REPOGEO REPORT · LITE

google-research/football

Default branch master · commit 3d9e7547 · scanned 5/19/2026, 12:42:20 AM

GitHub: 3,601 stars · 1,362 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

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 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

highabout#1

Update the repository description to be more specific

Why:

CURRENT

Check out the new game server:

COPY-PASTE FIX

The official Google Research Football repository: an open-source reinforcement learning environment for training AI agents in a realistic 3D football (soccer) simulation.

mediumtopics#2

Expand repository topics with more specific keywords

Why:

CURRENT

reinforcement-learning, reinforcement-learning-environments

COPY-PASTE FIX

reinforcement-learning, reinforcement-learning-environments, sports-simulation, football, soccer, multi-agent-systems, deep-learning

lowreadme#3

Add a 'Key Features' section to the README

Why:

COPY-PASTE FIX

## Key Features

*   **Purpose-built for Football RL:** A highly optimized 3D simulation environment specifically designed for multi-agent reinforcement learning in football (soccer).
*   **Lightweight & Performant:** Offers a ready-to-use, performant, and customizable platform tailored for AI research.
*   **Realistic Gameplay:** Based on open-source game Gameplay Football, providing realistic physics and game mechanics.
*   **Research-focused:** Created by the Google Brain team for advanced AI and machine learning research.

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 google-research/football

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Google Research Football (GRF)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Google Research Football (GRF) · recommended 1×
OpenAI Gym's Roboschool · recommended 1×
Unity ML-Agents · recommended 1×
DeepMind Lab · recommended 1×
Gym-Panda · recommended 1×

CATEGORY QUERY
What are good reinforcement learning environments for training agents in sports simulations?
you: not recommended
AI recommended (in order):
1. Google Research Football (GRF)
2. OpenAI Gym's Roboschool
3. Unity ML-Agents
4. DeepMind Lab
5. Gym-Panda
6. PyBullet
AI recommended 6 alternatives but never named google-research/football. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking open-source physics-based simulation environments for multi-agent reinforcement learning research?
you: not recommended
AI recommended (in order):
1. MuJoCo (deepmind/mujoco)
2. Isaac Gym
3. Isaac Sim
4. PyBullet (bulletphysics/bullet3)
5. Gazebo (osrf/gazebo)
6. Gymnasium (Farama-Foundation/Gymnasium)
7. Unity ML-Agents (Unity-Technologies/ml-agents)
AI recommended 7 alternatives but never named google-research/football. This is the gap to close.
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 named google-research/football explicitly
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)

[![RepoGEO](https://repogeo.com/badge/google-research/football.svg)](https://repogeo.com/en/r/google-research/football)

HTML

<a href="https://repogeo.com/en/r/google-research/football"><img src="https://repogeo.com/badge/google-research/football.svg" alt="RepoGEO" /></a>

Pro

Subscribe to Pro for deep diagnoses

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