This is something I wanted to create for the past three years: find out if the results of the drivers in a race are reflected on the sentiment of their fans’ posts on social media. After launching this blog, I found the motivation to actually implement it. In this post I present a nice R Shiny app showing the results and I describe the process behind creating it (code included).
Monaco is going to be a very tight race with Mercedes, Ferrari and (why not) Red Bull since this twisty circuit will hide the deficiencies of their Renault engine. Hamilton and Vettel are again expected to fight for the pole position while Ricciardo may qualify above the Ferrari of Kimi. Jenson Button will struggle although Monaco may be an opportunity for McLaren to get some points. Race predictions will be updated after the qualifying.
With the Spanish Grand Prix approaching, here I present the forecast for the qualifying and race results. For some not-so-clear reason, the model seems to favour Daniel Ricciardo to start 2nd with Hamilton starting as low as 4th. This could due to his crash last year with Nico Rosberg; the model might have thought that it was Hamilton’s fault after all! Surprisingly enough, Verstappen is expected to start only 6th despite his impressive win in 2016.
After getting very accurate predictions for Bahrain GP, I wonder whether the model will be able to replicate that success for the upcoming Russian GP. Vettel is expected to separate the two Mercedes while Ricciardo will qualify ahead of the 2nd Ferrari of Raikkonen. Race predictions have been updated after the qualifying.
With Bahrain Grand Prix approaching, here are my predictions for both qualifying and race. For the first time this year, the Mercedes drivers are expected to fill-up the front-row with Raikkonen starting 5th despite his phenomenal performance there last year. Race predictions will be updated after the qualifying.
Here are my predictions for the upcoming Chinese Grand Prix. After an impressive win in Albert Park, Vettel is the algorithm’s favourite to take the pole position with Hamilton filling up the front row. Surprisingly, Bottas is predicted to start fifth. Race predictions have been updated after the qualifying.
After discussing in my previous post about Data Acquisition, here I’m going to describe the most important part of any Machine Learning pipeline i.e. Feature Engineering.
Having defined what we are trying to achieve, it’s time to start thinking about what data we need and how we are going to obtain them in order to create features and train our models.
Hereby I present you with the first predictions made by the algorithm. The model has not seen any data from the recent testing in Barcelona so everything is based on the drivers’ and teams’ performance in the past years. I expect the accuracy of the predictions to increase as the season progresses. Race predictions have been updated after the qualifying.
In this series of posts I’m going to explain the process behind building a Machine-Learning model capable of predicting F1 race outcomes. This is not intented to be a complete guide so some background on building ML pipelines is assumed.