diff --git a/etl/eligibility/ha_15_32/app.py b/etl/eligibility/ha_15_32/app.py
index ccceb05f..48bfeb2c 100644
--- a/etl/eligibility/ha_15_32/app.py
+++ b/etl/eligibility/ha_15_32/app.py
@@ -833,6 +833,18 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
         results_df["warmfront_identified"]
     ]
 
+    # Aggregates of no eco and gbis jobs identified
+    n_eco = results_df["eco4_eligible"].sum()
+    # Gbis is rows where eco4 is not eligible
+    n_gbis = results_df[
+        (results_df["gbis_eligible"] == True) & (results_df["eco4_eligible"] == False)
+        ]["gbis_eligible"].sum()
+
+    pipeline_potential = results_df[
+        (results_df["warmfront_identified"] == True) | (results_df["eco4_eligible"] == True) | (
+            results_df["gbis_eligible"] == True)
+        ]
+
     success_rate = warmfront_identified["gbis_eligible"].sum() / warmfront_identified.shape[0]
     # For HA32, this is 89%
 
@@ -890,8 +902,16 @@ def analyse_ha_32_results(results, ha32, no_house_numbers):
 
     new_possibilities = results_df[
         (~results_df["warmfront_identified"]) &
-        (results_df["gbis_eligible"] | results_df["eco4_eligible"]) &
-        (results_df["tenure"] == "Rented (social)")
+        (results_df["gbis_eligible"] | results_df["eco4_eligible"])
+        ].copy()
+
+    new_possibilities_eco = results_df[
+        (~results_df["warmfront_identified"]) &
+        (results_df["eco4_eligible"] == True)
+        ].copy()
+    new_possibilities_gbis = results_df[
+        (~results_df["warmfront_identified"]) &
+        (results_df["eco4_eligible"] == False) & (results_df["gbis_eligible"] == True)
         ].copy()
 
     future_possibilities_eco = results_df[
@@ -959,6 +979,11 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
         "eligibility_classification"].value_counts()
     # For HA15 this is 50.3%
 
+    pipeline_potential = results_df[
+        (results_df["warmfront_identified"] == True) | (results_df["eco4_eligible"] == True) | (
+            results_df["gbis_eligible"] == True)
+        ]
+
     # of the properties we identify, what is the mix of confidenc
 
     missed = results_df[
@@ -977,32 +1002,32 @@ def analyse_ha_15_results(results_df, ha15, no_house_numbers):
         missed["sap"] < 69
         ]
 
-    sap_low_enough["walls"].value_counts()
-    z = ha15[ha15["row_id"].isin(sap_too_high["row_id"].values)]
-
-    investigate_1 = ha15[ha15["row_id"].isin(sap_too_high["row_id"])][
-        ["row_id", "Postcode", "Address Line 1", "Address Line 2", "Address Line 3"]]
-
-    investigate_2 = ha15[ha15["row_id"].isin(sap_low_enough["row_id"])][
-        ["row_id", "Postcode", "Address Line 1", "Address Line 2", "Address Line 3"]]
-
-    missed["message"].value_counts()
+    # Aggregates of no eco and gbis jobs identified
+    n_eco = results_df["eco4_eligible"].sum()
+    # Gbis is rows where eco4 is not eligible
+    n_gbis = results_df[
+        (results_df["gbis_eligible"] == True) & (results_df["eco4_eligible"] == False)
+        ]["gbis_eligible"].sum()
 
     # We now look for properties that we identified, that were not identified by Warmfront
 
     new_possibilities = results_df[
         (~results_df["warmfront_identified"]) &
-        ((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True)) &
-        (results_df["tenure"] == "Rented (social)")
+        ((results_df["gbis_eligible"] == True) | (results_df["eco4_eligible"] == True))
+        ].copy()
+
+    new_possibilities_eco = results_df[
+        (~results_df["warmfront_identified"]) &
+        (results_df["eco4_eligible"] == True)
         ].copy()
 
     # These are future possibilityies
-    new_possibilities_eco = results_df[
+    future_possibilities_eco = results_df[
         (~results_df["warmfront_identified"]) &
         (results_df["eco4_eligible_future"] == True) & (~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
         ].copy()
 
-    new_possibilities_gbis = results_df[
+    future_possibilities_gbis = results_df[
         (~results_df["warmfront_identified"]) &
         (results_df["gbis_eligible_future"] == True) & (results_df["eco4_eligible_future"] == False) & (
             ~(results_df["gbis_eligible"] | results_df["eco4_eligible"]))
diff --git a/etl/eligibility/ha_15_32/ha33_app.py b/etl/eligibility/ha_15_32/ha33_app.py
index 9af5eae2..42c8fa81 100644
--- a/etl/eligibility/ha_15_32/ha33_app.py
+++ b/etl/eligibility/ha_15_32/ha33_app.py
@@ -264,21 +264,21 @@ def get_ha_33data(data, cleaned, cleaning_data, created_at):
 
 
 def analyse_ha_33(results_df, data):
-    results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
+    # results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
+    #
+    # results_df_social["tenure"].value_counts()
 
-    results_df_social["tenure"].value_counts()
+    data[data["row_id"].isin(results_df["row_id"].values)]["PROPERTY TYPE"].value_counts()
 
-    data[data["row_id"].isin(results_df_social["row_id"].values)]["PROPERTY TYPE"].value_counts()
+    n_identified = (results_df["gbis_eligible"] | results_df["eco4_eligible"]).sum()
+    n_eco4 = results_df["eco4_eligible"].sum()
+    n_gbis = results_df[~results_df["eco4_eligible"]]["gbis_eligible"].sum()
 
-    n_identified = (results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]).sum()
-    n_eco4 = results_df_social["eco4_eligible"].sum()
-    n_gbis = results_df_social[~results_df_social["eco4_eligible"]]["gbis_eligible"].sum()
-
-    eco_eligibile = results_df_social[results_df_social["eco4_eligible"]]
+    eco_eligibile = results_df[results_df["eco4_eligible"]]
     eco_eligibile["walls"].value_counts()
     eco_eligibile["roof"].value_counts()
 
-    results_df_social[results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]]["tenure"].value_counts()
+    results_df[results_df["gbis_eligible"] | results_df["eco4_eligible"]]["tenure"].value_counts()
 
     results_df_social["eligibility_classification"].value_counts()
 
@@ -316,3 +316,11 @@ def app():
     created_at = datetime.now().isoformat()
 
     results_df, _, _ = get_ha_33data(data, cleaned, cleaning_data, created_at)
+
+    # Read in
+    import pickle
+    with open("ha33_results.pickle", "rb") as f:
+        data = pickle.load(f)
+    results_df = pd.DataFrame(data["results"])
+    scoring_data = data["scoring_data"]
+    nodata = data["nodata"]
diff --git a/etl/eligibility/ha_15_32/ha4_app.py b/etl/eligibility/ha_15_32/ha4_app.py
index 8a404eec..92b03539 100644
--- a/etl/eligibility/ha_15_32/ha4_app.py
+++ b/etl/eligibility/ha_15_32/ha4_app.py
@@ -241,15 +241,11 @@ def get_ha_4_data(data, cleaned, cleaning_data, created_at):
 
 
 def analyse_ha_4(results_df, data):
-    results_df_social = results_df[results_df["tenure"] == "Rented (social)"]
+    n_identified = (results_df["gbis_eligible"] | results_df["eco4_eligible"]).sum()
+    n_eco4 = results_df["eco4_eligible"].sum()
+    n_gbis = results_df[~results_df["eco4_eligible"]]["gbis_eligible"].sum()
 
-    results_df_social["property_type"].value_counts()
-
-    n_identified = (results_df_social["gbis_eligible"] | results_df_social["eco4_eligible"]).sum()
-    n_eco4 = results_df_social["eco4_eligible"].sum()
-    n_gbis = results_df_social[~results_df_social["eco4_eligible"]]["gbis_eligible"].sum()
-
-    eco_eligibile = results_df_social[results_df_social["eco4_eligible"]]
+    eco_eligibile = results_df[results_df["eco4_eligible"]]
     eco_eligibile["eligibility_classification"].value_counts()
 
     future_possibilities_eco = results_df[
@@ -299,3 +295,11 @@ def app():
     #             "scoring_data": scoring_data,
     #             "nodata": nodata
     #         }, f)
+
+    # Read in
+    # import pickle
+    # with open("ha_4.pickle", "rb") as f:
+    #     data = pickle.load(f)
+    # results_df = data["results_df"]
+    # scoring_data = data["scoring_data"]
+    # nodata = data["nodata"]