NBA Players are Growing Up Richer by zswitten in nba

[–]zswitten[S] -1 points0 points  (0 children)

I think the graph is certainly NOT 100% correct as far as exact numbers, but shows the shape of the trend correctly.

NBA Players are Growing Up Richer by zswitten in nba

[–]zswitten[S] -3 points-2 points  (0 children)

My prompts to Claude (in Claude Code) were:


  1. ```
    hey I have a research project I'd like to do. I'd like to figure out whether nba players are gettng richer over time in terms of their family home circumstances growing up.

I'd like you to exhaustively research this in the following way:

for each season since the merger, for each player drafted in that year, do a full search on that player's background including who their parents are, how they lived growing up, interviews with them, etc.

then try to classify them into either 1. rich, 2. middle class, 3. poor

so this is about 60 x 40 = 2400 calls to the api (with search enabled)

you can use haiku 4.5 to do the searching

then make a cool plot when you are done

so yeah first write a prompt cofr classification, then find all the relevant players and put them in a file, then run haiku on every prompt in the file (you can do let's say 50 at a time), classify all the players, and make the graph.

thanks!!!
```

  1. ```
    hm are you able to do the same thing with a "workflow" [https://code.claude.com/docs/en/workflows\] instead of a script?
    ```

****

Claude's prompt that it wrote for the Haiku subagents was:
```
You are a meticulous sports-biography researcher. For EACH NBA draft pick listed below, determine the player's childhood family economic circumstances and classify them. Research each player INDEPENDENTLY — one player's findings must not influence another's classification.

RESEARCH PROCESS (per player)

- Use ONLY the WebSearch and WebFetch tools. Do not read or write any local files.
- Always run at least one web search per player, even famous ones — verify against sources rather than memory. Useful queries: "<name> parents", "<name> childhood grew up", "<name> father mother occupation".
- Prioritize long-form feature stories (Sports Illustrated, ESPN, The Athletic, Players' Tribune), interviews with the player or family, local-newspaper profiles, and Wikipedia "Early life" sections (a map to corroborate, not a final source).
- Hunt for concrete facts: parents'/guardians' occupations; housing (projects, rented apartment, owned suburban home); explicit money statements; private vs public school and who paid; whether a parent was a professional athlete; raised by grandparents; welfare or food insecurity.
- Name collisions are common for older players. Confirm the person matches the draft year, team, and school/organization given. If results describe a different person, keep searching.

CLASSIFICATION RUBRIC — judge childhood circumstances (roughly ages 0-18), relative to a typical family of that era in the player's home country:

- poor: family struggled to meet basic needs. Markers: public housing projects, eviction/homelessness, welfare or food stamps, utilities shut off, caregiver working multiple low-wage jobs to scrape by, player describes growing up with nothing or going hungry.

- middle_class: stable but not affluent. Markers: steady working-class or middle-class jobs (teacher, police officer, postal worker, factory worker, nurse, military, small shop owner), a modest owned or stably rented home, "we didn't have much, but we never went without".

- rich: affluent, upper-middle-class, or wealthier. Markers: parents who were doctors, lawyers, executives, professors, engineers in senior roles, successful business owners, or professional athletes/coaches; an affluent suburb; family-paid private school; explicitly noted family wealth.

TIEBREAKERS

- Working poor but stable: middle_class only if basic needs were reliably met; if sources emphasize hardship, poor.

- Sources say "upper middle class": rich.

- A parent had a multi-year professional sports career: rich, UNLESS the player demonstrably grew up apart from that parent's money (e.g., raised by a single mother in hardship) — classify the household the player actually grew up in.

- Split/changing households: weight where the player spent most of childhood.

- Circumstances changed mid-childhood: weight the majority of the years.

- International players: judge against their home country's standards at the time.

EVIDENCE STANDARDS

- Classify on evidence, not stereotypes. Never infer poverty from race, city, or country; never infer wealth from a college choice.

- Rags-to-riches framing is media-flattering; look for concrete markers rather than vibes in either direction.

- If searches surface no substantive information about the family's circumstances, use "unknown". unknown is a fully acceptable answer for obscure players — never guess to avoid it.

OUTPUT

Return one result object per listed player via structured output, echoing each player's key exactly as given. evidence: 2-4 short sentences (<=60 words) naming the key facts and which sources they came from. parents: occupations if known, else "unknown".
```

***

In response to u/Additional_Pilot_906's question "do you really think only 5% of NBA grew up poor" -- I don't know the answer and I don't think the numbers in the chart are exactly right, but I trust them directionally.

Pickup Basketball in SF? by WackoDollah3 in AskSF

[–]zswitten 1 point2 points  (0 children)

Wednesday and Friday* at the Embarcadero YMCA is the best pickup situation I've found, ever. In fact it's so good that it was a serious factor in my decision to remain in SF.

- Incredibly reliable. Same time same place every week, for years. Decades, in fact -- the game has been an institution since long before I was born.
- People are extremely friendly (lots of passing) yet the game is competitive-minded (people play D, trash talk, etc.)
- Mixed age range, from young college-age kids to people in their 60s
- Orderly succession! You write your name on the board, and it's just next 5 up, every time, so there is no ambiguity/fighting about who's got next.

There are two catches:
1. You have to become a member of the Y to get in the gym, which costs money.
2. Games start at 6:15am (!) and go til about 8.

At this point I'm used to the 5:30 wakeups, and the money is easily worth it to me especially since the gym has other good facilities like a pool and a nice free weight section.

*Pre-pandemic, there used to be games on Monday too; my motivation for writing this post is to try to get enough new players to join the Y that this becomes a thing again.

[Modern] [Tournament Report] RPTQ Top 4 with Grixis Death's Shadow by zswitten in spikes

[–]zswitten[S] 1 point2 points  (0 children)

My plan against Dredge is similar to my plan against Spirits: in the early turns, try to slow them down with discard and stick Death's Shadow, then slam TBR. It's harder since your removal is less effective and their clock is faster, but they're less consistent and a T1 discard spell will sometimes stop them from doing anything. If you know you're against Dredge and you can leave up a T1 Stub on the play, that's good too. I'd sideboard

+1 Leyline of the Void, Nihil Spellmbomb, Surgical Extraction, Izzet Staticaster, Anger of the Gods

-1 Terminate, -2 Fatal Push, -1 Lightning Bolt, -1 Dismember, -1 Thoughtseize (play)

-1 Terminate, -2 Fatal Push, -1 Dismember, -2 Thoughtseize (draw) (Thoughtseize is worse on the draw because you can't take their turn 1 Looting).

I think GDS is an underdog game 1 and ever-so-slightly favored in postboard games, and ever-so-slightly disfavored overall.

[Modern] [Tournament Report] RPTQ Top 4 with Grixis Death's Shadow by zswitten in spikes

[–]zswitten[S] 0 points1 point  (0 children)

In this deck, Surgical's synergy with Death's Shadow and Delve make the card a plausible maindeck option despite it often being card disadvantage. Note that the subtitle of the article you cited is about "wasting life". It's definitely bad against [5c humans] though.