RRepoGEO

REPOGEO REPORT · LITE

google-research/football

Default branch master · commit 3d9e7547 · scanned 6/30/2026, 7:56:46 AM

GitHub: 3,630 stars · 1,366 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
73 /100
Needs work
Category recall
2 / 2
Avg rank #5.0 when recommended
Rule findings
2 pass · 0 warn · 0 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 google-research/football, 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
  • highreadme#1
    Reposition the README H1 and opening paragraph for clearer value proposition

    Why:

    CURRENT
    # Google Research Football

This repository contains an RL environment based on open-source game Gameplay Football. <br> It was created by the Google Brain team for research purposes.
    COPY-PASTE FIX
    # Google Research Football

This repository provides a challenging, high-fidelity, multi-agent reinforcement learning environment for competitive AI research in simulated football (soccer). Created by the Google Brain team, it's designed for training and evaluating intelligent agents in complex team sports.
  • mediumabout#2
    Update the repository description to be more informative

    Why:

    CURRENT
    Check out the new game server:
    COPY-PASTE FIX
    A high-fidelity, multi-agent reinforcement learning environment for competitive AI research in simulated football (soccer).
  • lowtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    reinforcement-learning, reinforcement-learning-environments
    COPY-PASTE FIX
    reinforcement-learning, reinforcement-learning-environments, multi-agent-reinforcement-learning, sports-simulation, competitive-ai, football-ai

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
2 / 2
100% of queries surface google-research/football
Avg rank
#5.0
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
Unity-Technologies/ml-agents
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Unity-Technologies/ml-agents · recommended 2×
  2. openai/gym · recommended 2×
  3. deepmind/lab · recommended 2×
  4. openai/gym-robotics · recommended 1×
  5. deepmind/mujoco · recommended 1×
  • CATEGORY QUERY
    What are good reinforcement learning environments for training agents in sports simulations?
    you: #2
    AI recommended (in order):
    1. Unity ML-Agents Toolkit (Unity-Technologies/ml-agents)
    2. Google Research Football (GRF) (google-research/football) ← you
    3. Gym-Robotics (openai/gym-robotics)
    4. MuJoCo (deepmind/mujoco)
    5. OpenAI Gym (openai/gym)
    6. DeepMind Lab (deepmind/lab)
    7. ViZDoom (mwydmuch/ViZDoom)
    8. PyBullet (bulletphysics/bullet3)
    Show full AI answer
  • CATEGORY QUERY
    Looking for a research platform to develop AI agents for competitive multi-agent game environments.
    you: #8
    AI recommended (in order):
    1. OpenAI Gym (openai/gym)
    2. Farama Gymnasium (Farama-Foundation/Gymnasium)
    3. PettingZoo (Farama-Foundation/PettingZoo)
    4. Unity ML-Agents (Unity-Technologies/ml-agents)
    5. DeepMind Lab (deepmind/lab)
    6. StarCraft II Learning Environment (SC2LE) (deepmind/pysc2)
    7. MAgent (PKU-MARL/MAgent)
    8. Google Football (GFootball) (google-research/football) ← you
    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 google-research/football?
    pass
    AI did not name google-research/football — 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 google-research/football in production, what risks or prerequisites should they evaluate first?
    pass
    AI named google-research/football 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 google-research/football solve, and who is the primary audience?
    pass
    AI named google-research/football 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 google-research/football. 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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MARKDOWN (README)
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HTML
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Pro

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google-research/football — 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