Retrospective scoring of Henrik Falchener against the corrected CB_ANCHOR model — run on data from before Viking FK signed him. The question: did his pre-Viking data already contain the same CB_ANCHOR markers he later showed at Viking?
Across both pre-Viking stints — Ørn Horten in the Norwegian second division and Egersund in Obos Ligaen — the corrected model returns STRONG BUY. The profile was there. The corrected model would have recognised the same CB_ANCHOR role profile in his pre-Viking data.
| duelPct | +2.8 pp vs target |
64.8% | / 62.0 |
| inter/90 | +39% vs target |
6.66 | / 4.80 |
| pass% | −2.0 pp vs target |
83.0% | / 85.0 |
| duelPct | +2.1 pp vs target |
64.1% | / 62.0 |
| inter/90 | +8% vs target |
5.20 | / 4.80 |
| pass% | +6.5 pp vs target |
91.5% | / 85.0 |
Falchener's CB_ANCHOR profile is built around two hardCore metrics: duelPct and inter/90. Both cleared target in every period of his career — at Ørn Horten (2. Division), at Egersund (Obos Ligaen), and now at Viking (Eliteserien). The pass% held between 83–92% throughout, consistently near or above the 85% target.
The key finding is not that the scores are high. It is that the scores are stable. A player whose profile inflates up a league tier will show high raw stats and low translation reliability. Falchener's inter/90 went from 6.66 (2. Division) to 5.20 (Obos) to 5.31 (Eliteserien). The natural compression as competition level rose is visible — and the numbers held above target at every step.
duelPct followed the same pattern: 64.8 → 64.1 → 62.4. Marginal compression, target always cleared. This is the signature of a player whose defensive fundamentals translate — not one whose numbers are a product of inferior opposition.
This retrospective was the direct catalyst for a model revision in April 2026. When Falchener's pre-Viking data was first run through the original model, rate metrics (passPct, duelPct) were mechanically downweighted by league coefficient — producing adjusted values of 46% duelPct and 66% passPct for the Egersund period. The model returned MONITOR.
That output was implausible. A CB who wins 64% of duels in Obos does not become a 46% duel winner at Eliteserien level. The original implementation conflated production volume (where league adjustment is defensible) with success rate (where it is not). The mismatch was observed during retrospective testing, documented, and corrected. Rate metrics are now evaluated at raw values against role benchmarks.
Under the corrected model: STRONG BUY in both pre-Viking periods. The corrected model would have recognised the same CB_ANCHOR role profile in his pre-Viking data.
| Period | Original model | Corrected model | Change |
|---|---|---|---|
| Ørn Horten 2022–23 | 0.92 BUY | 1.40 STRONG BUY | +0.48 |
| Egersund 2024 | 0.90 MONITOR | 1.27 STRONG BUY | +0.37 |
The corrected CB_ANCHOR model would have surfaced Falchener's pre-Viking data as a strong match to the CB_ANCHOR profile he later became at Viking. The key signal is not only the score, but the stability of his hardCore metrics across three levels: 2. Division, Obos Ligaen and Eliteserien. This case supports the internal consistency of the CB_ANCHOR archetype logic and demonstrates why the April 2026 rate-metric revision was necessary. Lower-league STRONG BUY outputs should still be treated as shortlist triggers requiring video, physical and role-context validation — but the profile was clearly visible in the data.