--- title: "Playeros.ai in the Machine Relations Index" description: "Canonical domain: playeros.ai" canonical: "https://machinerelations.ai/index/domains/playeros.ai" --- # Playeros.ai in the Machine Relations Index Canonical URL: https://machinerelations.ai/index/domains/playeros.ai Canonical domain: playeros.ai Source role: Vendor-owned source Release mri_score_v2.1+2026-11-01+0542f5da373e; methodology mri_score_v2.1; generated 2026-11-01; window 2026-05-10 to 2026-11-01; artifact 153bff98106d. playeros.ai appeared as a cited source in 2 of 34,788 monitored answer runs (0.01%) from 2026-05-10 to 2026-11-01. ## How the Site Describes Itself > The all-in-one casino marketing platform. Unified player database, multi-channel campaigns, Player Inbox web app, and journey automation for gaming operators. playeros.ai describes itself this way in the description tag on its homepage, read on September 22, 2026. It is the source's own wording, quoted as found, not a finding of this Index. ## Overall Evidence - Citation rate: 0.01% - Cited runs: 2 of 34,788 - Days cited: 2 of 169 - Engine breadth: 1 observed engines (chatgpt) - Confidence: Collecting - Standing: collecting · 0.01% across 2/34,788 observed runs ## Observed Segments | Category | Question shape | State | Cited runs | Observed runs | Run dates | Rate | Standing | |---|---|---|---:|---:|---:|---:|---| | [iGaming & Betting](https://machinerelations.ai/index/categories/igaming-betting) | [Is it worth it](https://machinerelations.ai/index/categories/igaming-betting/is_x_worth) | published | 2 | 191 | 11 | 1.05% | #123 of 239 | ## Citation History playeros.ai was first cited in a monitored answer run on September 22, 2026, and most recently on October 30, 2026, with cited runs in 2 of the 3 months from then to this release. The release window opens 2026-05-10, so an earlier citation would not show here. A month's rate is the share of all monitored answer runs that month that cited this domain. The latest month is still collecting. | Month | Cited runs | Observed runs | Rate | |---|---:|---:|---:| | September 2026 | 1 | 4,971 | 0.02% | | October 2026 | 1 | 16,496 | 0.01% | | November 2026 (to 2026-11-01) | 0 | 305 | 0.00% | ## By Answer Engine An engine's rate is its cited runs over every monitored run that engine answered in the window. Engines answer different numbers of runs, so compare rates, not counts. | Engine | Cited runs | Engine's observed runs | Rate | |---|---:|---:|---:| | [ChatGPT](https://machinerelations.ai/index/engines/chatgpt) | 2 | 8,151 | 0.02% | | [Claude](https://machinerelations.ai/index/engines/claude) | 0 | 8,189 | 0.00% | | [Gemini](https://machinerelations.ai/index/engines/gemini) | 0 | 6,873 | 0.00% | | [Google AI Mode](https://machinerelations.ai/index/engines/google_ai_mode) | 0 | 2,046 | 0.00% | | [Google AI Overviews](https://machinerelations.ai/index/engines/google_ai_overviews) | 0 | 1,260 | 0.00% | | [Perplexity](https://machinerelations.ai/index/engines/perplexity) | 0 | 8,269 | 0.00% | ## Peers Playeros.ai holds its best segment rank, #123 of 239, in iGaming & Betting / Is it worth it, at 1.05%. 50 sources on that leaderboard share that rate, and their order inside the tie is not a measured difference. Listed here: the nearest sources cited more and less often than it, and the sources beside it that share this best segment. Leaderboard: https://machinerelations.ai/index/categories/igaming-betting/is_x_worth | Rank | Source | Source role | Segment citation rate | Against this source | |---|---|---|---:|---| | #83 of 239 | [LiquidityFinder](https://machinerelations.ai/index/domains/liquidityfinder.com) | Editorial publication | 1.57% | +0.52 pts | | #84 of 239 | [Markets Media](https://machinerelations.ai/index/domains/marketsmedia.com) | Editorial publication | 1.57% | +0.52 pts | | #85 of 239 | [Nsoft.com](https://machinerelations.ai/index/domains/nsoft.com) | Other observed source | 1.57% | +0.52 pts | | #122 of 239 | [Onlyigaming.com](https://machinerelations.ai/index/domains/onlyigaming.com) | Editorial publication | 1.05% | Same rate | | #124 of 239 | [Psu.com](https://machinerelations.ai/index/domains/psu.com) | Editorial publication | 1.05% | Same rate | | #200 of 239 | [Jefferies.com](https://machinerelations.ai/index/domains/jefferies.com) | Analyst and consulting research | 0.52% | -0.53 pts | | #201 of 239 | [Lift.win](https://machinerelations.ai/index/domains/lift.win) | Other observed source | 0.52% | -0.53 pts | | #202 of 239 | [Lightreading.com](https://machinerelations.ai/index/domains/lightreading.com) | Other observed source | 0.52% | -0.53 pts | ## Citation Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai. Playeros.ai (playeros.ai), 0.01% citation rate, 2 of 34,788 monitored answer runs, 2026-05-10 to 2026-11-01. Release mri_score_v2.1+2026-11-01+0542f5da373e. https://machinerelations.ai/index/domains/playeros.ai. CC BY 4.0. APA Parrott, J. (2026). Playeros.ai (playeros.ai) in the Machine Relations Index (Version mri_score_v2.1+2026-11-01+0542f5da373e) [Data set]. Machine Relations Research. https://machinerelations.ai/index/domains/playeros.ai https://doi.org/10.5281/zenodo.23652423 BibTeX ```bibtex @misc{parrott2026mriplayerosai, author = {Parrott, Jaxon}, title = {Playeros.ai (playeros.ai) in the Machine Relations Index}, year = {2026}, version = {mri\_score\_v2.1+2026-11-01+0542f5da373e}, publisher = {Machine Relations Research}, url = {https://machinerelations.ai/index/domains/playeros.ai}, doi = {10.5281/zenodo.23652423}, license = {CC-BY-4.0}, note = {Machine Relations Index (MRI), Machine Relations Research — machinerelations.ai} } ``` DOI: https://doi.org/10.5281/zenodo.23652423 Citation file: https://machinerelations.ai/CITATION.cff ## Badge Badge image: https://machinerelations.ai/index/domains/playeros.ai/badge.svg [![Playeros.ai in the Machine Relations Index: 0.01% cited in monitored answer runs, 2 of 34,788 runs · Collecting. Machine Relations Research.](https://machinerelations.ai/index/domains/playeros.ai/badge.svg)](https://machinerelations.ai/index/domains/playeros.ai) The badge is live: it shows the current release, so it changes when the release changes, and it returns 404 if this domain leaves the released population. The link goes to this profile, and the alt text says what the badge shows. ## Use the data The public JSON is https://machinerelations.ai/data/machine-relations-index.json. The release manifest is https://machinerelations.ai/data/mri-release-manifest.json. The domain CSV is https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv. The Zenodo concept DOI is https://doi.org/10.5281/zenodo.23652423. ```bash curl -fsSL "https://machinerelations.ai/data/machine-relations-index.json" -o machine-relations-index.json curl -fsSL "https://machinerelations.ai/data/mri-release-manifest.json" -o mri-release-manifest.json curl -fsSL "https://huggingface.co/datasets/machine-relations/machine-relations-index/resolve/main/domains.csv" -o domains.csv curl -fsSL -L "https://doi.org/10.5281/zenodo.23652423" -o mri-zenodo.html ``` ```python import json from urllib.request import urlopen index_url = "https://machinerelations.ai/data/machine-relations-index.json" with urlopen(index_url) as response: index = json.load(response) print(index["generated"], index["total_domains"]) ``` ## Public Boundary The public dataset reports citation rates, rankings, and evidence counts. It excludes internal query identifiers, raw cited URLs, and answer-engine provider payloads. MRI measures observed root-domain citations in monitored prompts; it does not measure all AI answers, recommendation quality, website traffic, or commercial performance. A citation does not prove source support, and MRI confidence is not an Answer-Source Fidelity grade. ## Related Sources - [Machine Relations Index](https://machinerelations.ai/index) - [Public JSON artifact](https://machinerelations.ai/data/machine-relations-index.json) - [Release manifest](https://machinerelations.ai/data/mri-release-manifest.json)