I have recently talked about the best data sources for xG and the best ways to use xG data. In this post I’m providing the R code I use which: Scrapes non-penalty xG data (npxG) and expected assist (xA) data for all the players in the Premier League Scrapes npxG for and against data for… Continue reading R Code to Scrape xG Data From Fbref
Tag: R
xG Data Sources for FPL
Below are advantages and disadvantages of xG data sources one can use for FPL. Why xG and expected assists (xA) are better stats than goals and assists has been widely discussed. For example, Michael Caley (@MC_of_A) has done a lot of work on the benefits of xG: https://cartilagefreecaptain.sbnation.com/2014/2/28/5452786/shot-matrix-tottenham-hotspur-stats-analysis-expected-goals This twitter thread is another explanation on… Continue reading xG Data Sources for FPL
My Fantasy Football Model
With the Premier league season currently on hold, I thought now would be the ideal time to talk about the fantasy football model I made this season. Last season, I chose my players based on expected goals and fixtures, so a model that combined these to provide points predictions for each player seemed like the… Continue reading My Fantasy Football Model
Predicting Titanic Survivors
Kaggle is a website which hosts machine learning competitions. I recently completed the Titanic competition on Kaggle. The task was to predict who died and who survived on the Titanic, given data on approximately 900 passengers. I did this by using a random forest model to classify the passengers. I managed to correctly classify 74%… Continue reading Predicting Titanic Survivors
Making Data Visualizations
I entered the data visualization competition run by Chance Analytics. We were given a full season’s data from the Chinese Super League and told to visualize it as best as possible. I wanted to write about my visualization and creating data visualizations in general. For my entry, I plotted the locations of low crosses which… Continue reading Making Data Visualizations
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