I made use of a couple of python libraries to automate the data extraction and modeling process as well as using d3.js to complete the visuals.
The github project for the predictions is located here:
Arsenal the favorites against West Ham while the man city Liverpool game is a tossup.
Noise Over the last few months I have been developing a new application. It's purpose is to visualize Premier League trends and predictions by leveraging the freakishly awesome D3.js libraries found at http://d3js.org/ . I call the app noise , as it is in...
The tools in use here include: Python for the data munging, Orange for the modeling, and d3.js for the visualization.
The data prep approach includes the collection of 7 years worth of match results, calculating a rolling average for each team prior to the start of each game (about 2000 observations in total).
Variables include: goals scored, shots on target, penalties received etc for both the home and away teams.
I tried several different algorithms including decision trees, neural networks (bug prevented scoring - so I dropped that approach), SVMs and the old faithful -logistic regression. I also tried clustering - then training models against each of the clusters.
The SVM ended up proving the most robust when scoring validation data.
I spent the last week and a half reworking the model and building out the skeleton of a web app to better convey the results. I will expand ...
Using R and outlier analysis to explore NBA player statistics
Recently, I am interested in analyzing outliers in high dimensional data sets. Generally, the outlier analysis includes two parts, outlier d
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Smith Hanley Associates is a professional recruiting firm focused on recruitment in financial services and investments, pharmaceuticals, mar