After posting my analysis of on the beach teams last week, some people on twitter (@theFPLkiwi and @CoalportGlen) pointed out that by only looking at teams with less than 55 points, I was introducing bias into the analysis by removing teams who may have been ‘on the beach’, won lots of games and ended up… Continue reading On The Beach – Update
Tag: data
Secret xG Data
Most people use data from Fbref (Statsbomb) or Understat to assist their FPL decisions. However, an underlooked source of data is the FiveThirtyEight website. FiveThirtyEight use data to analyse sports, politics and science. With regards to football, they are well-known for their predictions on every tournament imaginable, from the Champions league to League two. It’s… Continue reading Secret xG Data
R Code to Scrape xG Data From Fbref
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
Finding West Ham a Striker
Since Sebastian Haller’s departure to Ajax in the January transfer window, West Ham’s only senior striker has been Michail Antonio. Combined with the fact that Antonio has only played in around half of all games due to injuries, it’s no surprise that West Ham are keen to sign a striker. This article identifies players West… Continue reading Finding West Ham a Striker
Off The Ball Runs
I used a sample of tracking data from Metrica sports and resources from Laurie Shaw (@EightyFivePoint) to build a model to identify and estimate the value added of off the ball runs (tracking data records a player’s position on the pitch at frequent intervals). Why is this useful? It’s main use would be to identify… Continue reading Off The Ball Runs
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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