feat(bokeh): implement scatter-pitch-events

[github-actions]

Implementation: scatter-pitch-events - bokeh

Implements the bokeh version of scatter-pitch-events.

File: plots/scatter-pitch-events/implementations/bokeh.py

Parent Issue: #4417

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a soccer pitch event map on a light green rectangular pitch with standard FIFA markings drawn in darker green: penalty areas, goal areas, center circle, halfway line, corner arcs, and goal posts (in dark gray). Four event types are scattered across the pitch using distinct marker shapes and colors: blue filled/hollow circles for successful/unsuccessful passes, red filled/hollow stars for shots, teal filled/hollow triangles for tackles, and gold filled/hollow diamonds for interceptions. Directional arrows connect start and end positions for passes and shots, with successful events shown at higher opacity and unsuccessful at lower opacity. The legend in the top-left lists all 8 event-outcome combinations. Title reads "scatter-pitch-events · bokeh · pyplots.ai". Axes are labeled "Pitch Length (m)" and "Pitch Width (m)" with numeric tick marks.

Score: 82/100

Category	Score	Max
Visual Quality	24	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	9	10
Library Mastery	7	10
Total	82	100

Visual Quality (24/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 60pt, labels 40pt, ticks 30pt, legend 28pt). Clearly readable at full resolution.
• VQ-02: No Overlap (4/6) — Legend with 8 entries overlaps data in the top-left corner of the pitch. Some arrow lines cross through legend area reducing readability.
• VQ-03: Element Visibility (5/6) — Markers at size 22 are visible. Successful vs unsuccessful differentiation via fill/alpha works well. Some unsuccessful markers (alpha 0.25-0.3) are slightly hard to see against the green pitch.
• VQ-04: Color Accessibility (3/4) — Blue, red, teal, gold palette is mostly distinguishable. Red (#E63946) and teal (#2A9D8F) could be challenging for deuteranopia, though not a pure red-green pair.
• VQ-05: Layout & Canvas (3/4) — Pitch fills canvas well with correct aspect ratio. Minor wasted space at margins.
• VQ-06: Axis Labels & Title (2/2) — "Pitch Length (m)" and "Pitch Width (m)" with units.

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Thoughtful custom palette, well-drawn pitch diagram, clean white background. Above defaults but not publication-level polish — the light green pitch fill is quite subtle and the overall look is functional rather than striking.
• DE-02: Visual Refinement (4/6) — Grid removed, axis lines removed, tick marks removed, outline removed. Clean white border. Good refinement but legend styling could be more polished.
• DE-03: Data Storytelling (3/6) — Spatial patterns are visible: shots cluster in the attacking third, passes spread across the pitch, tackles distributed in midfield/defense. Successful vs unsuccessful encoding adds narrative. However, no strong focal point or visual hierarchy guides the viewer.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct scatter plot on pitch diagram.
• SC-02: Required Features (4/4) — All features present: pitch markings (penalty areas, goal areas, center circle, halfway line, corner arcs, goals), distinct markers/colors per event type, outcome encoding (filled vs hollow + alpha), directional arrows for passes and shots, correct aspect ratio.
• SC-03: Data Mapping (3/3) — X = pitch length (0–105m), Y = pitch width (0–68m), correctly mapped.
• SC-04: Title & Legend (3/3) — Title format correct. Legend labels match data categories.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — All 4 event types shown with both outcomes, arrows for directional events, realistic spatial distribution per event type.
• DQ-02: Realistic Context (5/5) — Soccer match events — realistic, neutral domain. Event distributions match real match patterns.
• DQ-03: Appropriate Scale (4/4) — FIFA pitch dimensions (105×68m), shot success rate 30%, pass success 78%, sensible event counts.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data → pitch → events → styling → save.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) — All imports used.
• CQ-04: Code Elegance (1/2) — The per-row iterrows() loop for arrows is verbose; could use vectorized approaches. Double nested loop for markers is acceptable but adds length.
• CQ-05: Output & API (1/1) — Saves as plot.png and plot.html with current API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ColumnDataSource, figure with Range1d, scatter with marker parameter, Arrow layout with NormalHead. Idiomatic Bokeh patterns.
• LM-02: Distinctive Features (3/5) — Uses Bokeh-specific Arrow/NormalHead for directional annotations, HTML export with CDN resources, match_aspect for correct proportions. These are distinctive Bokeh capabilities.

Score Caps Applied

• None

Strengths

• Excellent spec compliance — all required features implemented including pitch markings, distinct markers, outcome encoding, and directional arrows
• Realistic data generation with domain-appropriate distributions (shots near goal, varied success rates per event type)
• Good visual refinement with removed grid, axis lines, and tick marks creating a clean look
• Proper use of Bokeh-specific features (Arrow, NormalHead, HTML export)

Weaknesses

• Legend with 8 entries is large and overlaps pitch data in the top-left corner — consider relocating or condensing
• Unsuccessful event markers (alpha 0.25–0.3) are quite faint against the light green pitch, reducing visibility
• No strong visual hierarchy or focal point to guide the viewer's eye through the data
• The per-row iterrows() loop for arrows is verbose

Issues Found

1. VQ-02 MODERATE: Legend overlaps with pitch data in the top-left
   • Fix: Move legend outside the pitch area (e.g., below the plot) or reduce to fewer entries by combining successful/unsuccessful into one entry with a note
2. VQ-03 MINOR: Unsuccessful markers too faint (alpha 0.25–0.3)
   • Fix: Increase unsuccessful alpha to 0.4–0.5 so markers remain visible on the light green pitch
3. DE-01 MODERATE: Good but not exceptional design
   • Fix: Consider a slightly darker pitch green fill for better contrast, add subtle pitch zone shading, or use a more sophisticated color palette
4. DE-03 MODERATE: No clear visual hierarchy or storytelling
   • Fix: Use size variation (e.g., larger markers for shots), add subtle pitch zone shading to highlight attacking/defensive thirds, or emphasize key events

AI Feedback for Next Attempt

Move or condense the legend to avoid overlapping pitch data. Increase alpha for unsuccessful events to improve visibility. Create visual hierarchy through size variation or pitch zone emphasis. Consider making the pitch background slightly more prominent for better contrast with markers.

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 displays a soccer pitch event map on a light green pitch background with standard FIFA markings including penalty areas, goal areas, center circle, halfway line, corner arcs, and goal posts drawn in dark green. Four event types are plotted: blue circles for passes, red stars for shots, teal triangles for tackles, and gold diamonds for interceptions. Successful events are filled with their respective colors while unsuccessful ones appear as hollow (white-filled) markers with colored outlines. Blue directional arrows indicate pass trajectories across the pitch, and red arrows show shot directions toward the right goal. Subtle zone shading highlights the attacking third (faint red) and defensive third (faint blue). The title "scatter-pitch-events · bokeh · pyplots.ai" appears at the top. Axes are labeled "Pitch Length (m)" and "Pitch Width (m)". A horizontal legend with 8 entries (4 event types × 2 outcomes) sits below the pitch in two rows of four.

Score: 89/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	89	100

Visual Quality (27/30)

• VQ-01: Text Legibility (8/8) — All font sizes explicitly set: title 60pt, axis labels 40pt, tick labels 30pt, legend 24pt. All clearly readable at 4800×2700.
• VQ-02: No Overlap (5/6) — Legend below the pitch avoids data overlap, but 8 entries in 2 rows are slightly cramped. Minor crowding between legend items.
• VQ-03: Element Visibility (5/6) — Markers are visible with appropriate size variation (20-28). Some smaller interception diamonds (size 20) and hollow unsuccessful markers are slightly hard to distinguish at a glance.
• VQ-04: Color Accessibility (3/4) — Blue, red, teal, and gold are generally distinguishable, but gold/yellow diamonds have low contrast against the green pitch background, especially hollow unsuccessful ones.
• VQ-05: Layout & Canvas (4/4) — Excellent canvas utilization with match_aspect preserving the 105:68 ratio. Pitch fills the frame well with appropriate margins.
• VQ-06: Axis Labels & Title (2/2) — "Pitch Length (m)" and "Pitch Width (m)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design with custom 4-color palette, zone shading for attacking/defensive thirds, filled/hollow outcome encoding, and detailed pitch markings including goal posts. Clearly above defaults.
• DE-02: Visual Refinement (5/6) — All chrome removed (grid lines, axis lines, tick marks), clean white background, outline removed. Excellent refinement for a pitch diagram.
• DE-03: Data Storytelling (4/6) — Zone shading contextualizes pitch areas, directional arrows convey pass/shot trajectories, and spatial clustering of shots in the attacking third tells a tactical story. Good visual hierarchy through marker differentiation.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct scatter plot on accurately scaled soccer pitch diagram.
• SC-02: Required Features (4/4) — All spec features present: pitch markings (penalty areas, goal areas, center circle, halfway line, corner arcs, goal posts), distinct markers per event type, outcome encoding (filled vs hollow + alpha), directional arrows for passes and shots.
• SC-03: Data Mapping (3/3) — X = pitch length (0–105m), Y = pitch width (0–68m). Correct FIFA dimensions maintained.
• SC-04: Title & Legend (3/3) — Title format "scatter-pitch-events · bokeh · pyplots.ai" correct. Legend labels clearly show all 8 event type/outcome combinations.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — All 4 event types with both outcomes represented. Passes distributed across the pitch, shots concentrated in the attacking third, tackles in midfield/defensive zones. Good spatial diversity.
• DQ-02: Realistic Context (5/5) — Soccer match event data with realistic distributions: 78% pass success, 30% shot success, appropriate zone-based positioning.
• DQ-03: Appropriate Scale (4/4) — FIFA standard 105×68m pitch, 120 events per match segment (within spec range), sensible coordinate distributions per event type.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Follows imports → data → plot → save. No functions or classes. Linear structure.
• CQ-02: Reproducibility (2/2) — np.random.seed(42) set at the top.
• CQ-03: Clean Imports (2/2) — All imports used: numpy, pandas, bokeh.io, bokeh.models, bokeh.plotting, bokeh.resources.
• CQ-04: Code Elegance (2/2) — Appropriate complexity for the pitch diagram. Iteration over groups is the right pattern for per-event arrows and per-type/outcome markers.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png and plot.html. Current Bokeh API.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ColumnDataSource, figure with Range1d, Arrow/NormalHead annotations. Uses scatter with marker parameter for different shapes. Could leverage factor_cmap or more declarative patterns.
• LM-02: Distinctive Features (3/5) — Uses Bokeh-specific Arrow + NormalHead annotation model for directional arrows, HTML export with CDN resources, and match_aspect. These are features distinctive to Bokeh.

Score Caps Applied

• None applied

Strengths

• Excellent pitch diagram with all FIFA-standard markings including goal posts, corner arcs, and penalty arcs
• Zone shading (attacking/defensive thirds) adds tactical context beyond spec requirements
• Clean visual refinement with all axis chrome removed — appropriate for a pitch diagram
• Filled vs hollow markers with alpha variation effectively encode outcomes
• Directional arrows for passes and shots fulfill spec requirements elegantly using Bokeh's Arrow model
• Perfect spec compliance and data quality

Weaknesses

• Gold/yellow interception diamonds have low contrast against the green pitch background — consider a darker or more saturated color
• Legend with 8 entries is slightly cramped — could benefit from more spacing or a different layout strategy
• Hollow unsuccessful markers (especially small interception diamonds) are hard to distinguish at a glance

Issues Found

1. VQ-04 MINOR: Yellow/gold interception markers (#E9C46A) have low contrast against the green pitch
   • Fix: Use a darker amber or orange tone (e.g., #D4850A or #F4A261) that stands out better on green
2. VQ-02 MINOR: Legend entries slightly cramped with 8 items in 2 rows
   • Fix: Increase spacing between legend items or use a single-column vertical layout outside the pitch area
3. LM-01 MINOR: Could use more declarative Bokeh patterns
   • Fix: Consider using factor_cmap/factor_mark for categorical marker/color mapping instead of manual iteration

AI Feedback for Next Attempt

Score is 89 — very close to approval. To reach 90+: (1) Change interception marker color to a darker amber/orange that contrasts better with the green pitch. (2) Slightly increase legend spacing or reorganize to reduce crowding. These two small fixes 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...

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🤖 impl-repair

[claude]

AI Review - Attempt 3/3

Image Description

The plot displays a soccer pitch event map rendered on a correctly proportioned 105m × 68m pitch diagram with a light green background. Standard pitch markings are present: penalty areas, goal areas, center circle with center spot, halfway line, penalty arcs, corner arcs, and goal posts (rendered as dark gray bars). Subtle zone shading distinguishes the defensive third (blue tint) and attacking third (red tint). Four event types are plotted with distinct markers and colors: blue circles for passes, red stars for shots, teal triangles for tackles, and orange diamonds for interceptions. Successful events appear as filled markers while unsuccessful ones are hollow with colored outlines. Blue directional arrows show pass trajectories across the pitch, and red arrows show shot trajectories toward the right goal. The legend at the bottom center displays all 8 categories (4 event types × 2 outcomes) in a horizontal layout across 2 rows. Axis labels read "Pitch Length (m)" and "Pitch Width (m)" with tick values matching FIFA dimensions. The title "scatter-pitch-events · bokeh · pyplots.ai" appears at the top left.

Score: 83/100

Category	Score	Max
Visual Quality	25	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	9	10
Library Mastery	7	10
Total	83	100

Visual Quality (25/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (60pt title, 40pt axis labels, 30pt ticks, 24pt legend). All text clearly readable at full resolution.
• VQ-02: No Overlap (5/6) - Legend positioned below pitch avoids data overlap. Some arrow congestion in the attacking third where many shots converge on goal, but this reflects real data density.
• VQ-03: Element Visibility (5/6) - Markers are well-sized (20-28px) for 120 data points. Filled markers are clearly visible; hollow unsuccessful markers are slightly less prominent but still distinguishable.
• VQ-04: Color Accessibility (3/4) - Blue (#306998), red (#E63946), teal (#2A9D8F), and orange (#D4850A) are generally distinguishable. Blue and teal could be challenging for tritanopia-type color vision deficiency.
• VQ-05: Layout & Canvas (3/4) - Pitch fills canvas well with match_aspect=True. Good use of horizontal space. The bottom margin for the legend creates some unused vertical space.
• VQ-06: Axis Labels & Title (2/2) - "Pitch Length (m)" and "Pitch Width (m)" are descriptive with units.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Thoughtful custom palette anchored on Python Blue, subtle zone shading for attacking/defensive thirds, filled/hollow outcome encoding, detailed goal post rendering. Clearly above configured defaults but not yet publication-grade.
• DE-02: Visual Refinement (5/6) - Grid lines removed, axis lines removed, tick marks removed, outline removed, clean white border. Strong attention to visual cleanliness.
• DE-03: Data Storytelling (3/6) - Zone shading provides tactical context, spatial distributions are meaningful (shots cluster in attacking third), and outcome encoding creates some hierarchy. However, no strong focal point or narrative emphasis — the viewer must find the story themselves.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Scatter plot on accurately scaled pitch diagram with all standard markings.
• SC-02: Required Features (4/4) - All spec features present: pitch markings (penalty areas, goal areas, center circle, halfway line, corner arcs, goal posts), distinct marker shapes/colors per event type, outcome encoding via fill/opacity, directional arrows for passes and shots, green pitch background, correct aspect ratio.
• SC-03: Data Mapping (3/3) - X = pitch length (0–105m), Y = pitch width (0–68m). All data visible within pitch bounds.
• SC-04: Title & Legend (3/3) - Title follows "scatter-pitch-events · bokeh · pyplots.ai" format. Legend labels clearly identify all 8 event type × outcome combinations.

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - All 4 event types with both outcomes present. Spatial distributions are realistic (passes spread across pitch, shots in attacking third). Arrows show direction. Minor: could have more variation in pass distances/angles to showcase more edge cases.
• DQ-02: Realistic Context (5/5) - Soccer match events with real-world plausible probabilities (78% pass success, 30% shot success, 65% tackle success). Neutral sporting context.
• DQ-03: Appropriate Scale (4/4) - 120 events on FIFA-standard 105×68m pitch. All values within realistic ranges.

Code Quality (9/10)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data generation → pitch drawing → event plotting → 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, pandas, bokeh.io, bokeh.models, bokeh.plotting, bokeh.resources).
• CQ-04: Code Elegance (1/2) - The nested loops for arrow rendering (iterating row-by-row) are verbose but necessary given Bokeh's Arrow API. The marker loop with groupby is clean.
• CQ-05: Output & API (1/1) - Saves as plot.png via export_png and plot.html via save with CDN resources. Current API usage.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Uses ColumnDataSource for scatter data, figure with Range1d, Arrow model with NormalHead for directional annotations. Good Bokeh patterns. Could use more high-level API features like color mapping or legend click policies.
• LM-02: Distinctive Features (3/5) - Arrow/NormalHead layout annotations are Bokeh-specific. HTML export with CDN resources is a Bokeh strength. match_aspect parameter is idiomatic. Could leverage HoverTool, TapTool, or interactive callbacks for more distinctive Bokeh showcase.

Score Caps Applied

• None — no score caps triggered.

Strengths

• Complete spec compliance with all pitch markings, event types, markers, arrows, and outcome encoding
• Clean visual refinement: grid removed, axis lines hidden, tick marks eliminated for professional look
• Zone shading for attacking/defensive thirds adds tactical context beyond the spec requirements
• Realistic data generation with domain-appropriate distributions and probabilities
• Dual output (PNG + HTML) leverages Bokeh's web-native strengths

Weaknesses

• Blue and teal markers could be closer in hue than ideal for some colorblind viewers
• Arrow congestion in the attacking third reduces clarity where shots cluster
• Data storytelling lacks a clear focal point — no single insight stands out to the viewer
• Row-by-row arrow loop is verbose (Bokeh API limitation but impacts code elegance)

Issues Found

1. VQ-04 MODERATE: Blue (#306998) and teal (#2A9D8F) markers may be hard to distinguish for some colorblind viewers
   • Fix: Consider replacing teal with a warmer yellow-green or adjusting one color for better separation
2. DE-03 MODERATE: No clear focal point or narrative emphasis in the visualization
   • Fix: Could highlight a specific tactical pattern (e.g., emphasize shots on goal with larger markers, or annotate key events)
3. CQ-04 MINOR: Row-by-row arrow loop is verbose
   • Fix: Limited by Bokeh's Arrow API requiring individual layout additions; acceptable trade-off

AI Feedback for Next Attempt

Solid implementation with full spec compliance and good visual refinement. The main gap to 90+ is Design Excellence: consider creating a stronger visual narrative (highlight shot outcomes near goal, or annotate key tactical zones), and refine the color palette for better colorblind accessibility. The code quality is good but the arrow rendering loop verbosity is a minor detractor.

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 soccer pitch event map on a light green background with subtle alternating mow-pattern stripes. Standard FIFA pitch markings are drawn in dark green: pitch outline, halfway line, center circle with center spot, both penalty areas, goal areas, penalty spots, penalty arcs, corner arcs, and gray goal posts at each end. A translucent red "danger zone" gradient highlights the attacking third (right side). Four event types are plotted with distinct markers and colors: blue circles for passes, red/pink stars for shots, gold triangles for tackles, and purple diamonds for interceptions. Successful events appear filled with high opacity; unsuccessful ones are hollow/white-filled with lower opacity. Directional arrows connect start-to-end positions for passes (blue) and shots (red), with shot arrows being thicker. A bold red annotation "16 shots · 3 on target" appears in the top-right corner. The horizontal legend at the bottom lists all 8 event type/outcome combinations. Title reads "scatter-pitch-events · bokeh · pyplots.ai". Axes are labeled "Pitch Length (m)" and "Pitch Width (m)" with clean gray text on a #FAFAFA background.

Score: 91/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	16	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	91	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title 52pt, axis labels 36pt, ticks 26pt, legend 22pt). Annotation text is readable but slightly small relative to the overall plot scale.
• VQ-02: No Overlap (5/6) — Legend with 8 items across 2 rows is slightly dense. Some arrow lines cross over markers in busy areas. Minor crowding, all text remains readable.
• VQ-03: Element Visibility (5/6) — Markers well-sized for 120 events. Unsuccessful markers (low alpha, white fill) are occasionally hard to distinguish against the light green pitch, especially interceptions.
• VQ-04: Color Accessibility (4/4) — Blue, red, gold, purple palette with shape redundancy. Colorblind-safe with good contrast.
• VQ-05: Layout & Canvas (4/4) — Pitch fills canvas well with balanced margins. Aspect ratio correctly maintained via match_aspect.
• VQ-06: Axis Labels & Title (2/2) — "Pitch Length (m)" and "Pitch Width (m)" — descriptive with units.

Design Excellence (16/20)

• DE-01: Aesthetic Sophistication (6/8) — Strong design: custom colorblind-safe palette, removed axis lines/ticks/grid, subtle mow-pattern stripes for texture, danger zone gradient, clean #FAFAFA background. Clearly above defaults with intentional hierarchy.
• DE-02: Visual Refinement (5/6) — Grid removed, axis lines and tick marks removed, clean background, generous whitespace, subtle pitch stripes add texture without distraction. Well-polished details throughout.
• DE-03: Data Storytelling (5/6) — Red danger zone gradient draws attention to the attacking third, shot annotation provides match context ("16 shots · 3 on target"), shots rendered larger as focal points. Visual hierarchy guides the viewer to the key action area.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct scatter plot on accurately scaled pitch diagram.
• SC-02: Required Features (4/4) — All required: pitch markings (penalty areas, center circle, goal areas, halfway line, corner arcs, goal posts), distinct marker shapes/colors per event type, outcome encoding via fill/opacity, directional arrows for passes and shots, green pitch background, correct aspect ratio.
• SC-03: Data Mapping (3/3) — X = pitch length (0–105m), Y = pitch width (0–68m). All data visible within pitch bounds.
• SC-04: Title & Legend (3/3) — Title "scatter-pitch-events · bokeh · pyplots.ai" correct. Legend labels clearly show all 8 event type/outcome combinations.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — All 4 event types present with both successful and unsuccessful outcomes. Passes distributed across the pitch, shots concentrated in the attacking third, tackles and interceptions in realistic zones. Good variation in event positions and trajectories.
• DQ-02: Realistic Context (5/5) — Soccer match event data with realistic proportions (45% passes, 15% shots, 22% tackles, 18% interceptions) and plausible success rates (78% pass completion, 30% shot accuracy).
• DQ-03: Appropriate Scale (4/4) — FIFA standard pitch dimensions (105m × 68m). Event coordinates fall within realistic pitch zones. 120 events is appropriate for a match segment.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear flow: imports → data generation → pitch drawing → event plotting → 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 used: numpy, pandas, bokeh.io (export_png, save), bokeh.models (Arrow, ColumnDataSource, Label, NormalHead, Range1d), bokeh.plotting (figure), bokeh.resources (CDN).
• CQ-04: Code Elegance (2/2) — Clean, well-structured code with appropriate complexity. Iteration over event types/outcomes is a reasonable pattern for this visualization.
• CQ-05: Output & API (1/1) — Saves as plot.png via export_png and plot.html via save. Current Bokeh API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (4/5) — Good use of ColumnDataSource, figure with Range1d, scatter with marker parameter, Arrow layout annotations, Label layout. Idiomatic Bokeh patterns throughout.
• LM-02: Distinctive Features (4/5) — Arrow with NormalHead (Bokeh-specific layout annotation), click_policy="hide" for interactive legend in HTML output, dual output (PNG + HTML with CDN resources). These are distinctive Bokeh capabilities.

Score Caps Applied

• None

Strengths

• Excellent pitch rendering with all FIFA standard markings including subtle details like corner arcs, penalty arcs, and goal posts
• Strong data storytelling through danger zone gradient, shot annotation, and visual hierarchy (shots rendered larger as focal points)
• Mow-pattern stripes add professional texture without distracting from data
• Dual PNG + HTML output leverages Bokeh's interactive capabilities (click-to-hide legend)
• Colorblind-safe palette with shape redundancy ensures accessibility
• Realistic data proportions and spatial distributions for soccer events

Weaknesses

• Unsuccessful event markers (white fill, low alpha) can be slightly hard to distinguish on the light green pitch in some areas
• Legend with 8 items across 2 rows is somewhat dense

Issues Found

None critical — minor visual density issues don't warrant deductions beyond what's already reflected in VQ-02 and VQ-03 scoring.

AI Feedback for Next Attempt

N/A — implementation meets quality threshold.

Verdict: APPROVED