diff --git a/config/application.yml b/config/application.yml
index 852e2fc..93d6049 100644
--- a/config/application.yml
+++ b/config/application.yml
@@ -20,7 +20,7 @@ resolver:
     1. dánsko: 119
     1. francie: 61
     1. itálie: 135
-    1. itálie - ženy: 794
+    1. itálie - ženy: 139
     1. německo: 78
     1. nizozemsko: 88
     1. polsko: 106
@@ -30,7 +30,7 @@ resolver:
     1. skotsko: 179
     1. slovensko: 332
     1. španělsko: 140
-    1. wales: 771
+    1. wales: 110
     # Second divisions
     2. anglie: 40
     2. česko: 346
@@ -39,7 +39,7 @@ resolver:
     2. německo: 79
     2. nizozemsko: 89
     2. rakousko: 219
-    2. slovensko: 333
+    2. slovensko: 506
     2. španělsko: 141
     # Third divisions
     3. francie: 63
diff --git a/src/beaky/resolvers/resolver.py b/src/beaky/resolvers/resolver.py
index 24d1082..7a3fac5 100644
--- a/src/beaky/resolvers/resolver.py
+++ b/src/beaky/resolvers/resolver.py
@@ -167,8 +167,11 @@ class TicketResolver:
         league_id, league_conf = self._resolve_league(bet.league)
         cache_key = (date_str, league_id)
 
+        window_end = center + timedelta(days=_DATE_WINDOW)
+        cache_may_be_stale = window_end >= date.today()
+
         if cache_key not in self._fixture_cache:
-            if cache_key in self._disk_cache:
+            if cache_key in self._disk_cache and not cache_may_be_stale:
                 self._fixture_cache[cache_key] = self._disk_cache[cache_key]
                 print(_ansi.gray(f"  │  /fixtures served from disk cache ({len(self._fixture_cache[cache_key])} fixtures)"))
             else:
@@ -183,10 +186,10 @@ class TicketResolver:
                 resp.raise_for_status()
                 self._fixture_cache[cache_key] = resp.json().get("response", [])
                 print(_ansi.gray(f"  │     {len(self._fixture_cache[cache_key])} fixtures returned"))
-                finished = [f for f in self._fixture_cache[cache_key] if _is_finished(f) == 1.0]
-                if finished:
-                    self._disk_cache[cache_key] = finished
-                    print(_ansi.gray(f"  │     {len(finished)} finished fixture(s) written to disk cache"))
+                cacheable = [f for f in self._fixture_cache[cache_key] if f.get("fixture", {}).get("status", {}).get("short") != "NS"]
+                if cacheable:
+                    self._disk_cache[cache_key] = cacheable
+                    print(_ansi.gray(f"  │     {len(cacheable)} non-NS fixture(s) written to disk cache"))
         else:
             print(_ansi.gray(f"  │  /fixtures (±{_DATE_WINDOW}d of {date_str}, league={league_id}) served from memory"))
 
@@ -200,11 +203,16 @@ class TicketResolver:
         if key in self._league_cache:
             return self._league_cache[key]
 
-        for pattern, league_id in self._league_map.items():
-            if pattern in key:
-                print(_ansi.gray(f"  │  league {league_name!r} -> id={league_id} (static map)"))
-                self._league_cache[key] = (league_id, 1.0)
-                return league_id, 1.0
+        # Use longest-match so "1. itálie - ženy" beats "1. itálie"
+        best_pattern, best_id = max(
+            ((p, lid) for p, lid in self._league_map.items() if p in key),
+            key=lambda t: len(t[0]),
+            default=(None, None),
+        )
+        if best_id is not None:
+            print(_ansi.gray(f"  │  league {league_name!r} -> id={best_id} (static map, pattern={best_pattern!r})"))
+            self._league_cache[key] = (best_id, 1.0)
+            return best_id, 1.0
 
         # Fall back to API search — lower confidence since first result is taken unverified
         print(_ansi.gray(f"  │  GET /leagues search={league_name!r}"))
@@ -278,10 +286,7 @@ def _best_fixture_match(fixtures: list[dict[str, Any]], team1: str, team2: str,
     for f in fixtures:
         home = f["teams"]["home"]["name"]
         away = f["teams"]["away"]["name"]
-        name_score = max(
-            _similarity(team1, home) + _similarity(team2, away),
-            _similarity(team1, away) + _similarity(team2, home),
-        ) / 2
+        name_score = (_similarity(team1, home) + _similarity(team2, away)) / 2
         date_prox = _date_proximity(f, center)
         # Name similarity is the primary signal; date proximity is a tiebreaker
         combined = name_score * 0.8 + date_prox * 0.2