debits the pitcher for performance of the team's defense, which I did here as well.

Now, he comes the most important easily most annoying part of this. Unlike in the regular season, we know exactly how many games a team has to win given their seeding in the postseason in order to win that season's championship; the 1906 Chicago Cubs won 116 games over 152 regular season contests but could not win 4 of 7 against the White Sox at the end. The 2014 Giants had to win 12 games to capture the World Series, since they were in the one-game WC round against Pittsburgh. But again, we are concerned not with the Giants themselves but an "average" postseason team. So how many games would an "average" postseason team in 2014 have to win in order to become champions? Well, this is where it gets quite annoying for 2012-2021 (excluding the nonsense of the 2020 postseason format). 10 teams qualified for the postseason in that timespan and 6 teams got a bye from the WC round, meaning that they had to win 11 games. 4 teams participated in the WC round, meaning they had to win 12 games. So the "average" postseason team would have had to win 11.4 games (40%*12 + 60%*11). Alright, with 10 teams in the postseason, each team has 10% odds of victory, right, hard part is over? Well, not quite, because if you think about it, there really aren't 10 teams in the postseason from a practical standpoint. Sure, 10 teams qualified for the postseason from 2012-2021, but two of those teams existed solely in the WC round, meaning they could not offer any sort of resistance to the 6 teams that had a bye to the LDS. This means that those 6 teams only played in a postseason with 8 teams total, including themselves. So really, 6 teams had a 1/8 chance of winning while 4 teams had a 1/10 chance of winning. So what would be a "average" postseason team's chances of winning. Well, using weighted average, we would get 8.8 teams in the postseason, (4/10 * 10 teams) + (6/10 * 8 teams). This gives the average team roughly a 11.3636% chance of winning the World Series from the outset. For reference, if were were to do this for 2022-present, it would be the case that 8 teams would have a 1/12 chance of winning the World Series and 4 teams would have a 1/8 chance because now only 4 teams get a free pass to the LDS and twice as many teams have to play in the WC. The good part is that technically, even if my calculations are completely wrong on the likelihood of a hypothetical average postseason team winning the WS, the below calculation will still work because it can also isolate the player's individual performance separate to the a-priori likelihood. What is important is that I have the calculation correct for the amount of wins a hypothetical average postseason team would have to get in order to win the World Series.

I get that I have written A LOT, but we are finally at the results portion of my post, yay. MadBum's 2014 postseason was worth 2.26 bWAA per my calculation. In 2014, the average postseason team would have to win 11.4 games. If you take 2.26 and divide it by 11.4, then Bumgarner increased an otherwise average postseason team's chances of winning the World Series by 19.80% (Baseball-Reference Championship Probability Added, or bcPA for short), which is insane. Considering that this is entirely non-contextual, it's just wild, beyond wild, really. His fcPA was 5.91% which we get by 0.67/11.4, still really, really impressive. But neither of those four figures (bWAA, fWAA, bcPA, and fCPA) are the best of all-time. With what I could come up with, Curt Schilling's 2001 postseason reigns supreme. Schilling had a 1.12 RA (and 404.7 ERA+) in those playoffs while pitching four of his six games in an absurdly hitter-friendly ballpark. Credit to his defense, as he was helped to the tune of 0.23 runs per game, but that still puts his PF-adjusted RA at 1.08. The postseason run-scoring environment adjustment reduced the 4.75 runs per game to 4.52, but that still left Schilling with a nigh-impossible 3.395 bRAA/G, which works out to 2.44 bWAA over the course of his 6 games. For fWAA, he earned 1.77, so still elite. If we take his 2.44 and divide it by the 11 games required for a 2001 postseason team to win the World Series, it comes out to a bcPA of 22.16% (minor rounding adjustments). That is the highest cPA I could find during my analysis, beating out MadBum by roughly 2.36%. These two stand alone amongst pitchers in my analysis for what I have seen, but we shall see what happens in future postseasons. Suffice it to say, it is going to be tough to eclipse either of them.

Now, for hitters. The aforementioned Bonds and Beltran were terminators those runs, Bonds getting a 336.3 OPS+ while Beltran had a 296.8 OPS+. Using standard WAR calculation, Bonds got 1.69 WAA while Beltran got 1.39 WAA, the former having a 15.40% cPA and the latter a 12.62% cPA. However, we have to remember that Bonds played in 5 more games that did Beltran, so on a per game basis, Bonds had a 0.89% cPA and Beltran had a 0.97% cPA. I can't really think of any more impressive volume position player postseason runs, but hey, we shall see what 2026 and beyond holds.

I’m trying to track down a specific game, hoping someone can help. Can anyone with a retrosheet database or stathead subscription do a search for me?

Looking for any Brewers home game between 1987-1991 where B.J. Surhoff struck out with the bases loaded.

I know he was a fairly high contact hitter, so hoping there are only a few possibilities.

Looking for dates, opponents, box score links. Any help is greatly appreciated. Thanks!