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About Schwubbeldiwubb

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  1. Thanks for mentioning that. Absolutely makes sense, Chansey will be corrected in the guide. :D (the chart will get updated later) I wondered myself and that's part of the reason why atm I'm trying to work in the main move sets and EV spreads, gathered from Gbush's guide and Smogon (in which unfortunately some builds don't make sense for PokeMMO because of the new gen physical/special spread). As I said, the current chart and results are to be taken with a grain of salt, but it will get a lot better approximated soontm. You'll still need salt for that, but not as much... :^) When I'm done with that, then there are still various builds, items, actual effect of speed stats in a battle, status attacks, healing and "switching pressure", that lack in the statistical consideration. I'd need waaay more detailed play rate statistics (distribution of different builds of Pkmn) and observations of countless battles over time, e.g. for effective damage calculation of moves like Will-O-Wisp and Toxic or Dragon Dance... I don't think I'll be doing this much. xD But when I'm done with WIP, the numbers should give you quite detailed "stationary" insights in respect to several specific averages.
  2. I'm studying Physics and am kinda used to this kind of stuff. Also, doing this is almost as fun for me as playing PokeMMO, so I'll take that as a compliment. :^)
  3. Hello World, like supposedly most people here, I've got accompanied by Pokemon my whole childhood through. Just a month ago I've started to actively play PokeMMO and at the same time begun to play competitively. And there we already have the first reason to warn you: Although I do know all Pokemon, their typings and specific tasks from Gen 1-3 by heart, I actually never played against human competitors until Jan 2017 due to lack of link partners. On the other hand, I do know a bit about statistical analysis and other hardcore nerd stuff, so I guess that can help me to get better at figuring out what to do in PvP. Of course, throwing yourself into queue is by far the best way to improve, but I also like theory crafting. From the latter I have got a buttload incoming for you! However, it should be noted, that statistics or maths in general are not representative for reality. Even Pokemon, although a game based on maths, is filled with such complexity and chaos, that it's very hard to predict the exact outcome of an encounter by numbers only. You either create a formula from a huge sample size and count in all imagineable variables, or handle the statistics with caution. I'd recommend you to do the latter with what I did. ^.^ This is based on information from the PokeMMO official usage statistics (as of February 8th 2017, but the data itself is from May 2016) and the Pokemon Database Pokedex. Google helped wording this ... I won't apologize for my English. :^) I created the chart with OpenOffice Calc and deeply hope, that you are able to download and open the file I uploaded. More on this in a second ... 1. Motivations First off, I wonder, if this kind of stuff is interesting at all or holds any value regarding competitive play... Display various properties using a (hopefully) easy-to-read chart and by approach of statistical weights and averages figure out: Best (and worst) attackers and move types Best (and worst) defending Pkmn Pkmn stats in comparison By that: Help creating a Team with good defense and offensive coverage Grant easy access to several nice-to-know values, e.g. during battles 2. The chart of charts (for OU) WIP-Note: At the moment I'm working on way more representative calculations counting in move sets and EV spreads (fow now, only a maximum of two spreads per Pkmn according to what I'd prefer), STABS and attack stats in addition to what I already considered. Problem is, this thing will be freaking huge and definitely not "easy to read" anymore. It could take some time, not only to pull this off, but to actually make it comprehendable... Nevertheless I'd be glad if the following file, the "alpha version" so to speak, somehow finds its way to you, functioning, if not, please message me. Download link to ODS version (recommended): battling statistics.ods?dl=0 Download link to XLS version (not tested with Excel yet): battling statistics.xls?dl=0 This chart is ... well, a chart with lots of stuff in it. For descriptions of individual properties, you can mouse-over the column headers and read the tooltips. The main feature is to be able to arrange values in ascending or descending order. That doesn't sound that cool, but it is, trust me. :^) As of now, I only have the German version of Calc, so sadly I can't tell you what exact command there is necessary to pull this off with Excel. Either way you should be able to select the whole column you want to organize by clicking onto the header with the letter and execute the sort function. And don't forget to extend the selection when arranging the table, else the data gets messed up big time! I should also note, that I only analyzed enough for it to be somewhat useful... I guess. The current chart is for the OverUsed tier only. A few OU Pkmn didn't even make it into the statistics this is based on, so please don't mind, if there is some (really) niche pick missing. Also, the weights I calculated for the arithmetic means could be waaay more refined, especially in the defense sector. Either way, in this state the approximations of weights are still quite lazy and I'm eager to count in a lot more than just play rate. But more on that later ... In the following I will show you some of the results and try to explain briefly how I calculated that stuff. Suppose all stat values have 31 IVs by default, when there reads "minimum" I mean base stat + 31 IVs. 2.1. OU Offense [WIP] 2.1.1. OU Attack Type Effectiveness 2.1.2. OU Top & Flop Speed - Sweepers 2.1.3. OU Speed - Walls 2.2. OU Defense The following applies to all of the defense results. The weighted arithmetic mean here represents the average effectiveness of a specific distribution of attack types. It is then used as an attack modifier to determine the damage dealt to the pokemon in question. By this process the defense and HP stats, EVs, IVs and nature find their way into the result. As of now, the weights I calculated are just taking into account, that almost every Pkmn uses one or two STAB moves, while unfortunately neglecting the bonus itself. E.g. a Water Pkmn will get a better defense result, when there are less Grass and Electric attackers. The more Pkmn there are, who can seriously harm the defender in question, the worse the latter's defense result. The calculation is still flawed, because its foundation is a lazy abstraction I made from the sample: Suppose all Pkmn only use their respective STAB moves. It's not entirely wrong, but I think I should be able to find a better approximation soon... The numbers you'll see represent the fraction of total HP these Pkmn lose on getting hit by an "average typed attack" with 100 strength and a 150 attack stat. At this point, no attack modifier other than type effectiveness is considered. To do: Again, analyze move pools and create weights, which count in the distribution of strengths of attacks, STABs and attack stats of offenders. [WIP] 2.2.1. OU Defenders - Walls [WIP] 2.2.2. OU Defenders - Walls in their "off-defense" [WIP] 2.2.3. OU Top Defenders - Bulky Sweepers [WIP] 2.2.4. OU Top & Flop Defenders - (more or less) Speedy Sweepers Thanks for reading! :)