3c76ce1cf0
Features: - Lineup Builder screen: set batting order, assign positions, save/load lineups - Gameday screen: integrated matchup scout + lineup builder side-by-side - Matchup Scout: analyze batters vs opposing pitchers with standardized scoring - Standardized scoring system with league AVG/STDEV calculations - Score caching for fast matchup lookups Lineup Builder (press 'l'): - Dual-panel UI with available batters and 9-slot lineup - Keyboard controls: a=add, r=remove, k/j=reorder, p=change position - Save/load named lineups, delete saved lineups with 'd' Gameday screen (press 'g'): - Left panel: team/pitcher selection with matchup ratings - Right panel: lineup builder with live matchup ratings per batter - Players in lineup marked with * in matchup list - Click highlighted row to toggle selection for screenshots Other changes: - Dynamic season configuration (removed hardcoded season=13) - Added delete_lineup query function - StandardizedScoreCache model for pre-computed scores - Auto-rebuild score cache after card imports
171 lines
6.1 KiB
Python
171 lines
6.1 KiB
Python
"""
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Stat weights and standardized scoring for matchup calculations.
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Converts raw card values into standardized scores (-3 to +3) based on
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league averages and standard deviations, then applies weights.
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"""
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from dataclasses import dataclass
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from typing import Literal
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from .league_stats import StatDistribution
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@dataclass
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class StatWeight:
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"""Weight and direction for a single stat."""
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weight: int
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high_is_better: bool # If True, high values get positive scores
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# =============================================================================
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# Batter Stat Weights (for matchup calculation)
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# =============================================================================
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BATTER_WEIGHTS: dict[str, StatWeight] = {
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"so": StatWeight(weight=1, high_is_better=False), # Strikeouts - low is better
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"bb": StatWeight(weight=1, high_is_better=True), # Walks - high is better
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"hit": StatWeight(weight=2, high_is_better=True), # Hits - high is better
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"ob": StatWeight(weight=5, high_is_better=True), # On-base - high is better
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"tb": StatWeight(weight=5, high_is_better=True), # Total bases - high is better
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"hr": StatWeight(weight=2, high_is_better=True), # Home runs - high is better
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"bphr": StatWeight(weight=3, high_is_better=True), # Ballpark HR - high is better
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"bp1b": StatWeight(weight=1, high_is_better=True), # Ballpark 1B - high is better
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"dp": StatWeight(weight=2, high_is_better=False), # Double plays - low is better
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}
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# =============================================================================
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# Pitcher Stat Weights (for matchup calculation)
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# =============================================================================
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PITCHER_WEIGHTS: dict[str, StatWeight] = {
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"so": StatWeight(weight=3, high_is_better=True), # Strikeouts - high is better for pitcher
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"bb": StatWeight(weight=1, high_is_better=False), # Walks - low is better for pitcher
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"hit": StatWeight(weight=2, high_is_better=False), # Hits - low is better for pitcher
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"ob": StatWeight(weight=5, high_is_better=False), # On-base - low is better for pitcher
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"tb": StatWeight(weight=2, high_is_better=False), # Total bases - low is better for pitcher
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"hr": StatWeight(weight=5, high_is_better=False), # Home runs - low is better for pitcher
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"bphr": StatWeight(weight=2, high_is_better=False), # Ballpark HR - low is better for pitcher
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"bp1b": StatWeight(weight=1, high_is_better=False), # Ballpark 1B - low is better for pitcher
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"dp": StatWeight(weight=2, high_is_better=True), # Double plays - high is better for pitcher
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}
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# =============================================================================
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# Standardized Scoring Functions
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# =============================================================================
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def standardize_value(
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value: float | None,
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distribution: StatDistribution,
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high_is_better: bool,
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) -> int:
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"""
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Convert a raw stat value to a standardized score (-3 to +3).
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Uses the following thresholds based on standard deviations from the mean:
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> AVG + 2*STDEV: -3 (or +3 if high_is_better)
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> AVG + 1*STDEV: -2 (or +2)
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> AVG + 0.33*STDEV: -1 (or +1)
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> AVG - 0.33*STDEV: 0
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> AVG - 1*STDEV: +1 (or -1)
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> AVG - 2*STDEV: +2 (or -2)
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else: +3 (or -3)
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Special case: value of 0 gets the best score (+3 for low_is_better, +3 for high after invert)
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Args:
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value: Raw stat value from card
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distribution: League average and standard deviation
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high_is_better: If True, high values get positive scores (inverted)
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Returns:
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Standardized score from -3 to +3
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"""
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if value is None or value == 0:
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# Zero value = best possible (for stats like SO, HR where 0 is rare/great)
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return 3 if not high_is_better else 3
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avg = distribution.avg
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stdev = distribution.stdev
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# Calculate thresholds
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thresh_plus_2sd = avg + (2 * stdev)
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thresh_plus_1sd = avg + (1 * stdev)
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thresh_plus_033sd = avg + (0.33 * stdev)
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thresh_minus_033sd = avg - (0.33 * stdev)
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thresh_minus_1sd = avg - (1 * stdev)
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thresh_minus_2sd = avg - (2 * stdev)
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# Determine base score (before inversion)
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# High values get negative scores in base formula
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if value > thresh_plus_2sd:
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base_score = -3
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elif value > thresh_plus_1sd:
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base_score = -2
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elif value > thresh_plus_033sd:
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base_score = -1
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elif value > thresh_minus_033sd:
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base_score = 0
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elif value > thresh_minus_1sd:
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base_score = 1
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elif value > thresh_minus_2sd:
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base_score = 2
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else:
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base_score = 3
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# Invert if high values are better
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if high_is_better:
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return -base_score
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return base_score
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def calculate_weighted_score(
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value: float | None,
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distribution: StatDistribution,
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stat_weight: StatWeight,
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) -> float:
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"""
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Calculate weighted score for a single stat.
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Args:
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value: Raw stat value
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distribution: League avg/stdev for this stat
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stat_weight: Weight and direction for this stat
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Returns:
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Weighted score (standardized_score * weight)
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"""
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std_score = standardize_value(value, distribution, stat_weight.high_is_better)
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return std_score * stat_weight.weight
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# =============================================================================
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# Maximum Possible Scores (for reference)
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# =============================================================================
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def get_max_batter_score() -> int:
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"""Get the maximum possible batter component score."""
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# All stats at +3, multiplied by weights
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return sum(3 * w.weight for w in BATTER_WEIGHTS.values())
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def get_max_pitcher_score() -> int:
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"""Get the maximum possible pitcher component score."""
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return sum(3 * w.weight for w in PITCHER_WEIGHTS.values())
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def get_max_matchup_score() -> int:
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"""Get the maximum possible combined matchup score."""
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return get_max_batter_score() + get_max_pitcher_score()
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# Max scores:
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# Batter: (1+1+2+5+5+2+3+1+2) * 3 = 22 * 3 = 66
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# Pitcher: (3+1+2+5+2+5+2+1+2) * 3 = 23 * 3 = 69
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# Combined max: 135
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