feat(bokeh): implement line-win-probability

[github-actions]

Implementation: line-win-probability - bokeh

Implements the bokeh version of line-win-probability.

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

Parent Issue: #4418

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot shows a win probability chart for an Eagles vs Cowboys NFL game. The x-axis displays "Play Number" (0–120) and the y-axis shows "Eagles Win Probability (%)" ranging from 0% to 100%. A dark line traces the probability over the game. The area above the 50% dashed baseline is filled with teal/dark green (Eagles color, #004C54) and the area below 50% is filled with light blue (Cowboys color, #3594), both at low alpha. Five key scoring events are annotated along the line: "TD Eagles 10-7", "TD Eagles 17-10", "TD Cowboys 17-17", "TD Cowboys 20-24", and "TD Eagles 27-24". Dotted vertical lines mark quarter transitions (Q2, Q3, Q4) with labels near the top. A bold teal label at the lower right reads "Final: Eagles 27 - Cowboys 24". The game starts near 50%, swings in both directions through momentum shifts, and ends at 100% as the Eagles win.

Score: 84/100

Category	Score	Max
Visual Quality	26	30
Design Excellence	14	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	5	10
Total	84	100

Visual Quality (26/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 42pt, labels 32pt, ticks 26pt, annotations 22pt). Annotation text is slightly small relative to the large canvas but still readable.
• VQ-02: No Overlap (5/6) — "TD Eagles 27-24" annotation is very close to the right edge and slightly overlaps the filled area. Minor crowding near play 105-112 where two annotations are close together.
• VQ-03: Element Visibility (5/6) — Main line is clearly visible (line_width=5). Filled areas at alpha=0.3 are quite subtle — the Cowboys blue fill is especially faint and hard to distinguish from background in some areas.
• VQ-04: Color Accessibility (3/4) — Teal (#004C54) and navy (feat(pygal): implement range-interval #3594) are both dark cool tones; distinguishable but could be more differentiated. Not a red-green issue but similar luminance makes them less distinct.
• VQ-05: Layout & Canvas (4/4) — Good proportions, plot fills canvas well, balanced margins.
• VQ-06: Axis Labels & Title (2/2) — "Play Number" and "Eagles Win Probability (%)" are descriptive with context.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (5/8) — Uses actual team colors, clean dark line, subtle fills. Above configured defaults but not quite publication-ready — the two fill colors are too similar in value/saturation, limiting visual impact.
• DE-02: Visual Refinement (4/6) — Grid alpha reduced to 0.15, outline removed, toolbar hidden, clean axis lines. Good refinement but not fully polished (spines still present, could benefit from more whitespace).
• DE-03: Data Storytelling (5/6) — Strong storytelling: annotations on key plays, quarter markers provide game structure, final score displayed, color fills show momentum. Viewer can follow the game narrative clearly.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct win probability line chart with filled areas above/below 50%.
• SC-02: Required Features (4/4) — Y-axis 0-100% ✓, 50% reference line ✓, filled areas with team colors ✓, scoring event annotations ✓, final score annotation ✓, quarter markers ✓.
• SC-03: Data Mapping (3/3) — X=play number, Y=win probability. Axes show full data range.
• SC-04: Title & Legend (2/3) — Title is "Eagles vs Cowboys · line-win-probability · bokeh · pyplots.ai" which prepends game context before the required format. No legend, but contextually appropriate since fills and annotations identify teams.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows probability swings in both directions, multiple scoring events (touchdowns, field goals, interception, turnover on downs), momentum shifts, game ending at 100%.
• DQ-02: Realistic Context (5/5) — Eagles vs Cowboys NFL game with realistic scoring events and play-by-play probability. Neutral sports content.
• DQ-03: Appropriate Scale (4/4) — Probabilities correctly range 0–1, scoring events produce realistic probability jumps, game flow is plausible.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → plot → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports are used (numpy, bokeh.io, bokeh.models, bokeh.plotting).
• CQ-04: Code Elegance (2/2) — Clean, well-organized, appropriate complexity for the visualization.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png. Also saves HTML for interactivity.

Library Mastery (5/10)

• LM-01: Idiomatic Usage (3/5) — Uses ColumnDataSource, figure, varea, line, Label, Span — standard Bokeh patterns. However, could better leverage Bokeh's data-driven approach (e.g., using transforms, color mapping).
• LM-02: Distinctive Features (2/5) — Uses HoverTool and HTML export which are Bokeh-distinctive, but the hover tool is basic (vline mode with simple tooltip). Could leverage callbacks, linked brushing, or more sophisticated interactive features that make Bokeh stand out.

Score Caps Applied

• None

Strengths

• Excellent data storytelling with annotated scoring events, quarter markers, and final score display
• Full spec compliance — all required features implemented
• Perfect data quality with realistic NFL game scenario and plausible probability dynamics
• Clean, well-structured code with appropriate complexity

Weaknesses

• Fill colors (#004C54 and feat(pygal): implement range-interval #3594) are too similar in value/saturation — hard to visually distinguish at alpha=0.3
• Library mastery is basic — HoverTool is simple, no use of Bokeh's more distinctive interactive capabilities
• Title format prepends "Eagles vs Cowboys" before the required spec-id format
• Annotation text could be larger and better positioned to avoid edge crowding

Issues Found

1. VQ-04 MODERATE: Fill colors too similar in luminance — both are dark cool tones at low alpha
   • Fix: Increase color contrast between team fills (e.g., use a warmer tone for one team or increase alpha differentiation)
2. LM-02 LOW: Basic Bokeh usage without leveraging distinctive features
   • Fix: Enhance HoverTool with richer tooltips (event names, score), consider using CustomJS callbacks or more interactive elements
3. DE-01 MODERATE: Fill areas too subtle, limiting visual impact
   • Fix: Increase fill alpha slightly (0.35-0.45) and ensure team colors are more visually distinct
4. SC-04 MINOR: Title format has extra prefix before required format
   • Fix: Use format line-win-probability · bokeh · pyplots.ai with game context as subtitle or annotation

AI Feedback for Next Attempt

Increase color contrast between the two team fills — they are both dark cool tones at low alpha and are hard to distinguish. Consider using a warmer/more saturated Cowboys color or increasing alpha. Enhance Bokeh-specific features: richer HoverTool tooltips showing event details, or use CustomJS for interactive highlighting. Fix the title to match the required {spec-id} · {library} · pyplots.ai format exactly. Slightly increase annotation text size and check positioning near canvas edges.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

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

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot shows a win probability chart for an Eagles vs Cowboys NFL game. The x-axis displays "Play Number" (0–120) and the y-axis shows "Eagles Win Probability (%)" with percentage labels at 0%, 25%, 50%, 75%, and 100%. A teal/green (#004C54) area fill appears above the 50% dashed reference line representing Eagles momentum, while a salmon/peach (#3594 at 0.3 alpha) fill below 50% represents Cowboys momentum. A dark line (line_width=5) traces the probability throughout the game. Dotted vertical lines mark quarter boundaries at plays 30, 60, and 90 with "Q2", "Q3", "Q4" labels at the top. Five key scoring events are annotated in bold text: "TD Eagles 10-7", "TD Eagles 17-10", "TD Cowboys 17-17", "TD Cowboys 20-24", and "TD Eagles 27-24". A bold teal "Final: Eagles 27 – Cowboys 24" label appears in the lower-right area. The title reads "line-win-probability · bokeh · pyplots.ai" at the top left. The toolbar is hidden. Grid lines are very subtle (alpha 0.15). The plot fills the canvas well with good margins.

Score: 88/100

Category	Score	Max
Visual Quality	28	30
Design Excellence	14	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (28/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 42pt, axis labels 32pt, ticks 26pt, annotations 22pt, final score 34pt. Everything is clearly readable.
• VQ-02: No Overlap (5/6) — Most text is clear, but the "TD Cowboys 20-24" annotation sits very close to the fill boundary and is slightly crowded against the line. Minor overlap issue.
• VQ-03: Element Visibility (6/6) — Line width of 5 is clearly visible, area fills have good alpha (0.3), scatter points for hover are appropriately invisible.
• VQ-04: Color Accessibility (3/4) — Teal and salmon fills are distinguishable for most colorblind types, but the dark teal (#004C54) and dark navy (feat(pygal): implement range-interval #3594) base colors are somewhat close in hue. Could benefit from more contrast.
• VQ-05: Layout & Canvas (4/4) — Plot fills the canvas well, margins are generous (min_border_left=140, min_border_bottom=120), nothing cut off.
• VQ-06: Axis Labels & Title (2/2) — "Play Number" and "Eagles Win Probability (%)" are descriptive with units.

Design Excellence (14/20)

• DE-01: Aesthetic Sophistication (5/8) — Uses team-themed colors (Eagles teal, Cowboys blue), intentional hierarchy with bold annotations, clean frame with outline removed. Above defaults but not quite publication-level polish — the annotation styling could be more refined (e.g., callout arrows, better positioning).
• DE-02: Visual Refinement (4/6) — Subtle grid (alpha 0.15), outline removed, toolbar hidden, axis lines at 2px. Good refinement but axis spines are still fully present on all four sides. Removing top/right spines would elevate this.
• DE-03: Data Storytelling (5/6) — Excellent narrative: the game flow tells a clear story with momentum swings, the filled areas instantly convey which team has the advantage, annotations mark pivotal plays, and the final score provides closure. The viewer immediately grasps the dramatic late-game swing.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) — Correct: line chart with area fill showing win probability over time.
• SC-02: Required Features (4/4) — All spec features present: 0-100% y-axis, 50% reference line, area fill above/below with team colors, annotated scoring events, final score annotation, quarter/period markers.
• SC-03: Data Mapping (3/3) — X=play number, Y=win probability. Axes show full 0-120 and 0%-100% range.
• SC-04: Title & Legend (2/3) — Title format correct (line-win-probability · bokeh · pyplots.ai), but no legend identifying which fill color corresponds to which team. The y-axis label implies Eagles perspective, but a small legend or color key would complete this.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows momentum swings in both directions, lead changes, scoring from both teams, a dramatic late comeback, and convergence to 100% at game end. Excellent coverage of all aspects.
• DQ-02: Realistic Context (5/5) — NFL game (Eagles vs Cowboys) is a real-world, neutral sports scenario. Team names, scoring events (touchdowns, field goals, interceptions, turnovers on downs) are all authentic.
• DQ-03: Appropriate Scale (4/4) — 120 plays is realistic for an NFL game, probabilities stay within realistic bounds (0.03-0.97 during play, converging to 1.0 at end), scoring patterns are plausible.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean linear flow: imports → seed → data generation → plot creation → styling → 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 are used: numpy, bokeh.io (export_png, output_file, save), bokeh.models (ColumnDataSource, HoverTool, Label, Span), bokeh.plotting (figure).
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity. The smoothing approach and event-driven simulation are well-structured. No fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png, also generates HTML. Uses current Bokeh API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ColumnDataSource, figure, varea, line, Label, Span. Uses Bokeh's declarative model well. Could leverage more advanced features like color mapping or custom formatters.
• LM-02: Distinctive Features (3/5) — HoverTool with vline mode is a distinctive Bokeh interactive feature. HTML export alongside PNG showcases Bokeh's dual-output capability. However, could go further with callbacks, CustomJS, or more sophisticated tooltip formatting.

Score Caps Applied

• None — no score caps triggered.

Strengths

• Excellent data storytelling with a realistic NFL game narrative showing dramatic momentum swings
• All spec requirements fully implemented: area fills, 50% reference, quarter markers, annotations, final score
• Clean, well-structured code with explicit font sizing for the 4800×2700 canvas
• Good use of Bokeh-specific features (HoverTool, varea, dual PNG+HTML output)
• Perfect data quality with authentic sports context and realistic probability values

Weaknesses

• Title in the plot doesn't include the game context ("Eagles vs Cowboys") visually prominently — it's in the code but the rendered title appears to show only the spec-id format
• No legend or color key identifying which fill color belongs to which team
• Axis spines on all four sides give a slightly boxy appearance — removing top/right would be cleaner
• Annotation positioning could be more polished (some annotations crowd near data points)

Issues Found

1. SC-04 MINOR: No legend or color key for team fill colors — viewers must infer from y-axis label
   • Fix: Add a simple legend or color-coded team labels
2. DE-02 MINOR: All four axis spines present, giving a boxy frame
   • Fix: Remove top and right spines for a cleaner look (set axis_line_color=None on those sides or use a minimal border approach)
3. DE-01 MINOR: Annotation styling is functional but could be more polished
   • Fix: Consider adding small markers/dots at annotation points, or use subtle connector lines

AI Feedback for Next Attempt

Score is 88 — very close to approval. To reach 90+: (1) Add a small legend or color-coded labels identifying Eagles/Cowboys fill colors. (2) Remove top and right axis spines for a cleaner, more modern look. (3) Consider adding small circular markers at annotated event points to make them more visually anchored. These three changes should push the score over the threshold.

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. The x-axis shows "Play Number" (0–120) and the y-axis shows "Eagles Win Probability (%)" ranging from 0% to 100%. A solid dark line traces the win probability over the course of the game. The area above the 50% dashed baseline is filled with a muted teal color representing the Eagles, while the area below is filled with a light gray for the Cowboys. Dotted vertical lines mark quarter transitions (Q2, Q3, Q4) with labels at the top. Five key scoring events are annotated with black dots and bold text labels (TD Eagles 10-7, TD Eagles 17-10, TD Cowboys 17-17, TD Cowboys 20-24, TD Eagles 27-24). A bold teal "Final: Eagles 27 - Cowboys 24" label sits in the lower-right area. A legend in the top-left identifies Eagles (teal) and Cowboys (gray). The overall design is clean with subtle grids, removed outline frame, and generous typography.

Score: 88/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	15	20
Spec Compliance	14	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title 42pt, axis labels 32pt, tick labels 26pt, annotations 22pt. All text clearly readable.
• VQ-02: No Overlap (5/6) - Minor crowding of annotation text near the right edge ("TD Cowboys 20-24" and "TD Eagles 27-24" are close together horizontally).
• VQ-03: Element Visibility (6/6) - Line width 5, fills with appropriate alpha, annotation dots size 20 — all well-adapted to the 4800×2700 canvas.
• VQ-04: Color Accessibility (4/4) - Teal (#004C54) and gray (#869397) are colorblind-safe with strong luminance contrast.
• VQ-05: Layout & Canvas (3/4) - Plot fills canvas well with set margins; minor issue with annotations approaching the right edge.
• VQ-06: Axis Labels & Title (2/2) - "Play Number" and "Eagles Win Probability (%)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Team-inspired color palette (Eagles teal, Cowboys gray), cohesive dark line, intentional typography hierarchy. Clearly above defaults but not quite FiveThirtyEight-level polish.
• DE-02: Visual Refinement (4/6) - Subtle grid (alpha 0.15), outline removed, minor ticks removed, toolbar hidden, clean axis styling. Good refinement visible.
• DE-03: Data Storytelling (5/6) - Strong narrative: dramatic back-and-forth game with annotated scoring events, quarter markers, and final score. The late-game collapse and comeback create a clear focal point. Visual hierarchy through fills and annotation placement guides the viewer.

Spec Compliance (14/15)

• SC-01: Plot Type (5/5) - Correct win probability line chart with area fills.
• SC-02: Required Features (4/4) - All spec features present: 0-100% y-axis, 50% reference line, team-colored area fills, annotated scoring events, final score display, quarter markers.
• SC-03: Data Mapping (3/3) - X = play number (game progression), Y = win probability. Correct mapping.
• SC-04: Title & Legend (2/3) - Title includes "line-win-probability · bokeh · pyplots.ai" but prepends "Eagles vs Cowboys ·" which deviates from the exact required format. Legend correctly identifies both teams.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - Shows momentum swings in both directions, multiple scoring event types (TD, FG, INT, turnover on downs), dramatic late-game reversal, and decisive finish at 100%.
• DQ-02: Realistic Context (5/5) - Realistic NFL game scenario with plausible team matchup, scoring progression, and play counts. Sports context is neutral and comprehensible.
• DQ-03: Appropriate Scale (4/4) - 120 plays is realistic for an NFL game, probability values follow sensible trajectories with appropriate drift and event-driven jumps.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean linear flow: imports → data generation → plot creation → styling → save. No functions or classes.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set before all random operations.
• CQ-03: Clean Imports (2/2) - All imports are used (output_file and save used for HTML export).
• CQ-04: Code Elegance (2/2) - Well-organized, appropriate complexity. Smoothing and fill logic are clean.
• CQ-05: Output & API (1/1) - Saves as plot.png via export_png. Also generates HTML for interactive viewing.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good use of ColumnDataSource, figure(), varea(), Span, Label, Legend with LegendItem, and HoverTool — all idiomatic Bokeh patterns.
• LM-02: Distinctive Features (3/5) - HoverTool with vline mode is a Bokeh-distinctive interactive feature. Dual output (PNG + HTML) leverages Bokeh's web-native design. Could push further with CustomJS callbacks or more advanced interactivity.

Score Caps Applied

• None — no caps triggered

Strengths

• Excellent data storytelling with a dramatic game narrative that naturally creates visual interest
• All spec requirements fully implemented including area fills, annotations, quarter markers, and final score
• Strong font sizing and explicit typography control for the 4800×2700 canvas
• Clean visual refinement: subtle grids, removed outlines, hidden toolbar
• Team-inspired color palette creates cohesive, professional look
• Perfect data quality with realistic NFL game simulation

Weaknesses

• Annotations crowd together near the right edge of the chart (plays 105-118)
• Title format includes extra "Eagles vs Cowboys" prefix beyond the required spec-id format
• Could leverage more Bokeh-distinctive features (e.g., CustomJS for richer interactivity in HTML output)

Issues Found

1. SC-04 MINOR: Title prepends "Eagles vs Cowboys ·" to the required format {spec-id} · {library} · pyplots.ai
   • Fix: Use exact title format from spec
2. VQ-02 MINOR: Right-edge annotations slightly crowded between plays 105-118
   • Fix: Adjust annotation positioning or reduce number of late-game annotations
3. LM-02 MODERATE: Bokeh's distinctive features could be used more extensively
   • Fix: Add CustomJS callbacks or Band annotations for richer HTML interactivity

AI Feedback for Next Attempt

Solid implementation at 88/100. The main areas preventing a 90+ score are: (1) minor annotation crowding at the right edge, (2) title format deviation from spec, and (3) opportunity to leverage more Bokeh-distinctive features. The design excellence and data storytelling are strong for a third attempt.

Verdict: REJECTED

[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 line chart for an NFL game between the Eagles and Cowboys. The x-axis shows "Play Number" (0–120) and the y-axis shows "Win Probability (%)" (0%–100%). A dark line traces the probability trajectory with a subtle confidence band around it. The area above the 50% dashed reference line is filled with muted teal (#004C54, Eagles) and the area below with gray (#869397, Cowboys). Quarter markers (Q1–Q4) appear at the top with dotted vertical lines at plays 30, 60, and 90, with alternating subtle background bands. Five key scoring events are annotated with teal dots and bold labels on white backgrounds. Semi-transparent "EAGLES" and "COWBOYS" team labels appear near the 50% line on the left. A legend in the top-left identifies the team fills. A prominent "Final: Eagles 27 - Cowboys 24" label sits in the lower-center area. The background is light gray (#fafafa) with white border fill, subtle grid lines, and clean axis styling with no tick marks.

Score: 92/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	92	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 40pt, axis labels 30pt, ticks 24pt, annotations 20-22pt. Everything clearly readable.
• VQ-02: No Overlap (5/6) — Minor crowding in the Q4 area where "TD Cowboys 20-24" and "TD Eagles 27-24" annotations are horizontally close. Otherwise clean.
• VQ-03: Element Visibility (6/6) — Main line has good width (4px + shadow), fill areas clearly visible, event markers well-sized (18px with white border).
• VQ-04: Color Accessibility (4/4) — Teal vs gray is easily distinguishable, including for colorblind users. Good contrast against light background.
• VQ-05: Layout & Canvas (4/4) — Plot fills canvas well with explicit margins (140/120/80/60). Good proportions at 4800×2700.
• VQ-06: Axis Labels & Title (2/2) — "Play Number" and "Win Probability (%)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Custom team color palette, professional typography hierarchy, confidence band adds depth, clean frame styling. Clearly above defaults but not quite FiveThirtyEight-level polish.
• DE-02: Visual Refinement (4/6) — Subtle grid (alpha 0.08/0.12), tick marks removed, clean axis lines, generous margins, #fafafa background. The confidence band adds slight visual noise that could be cleaner.
• DE-03: Data Storytelling (5/6) — Strong narrative: team-colored fills convey momentum at a glance, scoring annotations mark turning points, final score anchors the outcome, quarter markers provide temporal structure. The dramatic Q4 swings are the clear focal point.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct win probability line chart with area fills.
• SC-02: Required Features (4/4) — All features present: 50% reference line ✓, area fills with team colors above/below 50% ✓, scoring event annotations ✓, final score display ✓, quarter markers ✓.
• SC-03: Data Mapping (3/3) — X=play number (0–120), Y=win probability (0%–100%). Correct mapping with full data range.
• SC-04: Title & Legend (3/3) — Title "line-win-probability · bokeh · pyplots.ai" correct. Legend shows Eagles/Cowboys with matching fill colors.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows lead changes, momentum swings, close game (tie at 10-10, 17-17), dramatic Q4 comeback, interception impact, final decisive drive. Covers diverse game scenarios.
• DQ-02: Realistic Context (5/5) — Eagles vs Cowboys NFL game — real teams, neutral sports context. Scoring events are plausible (TDs, FGs, INT, turnover on downs).
• DQ-03: Appropriate Scale (4/4) — Probability values 0–1 with realistic fluctuations. Scoring events create believable jumps. Starts at 0.50, ends at 1.0.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Clean Imports → Data → Plot → Save structure. No functions or classes.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the top.
• CQ-03: Clean Imports (2/2) — All imports are used: Band, ColumnDataSource, CustomJS, HoverTool, Label, Legend, LegendItem, NumeralTickFormatter, Span.
• CQ-04: Code Elegance (2/2) — Clean, appropriate complexity. Smoothing and confidence band are reasonable additions. No fake functionality.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png(). Also saves HTML. Current Bokeh 3.9 API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ColumnDataSource, figure API, Band, Span, Label, Legend with LegendItem, NumeralTickFormatter. Slight deduction for the manual loop constructing quarter bands where a more idiomatic approach might use BoxAnnotation.
• LM-02: Distinctive Features (4/5) — HoverTool with rich HTML tooltips, CustomJS callback for crosshair effect, dual output (PNG + HTML) — these are distinctively Bokeh features. Could push further with linked selections or other widget interactivity.

Score Caps Applied

• None

Strengths

• Excellent spec compliance — all required features implemented (fills, 50% line, annotations, score, quarters)
• Strong data storytelling with team colors, event annotations, and final score creating a clear game narrative
• Good use of Bokeh-specific features (HoverTool, CustomJS crosshair, HTML export) that add value beyond the static PNG
• Professional visual refinement with custom colors, subtle grid, clean frame, and explicit font sizing
• Well-structured, reproducible code with clean imports

Weaknesses

• Minor annotation crowding in Q4 area where two event labels are horizontally close
• Confidence band adds slight visual noise without strong justification from the spec
• Quarter band construction could use BoxAnnotation for more idiomatic Bokeh

Issues Found

• None critical — implementation meets all requirements

AI Feedback for Next Attempt

Strong implementation. The win probability chart is well-executed with proper team colors, annotations, and interactive Bokeh features. Minor improvements could include better spacing of Q4 annotations and using BoxAnnotation for quarter shading.

Verdict: APPROVED