The Disappearing Home-Court Advantage: A Series

Draft

In the 1980s, NBA teams won about 65% of their home games. Today it’s about 55%, and hosting no longer rescues a weaker team the way it once did.

The long report is split here into eight shorter reads. Start at the top, or jump to the suspect you came for.

1. Home Court Advantage Is Fading: the case laid out, how much home court advantage has fallen, and why hosting stopped saving the underdog.
2. The Whistle: the friendly whistle was real in every decade; watch it fade as officiating got fairer and more uniform.
3. The Three-Point Suspect: the move to the arc is the obvious culprit, so how much of the crime does it actually cover?
4. The Mystery on the Glass: the single biggest driver is rebounding, and it is the one the data can measure but not explain.
5. The Alibis: the prime suspects (travel, crowd size, pace, rest, nearly every rule change), nearly all of them with airtight alibis.
6. The Playoffs Are Different (and the Same): home court advantage still swings a single playoff game, but a best-of-seven absorbs most of it, and the regular season hides an opposite twist.
7. The Buildings That Still Bite: why Denver and Utah stay hardest to win in, and why blowouts keep growing as home wins shrink.
8. How I Know This Isn’t Made Up: the battery of checks built to break the findings, and why they held.

Want the receipts? The last article, How I Know This Isn’t Made Up, links every check and the full investigation, for anyone who wants to verify a claim rather than take it on faith.

• One-Page Summary: the four questions answered on a single page, if you landed here first.
• Related Work: the wider academic and journalistic landscape, and where this report agrees, disagrees, or fills a gap.

About this series.

I’m trying to investigate questions about the NBA that deserve deep dives, and to see what can be understood with some statistical analysis. I’m starting out with this set of questions about NBA Home Court Advantage, but I hope to do more. I want to answer questions faster than I can write text, so I’m having AI/LLMs write most of the text.

Especially for this question, I started with data and analysis, everything is grounded in the data analytics. I have gone back-and-forth with AI/LLMs for many dozens of hours, making them make things clear so that I understand. AI/LLMs are more engaging and clear and concise than I could write. The result is also much better than AI/LLMs could do on their own. Never one prompt. Many prompts, many questions, asking for clarity and making sure it is referenced in fact.

AI/LLMs are not doing any of the calculation, that is Python using standard Python methodologies. Interpreting those results are AI/LLMs, but I make them make me understand, and I am counting on that being correct.

I’ve done everything I can think of to force it to be correct.

The numbers behind every article are the same public data: every NBA game result since the 1983-84 season, pulled from NBA.com’s stats with the open-source nba_api tool.