diff --git a/ippisite/ippidb/migrations/0067_metainformation_normalize_factor.py b/ippisite/ippidb/migrations/0067_metainformation_normalize_factor.py
new file mode 100644
index 0000000000000000000000000000000000000000..2d82a4d8aeb313bf983d9666fa361f4c733e134e
--- /dev/null
+++ b/ippisite/ippidb/migrations/0067_metainformation_normalize_factor.py
@@ -0,0 +1,18 @@
+# Generated by Django 2.2.1 on 2021-02-10 13:55
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ('ippidb', '0066_merge_20210128_0926'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='metainformation',
+            name='normalize_factor',
+            field=models.DecimalField(blank=True, decimal_places=8, default=4.5859973, max_digits=11, null=True, verbose_name='Normalize Factor'),
+        ),
+    ]
diff --git a/ippisite/ippidb/models/targetcentric.py b/ippisite/ippidb/models/targetcentric.py
index 050508d008340911d5648501bf0a7a420dbc5cc7..aaefeb9b68465b836ca10714595f6126c498d7af 100644
--- a/ippisite/ippidb/models/targetcentric.py
+++ b/ippisite/ippidb/models/targetcentric.py
@@ -426,6 +426,14 @@ class MetaInformation(models.Model):
     minimum = models.DecimalField(
         verbose_name="Minimum", max_digits=11, decimal_places=8
     )
+    normalize_factor = models.DecimalField(
+        blank=True,
+        null=True,
+        default=4.58599730,
+        verbose_name="Normalize Factor",
+        max_digits=11,
+        decimal_places=8,
+    )
 
     class Meta:
         verbose_name_plural = "MetaInformation"
diff --git a/ippisite/ippidb/templatetags/customtags.py b/ippisite/ippidb/templatetags/customtags.py
index 840921b726a107a0e862debbd786223090056086..91be01cfac97ad42094a16761772c0b269a17764 100644
--- a/ippisite/ippidb/templatetags/customtags.py
+++ b/ippisite/ippidb/templatetags/customtags.py
@@ -262,10 +262,10 @@ def get_zscore_threshold(avg_std):
 
 @register.filter
 def get_zscore(distance, avg_std):
-    std = float(avg_std.std)
+    factor = float(avg_std.normalize_factor)
     # mean = float(avg_std.average)
     dist = float(distance)
-    score = np.exp(-((dist ** 2) / (2 * (std ** 2))))
+    score = np.exp(-((dist ** 2) / (2 * (factor ** 2))))
     return score