Today a trend broke in Formula One. Max Verstappen didn’t win a Grand Prix. Of the last 35 Formula One Grand Prix, Max Verstappen has won all but 5. Last season he had something like 86% dominance.
For context I believe that I am overall pessimistic when asked to give a probability range about something “working out”. And since sports tend to vary in results if using a sport like Formula One would be a good source of data to make and compare predictions against?
Everything from estimating the range a pole position time, or the difference between pole and the last qualifier, from to a fastest lap in a race or what lap a driver will pit for fresh tyres.
What is the best way of doing it?
Every 2 weeks when there is a race at the start of a weekend making a series of estimates using only armchair-general knowledge of the sport, and then test and compare them with the actual numbers by the end of the racing weekend?
Should I actually attempt to build simple models and heuristics and deliberate and give specific reasoning?
Considering F1 stats go back to 1950 should I make guesses about historical races and championships rather than do it in real time?
The last idea raises the point that the intention is not to get better at predicting Formula One racing, but to reduce pessimistic bias from my own predictions on a wide variety of topics. Which gives me doubts about this exercise.
I wonder if I am wrong to think that making real time predictions would be better. Modern Formula One has become highly predictably after almost 15 years of Red Bull, Mercedes, and Red Bull dominance. It still is a sport and therefore no prediction model will ever be perfectly accurate. I think this hits the right balance of being able to predict within a range, while not being totally perfectly predictable and thus a good source of data to use. However, I don’t intend for it to be the only one.
I could be way-way off and invite thoughts and experiences from others who have tried to get better at calibrating. And suggestions of how they chose the data sets they compared against?
[Question] Are (Motor)sports like F1 a good thing to calibrate estimates against?
Today a trend broke in Formula One. Max Verstappen didn’t win a Grand Prix. Of the last 35 Formula One Grand Prix, Max Verstappen has won all but 5. Last season he had something like 86% dominance.
For context I believe that I am overall pessimistic when asked to give a probability range about something “working out”. And since sports tend to vary in results if using a sport like Formula One would be a good source of data to make and compare predictions against?
Everything from estimating the range a pole position time, or the difference between pole and the last qualifier, from to a fastest lap in a race or what lap a driver will pit for fresh tyres.
What is the best way of doing it?
Every 2 weeks when there is a race at the start of a weekend making a series of estimates using only armchair-general knowledge of the sport, and then test and compare them with the actual numbers by the end of the racing weekend?
Should I actually attempt to build simple models and heuristics and deliberate and give specific reasoning?
Considering F1 stats go back to 1950 should I make guesses about historical races and championships rather than do it in real time?
The last idea raises the point that the intention is not to get better at predicting Formula One racing, but to reduce pessimistic bias from my own predictions on a wide variety of topics. Which gives me doubts about this exercise.
I wonder if I am wrong to think that making real time predictions would be better. Modern Formula One has become highly predictably after almost 15 years of Red Bull, Mercedes, and Red Bull dominance. It still is a sport and therefore no prediction model will ever be perfectly accurate. I think this hits the right balance of being able to predict within a range, while not being totally perfectly predictable and thus a good source of data to use. However, I don’t intend for it to be the only one.
I could be way-way off and invite thoughts and experiences from others who have tried to get better at calibrating. And suggestions of how they chose the data sets they compared against?