diff --git a/beaky-backend/src/beaky/link_classifier/classifier.py b/beaky-backend/src/beaky/link_classifier/classifier.py
index 8df65f2..046bbab 100644
--- a/beaky-backend/src/beaky/link_classifier/classifier.py
+++ b/beaky-backend/src/beaky/link_classifier/classifier.py
@@ -133,7 +133,7 @@ def _extract_legs(page: Page, fallback_date: datetime | None) -> list[Bet]:
         title = leg.locator("h3").first.get_attribute("title") or ""
         date_text = leg.locator(".betslip-leg-date span").first.inner_text()
         bet_text = leg.locator("[data-selection-id]").first.inner_text()
-        league = leg.locator(".f-mt-1.f-leading-tight.f-line-clamp-2").first.inner_text()
+        league = leg.locator(".f-mt-1.f-leading-tight.f-line-clamp-2").first.inner_text().replace("Fotbal /", "")
 
         team1, team2 = _parse_teams(title)
         date = _parse_czech_date(date_text) or fallback_date or datetime.now()
diff --git a/beaky-backend/src/beaky/resolvers/resolver.py b/beaky-backend/src/beaky/resolvers/resolver.py
index 0f1511b..87f143b 100644
--- a/beaky-backend/src/beaky/resolvers/resolver.py
+++ b/beaky-backend/src/beaky/resolvers/resolver.py
@@ -213,13 +213,13 @@ class TicketResolver:
             return self._league_cache[key]
 
         # Static map — fuzzy match
-        patterns = list(self._league_map.keys())
+        patterns = [x.lower().strip() for x in self._league_map.keys()]
         idx, score = _best_match(key, patterns)
         if idx is not None:
             best_id = self._league_map[patterns[idx]]
             _ansi.log(_ansi.gray(f"  │  league {league_name!r} -> id={best_id} (static map, pattern={patterns[idx]!r}, score={score:.2f})"))
-            self._league_cache[key] = (best_id, 1.0)
-            return best_id, 1.0
+            self._league_cache[key] = (best_id, score)
+            return best_id, score
 
         # API fallback — fuzzy match all results
         _ansi.log(_ansi.gray(f"  │  GET /leagues search={league_name!r}"))
@@ -307,7 +307,10 @@ def _best_fixture_match(fixtures: list[dict[str, Any]], team1: str, team2: str,
     # Name similarity is the primary signal; date proximity is a tiebreaker
     home_names = [f["teams"]["home"]["name"] for f in fixtures]
     away_names = [f["teams"]["away"]["name"] for f in fixtures]
+    print(home_names)
+    print(away_names)
     name_scores = [(_similarity(team1, h) + _similarity(team2, a)) / 2 for h, a in zip(home_names, away_names)]
+    print(name_scores)
     date_proxies = [_date_proximity(f, center) for f in fixtures]
     combined = [n * 0.8 + d * 0.2 for n, d in zip(name_scores, date_proxies)]
     best_idx = max(range(len(combined)), key=lambda i: combined[i])