Flamengo modernizes data infrastructure with BlueShift and Google Cloud for performance analysis
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Olhar Digital
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Flamengo modernizes data infrastructure with BlueShift and Google Cloud for performance analysis

Flamengo is implementing a modernization of its data infrastructure by establishing a partnership with BlueShift and Google Cloud. This project aims to organize data related to games and players, thereby expanding analytical capabilities and assisting in the technical staff's decision-making.

The adopted architecture facilitates the processing of statistics and prepares the club to integrate future artificial intelligence applications. During the 90 minutes of a match, more than 3 thousand events are generated on the field, including passes, shots, and touches on the ball. To convert this large volume of data into relevant analyses, the project segregated the registration and analysis flows, centralizing the analytical environment in BigQuery, a service offered by Google Cloud.

The modernization also included the creation of a lakehouse architecture, which integrates the storage and analysis of large amounts of data into a single environment. The information is structured in layers, starting from raw records up to processed data ready for consultation.

This structure allows the technical staff to use various athlete performance indicators. Furthermore, the solution was developed considering the tools and skills already used by the club's professionals, which facilitated the platform's acceptance in the work routine. With the data properly organized, analysts can investigate different scenarios and provide support for sports planning, all within secure environments with cost and resource control.

Esteban Huerta, a Google Cloud specialist at BlueShift, pointed out that the biggest challenge lies in converting the vast records into truly useful information for the context of football. He explained that 'the difference is not just in gathering large volumes of data, but in transforming them into information that supports more accurate decisions.'

Additionally, the infrastructure was configured to receive future artificial intelligence applications through Vertex AI, another Google Cloud platform. The intention is to merge statistics, images, and other types of data to enrich the analysis without needing to rebuild the entire existing structure.

Huerta emphasized that this phase represents a potential evolution of the project, as the technological base was designed to keep pace with new demands, allowing Flamengo to incorporate more advanced tools without having to reconstruct its infrastructure with every new requirement.

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Technology allows for competitive games based on individual performance
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olhardigital.com.br

Technology allows for competitive games based on individual performance

The competitive environment of video games can take on new forms of participation. In addition to traditional esports tournaments, platforms are beginning to use performance data to allow regular players to compete without needing to join professional teams.

One such concept is Game Skill Monetization (GSM), a model that converts statistics gathered during matches into points that determine the winners of competitions.

A representative from Gamers Brawl, a platform using this system, explained how the technology works and the challenges of ensuring result fairness in an interview with Olhar Digital during the Brasil Game Show (BGS).

Unlike classic confrontations, participants do not necessarily have to play against each other. Each user plays their matches in a standard mode, while the platform tracks metrics such as kills, assists, accumulated gold, and captured objectives.

Using League of Legends as an example, the system takes into account different character roles. For instance, a support player is evaluated by different criteria than an ADC, as each position contributes to the team in a unique way. Ultimately, the statistics are converted into points used to determine the competition winner.

This approach allows players to participate in contests without abandoning familiar game modes. However, the use of individual statistics also involves difficulties, particularly concerning result manipulation.

Among the problems highlighted are the use of secondary accounts, profile sharing, and cheating software. To detect potential violations, anti-fraud systems can analyze performance history and look for unusual changes in player behavior. In this case, the technology employs machine learning and examines over 100 attributes to detect suspicious situations. A sudden and significant change in statistics, for example, could lead to the suspension of the competition for analysis.

Nevertheless, such monitoring does not completely eliminate the possibility of fraud and depends on adequate criteria to avoid unjustified penalties. The model also raises the issue of access to video game competitions. While the professional scene usually requires training, team participation, and constant dedication, performance-based competitions allow casual players to experience competitive dynamics using their own matches.

The concept still faces issues related to point balancing, fraud prevention, and comparing different playstyles. Nevertheless, applying statistics to create individual competitions demonstrates how data generated by video games can be used to develop new competitive formats beyond traditional championships.

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