Articles by Just_Flo 6

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The latest issue of the 9th Scroll is available! You can read all about it in the news.

  • First I want to appologize, for being late with this article. I originally wanted it to be ready when the first part of the update hits. Well, that did not work as intended. The good thing is, that now I should know what interests you most about the 2019 update.

    So what info will you get in this blog article?
    • The goals of this update and how the update was prepared and done
    • The future of the update and what you can do to improve it

    • The goals of this update and how the update was prepared and done

      The update had several different goals, I'm going to walk you through each goal seperateyly. Basically the update targeted 3 things treated with equal importance:

    • Improving external balance
    • Improving internal Balance
    • Bringing in line to strong designs for which pricing could not be used as a solution as it would cause to much collatteral damage

    • Improving external balance

      For that of course one needs to know where an army stands. As written in this Announcement we did collect data from multiple sources. Primary datasources were surveys (to both open community and a group of top tournamentplayers) and tournament data (single and team seperated).Than the ranking those datasets gave tot he armies were compared. If they didplace an army in the same area (= only did differentate about 1 -2 places), Thescources were calculated together giving the single data the highest value.After that with different weightenings oft he datascources and other methodesthe rest oft he armies were placed.That of course means, that none oft he scources will rank the armies total identical with the final tier list. If for example single data would put Army Asomewhere in the lower middle oft he pack, but External Experts, Community andTeam tournament data puts it on the last spot, than 3 scources vs 1 scourcemake it likely that the one scource might show the army potentially as a bitstronger than it really is. (The width oft he corridors the armyperformancedata puts the armies, makes that also a possibility). But more on the topic of theexternal performace tourney data at a later time (=around when the final updatehits or around december, which ever is nearer to me finishing that report.Basically to improve the external balance things should get cheaper and others more expensive. Tie 1 should get more rises in total on current builds (even after taking internal balance into account), than tier 5 (where rises on things would beinternal motivated and be much less, than if the same army would be Tier 2-4.)

    • Improving internal Balance

      You hopefully all have seen and contributed to your armies survey and the armylist analyse threads in the armyboards. A link to those threads can be found here. In those threads armylists from both, single and team tournaments of various size were entered in TA’s files and then automatically analyzed. This analysis is for you to see in each thread in one of the first three posts.

      Let me give you an example of how the internal balance adjustion works so far. Through data analysis, EE Reports and the Community surveys every entry gets a grade between 1 (hardcore seriously too point efficient) and 6 (hardcore serious too point inefficient). Then those grades are combined into a final grade. For that the different info scources have been weighted accordingly. Afterwards according to the final grade a factor is used to either increase or decrease the price.

      Let us get more concrete on the armylist data site, as about that I can explain more than about the other factors.

      We decided to use the taken "once or more often per army" dataset, as the basis for our analyses. Basically we belive it models the unit usage more fairly for units which can be taken in different quantities and/or for different utilities. Of course there are things where the "total taken" dataset might produce more exact results. (Yes, you guess it right, next year we will very likely have found a way to combine the usage of both datasets in a way to get the best out of both).

      From that we got how often something has been used at least once and how often from that category (Core, Characters, Noncore + Noncharacters, faction specific items, unit/character- options) would have been expected to be taken on average. There are several possible models to use for that each with its own pro's and con's and ultimately influences on the result. For example one can calculate the average expected shield usage for a character with the option for shield and great weapon as shield or no shield, which results in dividing the number of how often that character has been taken at least once by 2 or one could say shield, great weapon or no shield and divide by 3. This is an area where we will review our macros if we still believe to have taken the right decision.

      Now, how often things which were taken "at least once" were compared with that average. Things being used 190%+ are totally hardcore more cost efficient than what we want them to be. So they are very
    [Read More]
  • Tournament Analysis on the example of the WTC

    I want to give you all an example how an event can be analysed and processed by Tournament Analysis. I will use the WTC in Herford for that. I use it out of several reasons. First many of you tried/did their own analysis and started discussions about it, at least it was so on the HE board. Second it was a really big tourney and should still be one of the five biggest even at the end of the year. As we have real actual data=results of the games played and final ranking and it is a team tourney we can show almost everything which complicates the analysis.

    So how do we analyse a tourney?

    If it and the results are on,,, and on than we copy paste it into a prepared file where the macros process it into the format we work with. If it is somewhere else (and we find it), we have to enter it in the right format per hand.

    But what is the format we mostly work with? It is [(Number of participicants) – (Ranking reached) ] : [(Number of participicants) – 1 ]

    And now I will explain it in English.At a small Tourney (6 participicants) with Place 1 SA, Place 2 VC, Place 3 SE, Place 4 ID, Place 5 KoE and Place 6 SA we will award the armies the following points:

    Place 1 SA gets (6-1) : (6-1) =1

    Place 2 VC gets (6-2) : (6-1) = 0,8

    Place 3 SE gets (6-3) : (6-1) =0,6

    Place 4 ID gets (6-4) : (6-1) = 0,4

    Place 5 KoE gets (6-5) : (6-1) = 0,2

    Place 6 SA gets (6-6) : (6-1) = 0

    So as SA has both 1 and 0 points the mean would be 0,5 points.

    What does what number mean? Which average number shows an army to be over performing and which number shows that the army is underperforming? Well, between 0,45 and 0,55 we consider armies to be balanced. Under 0,45 they underperform and over 0,55 they overperform.

    So does in my example ID underperform? Well, I am sure we agree that one single army placement is a bit to few to say exactly how it really is. How much away the true value is assumed from the mean with a certain certainty (in our case 68 %) is measured with the certainty interval. It is calculate like that (here enter formula from arwaker).

    With the certainty Interval we can see if the corridor of the assumed true value is inside the corridor of 0,45 – 0,55. It also explains why we don’t go for example for 0,475 - 0,525. How does the certainty Interval explain that? Well we need quite a real lot of tourney ranking results per army to get a certainty Interval which is under 0,05. Everything above would be broader then the corridor between 0,475 -0,525 and so allow us no interpretation.

    That is one kind of analysis, but there are others, too. The more rounds are played on a tourney the more likely it is that an army shows it’s real strength. The more players are at a tourney the more likely it I that the pairing process brings players of equal skill to play each other. So naturally we have an additional calculation which takes the size and the length of a tourney into account. Later we compare both to see if tourney size and duration really have an influence and when which one. (Of course that only has a chance to have an effect if we have different tourneys with different size and duration.)

    We also used to do some complex calculations to see what would be if every country had the same number of results (= we treated them equal in one analysis). But as that and analysis which tried to capture the competiveness of the different countries scenes proved to influence the Certanity interval in a way which made results uninterpretable, we dropped that.

    The way we calculate the performance based on Ranking reached and the number of participicants naturally produces bigger differences between place 1 and place 2 depending on the number of participicants. That is different if we look at the actual games played. Those games always have a 20-0 / 0-20 matrix and so a 20-0 always brings 100 % or 1 and a 19-1 always brings 95% or 0,95. On that sort of analyse a totally balanced army would get an average of 10 points from it’s games. Here between 9.5 and 10.5 points average are the balance corridor. That kind of analyse can in theory potential produce more precise results than the ranking based, but we mostly get data for that analyse from big team tourneys and very few for smaller single tourneys. Later in this article I will go into more details how the difference between single and team tourneys influences our analysis.

    What does that mean?

    Well if we have the rankings reached by an army in a single tourney and the results of the matches it played, the result of an analysis based on ranking and the analysis of the actual played games natural will provide different numbers. But here again between 0,45 and 0,55 or between 9.5 and 10.5 points achieved on average (equalling 45 % - 55 %)… [Read More]
  • Hello and welcome to the newest article of the Tournament Support Blog. I know you all wait for the Report on Army Performance in 1.3.x, but that will be featured in the the next april article. Today I will talk about the merger of Data Analysis and Tournament Support and give you an overview of who we are and what we work on.

    The merger of Data Analysis and Tournament Support to Tournament Analysis

    For a long time Data Analysis and Tournament Support have worked close together and were kind of 2 sides of the same coin. Data Analysis prepared the tools for the analysis and Tournament Support hunted down the necessary data. After that it depended on the specific data who entered it into the tools. Both interpreted it (while Data analysis had the final say on interpretation). As the cooperation got closer and closer and more and more tools had been designed and more and more ways / standards of interpretation had been decided on the Deads of Data Analysis more and more moved in a more surveilling position to step in if or when Tournament Support screwed up or develop new tools if asked or doing long term planings and Tournament Support more and more took over the day to day buisness. Long story short, both Team Heads (That would be me Just_Flo and @arwaker) decided to merge our teams.

    That is the Result:

    Team Head of Tournament Analysis (coordinating and doing the day to day work): Just_Flo

    Assistant Head of Tournament Analysis (bringing in the statistic background, checking the results of analysis and always having a open ear and good advice): @arwaker

    Assistant Head of Tournament Analysis (bringing in the statistic background, coding analysis tools): @wazlawik

    Analysis of Armylists: @Old one

    Coding excel/spreadsheet genius: @Fleshbeast

    Collection of Armylists, Tourney results and army vs. army data and further analysis: @Ace Thackeray, @Celegil, @Dancaarkiiel, @Maelstorm, @mishagi, @simonbromley

    Of course we always welcome new recruits and my dream would be to have one person per army. So feel free to apply :) [Read More]
  • JimMorr undertook the project of coding an app for android to manage the Flux Cards, Magic Dice and Veil Tokens for both players of a match. So with his help the days of fickling with cards, searching cards one forgot, sharing card decks, ... are over.

    Sending match data made easy

    That app has an additional feature I am very thankfull for. JimMorr asked us what data would help us if we got them from individual games. Naturally if I am asked what data I want I say everything. And he made me happy and coded the possibility to send to me almost every possible data.

    What data can you send to the project?
    If you want, you can send us which armies played each other, which went first, what the deployment and the scenario were, which units performed best /worst, what the outcome of the game was, when the game was played, how long the game lasted and the number of turns played.

    Do you have to send data to the project if you want to use the app?

    No, you don't need to send us data to use the app. It is perfectly fine if you want to use the app but don't want to send data to the project. Nothing prevents you from that. To send data there are two things you need to actively do together:

    1) You have to enter the data (the managing of the flux cards functions problemless without you doing that)
    2) You have to actively tell the app to send us the data. (No data is automatically send to us. If you entered something you can still change your mind before pressing send)

    Can I use the App at a tourney?

    The Team which is responsible for the Tournament Pack has taken a look at the App.

    Be aware not everybody trusts electronic devices as much as i do. As we dont want to force its use on anybody the official stance is that you have to ask your opponent if he is okay with it.
    So you may use it under the Tournament Pack if you both agree to use it. If the other Player objects it is back to cards, tokens and pencil.

    Where do I get the App?

    You can find the Beta of the App and how to get it here. The final version will soon be avaiable in the Google playstore. [Read More]
  • In 1.3.x we tracked 5 334 Entries into Single Tourneys and 2 339 Entries into Team Tourneys. That leads to 7 673 Tourney entries in total. Of course there are numerous Tourneys out there which we did not track, because either they didn’t appear anywhere online, didn’t report results or just didn’t mention which army was used to reach which place (for example Italy collects all I could ever dream of, but not the armies used).
    Today I will talk about the popularity (not the power) of each army. As we have 16 different armies it would lead to a (100 : 16 =) 6,25 % distribution if all armies were played an equal amount of times. But that did not happen. I will start with the least played army and than get to the more often played armies.

    A global look at Single Tourneys:
    Infernal Dwarfs were the least played army, with 2,8 %.
    Than there came the Undying Dynasties with 3,2 %.
    After that there was the Kingdom of Equitaine with 5,0 %.
    It was followed by the Empire of Sonstahl (5,3%) and the Beast Herd (5,4 %).
    The Vermin Swarm came than with 5,5%.
    The Sylvian Elves didn’t hide in 5,7 % of all entries.
    Orcs (and Goblins) went to war in 6,5 % of all cases and so is the first army which is not underrepresented.
    The Demonic Legions found the way into our reality in 6,5 % of all armies played.
    The Dark Elves visited the coasts of our realms in 6,7 % of the games.
    The Saurian Ancients awaited their opponents in 7,0 % of the times.
    Vampir Counts were counted in 7,2 % of the armies.
    The Dwarfen Holds were besieged in 7,7 % of the games.
    The Ogre Khans and the Warriors of the Dark Gods battled it out in 8,4 % of the battles which army was most used (both have exactly the same numbers o appearance) and did not see the Highborn Elves marching past them in 8,6 % of all battlefields.

    A global look at Team Tourneys:
    Here Undying Dynasties (3,8%) stole the red latern from the Infernal Dwarfs (4,6%).
    Than there came the Dark Elves (4,7%) followed by the Vermin Swarm (4,8%).
    The Empire of Sonnstahl (5,2%) chased both the Beast Herds (5,6%) and the Kingdom of Equitaine (5,6%)
    In the Forests the Sylvan Elves (5,9%) did try to ambush the Warriors of the Dark Gods (6,3%).
    The Saurian Ancients (6,8%) and the (Orcs and) Goblins (6,9%) had a close race.
    Then there came the Dwarven Holds (7,4%) and the Demonic Legions (7,5%)
    The most played army in Single Events the Highborn Elves (7,7%) were next.
    Vampir Counts (8,6 %) were counted slightly less than the Ogre Khans (8,7%).

    A global look at Single and Team Tourneys thrown together:
    Here the Infernal Dwarfs (3,3%) recaptured the red flag from the Undying Dynasties (3,4%).
    The Kingdom of Equitain (5,1%) and the Empire of Sonnstahl (5,3%) followed.
    The Vermin Swarm (5,3%) and the Beast Herds (5,4%) battled it out close to a draw.
    Sylvan Elves (5,8%) and Dark Elves (6,1%) played hide and seek together.
    The Orcs and Goblins (6,6 %)) came slightly shorter than the Demonic Legions (6,8%).
    The ancient Saurian Ancients (7,0) and the ancient Dwarven Holds (7,6) went to war over who was more ancient.
    The Vampir Counts (7,6%) counted how many Warriors of the Dark Gods (7,8%) there were.
    The Highborn Elves (8,3 %) were overtaken just in front of the finish line by the Ogre Khans (8,5%)

    So all in all it was a race between Vampir Counts, Highborn Elves and Ogre Khans where the Ogres ate the competition most of the time. [Read More]
  • Many people speculate how good or bad their army performs / performed in 1.3.5 or 1.9. If one belives what is written often the own army is/ was underperforming and all others overperform. And even if people try to be as objective as they can, countries have different metas and so what is almost unbeatable in german may be weak in the USA and the other way around.

    That means we can’t just make a poll to get to know which army is the strongest. That is the reason we collect tourney results from all over the world. At the moment we collect data mostly using the following platforms as scources:


    You may wonder why I don’t use platform A or platform B where the national communities of country C and D are organized. Well there are 2 easy answers. Either I don’t know that platform or that platform doesn’t record or associate the armies used with the places reached.
    So if you know other ranking / reportings sites, please PM me.

    What do we collect?
    Here you can see the reporting table for TOs from communities not organized on the platforms above.
    Eventname:VersionusedPlacePlayer (maybe anonym)Armyused:Numberof PlayersNumberof TeamsNumber ofroundsCountrySingle/Team

    TOs can use that table to report their tourneys results here: Please report your 2.0 tourneys results and lists through here
    Now with the 1.9 Beta going on we want to have a much closer look at army vs. army matchups. As most of the ranking sites don’t record the exact results of the rounds we need your help. If you have acces to the pairings per Round and the results of that please share it in this thread (Please report your 2.0 tourneys results and lists through here) like that:

    Round:Player 1Army 1Victory-pointsPlayer 2Army 2PointsScenario (officialnames please)Scenariowinning Army

    You can also send data, files or links per email to or to
    We also collect armylists used at a tourneys. Please load them up here (please use the tournament name as file name):

    How can you contribute if you don’t play tourneys?
    Easy. You can report with the following table here: Please report your private games and lists through here ) That gives you the following table:

    Round:Player 1Army 1Victory-pointsPlayer 2Army 2PointsScenario (officialnames please)Scenariowinning Army

    What do we do with that data?
    We look at that data under quite a lot of perspectives:

    • We watch how the army perform in different countries
    • We watch how the armies perform if bigger tourneys have more influence than smaller
    • We watch how armies perform in single tourneys
    • We watch how armies perform in team tourneys.
    • We watch how armies perform if we throw single and team tourneys together.
    • We will watch if the armies performance come from 20-0 and 0-20 or more form 12:8 and 8:12s- = We will watch if an army has performance spikes or not.
    • We watch …
    • We collect how popular the armies are in tourneys.
    • We compare the results and make the data confess the truth as no method to interpret or analyse the data alone gives the whole picture

    Of course we also take confidence intervals, error bars, mean, standard deviation and so on into consideration.

    Thx for reading so far

    Just_Flo (Tournament Support) and Arwaker (Data Analysis) [Read More]