Recently, I worked on predicting the final driver standings for an F1 season using race results of the current season so far and historical data for the past few seasons. The linked blog post details the datasets, techniques used, and discussion on the results. Here's the github repository for the project : https://github.com/kvsingh/F1-standings-forecasting
An interesting thing I found was that the model's performance plateaued after 3 races into the season. Although this is more reflective of the model's use of the data rather than absolute predictability, it helps highlight the deterministic nature of such a technical sport.
I hope you enjoy reading it, and I would love any suggestions or comments, especially around interpretability of the results.
Recently, I worked on predicting the final driver standings for an F1 season using race results of the current season so far and historical data for the past few seasons. The linked blog post details the datasets, techniques used, and discussion on the results. Here's the github repository for the project : https://github.com/kvsingh/F1-standings-forecasting
An interesting thing I found was that the model's performance plateaued after 3 races into the season. Although this is more reflective of the model's use of the data rather than absolute predictability, it helps highlight the deterministic nature of such a technical sport.
I hope you enjoy reading it, and I would love any suggestions or comments, especially around interpretability of the results.
Thanks!
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