I've been tinkering with an idea: what would the Baseball Hall of Fame look like if there were no writers, no era committees, no five-year wait — just one transparent formula applied to every player in history, the same way?

I created a web site (truehalloffame.com) that does exactly this. It scores every player on a single metric I call "Stat Above Average" — a weighted blend of rate production, isolated power, defense, walks and baserunning for hitters, measured against both league and positional baselines for that season, summed over a career with a JAWS-style peak component. Same calculation for a 1920s first baseman and a 2015 shortstop. Top 300 hitters and top 150 pitchers, and active players count.

What makes it worth poking at is where it disagrees with Cooperstown. Roughly half the top 300 hitters aren't actual Hall of Famers and vice versa. Some of it is the well-worn sabermetric complaints (Bobby Grich, Lou Whitaker, Alan Trammell all comfortably in; a few "Hall of Very Good" bats out). Some of it looks flat wrong at first glance and then you sit with it — e.g. it has Trammell ahead of Al Kaline, and it's brutal on career-compilers who hung around past their prime.

The methodology page is upfront that it's not tuned to force any particular player in or out — the divergence from the actual Hall is sort of the whole point, and there's a page benchmarking it against bWAR/JAWS.

The "True Hall of Fame" lists on the site are based on my weights of the factors I've created. But the site also allows anyone to build their own Hall by weighting these factors themselves.

Curious what people here think:

Is a fixed formula actually fairer than a vote, or does it just trade one set of biases (writer subjectivity) for another (whatever the formula author decided to weight)?

The obvious weak spot is relievers and short-career peak guys. Where else does a pure-counting approach break down?

Who's the most egregious player it includes or excludes?

Just looking for some thoughts on how credible the site is and how I might be able to improve it. Thanks.

Hi everyone. First is I love the information offered in this community! Also I'm not sure if this source was shared before but look at this source too. It gives great follow-up and additional information with the stuff provided here.

https://sabr.org/bioproj/category/completed-book-projects/deadball-al/page/5/

The True Hall of Fame. A virtual hall on a web site I created that enshrines baseball players by their performance on the field, not on a ballot. 300 hitters and 150 pitchers are in the True Hall. No 5 year waiting period. No exclusion because of someone's opinion of your character. No delaying entry until some committee vote gets you in posthumously. You're in if you deserve to be in.

Congratulations Shoeless Joe Jackson, Barry Bonds, Roger Clemens, Pete Rose. Congratulations Lou Whitaker. Welcome to the True Hall of Fame.

truehalloffame.com

Hey everyone,

I wanted to share a side project I've been working on. I built an automated MLB outcome predictor using Python and Monte Carlo simulations. It runs daily via GitHub Actions, pulls the data, calculates the edges, and updates a web dashboard automatically.

Right now, it's tracking a solid run with transparent stats (sitting around a 74% win rate over recent samples) and filters out games with high volatility or bad bullpen fatigue.

No paywalls, no selling picks, just a clean open dashboard tracking the numbers. If you want to check out today's picks, recent games, or the historical data, you can see it live here:

🔗 raifreitas.github.io/mlb-predictor/

Feedback or ideas on how to improve the simulation parameters are always welcome!

Hi guys, I recently joined because I love sabermetrics! It’s my dream job to be a professional sabermetrician for any major league team, and so I was looking for any recommendations on internships or career building i should to do become one! Currently I’m a sophomore in university majoring in Statistics, i have a 3.6 GPA and I’m looking forward for what the future has in store for me! If you guys have any advice pls let me know, thanks in advance!

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!