In March 2025, video streaming platform Twitch had approximately *** million active streamers, down from a peak of **** million in January 2021. The platform experienced a boom during the COVID-19 pandemic, when many new users used the platform to connect with friends or try their hand at livestreaming. However, this trend normalized again towards the end of the year, and the streaming space has also grown more competitive as platforms apart from Twitch have evolved to attract streamers and viewers. Popular content categories on Twitch In 2024, most of the leading content categories on Twitch were all gaming-related – except for the top spot: Just Chatting. The general conversation category accumulated *** billion hours of viewing time in the measured period. In March 2025, global Twitch audiences spent around *** million hours watching Just Chatting content on Twitch, with the average viewer count of such content reaching *** thousand. HasanAbi was the most popular Just Chatting streamer on Twitch in the most recently measured month. Game streamers Twitch is very popular with gamers and gaming audiences, and the ranking of the most popular Twitch streamers reflects this. Ninja (real name: Richard Tyler Blevins), the top-ranked streamer on Twitch, had **** million followers in April 2025. Ninja saw a meteoric rise to fame when he was one of the first top-ranked players to stream the then-newly released Fortnite Battle Royale at the end of 2017. Second-ranked ibai (real name: Ibai Llanos Garatea) was ranked second with ***** million followers on Twitch. With more than **** million followers, Imane Anys, better known as Pokimane, was the only woman among the most-followed Twitch streamers worldwide. Overall, women only accounted for **** percent of the top-ranked Twitch channels.

The people from Czech are publishing a dataset for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, they omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During research, they divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

They have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. They also used several popular websites that primarily focus on the audience in Czech. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live Video Player DailyMotion, Stream.cz, Vimeo, YouTube Music Player AppleMusic, Spotify, SoundCloud File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive Website and Other Traffic Websites from Alexa Top 1M list

Live streaming is a unique form of media that creates a direct line of interaction between streamers and viewers. While previous research has explored the social motivations of those who stream and watch streams in the gaming community, there is a lack of research that investigates intimate self-disclosure in this context, such as discussing sensitive topics like mental health on platforms such as Twitch.tv. This study aims to explore discussions about mental health in gaming live streams to better understand how people perceive discussions of mental health in this new media context. The context of live streaming is particularly interesting as it facilitates social interactions that are masspersonal in nature: the streamer broadcasts to a larger, mostly unknown audience, but can also interact in a personal way with viewers. In this study, we interviewed Twitch viewers about the streamers they view, how and to what extent they discuss mental health on their channels in relation to gaming, how other viewers reacted to these discussions, and what they think about live streams, gaming-focused or otherwise, as a medium for mental health discussions. Through these interviews, our team was able to establish a baseline of user perception of mental health in gaming communities on Twitch that extends our understanding of how social media and live streaming can be used for mental health conversations. Our first research question unraveled that mental health discussions happen in a variety of ways on Twitch, including during gaming streams, Just Chatting talks, and through the stream chat. Our second research question showed that streamers handle mental health conversations on their channels in a variety of ways. These depend on how they have built their channel, which subsequently impacts how viewers perceive mental health. Lastly, we learned that viewers’ reactions to mental health discussions depend on their motivations for watching the stream such as learning about the game, being entertained, and more. We found that more discussions about mental health on Twitch led to some viewers being more cautious when talking about mental health to show understanding.

Path of Exile League statistic

Data contains stats of 59000 players, from 4th August of 2017 and before now.

Content

One file with 12 data sections. One league - Harbinger, but 4 different types of divisions:

• Harbinger
• Hardcore Harbinger
• SSF Harbinger
• SSF Harbinger HC

Each division has own ladder with leaders

Acknowledgements

I found this data at the pathofstats.com as a JSON format and exported at CVS. Data have been collecting by this API, and free to use for interested people. If GGG or pathofstats.com don't want to share it here, please contact me and it will be removed.
As I could understand, it is simple data for people, who are new at data science and want to have practice.

Questions for participants

1. A total number of players in each division, usage of each class in descending order.
2. Some of the players streaming their game (twitch colum). Do they play better than people, who does not?
3. Predict chance to be at top 30 in each division, if we are Necromancer. With and without stream.
4. Average number of finished challanges for each division, show division with highest and lowest average challanges.
5. Show dependency between level and class of died characters. Only for HC divisions.

[pt-br]

Contexto

Como jogador, estudante de Estatística e curioso, sempre que posso busco maneiras de aplicar meus conhecimentos em projetos práticos. Mais do que isso, tenho muita paixão em compartilhar minhas descobertas e aprendizados com a comunidade por meio de minhas lives na Twitch e vídeos no YouTube.

Tendo em vista recapitular um projeto que desenvolvi durante minha graduação, resolvemos fazer em live a obtenção dos dados de Partidas profissionais de Dota 2 a partir da API Open Dota. Os dados foram salvos em bancos de dados NoSQL (MongoDB) e também processados em diversas camadas de dados usando o conceito de Data Lake com a engine de processamento Apache Spark.

Você pode conferir nosso projeto em seu repositório no GitHub.

Conteúdo

Este dataset está longe de ser um dado crú, uma vez que passou por diversas etapas de transformações, cruzamentos e agregações. As informações presentes são estatísticas de cada time um dia antes da partida em questão ter início. Tais estatísticas são calculadas a partir das informações das partidas de cada jogador no 6 meses anteriores à partida em questão.

Assim, cada linha deste dataset possui a informação de qual time ganhou a partida, bem como estatísticas sumarizadas e 'não normalizadas' de cada time.

Agradecimentos

Muito obrigado a todos que acompanharam o desenvolvimento deste projeto em nossas lives e nos apoiaram com as inscrições na Twitch. O apoio de voc6es possibilita que levemos Data Science adiante, como por exemplo, compartilhando este dataset com mais pessoas que têm interesse em se desenvolver na área.

Inspiração

Nosso desejo enquanto comunidade é fazer com que o ensino chegue cada dia mais próximo das pessoas. E entendo que isso começa no Brasil. Por isso a descrição em pt-br, dando maior foco ao nosso público nacional.

Se tiver interesse em conhecer mais sobre nosso trabalho, nos acompanhe na Twitch: Téo Me Why .

[en - Google Translate]

Context

As a player, Statistics student and a curious person, I am always looking for ways to apply my skills in real time problems. I also am passionate about sharing my findings and learnings with others through my streaming sessions on Twitch or my Youtube channel.

With the goal of reusing a project that I worked on during my undergrad, we decided to stream the data acquisition of professional matches of Dota 2 through the Open Dota API. The dataset has been stored in a NoSQL (MongoDB) and it has been processed in several data layers using the Data Lake concept with the Apache Spark processing engine.

You can check out the project in this repository on GitHub.

Content

This dataset is far from being raw data, since it went through several stages of transformations, crossings and aggregations. The information present is each team's statistics one day before the match in question starts. Such statistics are calculated from each player's match information in the 6 months preceding the match in question.

Thus, each row of this dataset contains information on which team won the match, as well as summarized and 'non-normalized' statistics for each team.

Acknowledge

Many thanks to everyone who followed the development of this project in our lives and supported us with registration at Twitch. Your support enables us to take Data Science forward, such as sharing this dataset with more people who are interested in developing in the area.

Inspiration

Our desire as a community is to bring teaching closer to people every day. And I understand that this starts in Brazil. That's why the description in pt-br, giving greater focus to our national audience.

If you are interested in learning more about our work, follow us on Twitch: Téo Me Why .