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Showing posts from 2016

2016 General Election Prediction

The presidential election is just few days away and this is the perfect time to jump into the prediction game as many others had ( Nate Silver , Sam Wang ,  Drew Linzer , and others). One way to estimate the outcome of the election results is to use data from polls. Many factors can be taken into consideration when performing a projection, such as historical trends, media coverage, debates, etc., but the advantage of considering polls is that it gives a data that is chronologically close to the desired event. We can think about the election as a guessing game: How much does each candidate measure  on election day? We are measuring  each candidate on the population on election day and polls give a good source of information on how this measurement is currently changing. It is important to realize that these measurements are dynamic  and get affected by events.  Every time big polls are published, knowing the current measurement makes it to change . This is known in physical

How the Zika connects Puerto Rico

Given the importance of the recent Zika outbreak, I decided to perform some analysis on how the virus is spreading. I found very good data about Puerto Rico, so decided to focus there. With data from each municipality in Puerto Rico, it is possible to find a connectivity map of the island according to how the virus spreads there. By analyzing the growth of cases over time in each municipality treat them as independent series and performing correlation analysis, we have that the most relevant connectivity groups are given by the following graph. There are 5 clear connectivity groups based on their response to the virus, Group 1 Group 2 Group 3 Group 4 Group 5 And the strongest connectivity is given by the following graph, This connectivity is given by an adjacency matrix obtained by calculating the r² coefficients among the municipalities. In this graphs are displayed only the strongest r² coefficients. 

Financial models for the NBA

A portfolio analysis can be performed in order to analyze how effective the NBA teams were during the 2016 season. It is possible to think of both Eastern and Western Conferences to be a benchmark  that we can reference teams to. When we analyze each team's performance against the overall performance of the entire benchmark, it is possible to discover how effective and consistent teams are. This can tell us how risky or stable a team is, and how much of a pay off teams offer for being risky.  With this we can find that the most stable teams in the Eastern Conference are Chicago and Atlanta, and in the Western Conference were New Orleans and Utah. On the other hand, the most volatile teams were Philadelphia and New York in the Eastern Conference, and Houston and LA Lakers in the Western Conference.  Likewise, it can be found that the most representative teams for the performance of the Eastern Conference were Philadelphia and Miami, and for the Western Confere

The NBA champions and the Stock Market

Analyzing statistics in modern day sports is one of the prime sport-fan pastimes. However it is possible to capture more information than just a plain statistical description of the data. For example, it is possible to use financial models and techniques to understand performance in sports in a more insightful way. In  particular, in the case of the recent NBA finals, we can analyze the performance of each player as if it were a financial asset.  A financial asset or security is anything that has the potential of giving a financial return overtime, such as stocks, bonds, treasury bills, and others. In this way, our goal is to analyze and asses a player's performance, and identify how reliable and efficient he is. In the case of the 2016 NBA Champions, the Cleveland Cavaliers, we can perform this analysis by studying the player's individual performance compared to that of their conference during the 2016 season. Here we can determine how sensitive a player is wi