diff --git a/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py b/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py
new file mode 100644
index 0000000..fcde9797
--- /dev/null
+++ b/tools/gop_bitrate/python/bitrate_accuracy_percentage_error.py
@@ -0,0 +1,106 @@
+import numpy as np
+
+# Finds the coefficient to multiply A by to minimize
+# the percentage error between A and B.
+def minimize_percentage_error_model_a(A, B):
+    z = np.where(B == 0)[0]
+    A = np.delete(A, z, axis=0)
+    B = np.delete(B, z, axis=0)
+    z = np.where(A == 0)[0]
+    A = np.delete(A, z, axis=0)
+    B = np.delete(B, z, axis=0)
+
+    R = np.divide(A, B)
+    num = 0
+    den = 0
+    for r_i in R:
+        num += r_i
+        den += r_i**2
+    if den == 0:
+        x = 0
+    else:
+        x = (num / den)[0]
+    return x
+
+def minimize_percentage_error_model_b(r_e, r_m, r_f):
+    z = np.where(r_f == 0)[0]
+    r_e = np.delete(r_e, z, axis=0)
+    r_m = np.delete(r_m, z, axis=0)
+    r_f = np.delete(r_f, z, axis=0)
+
+    r_ef = np.divide(r_e, r_f)
+    r_mf = np.divide(r_m, r_f)
+    sum_ef = np.sum(r_ef)
+    sum_ef_sq = np.sum(np.square(r_ef))
+    sum_mf = np.sum(r_mf)
+    sum_mf_sq = np.sum(np.square(r_mf))
+    sum_ef_mf = np.sum(np.multiply(r_ef, r_mf))
+
+    # Divides x by y. If y is zero, returns 0.
+    divide = lambda x, y : 0 if y == 0 else x / y
+
+    # Set up and solve the matrix equation
+    A = np.array([[1, divide(sum_ef_mf, sum_ef_sq)],[divide(sum_ef_mf, sum_mf_sq), 1]])
+    B = np.array([divide(sum_ef, sum_ef_sq), divide(sum_mf, sum_mf_sq)])
+    A_inv = np.linalg.pinv(A)
+    x = np.matmul(A_inv, B)
+    return x
+
+# Calculates the average percentage error between A and B.
+def average_error_model_a(A, B, x):
+    error = 0
+    for i, a in enumerate(A):
+        a = a[0]
+        b = B[i][0]
+        if b == 0:
+            continue
+        error += abs(x*a - b) / b
+    error *= 100
+    error /= A.shape[0]
+    return error
+
+def average_error_model_b(A, M, B, x):
+    error = 0
+    for i, a in enumerate(A):
+        a = a[0]
+        mv = M[i]
+        b = B[i][0]
+        if b == 0:
+            continue
+        estimate = x[0]*a
+        estimate += x[1]*mv
+        error += abs(estimate - b) / b
+    error *= 100
+    error /= A.shape[0]
+    return error
+
+# Traverses the data and prints out one value for
+# each update type.
+def print_solutions(file_path):
+    data = np.genfromtxt(file_path, delimiter="\t")
+
+    prev_update = 0
+    split_list_indices = list()
+    for i, val in enumerate(data):
+        if prev_update != val[3]:
+            split_list_indices.append(i)
+            prev_update = val[3]
+
+    split = np.split(data, split_list_indices)
+
+    for array in split:
+        A, mv, B, update = np.hsplit(array, 4)
+        print("update type:", update[0][0])
+        xa = minimize_percentage_error_model_a(A, B)
+        xb = minimize_percentage_error_model_b(A, mv, B)
+        print("Model A coefficients:", xa, " | Model B coefficients:", xb)
+        error_a = average_error_model_a(A, B, xa)
+        error_b = average_error_model_b(A, mv, B, xb)
+        baseline_error_a = average_error_model_a(A, B, 1)
+        baseline_error_b = average_error_model_b(A, mv, B, [1, 1])
+        print("error a:", error_a, " | error b:", error_b)
+        print("baseline error a:", baseline_error_a, "baseline error b:", baseline_error_b)
+        print()
+
+if __name__ == "__main__":
+    print_solutions("data2/lowres_17f_target150_data.txt")