Valorant Data Analytics

Introduction

Valorant is a popular online multiplayer game that has gained a massive following worldwide. As with any competitive game, players are always looking for ways to improve their performance and gain a competitive edge over their opponents. This is where data analytics comes into play.

In this project, I will be using data analytics techniques to analyze the gameplay of Valorant players and gain insights into what factors contribute to success in the game. I will be collecting and analyzing data on various aspects of the game, such as weapon usage, map control, team composition, and individual player performance.

By analyzing this data, I hope to identify patterns and trends that can help players make more informed decisions in their gameplay, and ultimately improve their performance. This project has the potential to benefit both casual and competitive players alike, as the insights gained from the data analysis can be applied to any level of gameplay.

Gathering/Transforming Data:

When gathering and transforming data for my Valorant data analytics project, I faced a number of challenges. As there are no official match statistics available for the game, I had to rely on browsing for datasets on Kaggle and utilizing API's to collect data. However, this process was not without its limitations. I often found that the available data had limited time frames or was region-specific, making it difficult to gain a comprehensive view of the game's performance. Additionally, external validation of the data was challenging, as I only had access to statistics from professional Valorant players rather than the total player base.

While the data that I collected had already been preprocessed by Kaggle users, it was still important to remain critical of the data's trustworthiness, as it was not official. Despite these limitations, using preprocessed data did allow for the production of visualizations more quickly and with greater ease. One thing that I learned through this process was that the scope of investigation was limited by the availability of data. As the professional scene for Valorant is still relatively new, datasets are often not very large, which can limit the depth of analysis. Furthermore, the trustworthiness of the data was always in question, given that it was not official.

Source of the data (From Kaggle: VALORANT Masters 2021: Player & Map Data 🔥 | Kaggle)

Data Analysis

The data analysis for my Valorant data analytics project provided some interesting insights into the game's mechanics and player behavior. One of the key findings was that certain weapons and agents were being used more frequently in professional play than others. This may indicate imbalances in the game that need to be addressed by the developers. By identifying these imbalances, developers can make changes to the game to ensure that it is more balanced and enjoyable for players. To further explore the effectiveness of weapons and agents, I analyzed win rates and kill/death ratios. By doing this, I hoped to determine which agents and weapons were most effective in different situations. Additionally, I wanted to gain insights into map locations and why some were over/underpowered. Through my analysis, I discovered that certain combinations of agents were more successful than others. This information can be useful for professional teams looking to improve their performance and achieve better results in tournaments.

To visualize and present the data that I had collected, I used D3.js and Javascript to create interactive scatterplots, barplots, and a choropleth map based on regions. These visualizations made it easier to understand the data and identify trends. For example, the scatterplots allowed me to compare different metrics and determine if there were any correlations between them. The barplots helped me to visualize the frequency of weapon and agent usage, while the choropleth map allowed me to analyze regional differences in playstyle and performance. Here is the link for the demo. And here is a preview of some of the graphics I created to answer the questions below:

Interactivity

In addition to the insights gained from data analysis, the interactivity of the data visualizations was also an important aspect of my Valorant data analytics project. Interactive data visualizations allow readers to explore the data on their own terms and at their own pace, leading to a better understanding of the insights presented and making the information more digestible for non-technical audiences. Readers can focus on the specific data points or sections that interest them the most and manipulate the visualizations to see different angles or perspectives of the data. For Valorant players, interactive data visualizations can be especially helpful as they can see data on specific players, weapons, and maps, as well as the most commonly used tactics and team compositions in professional games to incorporate into their own gameplay.

One of the more layered graphs I made - with zoomable and hover-able points on a scatter plot representing professional players

Interactive data visualizations can help to uncover trends and patterns that may not be immediately obvious from static visualizations or raw data. For example, overlaying different data sets or filtering data by different criteria can reveal correlations or relationships that may not have been apparent otherwise. Interactive data visualizations can also help to identify outliers or anomalies that may be worth exploring further. Overall, interactive data visualizations offer a more engaging and informative way to present and explore data, allowing users to dive deeper into the insights and draw their own conclusions. By combining data analysis with interactivity, my Valorant data analytics project provided a unique and valuable perspective on the game, its mechanics, and player behavior.

This one would allow you to filter my Country

While this one would allow you to analyze individual player performance from each team

Conclusion

In conclusion, my Valorant data analytics project was a fascinating exploration into the world of professional Valorant gameplay. Through the use of API's, Kaggle datasets, and data analysis techniques, I was able to uncover insights into the most commonly used weapons, agents, and tactics, as well as gain a deeper understanding of the game's mechanics and balance.

One of the biggest challenges I faced in this project was the lack of official match statistics available. However, by browsing Kaggle datasets, I was able to find preprocessed data that was useful for my analyses, albeit with some limitations in scope and accuracy.

I also learned the importance of interactivity in data visualizations, which allowed readers to explore the data in a more engaging and meaningful way. The ability to manipulate and filter data sets helped me to uncover new insights that may not have been apparent from static visualizations.

Overall, my Valorant data analytics project provided me with a deeper appreciation of the complexities of professional gameplay, and the value of data analytics in uncovering insights that can help players and teams improve their performance. While there is still much to learn about the game, my project serves as a useful starting point for further exploration and analysis.