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

Luck seemingly repesents the antithesis of what statistics try to achieve. A concept with connotations of random chance and a quantity which most people would assume is an external factor that prohibits the sense of regularity that make statistics so powerful. Luck seemes like the tool which would always play on the mind of someone when predicting the outcome of a football match. But can it be accounted for? If so, how?

Skill is something that defintively exists in football. The void in skill can clearly be seen when comparing your average Sunday league player to the elite regularly featuring in the nation’s top league. But even within the top leagues where only the best can make it, a void still exists with talent levels clearly differing, between clubs and between leagues.

We can highlight one example of luck in the Premier League by looking at a game in 2009, where Darren Bent, at the time a Sunderland striker, was able to score from a rather tame shot against Pepe Reina in the Liverpool goal. In fact, the Spanish goalkeeper had not been to blame for this and the shot had take a nast deflection, but not from a player. In fact, the shot rebounded off a red beachball that had found its way on the pitch, wrong footing the keeper and confusing everyone in the stadium as well as everyone sitting at home. Liverpool found themselves 1-0 down and despite Liverpool taking 15 shots that game they couldn’t muster any points from the game. They had lost due to a beach ball.

Well how much of footall is based on skill then and how much of it is based on luck. If the game was pure skill then ultimately the best team would always end up winning, but we know that this is not always the case. If it was purely based on luck, then there would be no real division in points in the league and there would be no reason for teams to splash the cash on new signings or to develop their youth systems. Most academics have come to the conclusion that football consists of a 50:50 split between luck and skill, something which may be hard for any football purists reading to comprehend as they appreciate the intracicies of the beautiful game.

To further the football myths, the percieved beauty of a team, i.e the slick passes and long build up play leading to a finessed finish is no real measure for how objectively effective a team is, it is often just a by-product of a succesful team but is in no means what defines the success of a team. As many of us may have learnt, correlation does not indicate causation and this is a great example of this idea. The actual efectiveness of a team can be more accurately measured by looking at metrics such as ball retrieval, shot numbers and even as simply as goals. It is an objective fact, that the most succesful teams score more than others or concede less than others, usually a combination of both, however a beautiful style of play is most certainly not an indicator. The best example of this is Sir Alex Ferguson’s United who were a well drilled machine that would grind out results and always go fo the most pagmatic approach, which led them to winning 13 league titles in his tenure. A paticular example stands out when they played Arsenal in March 2011 and Sir Alex played 7 recognisd defenders in his starting line up against the visually pleasing Arsenal side, but still walked away with a 2-0 win.

If we were to look at bookmaker’s odds, the point behind this article is further illustrated. If we were to look at the success rates of pre-game favourites in a variety of sports in the 2010-11 season, in handball the bookmaker was corect 72% of the time, basketball 68%, american fooball 67%, baseball 61% and football sits right at the bottom of the pile with only a 52% success rate. This is other words, shows that bookies pick favourites less successfully in football alluding to the factor of luck or to be more scientific; random chance. Obviously the draw plays a significant part in the poor quality of predictions as this result is very common in football due to the rare nature of a goal, with the daw being a much less likely outcome in the other sports listed above.

When delving further into the odds we can almost classify the pre game favouits into two categories “strong” and “weak favourites”. To determine how much a favourite is advantaged over the opponent you can analyse the diffeence in odds between the favourites winning and the underdogs winning. In this we will define a strong favourite as one team’s oddds of winning as more than 2 times the other, suggesting, in an ideal world, if the two teams played three times then the favouite would for sure at least win twice and possibly win/draw or lose the other game. In football these strong favourites win only 65% of the time, less than the absolute minimum value according to the statistical theory, which would be 67%. In actuality it would be higher as in some cases the odds may be 3, 4 or even 5 times higher for on team than the other. In basketball the strong favourite wins 80% of the time, much closer to the value you would expect.

From these studies, including the one undertaken by Eli Ben-Niam from the Los Almos National laboatory, we can definitely conclude that football is the most uncertain of sports. According to Ben-Naim’s study above where he analysed 300,000 games and found that the chance of an underdog winning was 45.2%. However as established in the expected goals article, wins and even goals can be misleading. Even if you break it down to a statistics which is shots which we have established as one of the best indicators for determining the disparity in quality between sides, we can see that on average in Europe’s top five leagues the likelihood of the team that takes the most shots winning is only 47%, again hinting at that 50/50 proposition. Even if you use the even more precise metic of shots on target the tale is similar with teams that take more shots on target win only 55% of the time.

The real importance of this article however, is to show how football is rooted within statistics and chance. Within football we are seeing probability come to life and football fans need to acceptt that like so many other phenomenana that we see, football itself has this intrinsic random-element. Just like the decay of nuclei and the number you role on the dice, football also comes down to chance. In all these cases there are possible outcomes, either the nuclei decays or it doesn’t, you roll a 6 or you don’t you win or you don’t. Football seemingly comes down to probability as well, however unspurprisingly chance only takes into effect around 50% of the games. In half of the games we see the better side rewarded with the win. The other half comes down to the flip of a coin.

Aaron Wan-Bissaka has had a simply monstrous start to the season. Putting up a mamouth 5.6 tacles a game, Wan-Bissaka has silenced his critics which prviously mocked United for what was suggsted to be a vast overpayment for a young English talent showing glimmers of promise. His 5.6 tackles per 90 puts him top in the EPL this season with even the most labouring defensive midfielders such as the likes of Wilfred Ndidi, unable to match his remarkable output.

Whilst ability to pull off those perfectly timed “spiderman” challenges is now well known by the average football fan, many aspects of his game remain underrated. One of those is his dribbling ability, as his slick composure with the ball at his feet is most certainly one of his strongest assets. Attempting 2.4 dribbles per game, and completing 1.6 of them (giving him a better completion rate than Wilfred Zaha, highly rated for his dribbling), Wan-Bissaka has shown that he can be valuable in pushing the side forward and has the raw talent to beat a man. Making 1.2 interceptions and 1.8 clearances per 90, he has shown that he is able to dissect the game and really help to slow down the momentum of the team by winning the ball back or clearing the danger.

If you were to ask any United fan what their greatest issue was last season it would almost undoubtedly be their right hand flank. An ageing Ashley Young was thrown in out of position into the right back role with the added pressure and responsibility of the captain’s armband and United simply didn’t have a stable right wing option. With the addition of Wan-Bissaka and Daniel James ahead of him, United have looked much more lethal on that side both going forwad and in defence with the right hand channel being more defensively and ofensively productive this season for United than the left, this was certainly not the case last year.

Looking at Ashley Young’s stats from last year paints a clear picture of the discrepency in quality between him and Wan-Bissaka. Young averaged 0.3 tackles and 1 interception per 90 showing his lack of that intrinsic awareness top quality defenders have to win the ball back. He averaged 2.7 clearances per 90 which suggests he was required to do more last ditch defensing suggesting a lack of positional awareness but more likely a lack of composure or skill to bring the ball out from the back. He attempted only 0.6 dribbles per 90 and of those he only completed 50%. To even further condemn him, Young’s greatest historical asset, his crossing also took a sharp nose dive in output with him making over 4.4. inaccurate long balls per 90 out of the 5.6 he attempts and only 1.5 out of every 3.4 corners he takes are accurate as well.

Using the underlying statistics, United’s defence have been the best in the league tgis yar showing how remarkable a turn around Wan-Bissaka has made. They are top for goals against, only allowing 4 and have conceded the 2nd most shots in the league. Their XGA sits at 3.31 placing them top of the league above the likes of Manchester City and Liverpool with the former having an XGA of 4.48. Despite them curently sitting in 6th, looking at expected points they seem to be faily unlucky with expected points suggesting that really they should be sitting in second place, behing Manchester City. Looking at last season, United were 11th for goals against at 54 and XGA had them at 52.8, placing them 8th.

Obviously the addition of Harry Maguire has had a proound impact on this sudden improvement in defensive stability however the midfield has arguably gotten weaker. United have lost the extrem coverage that Ander Herrera brought as he moved to PSG and despite being a positive sale they did lose another defensively sound player in Fellaini. With Scott McTominay being worshipped by many United fans, his underlying numbers are simply not that impressive and rank him as an average or slightly below average Premier League quality midfielder. Matic is also a year older and his legs seem to have gone whilst Juan Mata is being employed in a midfield 3 despite him being completely immobile and offering hardly any defensive contribution. It has all simply been lft to Paul Pogba, who has simply had to focus on chance creation with the lack of creativity in United’s midfield. This simply makes Wan-Bissaka’s numbers even more astounding.

But why is he the best, or most important? Why not give this title to Pogba, or Rashord or De Gea. Simply put, Wan-Bissaka has solved a poblem looming over United since the departure of Gary Neville, with many players such as Antonio Valnecia and Ashley Young have had to convert postions in orde to fill the glaring gap in United’s lineup. The accquisition of Wan-Bissaka provides a long term solution with a generational talent who arguably is already the most defensively sound right back in world football at the momemnt, with only the likes of Joshua Kimmich and Trent Alexander Arold really challenging him to the top overall right back claim. Already showing improvements in his attacking game, Wan-Bissaka is most certainly heading towards being one of the best players to ever play for this prestigous club.