Since I started this blog, I’m always wondering how I can improve it, what should I write about and how to apply ‘data science’ on anything F1-related. Today, I’m happy to announce that the qualifying and race predictions plus some additional insights (see further down for details) will be exposed via an API and they will be available in JSON format.
The new modelling approach used in the Canadian GP offered accurate predictions especially for the qualifying session. Following Mercedes’ 1-2 in Canada, a Ferrari is again expected to split the Mercedes in qualifying with Hamilton having 72.4% chances of starting in front of Vettel. However, Sebastian has a slightly higher probability (56.7%) to actually win the race. Race predictions have been updated after the qualifying.
I’m proud to announce that the predictions for the next Grand Prix are based on an entirely new modelling approach that promises significantly improved prediction accuracy. The new approach also offers deeper insight by giving for each pair of drivers the probability of the former starting or finishing above the latter. For the upcoming qualifying session, Hamilton edges Vettel by the slightest of margins (50.3% probability of Hamilton starting in front of Vettel). Race predictions have been updated after the…
Having explored the data, checked what is available and what’s not, found any inconsistencies and potential problems, it is time to get creative. Feature extraction is the step where an experienced data scientist can really make the difference and improve the subsequent model’s accuracy; many times more than it will be possible through algorithm selection and fine-tuning.
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 have been 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.
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).
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.