diff --git a/src/winml/modelkit/eval/metrics/spearman_correlation.py b/src/winml/modelkit/eval/metrics/spearman_correlation.py
index 7113c9f69..3d93825ab 100644
--- a/src/winml/modelkit/eval/metrics/spearman_correlation.py
+++ b/src/winml/modelkit/eval/metrics/spearman_correlation.py
@@ -11,9 +11,14 @@
 
 from __future__ import annotations
 
+import logging
+import math
 from typing import Any
 
 
+logger = logging.getLogger(__name__)
+
+
 class SpearmanCorrelationMetric:
     """Spearman rank correlation between predicted and reference scores.
 
@@ -54,4 +59,12 @@ def compute(
             )
 
         corr, _ = spearmanr(predictions, references)
+
+        if math.isnan(corr):
+            logger.warning(
+                "Spearman correlation is NaN. This typically means the model "
+                "produced constant outputs (zero variance). Returning 0.0.",
+            )
+            corr = 0.0
+
         return {"cosine_spearman": round(float(corr) * 100, 4)}
diff --git a/tests/unit/eval/test_feature_extraction_evaluator.py b/tests/unit/eval/test_feature_extraction_evaluator.py
index a9abdadfd..518202c25 100644
--- a/tests/unit/eval/test_feature_extraction_evaluator.py
+++ b/tests/unit/eval/test_feature_extraction_evaluator.py
@@ -92,6 +92,12 @@ def test_score_range_is_0_to_100(self):
         result = metric.compute([0.1, 0.5, 0.9, 0.3], [1.0, 3.0, 5.0, 2.0])
         assert -100.0 <= result["cosine_spearman"] <= 100.0
 
+    def test_constant_predictions_returns_zero(self):
+        """Zero variance in predictions → correlation = 0.0, not NaN."""
+        metric = SpearmanCorrelationMetric()
+        result = metric.compute([1.0] * 10, [0.5, 1.2, 3.1, 4.0, 2.0, 0.1, 3.5, 4.8, 1.7, 2.9])
+        assert result["cosine_spearman"] == 0.0
+
 
 # ---------------------------------------------------------------------------
 # WinMLFeatureExtractionEvaluator._embed