Hello, this is part 3 of building a model to project the points a player might score in a given game week. If you have not read part 1 or 2 links below Part 1 – https://theparttimeanalyst.com/2021/03/09/building-a-model-in-r-to-predict-fpl-points/ Part 2 – https://theparttimeanalyst.com/2021/04/22/fpl-point-projection-in-r-part-2/ Now that the estimate of the score is created its time to work out […]
F1 Drivers Rated – Version 2
Hello, so a year and a half a go I created a new metric for measuring F1 drivers performance based around there performance in the race and the expected the performance in the race see blog here F1 Drivers Rated Since then my laptop BSOD’ed and me being useless I never committed the code to […]
Building a Model in R to Predict FPL Points
Fantasy premier league. That yearly ritual of thinking your team you spend hours agonising over whether to select this player or that player. I find you need to watch a lot of football to keep up with it so the challenge is how can I use data to shortcut this. This is probably the biggest […]
Win Probability Added – Batsman Review
Hello readers and welcome to the batsman review of my win probability added metric review. In the first part, linked here: https://theparttimeanalyst.com/2020/06/20/twenty20-win-probability-added/ I created the metric using a logistic regression machine learning model. Now its time to apply the model to real data and look at what insights it can show. The first question I […]
F1 2020 -Season So Far and Why Racing Point’s Method of Designing the Car is Controversial
Hello Readers, Today i’m going to do a little data explore of the data from the F1 2020 season so far. Exploring a number of questions about the season so far. First of all looking at qualifying and why a lot of teams are annoyed by (t)Racing Point and the strategy they have used to […]
Pole Position Prediction- A tidymodels Example
Hello readers, today’s blog I will be looking at predicting the formula 1 grid using the Tidymodels collection of R packages. The idea is to use data from the practice sessions on a Friday, to give an idea of what the grid is expected to be for the race on Sunday before qualifying on Saturday. […]
Twenty20 Win Probability Added
Hello readers, welcome to today’s blog. I am going to implement a win probability added model for twenty 20 cricket. Now this is nothing new a quick google and there are many sources for it. Cricviz is possibly the most famous version which you may have seen on the app. The idea is the model […]
Finding Undervalued Air Bnb’s
Hello, today I am going to do an EDA (exploratory data analysis) on AirBnB in the New York area. This data set is available here on Kaggle https://www.kaggle.com/dgomonov/new-york-city-airbnb-open-data Lets read the data into R and take a look of it So I can see there are 17 columns and over 48000 records with information covering […]
Predicting Qualifying — 2
In the last blog I outlined creating a model which predicts the fastest time for each driver in F1 qualifying. theparttimeanalyst.com/2019/07/10/predicting-f1-qualifying/ Today I am going to be dissecting the model to understands its strengths and weaknesses and to look if their is any bias within the model. First lets look at the importance matrix The […]
Predicting F1 Qualifying
Hello, welcome to this blog a few days ago I tweeted the below graph in a tweet It was the output from the model I have created which predicts the qualifying time for each driver. I will get into the review of the outputs of the model in the next blog but today im going […]
Sport Data Science 