feat(pygal): implement line-win-probability

[github-actions]

Implementation: line-win-probability - pygal

Implements the pygal version of line-win-probability.

File: plots/line-win-probability/implementations/pygal.py

Parent Issue: #4418

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a line chart with a teal/gray filled area showing Eagles win probability over the course of an NFL game against the Cowboys. The x-axis shows game progression with labels "Kickoff", "Q2", "Halftime", "Q4", and "Final". The y-axis shows "Eagles Win Probability (%)" from 0% to 100%. The title reads "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". The filled area extends from the win probability line down to 0% in a single muted teal color. Red/orange dots mark scoring events at key moments. A 50% baseline is present but barely visible as a very faint gray line. The legend at the bottom lists three series: "Eagles Win Probability", "50% Baseline", and "Scoring Events". The line shows momentum swings — dropping to ~35% after Cowboys scores, rising to ~74% after Eagles scores, then ultimately climbing to ~97% at the end.

Score: 76/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	11	20
Spec Compliance	11	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	7	10
Total	76	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=56, labels=40, major_labels=42). Readable throughout. Minor: some axis labels could be slightly larger for the canvas size.
• VQ-02: No Overlap (5/6) - No significant overlap. Minor: "Final" label is slightly crowded at the right edge.
• VQ-03: Element Visibility (4/6) - Main line and fill are clearly visible. Scoring event dots are prominent. However, the 50% baseline is barely visible — it blends into the background and fails as a reference line.
• VQ-04: Color Accessibility (3/4) - Teal fill and red/orange event markers are distinguishable and not a red-green pair. The faint baseline gray is too low-contrast.
• VQ-05: Layout & Canvas (3/4) - Plot fills canvas reasonably well. Minor right margin issue where "Final" label is slightly tight.
• VQ-06: Axis Labels & Title (2/2) - Y-axis "Eagles Win Probability (%)" has units. X-axis "Game Progression" is descriptive.

Design Excellence (11/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom teal color matching Eagles branding, white background, custom font families, red event markers. Clearly above defaults but not publication-level. The single-color fill limits visual impact.
• DE-02: Visual Refinement (3/6) - Custom grid styling with dashed guides, x-guides hidden, subtle foreground colors. Some refinement but baseline and fill could be more polished.
• DE-03: Data Storytelling (3/6) - The game narrative is compelling with visible momentum swings and event markers. However, event labels are not visible on the PNG (only in tooltips), and the missing two-color fill reduces the "at a glance" storytelling the spec calls for.

Spec Compliance (11/15)

• SC-01: Plot Type (5/5) - Correct chart type: line chart showing win probability over game progression.
• SC-02: Required Features (2/4) - Missing two-color fill: spec requires filling above 50% with home team color and below 50% with away team color — the implementation fills from line to 0 in one color. Missing visible annotations: scoring events are marked with dots but their text labels (e.g., "PHI TD (7-3)") are only in tooltips, not visible on the static PNG output. 50% reference line present but nearly invisible.
• SC-03: Data Mapping (3/3) - X is game progression (play number), Y is win probability. Axes show all data correctly.
• SC-04: Title & Legend (1/3) - Title format is correct: "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". However, legend labels are functional series names rather than meaningful team-based labels.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows multiple momentum shifts, lead changes, scoring events from both teams across all four quarters. Could show a wider variety (e.g., turnovers, defensive plays).
• DQ-02: Realistic Context (5/5) - NFL game between Eagles and Cowboys with realistic play-by-play scoring. Sports context is neutral and relatable.
• DQ-03: Appropriate Scale (4/4) - Win probability starts at 50%, swings realistically with scoring events, and converges to ~97% at game end. All values are plausible.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Linear structure: imports → data generation → chart config → series → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set at the start.
• CQ-03: Clean Imports (2/2) - All imports used: numpy, pygal, Style.
• CQ-04: Code Elegance (1/2) - Slightly verbose with repeated loop patterns for data generation. Each quarter uses the same append-loop pattern that could be more concise. No fake UI elements.
• CQ-05: Output & API (1/1) - Saves as plot.png (and plot.html). Uses current pygal API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of pygal API: custom Style, labeled data points with dicts, fill, interpolation, major x-labels, value_formatter. Follows pygal conventions well.
• LM-02: Distinctive Features (3/5) - Uses pygal-specific features: cubic interpolation, tooltip configuration, labeled data point dicts, stroke_style customization. These are somewhat distinctive to pygal.

Score Caps Applied

• None applied

Strengths

• Compelling game narrative with realistic NFL data and multiple momentum swings
• Good use of pygal-specific features (cubic interpolation, labeled data points, custom Style)
• Clean code structure with explicit font sizing and thorough style customization
• Correct title format with final score included

Weaknesses

• Missing two-color fill (above/below 50% baseline) — a core visual feature from the spec
• Scoring event annotations not visible as text on the static PNG output (labels only appear in SVG tooltips)
• 50% baseline is barely visible — needs much stronger contrast/styling
• Legend labels are series names rather than team-oriented labels

Issues Found

1. SC-02 MEDIUM: Missing two-color fill above/below 50% baseline
   • Fix: Create two separate filled series — one for values above 50% (home team color), one for below 50% (away team color), so momentum is visible at a glance
2. SC-02 MEDIUM: Scoring event labels not visible on PNG
   • Fix: Since pygal renders to PNG via cairosvg, tooltip labels won't show. Consider adding event labels as part of the chart title/subtitle, or use a secondary annotation approach visible in static output
3. VQ-03 LOW: 50% baseline barely visible
   • Fix: Use a more prominent stroke color and wider dash pattern for the baseline series, or use a contrasting color like dark gray (#666666)
4. SC-04 LOW: Legend labels could be more descriptive
   • Fix: Use team-oriented labels in the legend (e.g., "Eagles" instead of "Eagles Win Probability")

AI Feedback for Next Attempt

The core issue is the missing two-color fill above/below the 50% baseline — this is a defining visual feature of win probability charts. Create separate filled areas for home/away momentum. Make the 50% baseline much more prominent (darker color, wider stroke). Since PNG output doesn't show pygal tooltips, find a way to make scoring event labels visible in the static render (e.g., as part of data point labels or subtitle). These changes will significantly improve both spec compliance and visual storytelling.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot shows a win probability chart for an Eagles vs Cowboys NFL game (final score 24-17). The title reads "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai" at the top. The y-axis shows "Win Probability (%)" ranging from 0% to 100% with 10% increments. The x-axis shows "Game Progression" with rotated labels including quarter markers (Kickoff, Q2, Halftime, Q4, Final) and scoring events (DAL FG (3-0), PHI TD (7-3), etc.). Two filled areas represent team momentum: a darker teal (Eagles) and a lighter blue/navy (Cowboys) overlapping around the 50% baseline. Red/orange dots mark key scoring events. A dashed 50% baseline is present but partially obscured by the fill colors. The legend at the bottom shows DAL Cowboys, PHI Eagles, 50% Baseline, and Key Plays.

Score: 78/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	12	20
Spec Compliance	13	15
Data Quality	14	15
Code Quality	8	10
Library Mastery	7	10
Total	78	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set (title 56, labels 36, major labels 42). All text readable. Minor: rotated x-axis labels could be slightly larger for comfort.
• VQ-02: No Overlap (4/6) — X-axis labels are rotated at 35° but crowded; scoring event labels and quarter markers compete for space, with some labels appearing close together.
• VQ-03: Element Visibility (4/6) — Fill areas and key play dots are visible. However, the 50% baseline dashed line is largely obscured by the overlapping fill colors, making it hard to distinguish.
• VQ-04: Color Accessibility (4/4) — Cowboys navy (feat(pygal): implement range-interval #3594) vs Eagles teal (#004C54) is colorblind-safe. Red dots for key events provide strong contrast.
• VQ-05: Layout & Canvas (3/4) — Good canvas utilization overall. The lower half (0–50%) is a solid color block that doesn't convey much beyond "Cowboys territory," wasting some visual real estate.
• VQ-06: Axis Labels & Title (2/2) — "Win Probability (%)" with units, "Game Progression" is descriptive.

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Custom team colors, custom font family, styled grid guides with dashed patterns. Sports-themed palette is thoughtful and above library defaults, but not publication-level polish.
• DE-02: Visual Refinement (3/6) — White background, subtle dashed grid guides, no x-guides, legend at bottom. Some refinement visible but the two massive fill blocks dominate and feel heavy.
• DE-03: Data Storytelling (4/6) — The chart tells a game narrative: scoring events marked with red dots, fill colors show momentum shifts, final score in title. The viewer can follow the game's arc.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) — Line chart showing win probability evolution over game time. Correct.
• SC-02: Required Features (3/4) — Has: Y-axis 0–100% ✓, fill colors for team momentum ✓, scoring event annotations ✓, final score in title ✓, quarter markers ✓. Issue: The spec calls for a "prominent horizontal reference line at 50%" but the baseline is obscured by the fill areas.
• SC-03: Data Mapping (3/3) — X is game progression (play number), Y is win probability. Correct mapping showing full range.
• SC-04: Title & Legend (2/3) — Title includes "line-win-probability · pygal · pyplots.ai" with game context. Legend labels are functional but "50% Baseline" and "Key Plays" as separate legend entries add clutter.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) — Shows momentum shifts in both directions, multiple scoring events for both teams, a tie game, comeback, and decisive finish. Good coverage of win probability dynamics.
• DQ-02: Realistic Context (5/5) — NFL game between Eagles and Cowboys with realistic scoring (FGs, TDs). Neutral sports context.
• DQ-03: Appropriate Scale (4/4) — Win probabilities range from ~35% to 97%, starting at 50%. Scoring events at realistic play intervals.

Code Quality (8/10)

• CQ-01: KISS Structure (2/3) — Follows imports → data → plot → save. Data generation is verbose with many separate for-loops for each game segment.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used (numpy, pygal, Style).
• CQ-04: Code Elegance (1/2) — The two-fill overlay approach is clever but results in verbose code. Acceptable but could be cleaner.
• CQ-05: Output & API (1/1) — Saves as plot.png via render_to_png.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of pygal's Line chart with fill, custom Style, x_labels_major, cubic interpolation, value_formatter, per-series stroke_style overrides.
• LM-02: Distinctive Features (3/5) — Leverages pygal-specific features: cubic interpolation with precision control, x_labels_major for selective label display, per-series dict data points with labels for tooltip annotations.

Score Caps Applied

• None — no cap conditions triggered.

Strengths

• Strong data storytelling with realistic NFL game scenario and clear momentum shifts
• Good use of team-appropriate colors (Eagles teal, Cowboys navy) that are also colorblind-safe
• Effective use of pygal-specific features (cubic interpolation, x_labels_major, per-series fill overrides)
• Scoring event markers with labeled tooltips enhance the narrative

Weaknesses

• The 50% baseline dashed line is obscured by the fill areas and needs to be more prominent (spec explicitly requires this)
• X-axis labels are crowded with both quarter markers and scoring events competing for space
• The fill approach results in a heavy visual with the entire 0–50% range always filled in solid color
• Legend contains 4 entries including "50% Baseline" which could be cleaned up

Issues Found

1. VQ-03 MEDIUM: 50% baseline line is not visually prominent — lost behind fill areas
   • Fix: Ensure the baseline series renders on top with higher z-order, thicker stroke, and higher opacity so it stands out against the fills
2. VQ-02 MEDIUM: X-axis labels are crowded with scoring events and quarter markers overlapping
   • Fix: Consider reducing x-axis label clutter — either show fewer labels or increase rotation/spacing
3. DE-02 LOW: Fill areas feel visually heavy, dominating the chart
   • Fix: Increase transparency of fill areas or use a lighter fill to make the chart feel more refined
4. SC-02 MINOR: 50% baseline not prominent as spec requires
   • Fix: Move baseline to render last or use contrasting color/weight to ensure visibility

AI Feedback for Next Attempt

The main issues are: (1) Make the 50% baseline visually prominent — it should be the most visible horizontal reference, not hidden behind fills. Consider rendering it last with a strong contrasting color and thicker stroke. (2) Reduce x-axis label crowding — too many labels compete for space. (3) Consider lightening the fill opacity to reduce visual heaviness. The data storytelling and team color scheme are strong — preserve those.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 2/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a win probability chart for an Eagles vs Cowboys NFL game (final score 24-17) on a white background. Two overlapping filled area series create the visual effect: a navy blue (DAL Cowboys) fill from 0% to 50%, and a teal/slate (PHI Eagles) fill from 0% to the win probability value, with opacity blending at overlap. A prominent dashed dark line marks the 50% baseline. Seven red/orange circular dots mark scoring events along the probability curve. The y-axis ranges from 0% to 100% labeled "Win Probability (%)" with gridlines at 10% intervals. The x-axis shows "Game Progression" with five major labels: Kickoff, Q2, Halftime, Q4, and Final. The title reads "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". A four-item legend at the bottom shows DAL Cowboys, PHI Eagles, 50% Line, and Scoring Events. The probability curve starts near 50%, drops for early Cowboys scoring, rises through Eagles touchdowns, dips at a Cowboys tie, then surges to ~97% by game end.

Score: 82/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	13	20
Spec Compliance	13	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	7	10
Total	82	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=56, labels=36, major_labels=42, legend=34). All text clearly readable. Minor deduction: some sizing could be more proportional.
• VQ-02: No Overlap (6/6) - No overlapping text. X-axis uses only 5 major labels, well-spaced. Legend at bottom is clean.
• VQ-03: Element Visibility (4/6) - Fill areas are visible and scoring event dots are large and clear. However, the overlapping fill approach (both series fill from 0) creates muddy blending in areas where Eagles probability is near 50%, making it harder to distinguish team advantage at a glance.
• VQ-04: Color Accessibility (3/4) - Navy (feat(pygal): implement range-interval #3594) and teal (#004C54) are colorblind-safe but quite similar in darkness/saturation, reducing quick visual distinction.
• VQ-05: Layout & Canvas (3/4) - Good canvas utilization overall. "Final" label appears slightly cramped at right edge. Plot fills adequate area.
• VQ-06: Axis Labels & Title (2/2) - "Win Probability (%)" with units, "Game Progression" is descriptive.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom team color palette, custom font family, intentional styling choices. Above defaults but not publication-quality — the fill overlap creates visual confusion rather than clean team-color separation.
• DE-02: Visual Refinement (4/6) - Subtle dashed grid lines, x-guides hidden, white background, custom margins, generous whitespace. Good refinement beyond defaults.
• DE-03: Data Storytelling (4/6) - The data tells a clear game narrative: early Cowboys lead, Eagles comeback, dramatic tie, and final Eagles surge. Scoring event dots create focal points. Final score in title provides context. Quarter markers aid temporal orientation.

Spec Compliance (13/15)

• SC-01: Plot Type (5/5) - Correct: line/area chart showing win probability over game progression.
• SC-02: Required Features (2/4) - Y-axis 0-100% ✓, 50% reference line ✓, fill areas for team colors ✓ (approximated), quarter markers ✓, final score in title ✓. However, scoring event annotations are only dots — no visible text labels in the PNG output (labels only appear as tooltips in SVG/HTML). Spec requires "Annotate key scoring events" with visible labels.
• SC-03: Data Mapping (3/3) - X = game progression (play number), Y = win probability. Correct mapping, all data visible.
• SC-04: Title & Legend (3/3) - Title follows format: "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". Legend labels are descriptive.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - Shows momentum shifts in both directions, lead changes, different scoring types (FG, TD), close game and blowout phases. Could benefit from more dramatic swings.
• DQ-02: Realistic Context (5/5) - Real NFL teams (Eagles vs Cowboys), neutral sports topic, realistic game narrative.
• DQ-03: Appropriate Scale (4/4) - Win probabilities are realistic: starts at 50%, fluctuates appropriately with scoring, ends near certainty. Score values follow realistic NFL progression.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data generation → chart creation → save flow. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set at the start.
• CQ-03: Clean Imports (2/2) - All imports used: numpy, pygal, Style.
• CQ-04: Code Elegance (2/2) - Clean interpolation approach with S-curve smoothing. Well-organized data structures. No fake functionality.
• CQ-05: Output & API (1/1) - Saves as plot.png via render_to_png(). Also saves HTML. Current API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of pygal.Line(), Style class, fill parameter, per-series stroke_style, x_labels_major, value_formatter, tooltip configuration. Solid idiomatic usage.
• LM-02: Distinctive Features (3/5) - Uses pygal-specific features: per-point metadata dicts with labels, x_labels_major for selective labeling, tooltip_fancy_mode, fill with overlapping series technique. These leverage pygal's SVG-native approach.

Score Caps Applied

• None applied

Strengths

• Excellent data storytelling with a realistic NFL game narrative showing clear momentum shifts
• Clean code structure with smooth S-curve interpolation for natural-looking probability transitions
• Good use of pygal-specific features: per-point metadata, x_labels_major, custom Style
• All font sizes explicitly configured for the 4800×2700 canvas
• Proper title format with final score included

Weaknesses

• Scoring event annotations only appear as dots in PNG — text labels are not visible (only work as tooltips in SVG/HTML)
• Fill overlap approach (both series fill from 0) creates visual muddiness near the 50% line instead of clean team-color separation
• Navy and teal team colors are similar in darkness, reducing visual contrast between the two teams

Issues Found

1. SC-02 MEDIUM: Scoring event annotations not visible as text in PNG output — only dots without labels
   • Fix: Consider adding text labels near dots, though pygal has limited text annotation support in static renders
2. VQ-03 MEDIUM: Fill overlap creates visual confusion near 50% baseline
   • Fix: Difficult to resolve within pygal's fill constraints — the overlapping area approach is a reasonable workaround
3. VQ-04 MINOR: Team colors too similar in value/saturation
   • Fix: Use higher-contrast team color variants

AI Feedback for Next Attempt

This is attempt 3/3. The implementation is solid with good data storytelling and proper pygal usage. The main limitations (fill overlap approach, annotation visibility in PNG) are partly inherent to pygal's capabilities. Score of 82 merits inclusion in the repository.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 3/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

---

🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a win probability chart for an Eagles vs Cowboys NFL game (final score 24-17). A blue filled area (Cowboys, #3594) covers the region from 0% to 50%, while a green filled area (Eagles, #00843D) extends above 50% when the Eagles have the advantage. Red circular markers indicate scoring events (touchdowns and field goals) at their respective win probability values. The x-axis shows "Game Progression" with rotated labels for Kickoff, Q2, Halftime, Q4, Final, and all seven scoring events (e.g., "DAL FG 3-0", "PHI TD 7-3"). The y-axis shows "Win Probability (%)" from 0% to 100% with a dashed dark gray 50% baseline. A legend at the bottom lists DAL Cowboys, PHI Eagles, 50% Line, and Scoring Events. The title reads "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". The chart tells a clear momentum story: Cowboys take an early lead, Eagles fight back, the game ties at 17-17 around Q4, then Eagles pull away decisively to win.

Score: 84/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	84	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=56, labels=34, major_labels=42, value=28, legend=34). All text readable at full resolution. Minor deduction: rotated x-axis labels are dense.
• VQ-02: No Overlap (5/6) - X-axis labels rotated 45° to avoid overlap. Some crowding where scoring event labels sit close to quarter markers, but all remain legible.
• VQ-03: Element Visibility (4/6) - Filled areas and scoring dots are clearly visible. However, the win probability line (Series 4) uses the same green as the Eagles fill, making it blend in rather than stand out as a distinct trace.
• VQ-04: Color Accessibility (4/4) - Blue (feat(pygal): implement range-interval #3594) vs green (#00843D) is a strong colorblind-safe pairing. Red scoring dots provide additional contrast.
• VQ-05: Layout & Canvas (2/4) - Plot fills canvas reasonably but the 0-50% region is dominated by the flat blue Cowboys fill with no data variation, wasting nearly half the vertical space. Legend at bottom adds to vertical padding.
• VQ-06: Axis Labels & Title (2/2) - "Win Probability (%)" and "Game Progression" are descriptive with units.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Thoughtful team-color palette, custom font family, intentional opacity (0.50), dashed 50% baseline. Clearly above defaults with sports-broadcast-inspired design. Not quite publication-grade.
• DE-02: Visual Refinement (4/6) - White background, subtle dashed guide strokes (#e0e0e0), no x-guides, custom margins and spacing. Good refinement visible.
• DE-03: Data Storytelling (4/6) - Filled areas effectively convey momentum shifts. Scoring events marked with prominent dots and labeled on x-axis. The narrative arc (early Cowboys lead → Eagles comeback → tie → Eagles pull away) is visually clear.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Line chart showing win probability over game progression — correct.
• SC-02: Required Features (4/4) - Y-axis 0-100% ✓, 50% reference line ✓, fill above/below 50% with team colors ✓, annotated scoring events ✓, final score in title ✓, quarter/period markers ✓.
• SC-03: Data Mapping (3/3) - X = game progression (plays), Y = win probability percentage. Correct mapping.
• SC-04: Title & Legend (3/3) - Title follows format with game context: "Eagles vs Cowboys (24-17) · line-win-probability · pygal · pyplots.ai". Legend labels are clear and descriptive.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows momentum swings in both directions, lead changes, multiple scoring types (FG and TD), a tie, and a decisive finish. Exercises all aspects of a win probability chart.
• DQ-02: Realistic Context (5/5) - NFL game between Eagles and Cowboys with realistic scoring sequence (24-17 final). Neutral sports context.
• DQ-03: Appropriate Scale (4/4) - 120 plays for an NFL game is realistic. Win probabilities stay within plausible bounds (0.02-0.98), starting near 50% and ending near 97%.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → chart config → series → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set before any random operations.
• CQ-03: Clean Imports (2/2) - numpy, pygal, Style — all used.
• CQ-04: Code Elegance (2/2) - Clean interpolation with S-curve smoothing, well-organized multi-series approach with clear comments explaining each series' purpose.
• CQ-05: Output & API (1/1) - Saves as plot.png via render_to_png(). Current pygal API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of pygal.Line() with Style, fill=True per series, x_labels_major for selective labeling, value_formatter, legend_at_bottom. Solid idiomatic pygal.
• LM-02: Distinctive Features (3/5) - Leverages pygal's per-series fill control, tooltip formatting via value_formatter, x_labels_major vs x_labels distinction, dots_size parameter, and dict-based data points with custom labels. These are pygal-specific features.

Score Caps Applied

• None

Strengths

• Excellent spec compliance — all required features present including filled team-color areas, 50% baseline, scoring annotations, quarter markers, and final score
• Strong data storytelling with realistic NFL game data that creates a compelling momentum narrative
• Well-configured custom Style with team-appropriate colors, explicit font sizing, and subtle grid styling
• Clean, well-commented code with clear separation of data generation and chart configuration

Weaknesses

• Win probability line (Series 4) uses same green as Eagles fill, making the exact probability trace invisible against the fill
• Bottom half of chart (0-50%) is dominated by flat blue Cowboys fill with no variation, underutilizing vertical space
• X-axis labels are somewhat crowded with 12 labels (5 quarter markers + 7 scoring events) all competing for space

Issues Found

1. VQ-03 MODERATE: Win probability line blends into Eagles fill area — same color (#00843D) with no contrast
   • Fix: Use a darker or contrasting stroke color for the probability line, or increase stroke width significantly
2. VQ-05 MODERATE: Lower half of chart is visually static — flat blue fill from 0% to 50% wastes space
   • Fix: Consider adjusting y-range or visual approach to better utilize canvas

AI Feedback for Next Attempt

Solid implementation with full spec compliance and good design choices. Main visual issue is the win probability line blending into the fill area — a contrasting line color or heavier stroke would improve element visibility. The x-axis label density could be reduced by using fewer inline scoring labels.

Verdict: REJECTED