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 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. […]
Sliced – New York Air BnB
Hello, welcome to todays blog which I am going to go through my attmept at sliced. If you never hear of sliced its competitive data science which you have 2 hours to create a machine learning model. Catch the show here on tuesdays late a night for us Europeans https://www.twitch.tv/nickwan_datasci One of the recent rounds […]
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 […]
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 […]
Cricket Moneyball, pt 2
Hello readers, today we have part 2 of this cricket moneyball series. If you missed the first one its here: https://wordpress.com/view/theparttimeanalyst.com In it I looked at calculating the Pythagorean win percentage for each team in the IPL and then moving that forward to calculating the how many extra runs are needed to win one extra […]
The Most Unpredictable League?
Hello, I watch a lot of football in the championship and often the commentators on sky sports will say “this is the most unpredictable league in the world”. The thought occurred to me that this is actually quite easy to test. All you need is a model which predicts a range of league. The model […]
F1 Drivers Rated
Hello, welcome to today’s blog and in it I’m going to be developing methods to evaluate F1 drivers. Currently there is no real way to tell if an F1 driver is any good. It seems sort of arbitrary how a racing driver is decided if they are good or not. Being a data fan I […]
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 […]
Sport Data Science 