2bf3a6cee7
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>
91 lines
2.1 KiB
Python
91 lines
2.1 KiB
Python
from batters.models import BattingCardRatingsModel
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from pitchers.models import PitchingCardRatingsModel
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from creation_helpers import mround
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def test_batting_model_slg_formula_matches_canonical_weights():
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ratings = BattingCardRatingsModel(
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battingcard_id=1,
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bat_hand='R',
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vs_hand='R',
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hard_rate=0.3,
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med_rate=0.3,
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soft_rate=0.3,
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pull_rate=0.3,
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center_rate=0.3,
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slap_rate=0.3,
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homerun=1,
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bp_homerun=2,
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triple=3,
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double_three=4,
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double_two=5,
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double_pull=6,
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single_two=7,
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single_one=8,
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single_center=9,
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bp_single=10,
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)
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ratings.calculate_rate_stats()
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expected = mround(
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(
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ratings.homerun * 4
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+ ratings.bp_homerun * 2
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+ ratings.triple * 3
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+ ratings.double_three * 2
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+ ratings.double_two * 2
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+ ratings.double_pull * 2
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+ ratings.single_two
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+ ratings.single_one
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+ ratings.single_center
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+ ratings.bp_single / 2
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) / 108,
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prec=5,
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base=0.00001,
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)
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assert ratings.slg == expected
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def test_pitching_model_slg_formula_matches_canonical_weights():
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ratings = PitchingCardRatingsModel(
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pitchingcard_id=1,
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pit_hand='R',
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vs_hand='R',
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hard_rate=0.3,
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med_rate=0.3,
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soft_rate=0.3,
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homerun=1,
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bp_homerun=2,
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triple=3,
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double_three=4,
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double_two=5,
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double_cf=6,
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single_two=7,
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single_one=8,
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single_center=9,
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bp_single=10,
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)
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ratings.calculate_rate_stats()
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expected = mround(
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(
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ratings.homerun * 4
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+ ratings.bp_homerun * 2
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+ ratings.triple * 3
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+ ratings.double_three * 2
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+ ratings.double_two * 2
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+ ratings.double_cf * 2
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+ ratings.single_two
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+ ratings.single_one
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+ ratings.single_center
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+ ratings.bp_single / 2
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) / 108,
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prec=5,
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base=0.00001,
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)
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assert ratings.slg == expected
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