PageRank Utilized for Boxer Rankings
Page rank is a very useful tool, not just for Google’s search algorithm, but for many more applications as well. Mathematicians often use the Google page rank algorithm for unique calculations. This past winter, it was being debated which iconic heavyweight boxer should be ranked higher between Mike Tyson and Evander Holyfield. The same debate also included who should be ranked higher out of today’s competitors, between Canelo Alvarez and Gennady Golovkin. Two mathematicians developed a computerized method to determine who should be ranked higher. Unlike other sports, like baseball, where teams will have a game directly against each other and one will win, making that team the better team, not all boxers go directly against each other. Therefore, it can happen that the two best boxers at a given time in history never actually face off. For boxing, ranking systems influence who advances and who gets to compete in the finals. There are many different ranking systems. The rankings are typically based on point systems, where each individual fighter accrues points based on beating other boxers. Each system has a slightly different point trading system, depending on the effect of inactivity on a boxer’s points, or a knockout win, in addition to many other variables. Fans often do not know the actual ranking system being used behind the scenes.
It was decided by mathematicians that to benefit fans and make the system more cohesive, that they should turn to the Google PageRank algorithm and create a more concrete ranking. The two mathematicians who took on this challenge were Tien Chih and Demitri Plessas. They applied an algorithm similar to Google’s PageRank. The algorithm was initially created by Larry Page and Sergey Brin. The method is general enough that it can be applied to various things, like boxer rankings. One of the main algorithms of PageRank ranks webpages based on the referral pattern that links in-between webpages. PageRank ranks webpages based on how many times they are referenced by all webpages of the same topic. For example, if there are 50 webpages about soccer, and each page contains links to some of the other sites, PageRank ranks the webpages based on how many times they are referenced.
When looking at boxers, the algorithm can be seen as a form of Markov chains, where all boxers are connected by wins and losses. The algorithm sorts the boxers based on each individual’s referral pattern, which is made up by the number of victories. The two mathematicians tested their algorithm using data from the 1990s to rank the top 20 heavyweight boxers. Evander Holyfield came out on top. The algorithm does not recognize the method of winning, which is unusual for typical boxer ranking algorithms. The value of winning by knockout or points makes rankings all very different and difficult to compare, but these variables are still very important and should be recognized. Similar algorithms have also been applied to other sports. Overall, this algorithm and ranking system was very helpful for creating a cohesive and understandable ranking system, but it did take away from including some factors that can be helpful in considering which boxer should be ranked the highest.
This article relates to PageRank that we learned in class. PageRank is a kind of “fluid” that circulates through the network, passing from node to node across edges, and pooling at the nodes that are the most important. PageRank is an algorithm used by Google Search to rank websites in their search engine results, and ranks the sites based on how many times they are referred to. This is a principle we discussed in class, and one that is frequently applied on the web. It is a very unique concept that this algorithm can be so easily applied to completely other fields like sports, but makes sense that it is applicable to many forms of rankings. It is important to keep this algorithm in mind, when considering forms of ranking large groups.
https://www.insidescience.org/news/using-math-rank-boxers