As of the second quarter of 2025, gaming company Roblox Corporation had over 111.8 million daily active users of Roblox games worldwide. This figure represents a 41 percent increase from the corresponding quarter of the previous year. Roblox DAU has grown significantly since the beginning of 2020, when the global COVID-19 pandemic started to spread. Since then, the company has managed to retain and even increase its newly won audiences. Who are the Roblox users? Due to its colorful look and popularity among young gaming audiences, Roblox is often perceived as being just for children. Up until mid-2021, this was correct, as up until that point, the majority of Roblox gamers were aged 13 years or under. However, as of the first quarter of 2025, about 61 million Roblox gamers are aged 13 years or above, compared to 29.7 million younger users. According to the company, Roblox’s fastest-growing demographic are users aged 17 to 24 years, highlighting the platform’s efforts in attracting a wider audience. Building a Roblox for the future... is easier said than done Roblox generates nearly all of its revenues via sales of its own digital currency, Robux. The aging up of its audience is a vital factor in Roblox’s continued monetization strategy, as children under 13 years are not an addressable ad audience. Subsequently, in order to expand its advertising business to diversify its revenue streams, Roblox needs a bigger addressable ad audience. This is particularly relevant in context with Roblox’s metaverse ambitions which aim to foster stronger relationships with brands in the digital advertising space.However, by trying to tap into older gaming audiences, Roblox puts itself into direct competition with other online gaming developers that focus on the 13+ audience. Additionally, the company has been criticized for insufficient content moderation and user safety features, which have led to users experiencing inappropriate content or in-game harassment on the Roblox gaming platform.

Dataset card

The Luau dataset is a collection of code fragments collected from the Roblox Luau Data Sharing program. Only experiences where creators gave us permission to contribute to the public Luau Dataset were used for producing this dataset.

  Languages:

Lua, Luau

  License:

MIT Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without… See the full description on the dataset page: https://huggingface.co/datasets/Roblox/luau_corpus.

Roblox Centaura - A Gritty War Saga of Strategy, Lore, and Combat

Have you ever wished to experience the turmoil of war in a fully immersive Roblox game that blends tactical warfare, riveting drama, and a complex alternate history? If you answered yes, you've come to the correct place.

Centaura, developed by Roblox's ClassicMasterNoob, is not your usual run-and-gun shooter. Set in a hypothetical early twentieth-century planet, this war game immerses players in the horrific… See the full description on the dataset page: https://huggingface.co/datasets/gamegezz/roblox-centaura-game.

Complete database of all Grow A Garden pets with rarity tiers, hatching chances, abilities, and breeding information for Roblox players.

Complete database of all 106 Grow A Garden crops with values, rarity tiers, weights, and detailed attributes for Roblox players.

ChatMod_Dataset

A dataset used to train models and neural networks how to moderate properly. I made this because SOME platforms are absolutely TRASH at moderation (ah-hem, Roblox.), letting bad actors get away with diabolical stuff, while normal people are scratching their heads, wondering why they got banned for saying HI.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Roblox (RBLX): Stock Soaring, But Can It Maintain Momentum?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Roblox's (RBLX) Revenue Growth to Outpace Costs, Analysts Predict

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Roblox (RBLX): Building a Virtual Empire or Risky Bet?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Roblox (RBLX) Stock: Building a Metaverse Empire

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data