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feat(seaborn): implement line-win-probability (#5090)
## Implementation: `line-win-probability` - seaborn Implements the **seaborn** version of `line-win-probability`. **File:** `plots/line-win-probability/implementations/seaborn.py` **Parent Issue:** #4418 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/23342814414)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/line-win-probability/implementations/seaborn.py‎

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""" pyplots.ai
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line-win-probability: Win Probability Chart
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Library: seaborn 0.13.2 | Python 3.14.3
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Quality: 91/100 | Created: 2026-03-20
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"""
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import matplotlib.patches as mpatches
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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# Seaborn theme and context for global styling
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sns.set_theme(
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style="ticks",
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rc={
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"axes.facecolor": "#F7F9FC",
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"figure.facecolor": "#FFFFFF",
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"grid.color": "#D0D8E0",
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"grid.alpha": 0.3,
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"grid.linewidth": 0.6,
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"font.family": "sans-serif",
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},
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)
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sns.set_context("talk", font_scale=1.05, rc={"lines.linewidth": 2.8})
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# Palette from seaborn
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palette = sns.color_palette(["#306998", "#D4583B"])
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home_color = palette[0]
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away_color = palette[1]
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# Data
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np.random.seed(42)
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plays = np.arange(0, 121)
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win_prob = np.full(len(plays), 0.50)
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events = {
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8: ("FG Home", 0.07),
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22: ("TD Away", -0.15),
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35: ("TD Home", 0.18),
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48: ("INT Home", 0.10),
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55: ("FG Away", -0.08),
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65: ("TD Home", 0.16),
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78: ("TD Away", -0.14),
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85: ("FG Home", 0.09),
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95: ("TD Away", -0.20),
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105: ("TD Home", 0.22),
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115: ("FG Home", 0.08),
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}
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for i in range(1, len(plays)):
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noise = np.random.normal(0, 0.015)
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if i in events:
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shift = events[i][1]
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else:
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shift = 0
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win_prob[i] = np.clip(win_prob[i - 1] + shift + noise, 0.02, 0.98)
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win_prob[-1] = 0.95
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df = pd.DataFrame({"play": plays, "win_probability": win_prob})
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# Plot
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fig, ax = plt.subplots(figsize=(16, 9))
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sns.lineplot(data=df, x="play", y="win_probability", color=home_color, linewidth=2.8, ax=ax)
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ax.fill_between(plays, win_prob, 0.5, where=(win_prob >= 0.5), color=home_color, alpha=0.2, interpolate=True)
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ax.fill_between(plays, win_prob, 0.5, where=(win_prob < 0.5), color=away_color, alpha=0.2, interpolate=True)
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ax.axhline(y=0.5, color="#888888", linewidth=1.2, linestyle="--", alpha=0.5)
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key_events = {
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22: ("TD Away\n7-3", away_color),
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35: ("TD Home\n10-7", home_color),
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65: ("TD Home\n20-14", home_color),
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95: ("TD Away\n23-27", away_color),
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105: ("TD Home\n30-27", home_color),
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}
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annotation_offsets = {22: (-8, 0.10), 35: (0, -0.10), 65: (8, 0.10), 95: (-12, 0.10), 105: (0, -0.12)}
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for play_num, (label, color) in key_events.items():
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y_val = win_prob[play_num]
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x_off, y_off = annotation_offsets[play_num]
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ax.annotate(
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label,
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xy=(play_num, y_val),
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xytext=(play_num + x_off, y_val + y_off),
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fontsize=13,
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fontweight="bold",
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color=color,
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ha="center",
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va="center",
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arrowprops={"arrowstyle": "->", "color": color, "lw": 1.5, "connectionstyle": "arc3,rad=0.1"},
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)
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sns.scatterplot(
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x=list(key_events),
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y=[win_prob[p] for p in key_events],
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color=[key_events[p][1] for p in key_events],
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s=100,
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zorder=5,
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edgecolor="white",
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linewidth=1.5,
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ax=ax,
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legend=False,
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)
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# Quarter markers
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for q, label in [(30, "Q1"), (60, "Q2"), (90, "Q3"), (120, "Q4")]:
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ax.axvline(x=q, color="#C0C8D0", linewidth=0.8, linestyle=":", alpha=0.6)
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ax.text(q - 15, 0.025, label, fontsize=14, color="#8899AA", ha="center", fontweight="medium")
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# Style
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ax.set_xlabel("Play Number", fontsize=20)
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ax.set_ylabel("Home Win Probability", fontsize=20)
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ax.set_title("line-win-probability · seaborn · pyplots.ai\n", fontsize=24, fontweight="medium", pad=4)
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ax.text(
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0.5,
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1.02,
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"NFL Game — Home vs Away | Lead changes and momentum shifts across 120 plays",
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transform=ax.transAxes,
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ha="center",
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fontsize=14,
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color="#667788",
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fontstyle="italic",
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)
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ax.tick_params(axis="both", labelsize=16)
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ax.set_ylim(0, 1)
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ax.set_xlim(0, 120)
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ax.set_yticks([0, 0.25, 0.5, 0.75, 1.0])
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ax.set_yticklabels(["0%", "25%", "50%", "75%", "100%"])
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sns.despine(ax=ax)
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ax.yaxis.grid(True, alpha=0.2, linewidth=0.6)
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home_patch = mpatches.Patch(color=home_color, alpha=0.4, label="Home")
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away_patch = mpatches.Patch(color=away_color, alpha=0.4, label="Away")
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ax.legend(handles=[home_patch, away_patch], fontsize=16, loc="upper left", frameon=False)
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ax.text(
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118,
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0.015,
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"Final: Home 30 – Away 27",
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fontsize=15,
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ha="right",
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color="#556677",
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fontweight="semibold",
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fontstyle="italic",
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bbox={"boxstyle": "round,pad=0.3", "facecolor": "#E8EEF4", "edgecolor": "#C0C8D0", "alpha": 0.8},
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)
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# Save
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/line-win-probability/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: line-win-probability
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created: '2026-03-20T12:32:04Z'
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updated: '2026-03-20T12:45:51Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 23342814414
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issue: 4418
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python_version: 3.14.3
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library_version: 0.13.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-win-probability/seaborn/plot_thumb.png
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preview_html: null
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quality_score: 91
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review:
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strengths:
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- Excellent data storytelling with annotated scoring events showing running score
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- Polished design with custom background, cohesive palette, and styled final score
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box
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- Complete spec compliance — all required features implemented
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- Realistic NFL game data with compelling back-and-forth narrative
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weaknesses:
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- Q4 quarter label is obscured by the Final score box (minor overlap issue)
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- Library mastery could be stronger — core visualization relies heavily on matplotlib
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rather than seaborn-specific plotting features
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image_description: 'The plot displays a win probability line chart for an NFL game
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across 120 plays. A dark blue line (#306998) traces the home team''s win probability
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from ~50% at kickoff through various scoring events. The area above the 50% dashed
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baseline is filled with translucent blue (Home) and below with translucent salmon/red
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(#D4583B, Away). Five key events are annotated with curved arrows and bold colored
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text showing the scoring play and running score: "TD Away 7-3", "TD Home 10-7",
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"TD Home 20-14", "TD Away 23-27", and "TD Home 30-27". Dotted vertical lines mark
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Q1, Q2, and Q3 boundaries. The y-axis shows 0%-100% with the 50% reference line.
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A styled box at the bottom right displays "Final: Home 30 – Away 27". The title
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follows the required format. A legend in the upper left shows Home (blue) and
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Away (red) patches. The background is a subtle light blue-gray (#F7F9FC). An italic
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subtitle reads "NFL Game — Home vs Away | Lead changes and momentum shifts across
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120 plays".'
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criteria_checklist:
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visual_quality:
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score: 29
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt,
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annotations 13pt'
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- id: VQ-02
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name: No Overlap
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score: 5
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max: 6
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passed: true
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comment: Q4 quarter label hidden behind Final score box; remaining annotations
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well-spaced
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 6
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passed: true
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comment: Line, fills, and scatter markers all clearly visible with appropriate
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sizing
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- id: VQ-04
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name: Color Accessibility
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score: 4
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max: 4
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passed: true
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comment: Blue and salmon provide strong contrast, colorblind-distinguishable
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: Good 16:9 layout, balanced margins, nothing cut off
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: Descriptive labels with percentage units on y-axis
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design_excellence:
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score: 16
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 6
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max: 8
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passed: true
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comment: Custom background, cohesive palette, italic subtitle, styled score
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box — clearly above defaults
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- id: DE-02
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name: Visual Refinement
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score: 5
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max: 6
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passed: true
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comment: Spines removed, subtle grid, custom background, polished quarter
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markers
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- id: DE-03
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name: Data Storytelling
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score: 5
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max: 6
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passed: true
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comment: Annotations with running score, fill colors for momentum, final score
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box — strong narrative
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct line chart showing win probability over game progression
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: 'All features: 50% reference, fill areas, annotations, final score,
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quarter markers'
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: X=Play Number, Y=Win Probability, full range displayed
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: Correct title format and Home/Away legend patches
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data_quality:
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score: 15
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 6
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max: 6
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passed: true
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comment: Lead changes, momentum swings, multiple event types, both teams scoring
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: NFL game scenario, neutral sports context, plausible data
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: 120 plays realistic for NFL, probability values in valid range
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: Clean imports → data → plot → save flow, no functions or classes
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: np.random.seed(42) set
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All imports used
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Appropriate complexity, no fake functionality
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot.png with dpi=300
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library_mastery:
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score: 6
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 4
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max: 5
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passed: true
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comment: Good use of seaborn axes-level API, set_theme, set_context, despine
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- id: LM-02
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name: Distinctive Features
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score: 2
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max: 5
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passed: false
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comment: Seaborn features are mainly styling utilities; core viz relies on
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matplotlib
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- annotations
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- custom-legend
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- manual-ticks
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patterns:
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- data-generation
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- explicit-figure
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dataprep:
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- cumulative-sum
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styling:
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- alpha-blending
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- grid-styling
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- edge-highlighting

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