Solo Ranked Recon
Division 1A
Eight players and the league's ceiling: two enrolled above 2500, nobody below 2170. On lifetime outcomes the sub-division splits cleanly in half — four proven winners, four high-volume players ground down to equilibrium — and the seeding only partly agrees with that split.
Indonesia is 1A's fault line — a listed strength for two players and a listed weakness for three, with Australia its near-mirror (three strong, two weak). The headline individual stat belongs to Geooo: a 69.8% lifetime win-rate held across 14,153 duels. Volume normally drags win-rates toward 50%; his hasn't moved.
Salex and Harrier carry no live current rating (delisted for inactivity), so their current-form read leans on enrollment snapshots. Everyone's per-mode samples are healthy — this is one of the two cleanest datasets in the league.
State of the field
A snapshot before the detail: where the eight sit on rating, experience and volume. The headline is the gap between seeded and current form — this is a field well off its collective best.
Seeded ratings average 2341; current form averages 1963 — the division sits 378 points below its enrollment ratings. 2 players carry no live rating, so a seeded value stands in; the current curve is, if anything, flattered. The axis is scaled to this division's own range.
Tick marks are individual players. Mean ~5.5 years, but read it loosely: account age is only a proxy for GeoGuessr experience, and it breaks for fresh accounts of veteran players. Median lifetime volume is 5,043 ranked duels.
Geographic signal map
Every player against every country that appears in a top-3 or bottom-3 list. Warm = a ranked strength, cool = a ranked weakness, intensity = list position. Columns are ordered by how much the field cares about that country. Hover any cell for the value.
Ordinal signal only: a blank cell means the country isn't in that player's top or bottom three — not that they're necessarily neutral there.
Contested ground
Countries that are a strength for some players and a weakness for others — the regions where a matchup is won or lost rather than shared.
Field patterns
Where the field collectively excels and bleeds. Shared weaknesses are the soft ground worth drilling — common blind spots across skill tiers.
Common strengths
- AU — Eamonn, Lubec, Kalesalad
- ID — Salex, Math56
- CA — Lubec, Harrier
- US — Geooo, Lubec
- ZA — Geooo, Zakerr, Kalesalad
Common weaknesses
- CA — Math56, Eamonn
- US — Zakerr, Harrier
- RU — Lubec, Kalesalad
- ID — Lubec, Kalesalad, Harrier
- ES — Salex, Eamonn
Shared soft spots (2+)
- CA — Math56, Eamonn
- US — Zakerr, Harrier
- RU — Lubec, Kalesalad
- ID — Lubec, Kalesalad, Harrier
- ES — Salex, Eamonn
- AU — Math56, Harrier
Player clusters — win-rate model
Our primary segmentation, built on lifetime per-mode win-rates over hundreds-to-thousands of ranked duels per player — measured outcomes, not imputed ratings, so no synthetic data props it up. Features: current ELO, lifetime overall and per-mode win-rate, a win-rate-derived mode lean, accuracy and volume. Ward and k-means agree. Two players resolve as singletons for real reasons (below). A second, rating-based model in section 06 triangulates this one. At n=8 these are reading aids, not hard tiers — silhouette ≈ 0.31, a real but soft structure.
52–59% across heavy volume — Lubec's 26,111 duels are the second-largest sample in the league, at 52.3%. Nothing here is weak; it's what sustained play against matched opposition looks like. Kalesalad's 58.9% is the group's high-water mark.
All four hold 61–70% lifetime win-rates on serious samples (2,000–14,000 duels). Geooo's 69.8% at 14k volume is the league's single most impressive number. Harrier sits here despite carrying seed 8 — on outcomes, the most under-seeded player in the division.
How to read the two cluster views
The clustering runs in a 6-dimensional feature space; these two plots are different windows onto it. The PCA map flattens that space to its two most informative axes; the dendrogram shows the order in which players merged into groups. Same data, complementary views.
PCA map. Each player projected onto the two directions of greatest spread in this division's data. A principal component has no fixed meaning — it is whatever mix of the six features varies most here — so the axis readings below are derived from this division's own loadings, not carried over from any other division. PC1, the horizontal axis (71% of the variance): moving right means a restricted-mode lean, more even win-rates across modes and a wider average guess distance. PC2, the vertical axis (14%): moving up means more ranked volume, a higher current rating and a higher lifetime win-rate. Two of the six features deserve definition, since both are computed from win-rates rather than from how often a mode is played: mode lean = mean(NM%, NMPZ%) − Moving%, i.e. which way a player's results tilt; mode spread = the standard deviation across their three mode win-rates, i.e. how lopsided they are regardless of direction. A player who wins 74/75/71 across the modes and one who wins 49/52/47 both score flat on these; a 65/54/33 profile scores strongly on both. Distance on the plot ≈ overall dissimilarity. Together the two axes hold 85% of the variance in the six clustering features.
Dendrogram. Built bottom-up: each player starts alone, then the two most similar join, repeatedly, until all are one. Bar height = how different two groups were when they merged — low joins (Math56+Zakerr) are near-twins; high joins happen late and grudgingly. Reading down from the top, the first split separates the strong tier from everyone else; cut the tree at any height to get that many clusters. Dot colour marks final cluster membership.
Two models, triangulated
We trust two models, built on independent signals. The win-rate model (section 04) asks who beats their opponents, from measured lifetime outcomes. The ELO model asks where the ladder places them now, from current per-mode ratings with mode-lean computed only from real (non-imputed) data — the honest rating view, silhouette 0.29. Where they agree, a finding is robust; where they diverge, that disagreement is itself signal worth a second look.
| Player | Win-rate model | ELO model |
|---|---|---|
| Kalesalad | The equilibrium four | Higher rated |
| Lubec | The equilibrium four | Higher rated |
| Math56 | The equilibrium four | Higher rated |
| Zakerr | The equilibrium four | Isolated |
| Eamonn | Proven winners | Higher rated |
| Geooo | Proven winners | Higher rated |
| Harrier | Proven winners | Higher rated |
| Salex | Proven winners | Higher rated |
What the divergences mean. The two models read 1A very differently, and the difference is the story. The win-rate model halves the division; the ELO model puts seven of eight in one band and isolates Zakerr — the only player whose current rating sits above his enrollment snapshot (+115). Current ratings compress at the top of the ladder; lifetime outcomes still separate. Harrier is the divergence worth acting on: seed 8 by enrollment ELO, top-cluster by results, 76.8% lifetime moving. Treat his seed as an accounting artefact.
Geographic similarity
Where each player's competence is concentrated — thread weight is the overlap in best-country lists. GeoGuessr ranks these countries within each player, so the lists say where someone is strong relative to their own baseline, not how strong they are.
Reading it
Thick threads mean two players' strengths sit in the same places — on a map drawn from those countries, neither gets an edge from familiarity. Thin or absent threads mean their competence is concentrated elsewhere, so map choice cuts between them. Because each player's top-3 is ranked against their own record, this maps the shape of a player's geographic knowledge with their overall level divided out: a Division 8 player and a Division 1 player can share a thread while being nowhere near each other in strength. It is a map of where someone is strong, not how strong they are — which is why it is kept separate from the clustering, and why a thread is a matchup cue rather than a prediction.