Category Archives: Data Literacy

Skill Floors and Skill Ceilings: Which Age of Sigmar Factions Reward Skill the Most?

We all talk about faction strength a lot in Age of Sigmar, and the conversations usually focus around the overall win rates. However, we’ve mentioned before that this compresses too much information into a single number.

One interesting side effect of having our Elo system means that we are able to gather data on win rates for factions across different skill levels.

So we analysed GT games across two skills bands using our Elo ratings.

  • Low Skill Band: 200-400 Elo
  • High Skill Band: 500-700 Elo

These band represent newer tournament players versus experienced competitors. By comparing their performance we can show three different measurements.

  • Skill Floor – How well the faction performs in the hands of lower Elo players
  • Skill Ceiling – How well the faction performs in the hands of higher Elo players
  • Skill Gradient – How much performance improves as player skills increase

Together these three measurements should show which factions are forgiving, those that need to be mastered and those that struggle regardless of experience. Looking at you Gitz….

The Overall Pattern

Across the dataset shown below, most factions show a clear improvement as player skill increases. But the difference in this change changes quite a bit.

Some factions improve massively in the hands of skilled pilots while others remain pretty stable regardless of skill.

This gives us three categories of factions in the meta:

  • Forgiving factions that perform well for players regardless of their skill
  • Skill-dependent factions that reward experience and dedication
  • Struggling factions that remain difficult to win with ever for the skilled players

Forgiving Factions

Some armies do well even amongst lower Elo players. These appear more forgiving and allow players to achieve decent results even without lots of tournament experience.

FactionLow Skill Win Rate
Maggotkin of Nurgle52%
Ironjawz46%
Idoneth Deepkin46%
Flesh-eater Courts45%
Ossiarch Bonereapers44%

These combine a solid base performance with strong ceilings, meaning they remain competitive even as player skills increase.

For players new to tournaments, these armies may provide an easier learning curve.

High Skill Ceiling Factions

These factions show really strong performance among high Elo players.

FactionHigh Skill Win Rate
Idoneth Deepkin84%
Maggotkin of Nurgle77%
Flesh-eater Courts76%
Ironjawz74%
Ossiarch Bonereapers74%
Ogor Mawtribes74%

They are capable of strong results when played by the most skilled players.

Factions That Reward Skill the Most

The skill gradient shows how much a faction improves as player skills increase.

FactionGradient
Idoneth Deepkin+38%
Ogor Mawtribes+36%
Stormcast Eternals+32%
Flesh-eater Courts+31%
Seraphon+31%
Skaven+30%
Ossiarch Bonereapers+30%

These are factions with largest improvement between the skills bands. Experienced players can tap into a lot more performance from these than newer players.

Balanced Skill Scaling

Several factions fall into the middle ground, showing only moderate improvements as player skills increase

FactionGradient
Blades of Khorne+29%
Ironjawz+27%
Maggotkin of Nurgle+25%
Kharadron Overlords+24%

These are more balanced between being accessible and their skill depth. Newer players can still achieve reasonable results, while experienced players gain an advantage through better decision making.

Stormcast Eternals

Stormcast always seem to be a case unto themselves.

They are an example of a faction with a low skill floor but strong ceiling. Lower Elo players win only around 36% of their games, but higher Elo players reach 68% win rates.

This skill gradients suggest that Stormcast performance in strongly influenced by player skill.

The Concerning Faction

At the opposite end we have Gloomspite Gitz.

  • Low Skill Win Rate 33%
  • High Skill Win Rate 50%
  • Gradient +17%

Even amongst experienced players the factions struggles to push much beyond 50% win rate. They are vastly underpowered and very difficult to play.

What This Means for the Meta

These stats highlight that Faction strength is not experienced equally by everyone.

Some factions appear stronger because their elite players are able to extract more from them. Others are the same no matter the experience, while a few struggle across the board.

Understanding this helps explain why faction power can feel different depending on the player who uses them.

Final Thoughts

By looking at skill floors, ceilings, and gradients we can get a more complete picture of how factions perform across the competitive player base.

Some armies reward mastery, others offer a more forgiving learning curve, and a few may require further support to compete consistently.

Woehammer Data Literacy: Same Win Rate, Different Stories

One of the most misleading things about faction win rates is how convincing they look when two armies sit next to each other on the chart.

Cities of Sigmar and Gloomspite Gitz are a good example. Across the September Battlescroll both faction finished with almost identical headline win rates of 53%. To many players that reads as balance. Two factions that are equal in strength.

They are not the same, and this article is about why similar win rates can hide very different player experiences, and why relying on that one number can lead players to the wrong conclusions about what a faction is like when being used at a tournament.

The Illusion of Equality

If you only looked at win rates, then Cities and Gitz would be interchangeable. Both win roughly half their games and neither sits at the extremes. Neither faction looks like it is broken.

But win rates, as we’ve mentioned earlier in our series, answers only one question: How often the faction won. It won’t tell you how those wins were achieved, who’s achieving them and how often players walk away from events feeling like they had a decent weekend.

Once you start looking beyond that percentage, the picture changes.

Who is Actually Playing the Faction?

The first difference between the factions is who is taking them to events.

Cities of Sigmar has a relatively high average Elo but lower player numbers. A larger proportion of its games are played by experienced players, though it still has representation across the middle of the skill curve. It has slightly more appeal to competitive players and rewards strong fundamentals.

Gloomspite Gitz’ player base, while still high, is lower rated than Cities overall, with a much larger player base. It’s a faction that would appear to be chosen for its aesthetic and playstyle over being competitive.

That matters because a faction achieving a 53% win rate with a less experienced player base tells us something important. Gitz can generate wins without requiring tight, highly optimised play in every game.

Already we’re describing two different populations producing the same headline percentage.

What Does Success Look Like?

This is where faction win rate really starts to fall apart as player advice.

Cities of Sigmar shows a higher proportion of players finishing events with positive results. Almost 40% of players finished the weekend with 3-2 results, which reflects both consistency and the faction’s experienced player base.  Cities doesn’t spike events often, but many players walk away having felt competitive through the weekend.

While Gloomspite Gitz looks different. While its players are more likely than Cities to achieve three wins in the first three rounds of an event, there is a noticeable drop-off in later rounds. The 2-3 bracket is much larger, and far few players convert early success into strong final results.

What this suggests is a a faction that is tactically forgiving (players can win games even without perfect execution), but strategically volatile across a full tournament. As the event progresses Gitz struggle more at the top tables than Cities does.

Tactically Forgiving vs Strategically Forgiving

There is a difference. Gloomspite Gitz are tactically forgiving. Its mechanics and swing potential allow players to win games without flawless play or list optimisation. This helps explain why a lower Elo player base can still produce a strong overall win rate.

Cities of Sigmar is  less forgiving on a turn by turn bases. Mistakes can be punished more consistently and clean play matters.

But over the length of a weekend the roles reverse.

Cities is strategically forgiving. It recovers better across five rounds and produces steadier outcomes. Gitz by contrast are prone to late tournament collapse.

Both paths lead to the same win rate and the experience of getting there is very different.

How Fragile is Success?

Another difference lies in how dependent each faction is on certain choices.

Cities of Sigmar has reasonably large roster but would appear to have only a few viable builds. Its performance is spread across a smaller number of warscrolls with a vast amount of its roster being either ignored or experiencing very little play. Warscrolls like the Fusil-Major on Warhulk, Freeguild Cavaliers and Ironweld Great Cannon appear in the majority of lists.

This often happens when a smaller player bases identifies an optimised build and others follow. Success becomes concentrated, which can make the faction feel oppressive at times and fragile at others. Small rules or points changes may dramatically alter the performance even if the headline win rate barely moves.

Gitz shows broader warscroll usage. While not all choices are equally successful, far more of the roster sees meaningful pay. With a larger player base and lower average Elo, success is spread across different list types rather than being driven by a narrow core.

This contributes to both Gitz’ volatility and accessibility.

The Role of the Tournament Organiser and Battleplan Selections

Battleplans matter for each faction, but they matter unevenly.

Cities of Sigmars performs very well on five of the most popular battleplans, where it exceeds a 55% win rates, but struggles significantly on two others where their win rate is well below 45%.

Gits is more consistent across the battleplans with only one proving to be a significant advantage and one a clear problem.

As a result, Cities is more sensitive to the Battleplan selection than Gitz. Identical lists can feel dominant one weekend and underwhelming the next, depending entirely on which Battleplans the Tournament Organiser has chosen.

Again, this is invisible in the headline win rate.

Same Percentage, Different Questions

At this point the headline percentage of 53% becomes almost irrelevant.

Cities and Gitz may win their games at similar rates, but they ask very different things of their players.

Cities rewards experience, precision, and consistency. It performs better into top factions but pays for that with sensitivity to battleplans and narrower competitive cores.

Gloomspite Gitz is more accessible and tactically forgiving, allowing players to win games early and often, but struggles to convert that momentum into consistent late tournament success against top tier opposition.

Choosing between these factions based on the win rate alone, they are choosing blind.

What Players Should Take Away

This is the core of why I wrote the Data Literacy series.

Win rates aren’t lying to you, but they are summaries, and Summaries are dangerous when they’re treated as advice.

Two factions can sit on the same percentage and offer entirely different tournament experiences. One may feel consistent across battleplans but struggle into the top tier of factions. One may feel consistent but constrained. The other may feel explosive but unreliable.

Understanding which experience suits you matters more than the number reading 53%.

Final Thoughts

In our first article we talked about patience. In our second, interpretation. Now we’re talking above perspective.

Statistics don’t tell you what to play but they tell you what questions to ask.

Cities and Gitz look balanced at first glance. In practice, they are telling different stories and that gap is why win rates, taken alone, are such poor guides for players trying to understand their own results.

Same, same… but different.

Previous: Why Faction Win Rates Alone Are Bad

Woehammer Data Literacy: Why Faction Win Rates Alone Are Bad

One of the most common statements after attending a tournament is often:

“My army is bad”

It’s understandable sometimes. You’ve just played five games over a long weekend and walked away with one win. Maybe none. You check the win rates and see your faction sitting at 47%. You feel like the answer is staring back at you in one simple percentage.

But this is where win rates are not helpful.

Faction win rates are not useless, but taken on their own, they’re one of the worst tools a player can use to understand their own experience of the game. It compresses too much and hides too much. It doesn’t answer the question that you’re actually asking.

This article isn’t about telling you to ignore win rates. Far from it, it’s about explaining why win rates are only meaningful when read alongside other data.

What a Faction Win Rate Actually Tells You

All a win rate can answer is “Across all games played, how often did this faction win?”. That’s it.

It won’t tell you:

  • How hard the faction is to play
  • Whether mistakes are punished severely
  • Whether most players go 3-2 or 0-5
  • Whether success is driven by a few elite players
  • Whether the faction is forgiving or swingy

A win rate doesn’t know the difference between winning a game by only one victory point on turn five or tabling an opponent on turn three. Losing narrowly to an elite player will look the same as having your army rolled up by a newcomer. All of that is flattened into a percentage.

For GW and balancing the games they produce, that flattening is perhaps acceptable. But for individual players trying to decide what to play, what to stick with, and what factions they need to look out for, it’s terrible advice.

The Compression Problem

Win rates take a lot of variables and then compresses it into a single number. Player skill, list construction, internal balance of warscrolls, matchup spread, battleplans, learning curves is all compressed, and that is the source of most bad takes in competitive Warhammer.

A 52% faction can be brutally unforgiving and hard to learn. But that faction could be being carried by elite players who know how to get the most out of playing them. A 48% faction could be easily accessible, consistent, and easy to learn. The win rate chart won’t tell you which is which.

Why We Publish More Than Win Rates

This is the part that can get missed. We publish the faction win rates, but we try to also provide context around them. While win rates are often the headline, they won’t tell you the whole story. This is why we’ve deliberately expanded what we publish over time.

Here are some of the other views that you should be using.

Average Elo by Faction: Who’s Actually Winning?

We’ve now gathered a database of thousands of players across hundreds of tournaments across both third and fourth editions. We’ve also calculated the Elo of each player. An average Elo can tell you who is succeeding with an army and not just whether it wins.

When a faction has a high win rate and a high average Elo that’s often a sign the faction is being carried by experienced players. The success is real, but it comes with a skill tax (the faction rewards experience and punishes mistakes).

But you may also have factions with a very average win rate and an average or lower than average Elo. These factions are often easier to learn. They may not spike, but players can have decent weekends with them and not feel bad afterwards.

This difference matters far more than the raw percentage.

Popularity and Representation: Who’s Actually Playing the Army?

Popularity doesn’t mean power, but it can shape the outcomes.

High popularity factions are usually the ones that attract new players into the game. These are factions like Stormcast Eternals in Age of Sigmar, or Space Marines in 40k. High popularity factions will often have lists shared more frequently, get faced by more practiced opponents and generate more mirror matches. As a result, these factions often get “solved” quicker. Lists are either honed quickly, or counters are quickly found.

Rare factions benefit from unfamiliarity and are often played by specialists. They can appear stronger than they really are.

A 53% faction played by 250 people is different from a 53% faction played by 40.

This is why we include mirror matches within our data. Mirror matches are not just noise; they’re a direct result of faction popularity and part of the tournament experience players have.

Our previous approach before 2025 was to remove them to isolate the pure faction strength. Which is fine, but it answers a different question. Now we’re being deliberate, “What does it feel like to bring this army to events right now?”

Consistency: How Do Players Actually Perform?

Some of the most player relevant charts we publish barely get discussed. Charts like the percentage of players with positive results, the split between 4+ wins, 3-2, 2-3 finishes, and consecutive wins at events. These will help you shape your opinion a lot more than just the win rate alone.

A faction with lots of 3-2 finishes and very few highs or lows is often a better choice for most players than a spike army that occasionally wins events but collapses for the majority.

Most people don’t need to go 5-0 to enjoy a tournament, but they competitive games and a sense they were in the fight.

Battleplans and Battle Tactics

Another reason win rate is a poor advice on its own is that it ignores the situations that armies are playing in. Battleplan frequency, Battle Tactics pairing and faction performance by Battleplan all should be taken into account.

These charts show that some Battleplans favour certain playstyles and some Battle Tactic combinations are easier to score for some factions.

Results can be shaped by what Tournament Organiser’s decide is in the player pack, not just what you bring to the table. When an army feels strong one weekend and bland the next it can be the player pack talking and not the faction.

Transparency and the Database

One thing I think is important to say is that Woehammer doesn’t treat its charts as the final answers. For our Patreons we make the full Excel database available as a download. The database includes every list from every event we’ve recorded, the warcroll win rates (both included and excluded), Battle Tactic win rates by faction and breakdowns we don’t regularly publish on the site.

The point isn’t to overwhelm people with data. It’s to make the data we use and how we construct our charts visible. Anyone can see how the numbers are assembled, question our assumptions or explore the data themselves.

We’re trying really hard not to tell you what to think but to give you the tools to think more clearly.

What Should Players Do?

Look at the win rates, just don’t stop there.

If you’re choosing or judging an army ask a few questions:

  • Who is succeeding with it?
  • How many players are having decent weekends?
  • Is the success consistent or does it have spikes?
  • How popular is the faction?
  • How does it perform across the Battleplans?
  • How much data is there?

Win rate is the starting point, but to draw context you need to look at everything.

Final Thoughts

Faction win rates aren’t lying to you, they’re just summaries and summaries are dangerous when they’re treated as the be all and end all.

If our first article was about patience, then this one is about interpretation. We and others like us publish a lot of data because no single chart can explain what is a complex and evolving game. The aim is to give you some clarity not certainty. Statistics are just a tool in a toolbox.

In our next article we’ll compare two factions with similar win rates and look at just how differently they are when you take everything else into account.

Previous Article: Woehammer Data Literacy: Early Win Rates

Next Article: Same Win Rate, Different Story

Woehammer Data Literacy: Early Win Rates

This article is part of the Woehammer Data Literacy series, which focuses on how to read statistics.

Our aim is to explain what the data can reasonably tell us and what it’s limits are. We publish results once they become interesting, but we interpret them only when there is enough evidence to trust them. Statistics are a tool.

If you’re looking for instant tier lists, this series won’t give you those. If you want a clearer understanding of how to interpret the numbers, you’re in the right place.

Why Early Win Rates Lie

There is a familiar story to every Age of Sigmar rules cycle. A new battletome, or battlescroll lands and a handful of events are played. Someone posts a win rate chart and within days, the community has reached a conclusion. Sometimes within hours.

“This army is broken.”
“The army is dead.”
“GW didn’t test this.”

There’s nothing more certain, it’s like death and taxes, because the data never deserves it.

This article is the first in a new Woehammer Data Literacy series. It isn’t about defending or attacking any particular faction (Though I know you want to). It’s about how we read statistics, not just in Age of Sigmar, but for Old World and 40k as well. And how easily we mistake those early signals for final answers.

Because the biggest problem with data isn’t necessarily misinformation. It’s impatience.

Comfort in Numbers

Early win rates are enticing because they feel objective. A percentage carries an authority that anecdotes never seem to do. “Lumineth is on 60%” sounds strong, while “I keep losing with this army” does not.

The trouble is that early data is always fragile. At the start of a battlescroll, sample sizes are small. The meta hasn’t had time to adapt, and counter-play often hasn’t been identified yet. A single weekend of results can take up the picture. But that doesn’t make the data wrong, it just makes it provisional.

Pilot Effect

There is a pattern that appears again and again in early wargame statistics, the pilot effect.

A strong player picks up an army and they bring a sharp or unusual list. They go 5–0, sometimes the format of the event favours them, it could be a team events, an online TTS tournament, or a friendly local meta. Suddenly, the faction’s win rate spikes and screenshots circulate. Everyone then starts jumping to conclusions.

Nothing out of the ordinary happened. This is simply how small datasets behave. A single strong pilot can bend win rates out of shape, not because the army is dominant, but because skill differences matter when only a few games have been played.

The mistake comes later when those results are treated as universally replicable. Copying a list does not copy the decisions the player made when they went 5-0. Early win rates don’t really tell us whether we are seeing a powerful army, a powerful player, or a perfect matchup of factions on the way. They just flatten everything into one number and then we over-interpret it.

What a 5–0 Does to a Small Dataset

It’s easier to see the issue with an example.
Imagine a faction has 37 wins out of 68 games, thats a win rate of 54.4%. Now imagine the next event happens, and a strong player takes that faction and goes 5–0.

The new record becomes: 42 wins out of 73 games, the win rate jumps to around 57.5%.

No rules changed or points changed and the army didn’t get better overnight. One player simply added five wins to a small pool, and the conversation shifted from “healthy” to “dominant” in a single weekend.

Reverse the situation and the effect is just as dramatic. A new player goes 0–5, and the same faction suddenly looks mediocre or struggling. The only difference is a single player.

It’s not a flaw with the statistics; they’re just behaving as they should.

Decent Sample Sizes Aren’t Immune

Now take a sample size that feels more reassuring.

Imagine a faction with 80 wins out of 166 games, a win rate of 48.2%.

This looks stable. Then our strong player comes along (let’s call them Warson Chitlock), they add another 5 wins to that total. 85 wins out of 171 games, the win rate rises to 49.7%.

The shift is smaller than before, but it still has the potential to be meaningful. That could be enough to move a faction from “slightly underperforming” to “Ok”. Once again, nothing really changed, only five more wins were added.

This is what scale does. Larger sample sizes don’t remove variance, but they dampen it. Until you reach a point where individual players can’t move a number in a significant way, early conclusions are risky.

New Armies Don’t Start on a Level Playingfield

Another thing early data struggles to capture is who’s playing the faction.
Brand new armies like Helsmiths of Hashut attract new players to the hobby, and they attract people experimenting with unfamiliar mechanics. That often means a lower than average level of experience in the early days, combined with perhaps handful of very skilled players pushing the at the other end of the win rate scale. This results in polarisation.
Some players dominate and feel like the stats back their performance up. Others may struggle and feel as though the stats are telling them their experience doesn’t count. Both experiences are real and early win rates compress that complexity into a single percentage.

This is why early battlescroll debates so often feel like people talking past each other. They are describing different views of the same picture.

When Data Becomes a Conversation Killer

The most damaging misuse of early win rates is dismissal. Using a small, early dataset to tell someone that their frustration with a faction isn’t valid doesn’t help them improve and doesn’t help explain why they’re losing games. It simply shuts a conversation down.

Win rates are useful for spotting long term balance problems and identifying outliers. They are far less useful for explaining why someone went 1–4 at their local event, or why a new army feels punishing to learn.

A Note From Woehammer

It would be wrong of me to write an article like this without talking about our own history.

In the past, Woehammer has also published early win rates. We were keen to report what was happening in the first couple of weeks of a battlescroll. My intention was never to mislead, but I recognise now how easily those early numbers can be mis-interpreted.

I’ve learnt from my experiences, and while I still publish early results, I flag them clearly. On our win rate charts, factions that I feel do not yet have enough data are highlighted in bold italics to signal that the data set is small and should be treated with caution.

For me, a meaningful data set on a faction does not really begin until there are at least 100 GT games in the database. That’s a judgement call, based on watching early spikes. Below that threshold, it can still vary and pilot skill will skew results and conclusions. Sometimes the honest answer is simply: we don’t know yet.

A Calmer Take

Early win rates are not lies, but they are incomplete and easy to misinterpret. They should prompt questions, not panic and we should have more patience rather than treating them as a certainty.

Don’t let a two weeks worth of data convince you that the verdict is already in.

Next Article: Why Faction Win Rates Alone Are Bad