Subway Surfers
It includes both gamewide category leaderboards and level-specific category lead
@kaggle.willianoliveiragibin_subway_surfers
It includes both gamewide category leaderboards and level-specific category lead
@kaggle.willianoliveiragibin_subway_surfers
The provided dataset offers a comprehensive compilation of speedrun leaderboards for the immensely popular game Subway Surfers, sourced from the renowned speedrunning platform Speedrun.com. The dataset encompasses both gamewide category leaderboards and level-specific category leaderboards, providing a rich resource for delving into the intricate details of the Subway Surfers speedrunning community.
For those eager to explore the data, various intriguing aspects await investigation. One can scrutinize the fastest completion times across different categories, discern the prevalence of verified runs, and analyze the diverse timing methods employed. Additionally, the dataset facilitates an examination of the platforms used for speedruns, player information, and other pertinent details that contribute to the dynamic landscape of Subway Surfers speedrunning.
To fully appreciate the depth of the dataset, one might consider undertaking tasks such as tracking trends in speedrun times over time or investigating the correlation between platform choices and speedrun performance. Moreover, the dataset lends itself to the identification of key contributors to the Subway Surfers speedrunning community, allowing enthusiasts to recognize and celebrate the top-performing speedrunners within the community.
The dataset's structure is outlined, highlighting the distinction between gamewide leaderboards and per-level leaderboards, the latter being further segmented into different categories for each level. The 'place' column serves as a key metric, denoting the player's rank in the respective leaderboard.
For those seeking inspiration on how to leverage this valuable dataset, a list of interesting task ideas is provided. These range from analyzing trends in player signup dates and geographical distribution to determining the most popular platforms utilized for Subway Surfers speedruns. Furthermore, there is an encouragement to share the appreciation for the dataset by hitting the upvote button, fostering a sense of community engagement and recognition.
In essence, this Subway Surfers speedrun dataset presents an exciting opportunity for data enthusiasts, researchers, and the gaming community at large to embark on a captivating exploration of the intricate world of speedrunning, uncovering patterns, trends, and insights that contribute to the vibrant Subway Surfers speedrunning ecosystem.
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