Torpedo Bats. Tournament Model and PASE Leaderboard. NBA Future Rankings.
Issue One Hundred And Eighty Four
Welcome
Second issue with Substack. Was very pleased with how the first one went, and the overall post creation process. Has definitely restored an energy I was depleted of in creating issues each week.
This is a great week for sports! Three great Final Four games, and now we’re primed for The Masters (no teaser articles for it, I’ll correct this going forward). Now only if Mother Nature would get the hint and have winter see its way out for good for those in the north.
Keep the resource recommendations coming, thanks!
Issue Summary
From baseball's bizarre new bat tech and the physics behind its potential, to an AI model that achieved a solid 64% accuracy predicting March Madness outcomes, and a revealing look at which college hoops programs have truly dominated the tournament landscape. Plus, peer a decade into the NBA's future to see which franchises are primed for a dynasty run.
Weekly Unexpected Quote
"The pitcher has got only a ball. I've got a bat. So the percentage in weapons is in my favor and I let the fellow with the ball do the fretting."
Hank Aaron
This Week's Lineup
Torpedo Bats in Acquisition
Torpedo bats, with their redistributed mass, represent a simple yet potentially impactful baseball innovation, leveraging permissive rules to optimize bat performance based on individual swing mechanics and situational hitting, though adoption hinges on player acceptance and potential rule adjustments. fangraphs.com
The Physics of the Torpedo Bat
A physics-based analysis of "torpedo bats"—designed with reduced mass at the tip and increased mass at the sweet spot—suggests that while the peak exit velocity remains similar to traditional bats, the torpedo bat offers a wider sweet spot and better performance on inside pitches, likely due to altered vibrational properties resulting from the unique mass distribution, though experimental validation is still underway. fangraphs.org
Building a March Madness Model using XGBoost
Outlines how to build an XGBoost model in Python, leveraging the CBBD API for NCAA basketball data, to predict March Madness game outcomes with approximately 64% accuracy in the 2024 tournament, also illustrating its use for forecasting hypothetical future games. collegefootballdata.com
Men's Tournament Wins Leaderboard and PASE
Florida's recent title shakes up the post-1985 NCAA men's tournament wins leaderboard, with Duke, UNC, and Kansas leading. The article also dives into updated "Performance Against Seed Expectation" (PASE) stats, revealing surprising insights about top programs. johngasaway.com
NBA Future of the Franchise Rankings
After a chaotic NBA trade deadline,
ranks all 30 teams by their projected likelihood of winning championships over the next decade (2025-2034). The analysis prioritizes title contention, evaluating current rosters and future prospects based on factors like star players, draft picks, and organizational strength. natesilver.netSelf-Promotion
I am building +EV Bets, which identifies positive expected value (+EV) in the major sports betting markets based on popular prediction models and live betting odds. MLB is active, along with my own starting pitcher strikeout props. Check it out, let me know what you think.
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