After a very close battle between Hamilton and Vettel in Spa, F1 is heading to the temple of speed, Monza. The stronger power unit of Mercedes will play a big role here and we may end up seeing the lap record of Juan Pablo Montoya from 2004 being broken.
After a probably too long summer break, F1 returns to Spa circuit in Belgium. The championship leader is expected to take the pole position with the two Mercedes of Hamilton and Bottas following. The Spa-master, Kimi Raikkonen, is predicted to start 4th, however it could be a chance for him to get the pole. Sebastian and Lewis almost have 50-50 chances of winning the race.
Before the summer break, F1 heads to Budapest for the Hungarian GP. It’s a very twisty track where passing another car is notoriously difficult. The top-4 teams are expected to occupy the first four rows with just one team per row, namely Mercedes, Ferrari, Red Bull and Force India. Bottas is predicted to start ahead of Hamilton although the latter has 5 pole positions in this circuit; the most of any current driver. It’s remarkable how little the order changes…
As I was brainstorming possible features for the ML model, I created a lot of graphs that helped me get a better understanding of what is hidden in the data and what features may be helpful for the ML algorithms. Here, I share some of the most interesting and weird ones!
After a race to forget for Lewis, F1 returns to Silverstone. Hamilton triumphed there last year in tricky conditions and I expect him to do the same this year; at least in qualifying. Bottas is expected to close a Mercedes 1-2 while the usual suspects (Vettel, Ricciardo, Kimi and Max) will follow in the next positions. It’ll be interesting to see if Stroll’s momentum carries on. Race predictions have been updated after the qualifying.
Following the most unpredictable Grand Prix so far this season, F1 is heading to the Red Bull Ring in Austria. The Mercedes are expected to occupy the first row off the starting line. Will this remain till the end of the race? I doubt so. Race predictions have been updated after the qualifying.
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.