RRepoGEO

REPOGEO REPORT · LITE

SkalskiP/sports

Default branch master · commit 7aaf3a53 · scanned 6/8/2026, 5:57:52 PM

GitHub: 550 stars · 41 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 SkalskiP/sports, 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

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

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory of the repository with the text of a suitable open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • highreadme#2
    Reposition the README's opening to clarify its purpose as a collection of experiments

    Why:

    CURRENT
    # ⚽ Football Players Tracking with YOLOv5 + ByteTrack
    COPY-PASTE FIX
    # ⚽🏃 SkalskiP/sports: Computer Vision Experiments & Tutorials for Sports Analytics

This repository serves as a practical collection of computer vision experiments and tutorials, showcasing the application of deep learning techniques to various sports analytics challenges. Explore examples for player tracking, pose estimation, and more, built with frameworks like YOLO and ByteTrack.
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Set the 'Homepage' URL in the repository settings to `https://github.com/SkalskiP/sports` (or a personal portfolio/blog if one exists that aggregates these projects).

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 SkalskiP/sports
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
YOLO
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. YOLO · recommended 1×
  2. Mask R-CNN · recommended 1×
  3. EfficientDet · recommended 1×
  4. DeepSORT · recommended 1×
  5. ByteTrack · recommended 1×
  • CATEGORY QUERY
    How can I track multiple football players in video using computer vision techniques?
    you: not recommended
    AI recommended (in order):
    1. YOLO
    2. Mask R-CNN
    3. EfficientDet
    4. DeepSORT
    5. ByteTrack
    6. FairMOT
    7. LabelImg
    8. CVAT
    9. PyTorch
    10. TensorFlow
    11. OpenCV
    12. Python

    AI recommended 12 alternatives but never named SkalskiP/sports. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks help with 3D human pose estimation for sports performance analysis?
    you: not recommended
    AI recommended (in order):
    1. OpenPose
    2. AlphaPose
    3. Mediapipe Pose
    4. HRNet
    5. VIBE
    6. SMPL
    7. SPIN

    AI recommended 7 alternatives but never named SkalskiP/sports. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    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 SkalskiP/sports?
    pass
    AI named SkalskiP/sports explicitly

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

  • If a team adopts SkalskiP/sports in production, what risks or prerequisites should they evaluate first?
    pass
    AI named SkalskiP/sports 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 SkalskiP/sports solve, and who is the primary audience?
    pass
    AI named SkalskiP/sports explicitly

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

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SkalskiP/sports — 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