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u/vanisle_kahuna

▲ 0 r/NBAConvo

How to fix tanking and meaningless games

How to fix tanking and meaningless games

Tanking has become embarrassing. Nine or ten teams are openly punting this season and the league's own fixes haven't worked. Here's a fun ground-up solution I'm proposing that could potentially solve it.

THE REGULAR SEASON — The League Cup

  • League expands to 32 teams

  • Double round robin format — every team plays every other team twice (62 games)

  • Best overall record at the end wins the League Cup

  • Every game matters because every game affects standings. No more dead rubber months.

MIDSEASON TOURNAMENT - The Dominion Shield Tournament

  • Season pauses at the halfway point after ever team has played each other once

  • All 32 teams enter a single-elimination, best-of-3 tournament called the Dominion Shield

  • Seeded by current standings at the halfway mark

  • Shield results do NOT affect regular season standings

  • Winner earns a direct path to the NBA Supercup Championship

The Shield is essentially March Madness dropped into the middle of the NBA season. Every team is alive. Upsets happen. It's must-watch television.

END OF SEASON — The Lottery Tournament

  • Bottom 16 teams by record enter a single-elimination, best-of-3 tournament

  • Winner gets the #1 draft pick

  • Teams that lose keep their original seeding-based draft position — no punishment for losing

  • Remaining picks 2–16 ordered by regular season record

This is the key tanking fix. Bad teams are now competing to win for the top pick instead of competing to lose. The worst games of the year become meaningful overnight.

THE POSTSEASON SUPERCUP — Three Round Championship

After the Lottery Tournament concludes, giving all teams time to rest:

Round 1 — Wildcard Series (7 games)

Shield runner-up vs League Cup runner-up

If the same team finished 2nd in both, the spot goes to 3rd place in the League Cup, then 3rd in the Shield, cascading down.

Round 2 — Semifinal (7 games)

Wildcard winner vs Shield winner

Round 3 — NBA Championship (7 games)

Semifinal winner vs League Cup winner

  • League Cup winner gets a full bye — they only play ONE series to win the championship. That's the reward for sustained excellence over 62 games.

  • Shield winner plays two series. They have to prove the midseason run wasn't a fluke.

  • Wildcard winner has to win three straight series. If they pull it off, it's the greatest underdog run in league history.

  • If one team wins both the League Cup AND the Shield, they are automatically NBA Champions. No Supercup needed.

WHY THIS WORKS

✓ Tanking becomes irrational — bad teams fight to win their way to the #1 pick

✓ The regular season is watchable from game 1 to game 62

✓ The Shield creates a brand new tentpole event mid-calendar

✓ The postseason has three escalating 7-game series with distinct storylines

✓ The reward structure is fair — the hardest competition (League Cup) earns the easiest postseason path

What do you think? Does this fix the league's tanking, revenue, and overall anticompetitiveness problem?

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u/vanisle_kahuna — 6 days ago
▲ 40 r/datascience+2 crossposts

Went down a rabbit hole on causal reasoning and came back up having learned about DAGs, mediators, and why predictive accuracy shouldn’t always be the target.

The past few months, I've been teaching myself Bayesian stats from the Statistical Rethinking textbook (highly recommend btw) and I went down a rabbit hole on causal reasoning which I found really compelling! It's a completely different framework from the "maximize predictive accuracy, throw everything in" approach I learned in bootcamps and instead called for thinking deliberately about the causal mechanisms generating your data.

Anyways, I thought it might be useful to write up an article summarizing some key ideas of causal reasoning like DAGs, mediators, and confounders for those that haven’t come across it yet. I also made a case for why adding more predictors may actually make your models worse if you don’t think carefully about the relationships your predictors have with one another. And to make these concepts more practical, I applied them towards a wildfire dataset to form a hypothesis on the data generating process behind total hectares burnt in a wildfire.

This is Part 1 (theory + DAG construction) of a two-part series. Part 2 will test the causal model with regression.

If you find this stuff interesting, useful, or even just inaccurate, I’d love to hear your feedback! Has anyone else gone down the causal inference rabbit hole? It feels like a whole different lens on ML that doesn't get talked about much but definitely needs more attention.

https://medium.com/towards-artificial-intelligence/rethinking-predictors-why-causal-reasoning-matters-in-data-science-part-1-f1d4c1e08068

https://preview.redd.it/n7isqm44v00h1.png?width=2779&format=png&auto=webp&s=fb4def19be69150c19bff3805d80243540eb6f2c

reddit.com
u/vanisle_kahuna — 6 days ago
▲ 15 r/pokemonanime

I'll start:

Professor Oak — Zoology or taxonomy:

He's the original "Pokédex" guy obsessed with cataloguing every species and how they interact with humans.

Professor Elm — Developmental Biology or Reproductive Biology

This is pretty straightforward. The guy studies how Pokemon develop from egg to adult and was introduced in the same generation as the breeding mechanic.

Professor Birch — Field Ecology or Wildlife Conservation:

This is the study of organisms in their natural habitats and how species distribute geographically. I think this makes sense as he's permanently out in the field getting chased by Poochyena and studying pokemon in their natural habitats.

Professor Rowan — Evolutionary Biology:

Literally his entire research focus is Pokémon evolution. One of the easiest match on the list.

Professor Juniper — Paleobiology:

She studies how Pokémon came to exist and where they originated. Combines fossils, ancestry, and species emergence. Another alternative for her could be the anthropology of human–Pokémon coexistence

Professor Sycamore — Also Evolutionary Biology:

To me, he's one of the harder ones so I would probably default to evolutionary biology as well given he was mentored by Rowan and he also studied evolution as well, but obviously a newer and more recent phenomenon in mega evolution.

Professor Kukui — Sports Science. A hot take alternative in my mind can also be a specific subfield of AI which is Reinforcement Learning with a focus on video game environments:

The sports science feels fairly obvious as he studies how bodies move and generate force. He's obsessed with Pokémon *moves* and famously takes hits himself to study them.

On the other hand, reinforcement learning is fundamentally about agents learning optimal strategies through trial, reward, and competition which is kinda how Kukui approaches Pokémon moves and battling. He founded the Alola League specifically to create a competitive testing ground, similar to essentially building a benchmark environment. The video game application angle also fits perfectly given the Battle Royal format and his fascination with novel battle structures.

Professor Magnolia — Honestly, no idea. Would love to get ideas here.

Professor Sonia — History:

This is pretty easy too. She's writing a book about the academic history of a country, working from primary sources, and doing interviews.

Professor Sada — Paleontology + Theoretical Physics:

She studies ancient Paradox Pokémon pulled from the past, which requires both fossil/ancient-life expertise and a working theory of temporal mechanics.

Professor Turo — Broadly AI and Robotics:

The future Paradox Pokémon are essentially biomechanical/robotic and he was capable enough to build an advanced AI version of himself.

Professor Friede — Material Sciences, Pharmacology, or Astrobiology:

Friede is probably the toughest one for me. I'm basing his specialization off his backstory where he was working for Exceed studying the effects of the mineral on Pokemon so that to me could be either material sciences of pharma. I also included Astro Biology given the guy went up to space but I can't remember why he was there so I'll just throw in this general field of specialization.

Are there any fields that would be a better fit? Excited to know what you guys think!

u/vanisle_kahuna — 10 days ago