Nba First Basket Scorer Predictor
D 54 completed
Other
unknown / json · tiny
47
Files
35,695
LOC
0
Frameworks
5
Languages
Pipeline State
completedRun ID
#396960Phase
doneProgress
1%Started
Finished
2026-04-13 01:31:02LLM tokens
0Pipeline Metadata
Stage
SkippedDecision
skip_scaffold_dupNovelty
34.07Framework unique
—Isolation
—Last stage change
2026-04-16 18:15:42Deduplication group #47296
Member of a group with 1 similar repo(s) — canonical #110080 view group →
Top concepts (2)
Project DescriptionData/ML
Repobility — same analyzer, your code, free for public repos · /scan/
AI Prompt
Create a Python application to predict which player will score first in NBA games. The system should use jump ball data and player tendencies, and it needs to include a module for betting optimization using the Kelly Criterion. I want a Streamlit interface to run the predictions, and the core logic should handle collecting raw NBA data, processing features, training the necessary machine learning models, and finally displaying the betting recommendations. Please structure the code to handle data collection, analysis, training, and UI presentation.
python streamlit machine-learning nba data-science betting prediction json
Generated by gemma4:latest
Catalog Information
Predict which player will score first in NBA games using jump ball data, player tendencies, and machine learning. Includes betting optimization with Kelly Criterion.
Description
Predict which player will score first in NBA games using jump ball data, player tendencies, and machine learning. Includes betting optimization with Kelly Criterion.
Novelty
3/10Tags
python streamlit machine-learning nba data-science betting prediction json
Technologies
streamlit
Claude Models
claude-opus-4-6
Quality Score
D
54.5/100
Structure
43
Code Quality
74
Documentation
61
Testing
0
Practices
62
Security
90
Dependencies
60
Strengths
- Consistent naming conventions (snake_case)
- Good security practices \u2014 no major issues detected
Weaknesses
- No LICENSE file \u2014 legal ambiguity for contributors
- No tests found \u2014 high risk of regressions
- No CI/CD configuration \u2014 manual testing and deployment
- 3 bare except/catch blocks swallowing errors
- Potential hardcoded secrets in 1 files
- 892 duplicate lines detected \u2014 consider DRY refactoring
- 4 'god files' with >500 LOC need decomposition
Recommendations
- Add a test suite \u2014 start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Add a LICENSE file (MIT recommended for open source)
- Replace bare except/catch blocks with specific exception types
- Move hardcoded secrets to environment variables or a secrets manager
Security & Health
5.3h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
All rows above produced by Repobility · https://repobility.com
Unknown
License
17.8%
Duplication
Languages
Frameworks
None detected
Concepts (2)
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| auto_description | Project Description | Predict which player will score first in NBA games using jump ball data, player tendencies, and machine learning. Includes betting optimization with Kelly Criterion. | 80% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| auto_category | Data/ML | data-ml | 70% | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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