Stuff I Re‑read

Caleb Ortiz

@boxscorecaleb

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These are the papers I end up dragging into bar arguments, whiteboard sessions at the office, and group chats during the fourth quarter. They’re not all about sports, but they all change how you see a box score, a betting line, or a bad coaching decision. Think of this as my personal playbook: a mix of hot-hand myths, weird human psychology, and the math that quietly runs every season we watch.

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Stuff I Re‑read — 8 items
Name	Authors	Year	Field	Why it matters	My take
1The Hot Hand in Basketball: On the Misperception of Random Sequences	Thomas Gilovich, Robert Vallone, Amos Tversky	1985	Psychology / Behavioral Statistics	Argues that the beloved “hot hand” in basketball is mostly our brain seeing patterns in random streaks.	This is the classic that ruins barstool myths. You read it, you look at a 10–0 scoring run, and you immediately wonder how much is real momentum versus coin-flip chaos.
2Prospect Theory: An Analysis of Decision under Risk	Daniel Kahneman, Amos Tversky	1979	Behavioral Economics	Shows how people hate losses more than they love equivalent gains, and why we make weird, inconsistent choices under risk.	Once you see these value curves, you can’t unsee them in sports: coaches punting scared, GMs avoiding trades, fans melting down over a blown 10‑point lead like it’s the end of the world.
3Regression Shrinkage and Selection via the Lasso	Robert Tibshirani	1996	Statistics / Machine Learning	Introduces LASSO, a way to pick out the truly important variables while keeping models simple and avoiding overfitting.	If you’ve ever tried to jam a hundred tracking stats into one model and gotten mush back, this is the paper that teaches you to cut dead weight like a GM on deadline day.
4Statistical Modeling: The Two Cultures	Leo Breiman	2001	Statistics / Data Science	Calls out the divide between traditional parametric statistics and algorithmic modeling, pushing toward prediction-focused methods.	Feels like a locker-room speech for data people: stop worshipping pretty equations, start caring if your model actually calls the game right on Tuesday night.
5The Baseball Players’ Labor Market	Gerald W. Scully	1974	Sports Economics	Uses economic modeling to estimate player marginal revenue product and show how underpaid players were under the reserve clause era.	This is where “player value in dollars” starts to feel real. Every heated debate about whether a contract is an overpay is basically living in Scully’s shadow.
6The Strength of Weak Ties	Mark Granovetter	1973	Sociology / Network Theory	Shows how loose, casual connections spread information better than your tight inner circle.	Explains everything from how rumors fly through a fanbase to how one random scout report changes a draft board. Your group chat is loud, but the weak ties move the market.
7Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers	Joshua B. Miller, Adam Sanjurjo	2018	Statistics / Behavioral Economics	Re-examines the original hot-hand work and shows that the classic tests undercount streaks because of subtle sampling bias.	This is the plot twist. After decades of saying the hot hand was a mirage, this paper basically says, “well, actually…” and gives hoop fans some ammo back.
8Computing Machinery and Intelligence	Alan M. Turing	1950	Computer Science / AI	Introduces the idea of machines “thinking” and proposes the famous imitation game, now called the Turing test.	Feels wild, reading this and then looking at modern models picking plays or generating scouting notes. It’s like reading the league’s original rulebook and then watching today’s pace-and-space offense.