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If you’d like to see the code behind this analysis you can find it here. the worst possible combinations) involves karts from the Badwagon class.
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Nearly every configuration on the ‘anti-Pareto frontier’ (i.e. Metal Mario / Pink Gold Peach are the only characters that have no configurations on the Pareto frontier.While Wario’s possible configurations can achieve about 77% of the max acceleration, Baby Mario can only get up to 50% of the max speed. Heavy characters are more versatile than light characters.
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Let’s have a look at how their stats compare. The complete class memberships are listed at the end of the post in case you want to see where your favorite character lands. For example, in the heatmap below the row labelled ‘Peach’ also describes the stats for Daisy and Yoshi. From here on out, I’ll refer to the character (or kart, or tire) class by the name of one of its members. One particular quirk of Mario Kart is that while there are a couple dozen characters, many of them have identical stats. We’ll start by examining the stats of each character, kart, and tire independently using some fan-compiled data. Speed and acceleration are generally the two most important attributes in Mario Kart, so the goal of this analysis is to identify the character / kart / tire configurations that lie on the Pareto frontier for speed and acceleration. Allocations in grey are not Pareto efficient because you can improve both outcomes with a different allocation of resources. The allocations in red lie on the Pareto frontier: for each of these allocations, an improvement in one outcome requires a decrease in the other. The position of each circle represents the outcome of that allocation on two competing dimensions, for example speed and acceleration. This is more easily explained with a picture (courtesy of Wikipedia).Įach circle is a potential resource allocation, which in our case means a distribution of stat points across the different attributes like weight, handling, and traction (characters in Mario Kart have about the same number of total stat points, and differ only in their distribution). The “Pareto efficient” allocations are those in which it’s impossible to improve one outcome without worsening another outcome. The concept of Pareto efficiency applies to situations where there is a finite pool of resources and multiple competing outcomes that depend on how those resources are allocated. The question for an aspiring Mario Kart champion nowadays is “How can I pick a character / kart / tire combination that is in some sense optimal, even if there isn’t one ‘best’ option?” To answer this question we turn to one of Mario’s compatriots, the nineteenth century Italian economist Vilfredo Pareto who introduced the concept of Pareto efficiency and the related Pareto frontier. In general, it isn’t possible to optimize across multiple dimensions simultaneously, however some setups are undeniably worse than others. This is a tricky question because there are tons of potential character / kart / tire configurations now and they all have widely varying stats across a number of attributes. My Mario Kart reflexes aren’t what they used to be, but I am better at data science than I was as a fourth grader, so in this post I’ll use data to finally answer the question “Who is the best character in Mario Kart?” Pareto Efficiency
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Back then there were only eight options to choose from fast forward to the current iteration of the Mario Kart franchise and the question is even more complicated because you can select different karts and tires to go with your character. Some people swore by zippy Yoshi, others argued that big, heavy Bowser was the best option. One thing that always vexed our little group of would-be speedsters was the question of which character was best. Mario Kart was a staple of my childhood - my friends and I would spend hours after school as Mario, Luigi, and other characters from the Nintendo universe racing around cartoonish tracks and lobbing pixelated bananas at each other.