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who's shaking · 7 days
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Total War: SHOGUN 2

Steam ↗

Too early to tell: the game's usual hasn't settled yet

How the game is doing

76% positive reviews over the last 7 days; the usual for the game hasn't settled yet · Oct 29 – Nov 4

What players write this week

  • «CA wanted to get rid of this version of the game for a remaster that added more bugs. This version of the game better stay on the store in case anyone else wants to purchase it» ▼ not recommended
  • «you shouldn't have de-listed this game, and pushed the Complete edition out of the steam workshop in favour of some ai-based cash grab. Zero Trust, work to get it back or lose this customer.» ▼ not recommended
  • «Really CA?» ▼ not recommended

Shift log

Shifts over the past year: 4

  • Sharp rise (M2). Oct 29, 2026 · 53% before, 76% after.
  • Rising, but below usual (M2). Oct 20, 2026 · usually 88%, 53% over the 7 days since the event; 38% in the week before the event.
    AI breakdown: The share of Not Recommended reviews fell from 62% to 47%, while complaints about pricing and purchases dropped from 65% to 46%. The mood shifted sharply that same week, with many games on the dashboard moving the same way — a platform-wide shift, not a story specific to this game.
  • "Total War: Shogun 2 Complete Edition has fallen short of both your expectations and our own standards", Sega say as they pull it from stores and restore the original — Mood dipped (M4). Oct 15, 2026 · usually 90%, 42% over the 7 days since the event; 40% in the week before the event.
    AI breakdown: On October 15, 2026, Sega pulled the Complete Edition from sale and restored the original game. The share of Not Recommended reviews fell from 61% to 58%, while complaints about access and accounts dropped from 5% to 1%.
  • Sharp drop (M4). Oct 9, 2026 · usually 90%, 42% over the 7 days since the event; 69% in the week before the event.
    AI breakdown: On October 9, the share of Not Recommended reviews rose from 31% to 58%, while complaints about pricing and how the company treats players climbed from 35% to 63%. The mood turned sharply, with no game news around that date.

Digest · Oct 22, 2026 – Oct 29, 2026

AI breakdown: Shogun 2 reviews climb back to 52% after the delisting Sega pulled the Complete Edition on October 15 and restored the original. Positive reviews rose from 42% to 52% on a much smaller flow of reviews. Complaints about business conduct and the content access model dropped sharply; players now praise the visuals and grumble about stability and crashes.

How the game is doing

How it's computed

Positive share
Positive reviews are divided by all reviews in the smoothing window as sums, not as an average of daily shares: a day with three reviews does not weigh as much as a day with three thousand. Each day gets its own window: it grows until it holds 100 reviews, up to 29 days; with fewer than 10 reviews in the window there is no share, and the line breaks; with fewer than 30 the line is gray and dashed — “too little data” — with no fill above or below the usual and no shifts: a share from a handful of reviews jumps around on its own. A week or more without a single review on Steam is hatched — “no reviews”; the line is dashed there too, since it rests on reviews from nearby days. The window never crosses the release date: different players write before and after launch.
Usual for the game
The median of weekly positive shares over the 90 days before the week in question (for the scoreboard — before the last 7 days): the 90 days are cut into weeks counting back from it, each week gets its own share, and the usual is the one in the middle. That way a review bomb or a one- or two-week wave of reviews does not move the baseline, however many reviews it brings. A week with fewer than 20 reviews is merged with its neighbor: a share from a handful of reviews means nothing. Never earlier than the release date: before it, different players were writing. Fewer than 14 days of baseline after release — there is no “usual” yet, and the game runs in release mode without a magnitude.
Big events
Milestones, major updates and announcements, as labeled by the model from game news; without labeling — expansions, seasons and patches whose notes are several times longer than usual for the game. Several posts about one release within three days count as one event.
Reaction to an event
The positive share over the 7 days from the event day (fewer while the week is still running) compared with the game's usual before it — the same measure as the scoreboard and the home page; for an event this week, the usual is the scoreboard's. “Players loved it” or “mood dipped” starts at M1: a shift of at least 2 pp and more than two standard errors after correcting for the game's noise; otherwise “within the game’s usual swings”. The second line is the week after against the week before, with a 95% interval and growth in total and negative reviews: it shows a separate rise or dip before the event. Until the week is over, the delta is preliminary: no M and no “significant” until a full week; under 5 days it is “too early to tell”. Without a settled usual (a launch, a new game) — only the week before and after. For labeled games — also the topics whose criticism shifted by 5 pp or more. A rebound is a significant shift within two weeks after a shift the other way, if the share came back to its earlier level (within 3 pp or half the margin); if it went further, it is a rise or a dip “after”, with what came out in between. For launches, seasons, expansions and major updates the outcome is read from the event and is never a rebound.
Shifts
Days when the 7-day average share after differs markedly from the 7 days before. At the edge of the series the window may be shorter — at least 5 days on each side; such a day is marked “window truncated” and read more cautiously. A shift next to a serious event becomes part of it; a shift without an event stays in the log only if the shift exceeds three standard errors adjusted for the game's noise — stricter than for an event: the detector picks such a day itself, and a softer test would find shifts in pure noise. Timing is a hint, not proof of cause.
Game noise
How many times more the daily positive share swings than pure chance would explain: a big game has its own “moods of the day” — weekends, streams, review waves. The estimate is a median across days, so one review-bomb day does not move it. Every estimate divides the evidence by this noise; otherwise any trifle would be significant for a game with thousands of reviews a day.
Weekly shake
The last 7 days against the 90 days before. Mood is the positive share and its shift from the baseline, adjusted for the game's noise; activity is reviews per day against the usual level. The mood shift becomes the magnitude M. A surge in reviews without a mood shift does not raise the magnitude and is labeled separately — “reviews ×3”. “Shaking now” in the filter and the top of the “by shake” order mean M2 or higher or three times the usual reviews; next come M1 or 1.8 times the usual reviews.
Magnitude M0-M5
The strength of the week's mood shift — the lower of two steps: by significance (from 2, 3, 5, 8 and 13 standard errors adjusted for the game's noise) and by size (from 2, 3, 5, 8 and 12 pp). So a shift counts only if it is both not random and noticeable: for a game with thousands of reviews any trifle is significant, for a small one even 10 pp can be noise. M0 — calm, M1 — tremor, M2-M3 — shaking, M4-M5 — quake. M compares the week with the previous three months, so slow mood drift raises it too: it means “not as usual”, not necessarily “something happened”. What happened is in the shift log.
    Words
    Share of reviews with a word over the period compared with the 90 days before it, with add-one smoothing so rare words don't shoot up. English and Russian stop words are dropped; other languages are left out.
    Model labeling and analyses
    The model tags a review with catalog topics marked “praised/criticized”. A sample is labeled — up to 30 reviews a day, more on shaking days — so topic shares are of labeled reviews. Event analyses and weekly digests are written by the model from ready numbers in both languages at once, and code checks every number, every supporting review and that both languages say the same thing; anything that fails isn't shown.

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