diff --git a/src/helm/benchmark/presentation/summarize.py b/src/helm/benchmark/presentation/summarize.py
index 82828ae5ba..d6cda11fdb 100644
--- a/src/helm/benchmark/presentation/summarize.py
+++ b/src/helm/benchmark/presentation/summarize.py
@@ -226,17 +226,27 @@ def compute_aggregate_row_win_rates(table: Table, aggregation: str = "mean") ->
     """
     assert aggregation in ["mean", "median"]
     win_rates_per_row: List[List[float]] = [[] for _ in table.rows]
-    for i, header_cell in enumerate(table.header):
+    for column_index, header_cell in enumerate(table.header):
         lower_is_better = header_cell.lower_is_better
         if lower_is_better is None:  # column does not have a meaningful ordering
             continue
-
-        values = [(row[i].value, j) for j, row in enumerate(table.rows) if row[i].value is not None]
-        if len(values) < 2:  # don't rank a single model
+        value_to_count: Dict[float, int] = defaultdict(int)
+        for row in table.rows:
+            value = row[column_index].value
+            if value is not None:
+                value_to_count[value] += 1
+        value_to_wins: Dict[float, float] = {}
+        acc_count = 0
+        for value, value_count in sorted(value_to_count.items(), reverse=lower_is_better):
+            value_to_wins[value] = acc_count + ((value_count - 1) / 2)
+            acc_count += value_count
+        total_count = acc_count
+        if total_count < 2:
             continue
-        for wins, (v, j) in enumerate(sorted(values, reverse=lower_is_better)):
-            win_rate = wins / (len(values) - 1)  # normalize to [0, 1]
-            win_rates_per_row[j].append(win_rate)
+        for row_index, row in enumerate(table.rows):
+            value = row[column_index].value
+            if value is not None:
+                win_rates_per_row[row_index].append(value_to_wins[row[column_index].value] / (total_count - 1))
 
     # Note: the logic up to here is somewhat general as it simply computes win rates across columns for each row.
     # Here, we simply average these win rates but we might want some more involved later (e.g., weighted average).
diff --git a/src/helm/benchmark/presentation/test_summarize.py b/src/helm/benchmark/presentation/test_summarize.py
index cb6552989f..893189fd39 100644
--- a/src/helm/benchmark/presentation/test_summarize.py
+++ b/src/helm/benchmark/presentation/test_summarize.py
@@ -163,4 +163,21 @@ def test_compute_win_rates_ties():
     ]
     rows = [[Cell(value) for value in row_values] for row_values in values]
     table = Table(title="Test Table", header=header, rows=rows)
-    assert compute_aggregate_row_win_rates(table) == [0.0, 0.25, 0.5, 0.75, 1.0]
+    assert compute_aggregate_row_win_rates(table) == [0.25, 0.25, 0.25, 0.75, 1.0]
+
+
+def test_compute_win_rates_lower_is_better():
+    header = [
+        HeaderCell(value="Model"),
+        HeaderCell(value="Scenario A", lower_is_better=False),
+    ]
+    values = [
+        ["Model A", 1],
+        ["Model B", 2],
+        ["Model C", 3],
+        ["Model D", 4],
+        ["Model E", 5],
+    ]
+    rows = [[Cell(value) for value in row_values] for row_values in values]
+    table = Table(title="Test Table", header=header, rows=rows)
+    assert compute_aggregate_row_win_rates(table) == [0, 0.25, 0.5, 0.75, 1]