Baseball Mlops

D 56 completed
Other
containerized / python · tiny
19
Files
2,199
LOC
1
Frameworks
4
Languages

Pipeline State

completed
Run ID
#394584
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
31.17
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47563
Member of a group with 1 similar repo(s) — canonical #117037 view group →
Top concepts (2)
Project DescriptionWeb Backend
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot

AI Prompt

Create a full MLOps pipeline for predicting MLB player performance using MLB Statcast data. The system should use LightGBM trained on features like EV, Barrel%, and xwOBA to predict a hitter's next year wOBA and a pitcher's xFIP. I need a FastAPI backend (running on port 8002) that automatically loads the latest model from W&B and exposes endpoints for predictions (e.g., `/predict/hitter/{name}`) and rankings. Additionally, build a Streamlit dashboard to visualize the predictions, compare ML results against the traditional Marcel method, and include a mechanism for weekly auto-retraining via GitHub Actions that updates the model in W&B.
python fastapi streamlit mlops lightgbm mlb statcast docker github-actions
Generated by gemma4:latest

Catalog Information

MLB Statcast × MLOps — Weekly auto-retrained player performance prediction

Description

MLB Statcast × MLOps — Weekly auto-retrained player performance prediction

Novelty

3/10

Tags

python fastapi streamlit mlops lightgbm mlb statcast docker github-actions

Technologies

fastapi streamlit

Claude Models

claude-opus-4-6

Quality Score

D
56.4/100
Structure
60
Code Quality
55
Documentation
56
Testing
15
Practices
66
Security
100
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Containerized deployment (Docker)
  • Properly licensed project

Weaknesses

  • No tests found \u2014 high risk of regressions
  • 199 duplicate lines detected \u2014 consider DRY refactoring

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Add a linter configuration to enforce code style consistency

Security & Health

4.1h
Tech Debt (D)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (4)
Repobility (the analyzer behind this table) · https://repobility.com
MIT
License
4.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
76.8%
yaml
16.7%
markdown
5.3%
text
1.2%

Frameworks

FastAPI

Concepts (2)

Page rendered by Aljefra Mapper · scored by Repobility (https://repobility.com)
CategoryNameDescriptionConfidence
Repobility · code-quality intelligence platform · https://repobility.com
auto_descriptionProject DescriptionMLB Statcast × MLOps — Weekly auto-retrained player performance prediction80%
auto_categoryWeb Backendweb-backend70%

Quality Timeline

1 quality score recorded.

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