Add forensic diagnostic logging to fetch_all_upcoming_hr_props

Instruments the per-event odds path with full visibility into every step:
- Step 1: log exact events request params (apiKey masked)
- Step 2: log exact per-event odds params + HTTP status before raise_for_status
- Response: log type, top-level keys, books returned, markets per book, outcome counts
- Row-build loop: counters for bookmakers/markets/outcomes/rows plus per-reason skip counts
- Sample first 3 outcomes verbatim (keys, name, description, price, point)

No fixes applied. Diagnostic only — break point to be identified from log output.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

data/provider_theoddsapi.py

@@ -221,7 +221,11 @@ class TheOddsAPIProvider(MarketProviderBase):
             "commenceTimeTo": (now + timedelta(days=7)).strftime("%Y-%m-%dT%H:%M:%SZ"),
         }
 
-        _diag_log.info("[upcoming_hr_props] Step1 events window=now+7d")
+        _diag_log.info(
+            "[upcoming_hr_props] Step1 GET %s params=%s",
+            events_url,
+            {k: (v if k != "apiKey" else v[:6] + "...") for k, v in events_params.items()},
+        )
         try:
             r1 = requests.get(events_url, params=events_params, timeout=30)
             _diag_log.info(
@@ -262,8 +266,24 @@ class TheOddsAPIProvider(MarketProviderBase):
                 "oddsFormat": "american",
                 "dateFormat": "iso",
             }
+            _diag_log.info(
+                "[upcoming_hr_props] Step2 event_id=%s %s@%s url=%s params=%s",
+                event_id,
+                away_team,
+                home_team,
+                odds_url,
+                {k: (v if k != "apiKey" else v[:6] + "...") for k, v in odds_params.items()},
+            )
             try:
                 r2 = requests.get(odds_url, params=odds_params, timeout=30)
+                _diag_log.info(
+                    "[upcoming_hr_props] Step2 HTTP %s | remaining=%s | event_id=%s %s@%s",
+                    r2.status_code,
+                    r2.headers.get("x-requests-remaining", "?"),
+                    event_id,
+                    away_team,
+                    home_team,
+                )
                 r2.raise_for_status()
             except (requests.HTTPError, requests.RequestException) as exc:
                 _diag_log.warning(
@@ -276,30 +296,71 @@ class TheOddsAPIProvider(MarketProviderBase):
             bookmakers = (
                 event_data.get("bookmakers", []) if isinstance(event_data, dict) else []
             )
+            _data_type = type(event_data).__name__
+            _top_keys = list(event_data.keys()) if isinstance(event_data, dict) else f"LIST len={len(event_data)}"
+            _bk_keys = [b.get("key") for b in bookmakers]
+            _markets_by_book = {
+                b.get("key"): [m.get("key") for m in b.get("markets", [])]
+                for b in bookmakers
+            }
+            _outcome_counts = {
+                f"{b.get('key')}:{m.get('key')}": len(m.get("outcomes", []))
+                for b in bookmakers
+                for m in b.get("markets", [])
+            }
             _diag_log.info(
-                "[upcoming_hr_props] %s@%s books=%s",
-                away_team, home_team, [b.get("key") for b in bookmakers],
+                "[upcoming_hr_props] %s@%s type=%s top_keys=%s books=%s markets=%s outcome_counts=%s",
+                away_team, home_team, _data_type, _top_keys, _bk_keys,
+                _markets_by_book, _outcome_counts,
             )
 
+            _bookmakers_seen = 0
+            _markets_seen = 0
+            _outcomes_seen = 0
+            _rows_appended = 0
+            _skip_market_mismatch = 0
+            _skip_empty_name = 0
+            _skip_missing_price = 0
+            _sample_logged = False
+
             for bookmaker in bookmakers:
+                _bookmakers_seen += 1
                 book_key = str(bookmaker.get("key", "") or "")
                 book_name = BOOK_KEY_MAP.get(book_key, book_key)
 
                 for market in bookmaker.get("markets", []) or []:
+                    _markets_seen += 1
                     market_key = str(market.get("key", "") or "")
                     if market_key != "batter_home_runs":
+                        _skip_market_mismatch += 1
                         continue
                     market_name = MARKET_NAME_MAP.get(market_key, market_key)
 
                     for outcome in market.get("outcomes", []) or []:
+                        _outcomes_seen += 1
+
+                        if not _sample_logged and _outcomes_seen <= 3:
+                            _diag_log.info(
+                                "[upcoming_hr_props] sample outcome keys=%s name=%r description=%r price=%r point=%r",
+                                list(outcome.keys()),
+                                outcome.get("name"),
+                                outcome.get("description"),
+                                outcome.get("price"),
+                                outcome.get("point"),
+                            )
+                        if _outcomes_seen >= 3:
+                            _sample_logged = True
+
                         player_name_raw = str(
                             outcome.get("description", "") or outcome.get("name", "") or ""
                         ).strip()
                         if not player_name_raw:
+                            _skip_empty_name += 1
                             continue
 
                         price = outcome.get("price")
                         if price is None:
+                            _skip_missing_price += 1
                             continue
 
                         rows.append(
@@ -319,6 +380,15 @@ class TheOddsAPIProvider(MarketProviderBase):
                                 "line": _safe_float(outcome.get("point")),
                             }
                         )
+                        _rows_appended += 1
+
+            _diag_log.info(
+                "[upcoming_hr_props] %s@%s counters: bookmakers=%d markets=%d outcomes=%d rows=%d "
+                "skip_market=%d skip_name=%d skip_price=%d",
+                away_team, home_team,
+                _bookmakers_seen, _markets_seen, _outcomes_seen, _rows_appended,
+                _skip_market_mismatch, _skip_empty_name, _skip_missing_price,
+            )
 
         _diag_log.info("[upcoming_hr_props] total rows=%d", len(rows))
         return pd.DataFrame(rows)