bA

bAIseball.org – Google for historical baseball stats

Hacker News

bAIseball.org – Google for historical baseball stats

I launched a new website, bAIseball.org. Think of it as Google for baseball stats: it answers just about any freeform question you might have about baseball statistical history from pre-1900 through the current season, using free data sources like the Sean Lahman database, Retrosheet.org, and MLB.com. Questions it can answer include (but are certainly not limited to): - 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

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, single · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: google, answers, way · Missing: mobile apps, ios, personal
62%62% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

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