Stat Distribution Analysis: Part 2

One week later and I think I’ve cracked this. Firstly, go back and read last weeks post if you haven’t already, and this old one on stat weights if you don’t understand why replacing stat weights is a good thing.

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Stat Distribution Analysis

One of the things I’ve wanted to do for a while is work out a decent way of estimating the ideal “stat ratio” based on some previously generated data vs a players current gear. My initial ideas were based around either building a grand “formula of everything” for a spec or using the stat scaling graphs to somehow build a model, but these were fairly complicated and required a lot of data.

I may have, by complete accident, worked out how to do this in a much easier way.

This started with Bloodmallet sharing his secondary distribution graph on his website  with one of those 3D charts showing DPS output as colour shading vs crit/haste/mastery as the x/y/z axis. I grabbed a copy of the raw results, planning on messing around with it to see what sort of patterns I could get out of it.

Messing around with data

About the third or fourth thing I tried was averaging the results for the percentage increments for each stat, like so:

stat distribution table 1

The next step in these situations is to look at the difference between one row and the next:


stat distribution table 2

The interesting thing I noticed was that the best combo, C35 H15 M40 V10, had that massive value drop between H15 & H20. V10 was fairly obvious as the DPS value as you add more versatility is fairly low, but why were C35 & M40 the best results? After looking at the difference between C25/35 & M30/40 I noticed that the value increases after the large drops were still the largest gains out of any of the other steps.

DPS Analysis vs Election Systems

This is where we take a slight detour. As a numbers orientated spreadsheet person I made a few spreadsheets looking at how poll results from the 2017 New Zealand general election would generate the final seat results in Parliament. This is done via the “Sainte-laguë Allocation Formula” where the total number of votes gets divided by sequential odd numbers and then the top 120 values determine how many seats each party receives.

The reason I bring this up is that the step differences looked very similar to this, where the top 20 values were used to return the best secondary stat ratio that makes up a total of 100% (each step is 5%, so 20 steps available).

How could this be useful?

If this works out at the very least I’ll have a set of templates to indicate what sort of secondary stat distribution a player should have and indicate the best one to increase next without resorting to countless stat weight sims. If everything works out like I hope then I should be able to have a “stat ratio path” in a single table that indicates how secondaries should be increased for any level of gear. Talent selection will modify this, but if the basic premise holds then looking at the individual effect of each talent would just require another small set of adjustment data, unless there’s a large stat priority switch caused by one or more talent choices.

This data could then be used within an addon as a replacement for Pawn-like addons (or even an update to Pawn) to help guide players towards better gear choices without sims or stat weights.

It’s still early days on this, as I need to gather a lot more data to analyse, but the initial idea seems promising.

Arcane Mastery Problems

Mastery effects vary from spec to spec, and I’ve already highlighted the issues around change based masteries like Overload, but there’s another I want to look at: Savant for Arcane Mages. This increases maximum mana and regeneration, as well as increasing the damage bonus from Arcane Charges, but there are some interesting interactions in how the spec is designed. Read more of this post

Min/Maxing: Are these extra 1000 DPS increases worth it?

One of my recent theorycrafting thoughts was on looking at APL’s and reducing the complexity to see how this alters DPS output. The idea is that a less complex rotation will be easier to use in-game, and maybe even counter-intuitively increase damage.

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Stat Weight Graphs Part 3: Raidbots edition

Thanks to Seriallos from I now have full stat weight plots for every DPS spec, and this data should be easily updateable as I’m reading directly from JSON files he’s hosting. I’ll link in the spreadsheets as I set them up, along with interesting stat charts that indicate something different/unusual. Read more of this post

Simulations, Logs & Dummies

Meters, Warcraft Logs & Statistics: Part 3

A continuation of the blog series I started recently (Part 1, Part 2).

Another common question raised by players is the “My sims say I should do X DPS but I only get Y DPS on dummies or from WCL parses” one. Unfortunately all three approaches can’t reliably be cross-compared with each other for specific details. Read more of this post

Rankings & Detrimental Behaviour

Meters, Warcraft Logs & Statistics: Part 2

Earlier in the week I covered how there’s a feedback loop (or “snowball effect”) on players moving away from specs based on statistics. Today I’ll be looking at rankings, how these can distort the aggregated data on Warcraft Logs, and why taking non-optimal actions to increase rankings are bad.

In other words, padlords gonna pad, and it’s bad. Sorry, no graphs this time. Read more of this post