Fix SLG formula drift in extracted rating models

The extracted batting and pitching models used malformed SLG equations that double-counted and omitted outcomes, skewing slash lines. Align formulas with canonical weighting and add regression tests to prevent recurrence.

Co-Authored-By: Claude GPT-5.3-Codex <noreply@anthropic.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

batters/models.py

@@ -269,8 +269,9 @@ class BattingCardRatingsModel(pydantic.BaseModel):
         self.avg = mround(self.total_hits() / 108, prec=5, base=0.00001)
         self.obp = mround((self.total_hits() + self.hbp + self.walk) / 108, prec=5, base=0.00001)
         self.slg = mround((
-            self.homerun * 4 + self.triple * 3 + self.single_center + self.single_two + self.single_two +
-            (self.double_two + self.double_three + self.double_two + self.bp_homerun) * 2 + self.bp_single / 2) / 108, prec=5, base=0.00001)
+            self.homerun * 4 + self.bp_homerun * 2 + self.triple * 3 + self.double_three * 2 +
+            self.double_two * 2 + self.double_pull * 2 + self.single_two + self.single_one +
+            self.single_center + self.bp_single / 2) / 108, prec=5, base=0.00001)
 
     def custom_to_dict(self):
         self.calculate_rate_stats()

pitchers/models.py

@@ -89,8 +89,9 @@ class PitchingCardRatingsModel(pydantic.BaseModel):
         self.avg = mround(self.total_hits() / 108, prec=5, base=0.00001)
         self.obp = mround((self.total_hits() + self.hbp + self.walk) / 108, prec=5, base=0.00001)
         self.slg = mround((
-            self.homerun * 4 + self.triple * 3 + self.single_center + self.single_two + self.single_two +
-            (self.double_two + self.double_three + self.double_two + self.bp_homerun) * 2 + self.bp_single / 2) / 108, prec=5, base=0.00001)
+            self.homerun * 4 + self.bp_homerun * 2 + self.triple * 3 + self.double_three * 2 +
+            self.double_two * 2 + self.double_cf * 2 + self.single_two + self.single_one +
+            self.single_center + self.bp_single / 2) / 108, prec=5, base=0.00001)
 
     def custom_to_dict(self):
         self.calculate_rate_stats()

tests/test_rate_stats_formulas.py

@@ -0,0 +1,90 @@
+from batters.models import BattingCardRatingsModel
+from pitchers.models import PitchingCardRatingsModel
+from creation_helpers import mround
+
+
+def test_batting_model_slg_formula_matches_canonical_weights():
+    ratings = BattingCardRatingsModel(
+        battingcard_id=1,
+        bat_hand='R',
+        vs_hand='R',
+        hard_rate=0.3,
+        med_rate=0.3,
+        soft_rate=0.3,
+        pull_rate=0.3,
+        center_rate=0.3,
+        slap_rate=0.3,
+        homerun=1,
+        bp_homerun=2,
+        triple=3,
+        double_three=4,
+        double_two=5,
+        double_pull=6,
+        single_two=7,
+        single_one=8,
+        single_center=9,
+        bp_single=10,
+    )
+
+    ratings.calculate_rate_stats()
+
+    expected = mround(
+        (
+            ratings.homerun * 4
+            + ratings.bp_homerun * 2
+            + ratings.triple * 3
+            + ratings.double_three * 2
+            + ratings.double_two * 2
+            + ratings.double_pull * 2
+            + ratings.single_two
+            + ratings.single_one
+            + ratings.single_center
+            + ratings.bp_single / 2
+        ) / 108,
+        prec=5,
+        base=0.00001,
+    )
+
+    assert ratings.slg == expected
+
+
+def test_pitching_model_slg_formula_matches_canonical_weights():
+    ratings = PitchingCardRatingsModel(
+        pitchingcard_id=1,
+        pit_hand='R',
+        vs_hand='R',
+        hard_rate=0.3,
+        med_rate=0.3,
+        soft_rate=0.3,
+        homerun=1,
+        bp_homerun=2,
+        triple=3,
+        double_three=4,
+        double_two=5,
+        double_cf=6,
+        single_two=7,
+        single_one=8,
+        single_center=9,
+        bp_single=10,
+    )
+
+    ratings.calculate_rate_stats()
+
+    expected = mround(
+        (
+            ratings.homerun * 4
+            + ratings.bp_homerun * 2
+            + ratings.triple * 3
+            + ratings.double_three * 2
+            + ratings.double_two * 2
+            + ratings.double_cf * 2
+            + ratings.single_two
+            + ratings.single_one
+            + ratings.single_center
+            + ratings.bp_single / 2
+        ) / 108,
+        prec=5,
+        base=0.00001,
+    )
+
+    assert ratings.slg == expected