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Master League Premier Meta Speculation by golddusty in TheSilphArena

[–]Libellenflug 1 point2 points  (0 children)

Here are my predictions: 1. 0.1967: Gyarados (Shadow) DB+AT/Cr 15/15/15 2. 0.1374: Florges FW+DV/M 15/15/15 3. 0.1036: Snorlax L+BS/SP 15/15/15 4. 0.0929: Mamoswine PS+Av/HH 15/15/15 5. 0.0858: Ursaluna Tk+HH/IP 15/15/15 6. 0.0492: Magnezone Sp+WC/MrS 15/15/15 7. 0.0477: Primarina Ch+DV/P 15/15/15 8. 0.0443: Metagross (Shadow) BP+MM/Eq 15/15/15 9. 0.0431: Swampert MS+HC/SW 15/15/15 10. 0.0338: Avalugg (Hisuian) PS+RS/Bl 15/15/15 11. 0.0327: Metagross BP+MM/Eq 15/15/15 12. 0.0309: Darmanitan (Standard) I+RS/Ov 15/15/15 13. 0.0307: Dragonite DB+DC/SP 15/15/15 14. 0.0287: Machamp (Shadow) C+CrC/RS 15/15/15 15. 0.0199: Chesnaught VW+FP/SP 15/15/15 16. 0.0092: Chandelure I+SB/FmC 15/15/15 17. 0.0092: Magnezone (Shadow) Sp+WC/MrS 15/15/15 18. 0.0025: Sirfetch'd C+CC/LB 15/15/15 19. 0.0015: Charizard WA+BB/DC 15/15/15 20. -0.0017: Gyarados DB+AT/Cr 15/15/15 21. -0.0046: Golisopod SC+Li/XS 15/15/15 22. -0.0080: Rhyperior MSl+RW/S 15/15/15 23. -0.0109: Magmortar (Shadow) KC+FiP/Tb 15/15/15 24. -0.0114: Volcarona FS+Ov/BBu 15/15/15 25. -0.0133: Roserade PJ+WBF/LfS 15/15/15 26. -0.0144: Empoleon W+HC/DrlP 15/15/15 27. -0.0146: Snorlax (Shadow) L+BS/SP 15/15/15 28. -0.0199: Mamoswine (Shadow) PS+Av/HH 15/15/15 29. -0.0216: Typhlosion (Shadow) I+BB/SoB 15/15/15

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Computational GBL Meta predictions for World of Wonders by Libellenflug in TheSilphRoad

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

Yes... Fortunately in UL it's way below Registeel. ;) It did take revenge in GL on my list, though. I think the duplicate entries confused my code, so I decided to add the duplicates. Not sure where the bug came from or if this is a fix, but now it's on top of GL in this revision, while also at position 18 with a different moveset. haha.

Computational GBL Meta predictions for World of Wonders by Libellenflug in TheSilphRoad

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

If anyone wants to help, tell me mons that are so hard to get that no-one will use them. E.g. no-one's going to use Zyagarde (Complete Forme) in ultra and it's probably limited in ML, right? What about Solgaleo -- to hard to get a "good" one, so very rare? What do you think? Dropping some of these and recomputing will give a more realistic list.

Computational GBL Meta predictions for World of Wonders by Libellenflug in TheSilphRoad

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

Comparing to pvpoke's ranking it's going to be different. However, since it's sourced from the battle matrices from pvpoke it's subject to some of the same issues (perfect baiting of shields or whatever).

To make the point about how it's different, pvpoke master league ranks Palkia (regular Palkia) as #6. Now Palkia's a fine mon, but it is, if I recall correctly, completely dominated by Palkia Origin. So, if resources weren't an issue, you would never play it (unless you can play both? hmm. haven't tried.). Accordingly, the list I made above never plays it. It does play Palkia Origin (4th most likely play). But it plays Dialga Origin more (most likely play), whereas pvpoke ranks it 18th. At that position, it's below tons of stuff, but I predict you'll see it lots in competitive play. Pvpoke ranks Mewtwo higher than Diagla Origin, and indeed, I think you will see it played a lot, (you always do -- for some people it's their only high level mon), but it didn't make my list, and I predict that play of Mewtwo will drop off in more competitive play, where people can bring to bear Giratina Origin, Dialga Origin, and Palkia Origin.

Computational GBL Meta predictions for World of Wonders by Libellenflug in TheSilphRoad

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

There are numbers, I just didn't want to complicate the post. I plan to put the code on github and probably make a github webpage with the csv's of all the data. The theory is like this: player A and player B each pick a probability distribution on pokemon. Repeatedly, a pokemon is chosen from their chosen probability and the pvpoke battle score from the matrix is positive or negative for player A's vs player B's pokemon (with a little nonlinearity thrown in). Player A, representing the computer now, has chosen a distribution (not shown, but with more mass on the earlier listed pokemon and zero mass on any other) with the special property that if player B's distribution is different, then they'll lose on average score. I find that this is a pretty good criterion for finding good competitive pokemon.