Statistics are starting to be integrated into more and more sports and we have discussed its increasing use in football in many previous blogs. Perhaps the hardest sport to try and integate statistics into is Formula 1 as assessing driver competency is extremely dificult due to the vastly varying levels of the car.
To understand why the different cars vary so much, we need to delve deep within how funding works in F1. In 2017, the top earning team made almost 10 times the amount of the lowest earner. It simply does not boil down to the constructors championship as many people believe as there are a variety of other factors to consider. The first funding a team recieves is $36 million if the team has been classified for at least 2 of the previous 3 seasons. In 2017, this bonus was recieved by everyone except a new team, Haas F1, Putting them well behind their competition already with the bonus equating to 240 front wings. The teams then get payed according to their standing in the constructors championship ranging, with Merceded recieveing $61 million for winning the championship.
The next source of revenue is known as Long-Standing-Team or LST for short. In 2017, Ferarri were the only team to recieve this bonus of $68 million. As well as some other hefty bonuses that Williams, McLaren , Mercedes and Red Bull recieve for different reasons. The final finances look something like this:
As you can see here there is a lage disparity between the top 3 teams of Ferrari, Mercedes and Red Bul who can dominate other teams due to their much larger revenue streams . You can also see how difficult is for a new team to posper in F1 as can be seen in the extremely limited revenue that Haas recieve.
Obviously the drivers in the slower cars in the midfield and back of the grid will be at a severe disadvantage to those who drive the Ferraris and the Mercedes wih their top spec power units and extreme downforce cars. So how can drivers be ranked fairly, factoring in their car performance, and what is the obsession and importance with ranking these drivers.
Well first off the importance of being able to measure driver performance in the fairest way possible lies within the whole seat system of F1. Every year drivers are fighting with each other to secure the limited 20 seats that F1 has to offer. Not only are they battling the current F1 drives but also up and coming drivers from F2, F3 and drivers from the Junior driver academies of the different constructors. Because of this each drivers greatest cmpetiton has typically been their teammate as they are both in the same car so comparison an be made between the two easily as to who is the better driver. Why the statistics regarding driver performance is so important is that say a driver is droppped by a team, a different team may want to sign the driver who has just been sacked, but for them to compare it to the drivers they currently have would be very difficult as they were both in different cars, making the decision extremely subjective.
So what factors should be considered when assessing the ability of an F1 driver. It can be broken down into two basic categories, race performance and qualifying performance. Both are impotant as qualifying shows how fast the drive can be when pushing to the absolute limit, almost giving an idea of the potential of the driver. The race shows whether has the focus and enduance to sustain a high level for long periods of time and ultimately the points or the team are won in the race and not qualifying. This would mean that if we were to create an algorithm in order to try and rank the drivers that final qualifying position would have to be factored in, both in relation to the teamate as well as the general grid positon. Furthermore the race finish would have to have a slightly higher weighting in comparison to the qualifyng position as this obviously has more importance.
Delving deeper into how we could score drivers in the algorithm based on race finsh begs the question whether it should be done using points scored or simply just from driver position in the grid. Obviously points scored is what counts in the end but this can often be misleading. For example looking at the 2019 F1 season George Russel has been dominating his teamate Robert Kubica, beating him every single qualifying session and finishing above him almost every race. However as both drive for current backmarker’s Williams, Kubica is ahead of Russel in the driver’s championship as h has scored 1 point and ussel has not yet so far despite his highly promising rookie season.
If we are looking at complete driver performance then we obviously need to facto in some consideration of how fast the car really is and how much the drivers have been able to utilise the car that they have been given as it would obviously not to blindly compare points scored for one of the Ferrari drivers with one of the Torro Rosso’ s. Perhaps one way o doing this would be to really analyse the staistics o the car through winter testing. Yes this data is not readily avaliablebyt say this data was given to an FIA regulaed body and or the eyes of no one else then perhaps the teams would be happy to share this. Another way perhaps of assessing speed of the car is though the use of simlation, taking data such as max speed and downfoce into account by looking at laps done by drivers. You could even get one driver to test each car and compare those lap times in order to analyse how fast the cars really are.
Another factor which will certainly need to be considered is the reliaibility of the car and if retirements are caused simply due to mechanical failures then perhaps the effect of the final postion on the score of the driver for that weekend could be lowered or pehaps the score for that race could just be ignored. Often retirements are cause by reckless driving where you may hit a kerb and damage he suspension, then race postion shoud be considered as it also comes down to driver skill to see if they can recover using a damaged car.
Crashes are another big part of the adjustment of the race position and is one of the key areas where subjectivity will have to be incorporated between the algortihm. Perhaps a multiplier culd be used from 0 to 1 based on how at fault the driver was. For example :
Looking at the first crash in this video there are a number of cars involved in this. If we were to spread the blame of the crash with the total blame of all the cars involved in the crash being 0 to 1 then Raikonnen and Vettel would have to share a large poportion of the blame their perhaps 0.5 for Raikonnen and 0.4 for Vettel. Verstappen seemed fairly helpless in that incident but perhaps could have braked and given in so 0.1 of the blame lies with him. The other two cars of Alonso and Riccardo that Raikonnen collides into really have no liability for this crash whatsoever so would ecieve some sort of bonus to the score or that wekend as it simly was not their fault that they got caught up in a racing incident that jeprodized their race.
Another thing to consider is age and experience for a team who may be looking to decide whether they go forward with a current driver or not. There are many ways to jusge this, perhaps by looking at the age of world champions or by looking at the mean points scored in the driver’s championship for 20 year olds in the hybrid era, then 21 year olds, then 22 and so on to almost predict how the development of a racing driver is on average. Perhaps the progress of the current driver could be compared to the standard cuve over what data is avaliable and it can be seen whether they are above or below that ranking. Obviously there are many flaws and assumptions involved in this such as the driver may have a worse car the next year so despite them seemingly getting worse by getting less points their skill as a racing driver may have drastically improved. Also a small sample size for the extreme ends of the spectrum would restrict how accurate we could possibly be. There could also be some sort of rookie bonus for drivers in their first year of F1 as this is a paticulaly tough year or drivers as they enter a new sport. In the end, this measure would be more useful or teams deciding on driver lineups or he next season and should not be included in the aim to objectively measure driver performance.
In the end, F1 is an extremely unfair and dominated sport, with very year in the hybrid era, mercedes have won both the driver’s and constructor’s championship. Although Ferrari and Red Bull can keep them on their toes the rest of the teams in the midfield and at the back of the grid have really struggled to keep up with the big three due to thei budgets being no way near comparable. To try and eliminate this differnce in car and to just simply understand how good the driver is behind the wheel would be extremely imprtant. It could almost be used as another measure that divers fight for like the diver’s p constuctors championship. It highights the work of drivers towards the back of the grid who may not get the plaudits they deserve as their cars restict them from getting anywhere nea the podium or race wins.
Simply this measure would be good for the teams to help rate their drivers, it gives all the drivers a fair and even playing field to compete from and it helps the fans to understand who truly are the best drivers in the paddock. Funding could also be given to teams based on their performance on this metic which would help to lower the disparity in budgets towards the teams, also hlping new teams to suvive in Formula 1, leading to moe intense racing for fans to enjoy and more competition, forcing teams to up their game. This metric could be revolutionary for F1
Stats In Sport 