diff --git a/rowers/dataprep.py b/rowers/dataprep.py
index ced2f2ac..d916934b 100644
--- a/rowers/dataprep.py
+++ b/rowers/dataprep.py
@@ -1026,6 +1026,14 @@ from rowers.datautils import p0
 from rowers.utils import calculate_age
 from scipy import optimize
 
+def workout_goldmedalstandard(workout):
+    if workout.goldmedalstandard > 0:
+        return workout.goldmedalstandard
+    goldmedalstandard,goldmedalduration = fitscore(workout.user,workout)
+    workout.goldmedalstandard = goldmedalstandard
+    workout.save()
+    return goldmedalstandard
+
 def fitscore(rower,workout):
     cpfile = 'media/cpdata_{id}.parquet.gz'.format(id=workout.id)
     try:
@@ -1033,21 +1041,30 @@ def fitscore(rower,workout):
     except:
         df, delta, cpvalues = setcp(workout)
 
+    if df.empty:
+        df, delta, cpvalues = setcp(workout)
+
     age = calculate_age(rower.birthdate,today=workout.date)
+
     agerecords = CalcAgePerformance.objects.filter(
         age=age,
         sex=rower.sex,
         weightcategory = rower.weightcategory
     )
+
     wcdurations = []
     wcpower = []
+    getrecords = len(agerecords) == 0
     for record in agerecords:
-        wcdurations.append(record.duration)
-        wcpower.append(record.power)
+        if record.power > 0:
+            wcdurations.append(record.duration)
+            wcpower.append(record.power)
+        else:
+            getrecords = True
 
-    if len(agerecords)==0:
-        durations = [1,4,10,20,30,60]
-        distances = []
+    if getrecords:
+        durations = [1,4,30,60]
+        distances = [100,500,1000,2000,5000,6000,10000,21097,42195]
         df2 = pd.DataFrame(
             list(
                 C2WorldClassAgePerformance.objects.filter(
@@ -1066,12 +1083,13 @@ def fitscore(rower,workout):
     fitfunc = lambda pars,x: pars[0]/(1+(x/pars[2])) + pars[1]/(1+(x/pars[3]))
     errfunc = lambda pars,x,y: fitfunc(pars,x)-y
 
-    if len(wcdurations)>4:
+    if len(wcdurations)>=4:
         p1wc, success = optimize.leastsq(errfunc, p0[:],args=(wcdurations,wcpower))
     else:
         factor = fitfunc(p0,wcdurations.mean()/wcpower.mean())
         p1wc = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
         success = 0
+        return 0,0
 
 
     times = df['delta']
@@ -1079,6 +1097,7 @@ def fitscore(rower,workout):
     wcpowers = fitfunc(p1wc,times)
     scores = 100.*powers/wcpowers
 
+
     try:
         indexmax = scores.idxmax()
         delta = df.loc[indexmax,'delta']
@@ -1127,7 +1146,6 @@ def setcp(workout,background=False):
         return job.id
 
 
-
     if not strokesdf.empty:
         totaltime = strokesdf['time'].max()
         try:
diff --git a/rowers/tasks.py b/rowers/tasks.py
index 103f09fe..57cbdbae 100644
--- a/rowers/tasks.py
+++ b/rowers/tasks.py
@@ -16,6 +16,7 @@ import json
 
 from scipy import optimize
 from scipy.signal import savgol_filter
+from scipy.interpolate import griddata
 
 import rowingdata
 from rowingdata import make_cumvalues
@@ -333,7 +334,11 @@ def getagegrouprecord(age,sex='male',weightcategory='hwt',
 
             power = 0.5*(np.abs(power)+power)
         else:
-            power = 0
+            new_age = np.range([age])
+            ww = griddata(ages.values,
+                          powers.values,
+                          new_age,method='linear',rescale=True)
+            power = 0.5*(np.abs(power)+power)
     else:
         power = 0
 
diff --git a/rowers/views/workoutviews.py b/rowers/views/workoutviews.py
index fe44f91d..ac877885 100644
--- a/rowers/views/workoutviews.py
+++ b/rowers/views/workoutviews.py
@@ -3497,6 +3497,15 @@ def workout_stats_view(request,id=0,message="",successmessage=""):
 
     # Normalized power & TSS
     tss,normp = dataprep.workout_rscore(w)
+    goldmedalstandard = dataprep.workout_goldmedalstandard(w)
+    
+
+    if not np.isnan(goldmedalstandard) and goldmedalstandard > 0:
+        otherstats['goldmedalstandard'] = {
+            'verbose_name': 'Gold Medal Standard',
+            'value': int(goldmedalstandard),
+            'unit': '%',
+        }
 
 
     if not np.isnan(tss) and tss != 0: