accessible-space

v2.1.0 suspicious
5.0
Medium Risk

Implementation of the Dangerous Accessible Space (DAS) passing model for football analytics.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to its execution of external commands and network calls, though no direct evidence of malicious behavior is present.

  • Moderate shell risk due to use of subprocess.run
  • Potential network risks associated with fetching data from external sources
Per-check LLM notes
  • Network: The network calls appear to be fetching data from predefined URLs, which could be part of the package's functionality but may need verification of the data sources.
  • Shell: Executing external commands via subprocess.run can be risky if not properly sanitized and controlled, suggesting potential for misuse or unintended consequences.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating secure handling of sensitive information.
  • Metadata: The author's details are sparse and they appear to be new or inactive, which raises some concern but does not conclusively indicate malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • PV-benchmark" response = requests.get(url) files = response.json() import accessible_space
  • s.json")) json_data = requests.get(f"{metrica_open_data_base_dir}/Sample_Game_3/Sample_Game_3_e
  • meta_data = xmltodict.parse(requests.get(f"{metrica_open_data_base_dir}/Sample_Game_3/Sample_Game_3_m
  • on")) json_data = requests.get(f"{metrica_open_data_base_dir}/Sample_Game_3/Sample_Game_3_e
  • todict.parse( requests.get(f"{metrica_open_data_base_dir}/Sample_Game_3/Sample_Game_3_m
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • in with streamlit subprocess.run(['streamlit', 'run', os.path.abspath(__file__), "run_dashboa
  • rts) else: return subprocess.run(['streamlit', 'run', os.path.abspath(__file__), f"{dummy}",
  • treamlit # return subprocess.run(['streamlit', 'run', os.path.abspath(__file__), key_argument
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: univie.ac.at>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository jonas-bischofberger/accessible-space appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with accessible-space 
Create a mini-application that analyzes football matches using the 'accessible-space' Python package. This application will help coaches and analysts understand player movement and space utilization on the field during matches. The app should be able to ingest match data, such as player positions at various times during the game, and then apply the Dangerous Accessible Space (DAS) model to generate insights about offensive and defensive strategies.

### Steps to Build the Application:
1. **Setup Environment**: Set up a Python environment and install necessary packages including 'accessible-space'.
2. **Data Ingestion**: Design a user-friendly interface where users can upload match data in a CSV format. The CSV file should contain columns like 'PlayerID', 'Time', 'XPosition', 'YPosition', etc.
3. **Preprocessing**: Implement functions to preprocess the data, ensuring it's in the correct format for analysis. This includes handling missing values, normalizing coordinates if needed, and filtering out irrelevant data points.
4. **DAS Analysis**: Use the 'accessible-space' package to calculate DAS for each player at different time intervals during the match. Display these results visually using plots or heatmaps.
5. **Insight Generation**: Based on the DAS calculations, generate actionable insights. For example, identify periods when a team had a high concentration of accessible dangerous space, indicating potential offensive opportunities.
6. **Visualization**: Develop interactive visualizations showing player movements, DAS zones, and key moments from the match where strategic decisions could have been made based on the DAS analysis.
7. **Reporting**: Allow users to export reports summarizing the findings, including visualizations and textual explanations of the insights generated from the DAS analysis.

### Suggested Features:
- Real-time data streaming support for live match analysis.
- Comparison tools allowing users to analyze multiple matches side-by-side.
- Customizable alerts based on DAS thresholds, notifying analysts when certain conditions are met.
- Integration with existing football analytics platforms via APIs.