What I ended up forging was a matrix of champion matchup win-rates based on every professional game from 2018-19 across all major regions and a few minor regions. Professional League of Legends players love nothing more than to troll their opponents with riot-inciting hovers, pick off-meta Champions that they’ve only practiced on their 95th alt in SoloQ, and of course, the historic pocket picks that come back after months, and sometimes years, of dormancy. Before we get neck-deep in data, let’s take a step back and look at the bigger picture.
From the sound of it, we can utilize one of Machine Learning’s most well-loved binary classifiers, the Logistic Regression. Mysterious and eerie atmosphere that will keep you guessing. How many times have you been watching a hyped rivalry match in the LEC and out of nowhere, during Pick-and-Ban of all times, the crowd starts going off as if a game-changing gank was coming and the other team had absolutely no vision on it? Thank you for reading, and don’t forget to share it with your friends. I’ll be leaving the article as-is so people can see my mistakes, but I’ve amended it with the updated prediction accuracy rating. Arguably the largest factor in Pick/Ban, and the one I’ve decided to base this project on, is going to be Matchups, or how any given Champion plays into and against team compositions. How do you quantify a matchup? Not too bad for a quick day-long challenge! 9 characters with various fighting profiles, personalities and backgrounds. After correcting for the accidental oversight when creating the win-rate matrix, I ran just the Win/Loss Result prediction again and arrived at what I think is still a pretty interestingly high accuracy rating of 73.52%. Huge interactivity.
I told you I felt like my intuition behind the math was a solid B+. Inspirational Sharing Card Game "We are our choices." Enjoy! This game offers a great deal of replayability. All that’s left to do is fit the model, and test it out! You, as the player, can decide who makes it out alive. Choosing Wisely is an initiative of the ABIM Foundation that seeks to advance a national dialogue on avoiding unnecessary medical tests, treatments and procedures. To my surprise, on the blue sum and red sum features alone, I had an 84.9% Accuracy Rating for predicting the binary outcome of a match! MoodMission - Cope with Stress, Moods & Anxiety, Cookies help us deliver our services. After I had my 5 scores per-team, I added those together to get the blue team sum and red team sum features. Use evidence-based CBT techniques to deal with depression, anxiety, and stress. I downloaded all the match data from 2018-19, and through some creative data engineering, I created our second data set which consists of the following features: first blood, first tower, first baron, and the result (0-1 scaled floats). You, … My intuition when deciding on this method was that given a score for each Champion based on the 5 Champions it’s opposing will give an accurate representation of not only the positional matchup, but also of the team play abilities towards the mid-late game.
One thing you don’t have, at least not yet, is a built-in computer in your brain that can run through thousands of epochs of training data and give you more than those recency-based intuitions. What we’re doing is determining the outcome of a game based only on factors from the pre-game. I calculated the win-rate matrix with the entirety of my data, not just the test data. These single numbers are what the Logistic Regression uses to predict the outcome of a match. Your actions determine the outcome of the game. 38 talking about this. If you’re like me, it happens pretty much every time two powerhouse teams load into the server. Mobile Event Guide for ABIM Foundation Forum. SDK updates and other refactoring to handle deprecated libraries. Now that we have our data set, let’s quantify the matchups! WARNING: VERY STRONG LANGUAGE. In fact, around that same time, I learned just how much the Pick/Ban phase can alter the course of a match, which leads me to our topic: Can you predict the outcome of a match solely based on data from the Pick/Ban phase?
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