The excitement of Formula 1 racing lies in the intense competition between drivers and teams as they navigate the twists and turns of each race to claim the coveted championship title. In recent years, data-driven predictions have become increasingly popular in sports, and F1 is no exception. Inspired by FiveThirtyEight’s comprehensive soccer predictions, we set […]
Kaggle Playground Series – Tidymodels
Hello readers, we are entering another Kaggle playground competition, so get your Yorkshire tea ready and enjoy the process of joining. This month the competition I entered is this one https://www.kaggle.com/competitions/playground-series-s3e7It’seiew It’s looks like looks are canncellations from hotels and spoiler alert – I had a lot of fun with this dataset. EDA First, I […]
Cricket Weighted Batting Average in R
Hello, I hope you have your Yorkshire tea ready as today I am going to be exploring weighted averages using R. I used the code above to generate the table of the top 15 players by batting average in the 2022 county championship. Now the whole point of this blog is to devise a weighted […]
Kaggle January Playground Series – Tidymodels
Hello, hope you have your Yorkshire tea ready this is going to be a new series on the blog in which each month I am going to be tackling Kaggles monthly playground series. Find the link to Januarys below feel free https://www.kaggle.com/competitions/playground-series-s3e1 So let’s get started EDA Above is the structure of the training dataset. […]
Predicting Twenty 20 Cricket Result with Tidy Models
Hello, hope you have your Yorkshire tea to hand and sitting comfortably ready to read today’s blog. In it I am going to be doing some machine learning with tidymodels to predict the outcome of some twenty20 cricket matches. I am using the data from cricsheet as used in this blog and using the win […]
FPL – Point Project Part 3
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 […]
Sport Data Science 