Hierarchical bayesian rating model in PyMC3 with application to eSports

Suppose you are interested in measuring how strong a counterstrike eSports team is relative to other teams.  This measure will need to be able to predict the outcome of a  heads-up matches between two teams. We can use bayesian analysis to do this through inferring a latent rating variable for each team that predicts match results.  This rating variable should reflect the relative strength and consistency of a team since all sports outcomes are inherently probabilistic. Quick debrief on Counterstrike Professional counterstrike consists of two teams of 5 players playing head to head on one of the seven available maps with first to reach 16 rounds […]

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Optimal Alphadraft Bankroll Management with Beta-Beta Model and Kelly Criterion

For context, you can read my previous post on alphadraft betting for CS:GO here. After we have developed a concrete model for drafting our line-ups, we want to focus more on the bettor's bankroll management over time to minimize risk, maximize return and reduce our probability of ruin.  In this blog post, I will first describe the typical scenarios behind Alphadraft contests and how that can be translated to our probability model for win/loss (Beta-Beta Model).  We can then modify the standard Kelly Criterion to fit our case and calculate the optimal % bankroll to bet per game. Note that the concepts […]

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Guns, Bombs and eSports: Applying Data and Portfolio Analytics to Counter-Strike Gambling

Since the publication of Bill James' seminal work, Baseball Abstract, and the rise to stardom for the Oakland A's, Sports Analytics - the application of statistics to competitive sports - has been (and still is) a prominent topic within the industry.  Thus, it is only reasonable for practitioners to apply this movement to the new and upcoming playing field called eSports, which has gained a large following over the years with many online games such as League of Legends, Dota 2 and Counter-Strike: Global Offensive (CSGO).  I would like to argue that the data drawn from eSports is definitely more abundant and easier to acquire whereas, real life sporting data requires physical measurements, whether it's measured by a person […]

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