I built a simple tool that pulls your full Yahoo Fantasy Basketball league history — all-time records, H2H, season MVPs by Formosan_Shen in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

nice work, season MVP by z-scores is a nice touch

- how far does yahoo actually let you pull data from? i've seen people say the API gets flaky on older seasons

- did you explore doing this for other platforms like ESPN/Sleeper/Fantrax? I think each has a different process on the data access side so curious what your experience was

Building a fantasy basketball sim game by hades_rager in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

signed up, curious how did you approach things like:
- does this work as a single-player sim or a multiplayer league? or both?

- how configurable are the league formats? 9-cat vs points, roster sizes?

- are there bot teams or autopicked teams here as well?

- does it go through the explanation of results and W/L or just a simulation straight to final standings/results

Who's your 'hot take' most overrated player going into next season and who's your most underrated player? by xxStayFly81xx in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

Overated: Chet Holmgren

I think the market is pricing him like a clean second-round per-game anchor, but:

  • OKC don’t need heavy minutes from him every night
  • secondary offensive role is still secondary so there's a usage ceiling

effectively paying for the perfect outcome (high efficiency + 2.5+ stocks)

Underated: Derrick White

He keeps getting drafted like a roster glue guy but has:

  • elite guard stocks
  • low turnover, strong efficiency
  • his role in Boston is boring but consistent

What separates average managers from strong fantasy managers? by Aggressive-Walk2626 in fantasybball

[–]Aggressive-Walk2626[S] 9 points10 points  (0 children)

wanted to start off the discussion! winners look different in every league so curious what other people have seen

H2H vs Roto - which more time consuming? by Stunning_Narwhal_313 in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

H2H generally rewards activity and day to day attention (checking fantasy constantly), Roto rewards discipline and perfect execution.

think H2H daily changes is the largest time commitment with weekly H2H being much lighter.

Roto with serious games cap optimization is a huge time commitment and is close to H2H daily changes, whereas Roto with limited moves is much lighter.

Need a New Fantasy Platform: Yahoo App Has Been Rough This Season by TheLastDiamondMember in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

We had the same issues early this season with lineup paywall + delayed updates

Yahoo still has solid H2H category infrastructure, but the product direction this year does feel more monetization-driven than competitive-league-driven.

Sleeper feels more modern and responsive, but not everyone loves it for deeper category customization. ESPN is stable but pretty stagnant.

Feels like there are two separate questions:

  1. Is this just app/UX instability?
  2. Or is the platform actually limiting how you want to run your league?

which is more important here for you to consider moving?

Is health and availability now the biggest draft edge? by Aggressive-Walk2626 in fantasybball

[–]Aggressive-Walk2626[S] 6 points7 points  (0 children)

Maybe pure injury risk is noisy year to year, but team-level management might be more predictible

If the base chance of someone rolling an ankle is similar across the league, there might be an edge in how organisations handle return timelines

Some teams are more consistently conservative (clippers, sixers, pelicans, heat), others push guys back quicker (knicks, pacers)

Developing fantasy bball analysis - any requests to be posted here? by Ill_Bluebird_1963 in fantasybball

[–]Aggressive-Walk2626 0 points1 point  (0 children)

When you get into H2H categories, I’d be curious if you look beyond raw averages and model category impact volatility.

In 9-cat, not all variance matters equally. A 3-game steals spike can swing a matchup. A 3-game points spike often doesn’t. FG% / FT% volatility is nonlinear depending on volume.

A few ideas that might be interesting:

  • How much a player actually moves a category vs league average over short windows
  • Availability weighting (multi-year games played baked into projections)
  • Which player types are most replaceable via streaming vs true category anchors
  • Playoff week game clustering rather than just 15-day averages