The reviewer view for How To Punish A Witch is based on an identified sample of public reviewers. Player, follower, and wishlist geography are directional models that use public reviewer geography and available game-level signals; they are not exact country-by-country account counts.
Use the tabs to compare relative geographic shares and check each view's confidence label. Modeled percentages should be treated as directional context, especially when the identified reviewer sample is small or contains unknown locations.
Regional Audience Explorer
Open a tab to inspect the full country ranking, the highlighted world map, and the current base or identified sample behind each region view.
Top Players Region Overview
SteamPulse's directional player-region model for How To Punish A Witch assigns its largest shares to United States (42.8%), Latvia (17.7%), China (7.6%). The model blends the available public-reviewer geography, reviewer playtime signals, current player scale, and a disclosed Steam market prior. The dataset labels confidence as exploratory.
These percentages are relative model weights, not observed locations or verified counts of active players. Use the map to compare the model's country distribution, not to infer exactly how many people are playing in any country.
Reviewers Region Overview
How To Punish A Witch's identified public-reviewer sample spans 2 countries and is led by United States (66.7%), Latvia (33.3%). United States accounts for 66.7% of reviewers with an identified country (2 reviewers).
This is an observed sample of 3 public reviewer profiles with visible country data, not a census of all reviewers, owners, or players. Another 17 sampled reviewer profiles did not expose a country and are excluded from the percentages. The dataset labels confidence as exploratory.
Followers Region Overview
SteamPulse's directional follower-region model for How To Punish A Witch assigns its largest shares to United States (51.1%), Latvia (19.7%), China (6.4%). It blends the modeled player and wishlist distributions with the available public-reviewer geography. The dataset labels confidence as exploratory.
Steam does not publish account-level follower locations for this view. The percentages are normalized model weights, not observed follower counts, and they should not be read as proof of attention or future activity in a country.
Wishlist Region Overview
SteamPulse's directional wishlist-region model for How To Punish A Witch assigns its largest shares to United States (37.4%), Latvia (13.9%), China (9.4%). It combines the modeled player distribution, available public-reviewer geography, review penetration, release recency, and a disclosed market prior. The dataset labels confidence as exploratory.
Steam does not publish account-level wishlist locations for this view. The percentages are normalized model weights, not observed wishlist counts or evidence of purchase intent, and should be used only to compare the model's relative country distribution.
Player Region Distribution
| Country | Modeled share |
|---|
| United States | 42.8% |
| Latvia | 17.7% |
| China | 7.6% |
| Russia | 3.6% |
| Brazil | 3.1% |
| Germany | 2.7% |
Directional model - exploratory confidence; percentages are estimates, not counted accounts.
Wishlist Region Distribution
| Country | Modeled share |
|---|
| United States | 37.4% |
| Latvia | 13.9% |
| China | 9.4% |
| Russia | 4.4% |
| Brazil | 3.9% |
| Germany | 3.3% |
Directional model - exploratory confidence; percentages are estimates, not counted accounts.
Reviewer Region Distribution
| Country | Identified reviewers | Share |
|---|
| United States | 2 | 66.7% |
| Latvia | 1 | 33.3% |
Observed public-reviewer sample: 3 identified accounts - exploratory confidence
Follower Region Distribution
| Country | Modeled share |
|---|
| United States | 51.1% |
| Latvia | 19.7% |
| China | 6.4% |
| Russia | 2.8% |
| Brazil | 2.4% |
| Germany | 2.0% |
Directional model - exploratory confidence; percentages are estimates, not counted accounts.
