RRepoGEO

REPOGEO REPORT · LITE

llSourcell/ChatGPT_Sports_Betting_Bot

Default branch master · commit 9a907d1c · scanned 5/31/2026, 8:47:56 PM

GitHub: 507 stars · 186 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface llSourcell/ChatGPT_Sports_Betting_Bot, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition the README's opening paragraph to emphasize core utility

    Why:

    CURRENT
    # ChatGPT Sports Betting Bot

This is the code for the "ChatGPT Sports Betting Bot" Video by Siraj Raval on Youtube. This repository is a starter template for you to build your own sports betting bot.
    COPY-PASTE FIX
    # ChatGPT Sports Betting Bot

Build your own AI-powered sports betting bot using ChatGPT. This repository provides a starter template and code demonstrated in Siraj Raval's YouTube video, enabling you to develop arbitrage or deep learning-based prediction systems for sports outcomes.
  • mediumlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory of the repository with a standard open-source license, such as the MIT License.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface llSourcell/ChatGPT_Sports_Betting_Bot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pandas
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pandas · recommended 2×
  2. PostgreSQL · recommended 2×
  3. SQLite · recommended 2×
  4. Scrapy · recommended 2×
  5. Scikit-learn · recommended 1×
  • CATEGORY QUERY
    How to build an automated system for sports outcome predictions using AI?
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn
    2. Pandas
    3. TensorFlow
    4. Keras
    5. PyTorch
    6. XGBoost
    7. LightGBM
    8. Streamlit
    9. Dash
    10. PostgreSQL
    11. SQLite
    12. Beautiful Soup
    13. Scrapy

    AI recommended 13 alternatives but never named llSourcell/ChatGPT_Sports_Betting_Bot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open-source tools to develop a sports arbitrage betting strategy.
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Scrapy
    3. Pandas
    4. NumPy
    5. SciPy
    6. Requests
    7. BeautifulSoup
    8. R
    9. rvest
    10. dplyr
    11. data.table
    12. ggplot2
    13. Node.js
    14. Puppeteer
    15. Cheerio
    16. Axios
    17. PostgreSQL
    18. SQLite
    19. Jupyter Notebooks
    20. Git
    21. GitHub
    22. GitLab

    AI recommended 22 alternatives but never named llSourcell/ChatGPT_Sports_Betting_Bot. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of llSourcell/ChatGPT_Sports_Betting_Bot?
    pass
    AI did not name llSourcell/ChatGPT_Sports_Betting_Bot — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts llSourcell/ChatGPT_Sports_Betting_Bot in production, what risks or prerequisites should they evaluate first?
    pass
    AI named llSourcell/ChatGPT_Sports_Betting_Bot explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo llSourcell/ChatGPT_Sports_Betting_Bot solve, and who is the primary audience?
    pass
    AI named llSourcell/ChatGPT_Sports_Betting_Bot explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of llSourcell/ChatGPT_Sports_Betting_Bot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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llSourcell/ChatGPT_Sports_Betting_Bot — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite