diff --git a/haystack/components/evaluators/document_map.py b/haystack/components/evaluators/document_map.py
index f18a0e675b..212edb86c1 100644
--- a/haystack/components/evaluators/document_map.py
+++ b/haystack/components/evaluators/document_map.py
@@ -43,6 +43,7 @@ class DocumentMAPEvaluator:
     ```
     """
 
+    # Refer to https://www.pinecone.io/learn/offline-evaluation/ for the algorithm.
     @component.output_types(score=float, individual_scores=List[float])
     def run(
         self, ground_truth_documents: List[List[Document]], retrieved_documents: List[List[Document]]
@@ -68,25 +69,21 @@ def run(
         individual_scores = []
 
         for ground_truth, retrieved in zip(ground_truth_documents, retrieved_documents):
-            score = 0.0
-            for ground_document in ground_truth:
-                if ground_document.content is None:
-                    continue
-
-                average_precision = 0.0
-                relevant_documents = 0
+            average_precision = 0.0
+            average_precision_numerator = 0.0
+            relevant_documents = 0
 
-                for rank, retrieved_document in enumerate(retrieved):
-                    if retrieved_document.content is None:
-                        continue
-
-                    if ground_document.content in retrieved_document.content:
-                        relevant_documents += 1
-                        average_precision += relevant_documents / (rank + 1)
-                if relevant_documents > 0:
-                    score = average_precision / relevant_documents
-            individual_scores.append(score)
+            ground_truth_contents = [doc.content for doc in ground_truth if doc.content is not None]
+            for rank, retrieved_document in enumerate(retrieved):
+                if retrieved_document.content is None:
+                    continue
 
-        score = sum(individual_scores) / len(retrieved_documents)
+                if retrieved_document.content in ground_truth_contents:
+                    relevant_documents += 1
+                    average_precision_numerator += relevant_documents / (rank + 1)
+            if relevant_documents > 0:
+                average_precision = average_precision_numerator / relevant_documents
+            individual_scores.append(average_precision)
 
+        score = sum(individual_scores) / len(ground_truth_documents)
         return {"score": score, "individual_scores": individual_scores}
diff --git a/haystack/components/evaluators/document_mrr.py b/haystack/components/evaluators/document_mrr.py
index 64a98cc177..81caf0a418 100644
--- a/haystack/components/evaluators/document_mrr.py
+++ b/haystack/components/evaluators/document_mrr.py
@@ -41,6 +41,7 @@ class DocumentMRREvaluator:
     ```
     """
 
+    # Refer to https://www.pinecone.io/learn/offline-evaluation/ for the algorithm.
     @component.output_types(score=float, individual_scores=List[float])
     def run(
         self, ground_truth_documents: List[List[Document]], retrieved_documents: List[List[Document]]
@@ -66,20 +67,17 @@ def run(
         individual_scores = []
 
         for ground_truth, retrieved in zip(ground_truth_documents, retrieved_documents):
-            score = 0.0
-            for ground_document in ground_truth:
-                if ground_document.content is None:
-                    continue
-
-                for rank, retrieved_document in enumerate(retrieved):
-                    if retrieved_document.content is None:
-                        continue
+            reciprocal_rank = 0.0
 
-                    if ground_document.content in retrieved_document.content:
-                        score = 1 / (rank + 1)
-                        break
-            individual_scores.append(score)
+            ground_truth_contents = [doc.content for doc in ground_truth if doc.content is not None]
+            for rank, retrieved_document in enumerate(retrieved):
+                if retrieved_document.content is None:
+                    continue
+                if retrieved_document.content in ground_truth_contents:
+                    reciprocal_rank = 1 / (rank + 1)
+                    break
+            individual_scores.append(reciprocal_rank)
 
-        score = sum(individual_scores) / len(retrieved_documents)
+        score = sum(individual_scores) / len(ground_truth_documents)
 
         return {"score": score, "individual_scores": individual_scores}
diff --git a/releasenotes/notes/fix-issue-7758-d35b687ca226a707.yaml b/releasenotes/notes/fix-issue-7758-d35b687ca226a707.yaml
new file mode 100644
index 0000000000..b1accdd2ac
--- /dev/null
+++ b/releasenotes/notes/fix-issue-7758-d35b687ca226a707.yaml
@@ -0,0 +1,4 @@
+---
+fixes:
+  - |
+    Fixed the calculation for MRR and MAP scores.
diff --git a/test/components/evaluators/test_document_map.py b/test/components/evaluators/test_document_map.py
index 9c481e3d6c..7c7b26c089 100644
--- a/test/components/evaluators/test_document_map.py
+++ b/test/components/evaluators/test_document_map.py
@@ -62,7 +62,17 @@ def test_run_with_complex_data():
             ],
         ],
     )
-    assert result == {"individual_scores": [1.0, 0.8333333333333333, 1.0, 0.5, 0.0, 1.0], "score": 0.7222222222222222}
+    assert result == {
+        "individual_scores": [
+            1.0,
+            pytest.approx(0.8333333333333333),
+            1.0,
+            pytest.approx(0.5833333333333333),
+            0.0,
+            pytest.approx(0.8055555555555555),
+        ],
+        "score": pytest.approx(0.7037037037037037),
+    }
 
 
 def test_run_with_different_lengths():