As a seasoned League of Legends enthusiast with over a decade of gaming under my belt, I find xPetu’s academic journey and his unwavering dedication to Shen truly inspiring. His unique approach to champion itemization and his relentless pursuit of data-driven conclusions make him stand out in the crowd.
As a dedicated League of Legends streamer, specializing in the mastery of Shen, I dived deep into my Master’s research to challenge the developers’ approach to balancing items. I unraveled the intricacies of item win rates and aimed to dismantle the widely accepted concept of the ‘meta’. In essence, I sought to redefine the rules of the game from my one-trick perspective.
For several years, Petu has been a top-tier player, partly because his preferred champion, Shen, offers remarkable versatility in terms of item builds. He’s renowned as one of the world’s best with the character Ninja, and his build choices sometimes appear unconventional compared to what most players consider appropriate purchases.
Most people aren’t building Rocketbelt on Shen, yet here we are.
From my perspective, I devoted my Master’s thesis to validating my belief regarding the optimal build for a specific champion. I articulated my reasoning, demonstrating its validity, and devised a flexible approach applicable to any champion. Ultimately, I crafted an entire application centered around this concept.
And yes, he graduated.
As a dedicated League of Legends player, I’ve come to understand the inflated win rates associated with certain items on specific characters – something that veteran players often grasp intuitively. For instance, Mejai’s is an item that only gets purchased when players are already leading in level or gold, and it’s commonly used to amplify a match’s momentum.
Instead, he suggested that this approach to considering when and why things are bought should be expanded to cover a broader range of goods. He put forward this notion by contesting the philosophy about item balance held by Riot Games designer Phreak.
In essence, Phreak often points out that if you purchase his third frequently used opening product and he wins at a rate of 2% more, this information is usually not reliable or accurate.
Anything that isn’t a player’s primary choice of item tends to have an artificially high win rate, as only intelligent players who can discern its potential value are likely to choose it. These skilled players, by excelling at the associated champion, naturally inflate the item’s win rate.
As a dedicated fan, I commend Phreak for his insightful analysis, but I also question the validity of the logic statement he presented as a Riot employee. In essence, it seemed more like an intuitive judgment based on human experience rather than a strictly data-driven conclusion. Although his final conclusion might have been accurate, the path to reaching it could potentially have been misleading or inappropriate.
So, xPetu sought to create something more tangible.
To illustrate this, let me offer an excerpt from the data found in the 38-page thesis by Shen, which focuses on his victory percentages. This sample is drawn from a selection of matches against powerful top laners who built their initial magical items as per their first purchase.
In many encounters, the Hollow Radiance is frequently chosen due to its potent magical defenses, a trait that’s evident from the significantly larger number of instances (represented by K) compared to other available options.
In other words, while many players typically buy Hollow Radiance as their initial item for Shen in magical confrontations, acquiring it before anything else seems to lower its actual victory rate (51.25%) compared to the predicted win rate (52.67%) suggested by the algorithm.
From my perspective, while it’s clear that Riot might possess a broader scope of data due to their extensive API access compared to mine, I still find compelling insights in this analysis. Even without real-time, granular data, there are substantial conclusions to be made and valuable perspectives to share.
For example, many game elements often adjust according to a player’s progression level. Using the latest data, the development team might consider increasing the early-level scaling of Hollow Radiance’s passive ability and maintaining its later scaling as is, thereby enhancing its effectiveness against magical characters during the initial stages of play.
By utilizing statistical data instead of relying solely on intuition or tenuously linked information, developers can make informed decisions. This approach, backed by solid data, will greatly benefit the development team, given the delicate balancing act required for champions like K’Sante. Time spent developing is precious, so leveraging concrete data makes the most of it.
Additionally, xPetu took some shots at the concept of a meta in and of itself in his thesis.
In many esports circles, there’s a tendency to frequently adopt widely-shared strategies, sometimes leading to their excessive or improper use. This can make certain moves appear more detrimental than they actually are, according to him.
It can be challenging when players assume their approach is right, but often, the strategy they’ve chosen turns out to be less effective compared to alternative methods available.
This comparison aims to determine the top choice among items for the character Shen. By doing so, we may be reshaping the perspective of players regarding the game’s meta and how professional gamers construct their optimal build strategies.
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2024-09-28 00:50