FORMULA 1 CRYPTO.COM MIAMI GRAND PRIX 2023 - Miami....Race.... ▄▀▄▀▄▀ SPOILERS ▄▀▄▀▄▀

Hey, we can disagree (on this one) but remain friends on this forum.

There is a lot of impish talk in the media to whip up interest and create talking points, if not then they are left sucking their thumbs. I have no doubt about that.

Best
tony

 

I'm starting to think maybe he wanted Angela to wear matching outfits.

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Intrigued about race strategy I cam across this interesting article. For me statistical analysis can have a role in race prediction but it been made all to important in F! today. Sure it can be used as a tool but like any tool, if misused it can hurt. The days of the team leader leaning out of the pit wall and watching the race unfold can be more powerful. Thats called experience and sadly it seems a lot of team leaders hide behind the strategists who really are only betting on the roulette wheel.

Part science, part dark art of guessing- a race strategy is essential to winning races. Or losing them. There is a load of basic variables strategist have to put into equation: fuel load, tire wear, overtaking possibilities on given track, traffic, weather forecasts, the length of the pit lane, likelihood of the safety car, quality of the driver, constantly changing situation on the track, along with a host of other factors. Also, race strategy is constantly evolving, passing through transitions when rule changes are introduced. Each year on same track, race strategy will evolve in something different, with only general guidelines taken from previous experiences.

It seems like common sense to think that a car that never stops is going to finish ahead of someone who is heading into the pit lane several times over a race. Yet pit stops remain a key part of Formula One. By stopping and making changes to the car you can actually go quicker between pit stops, overcoming the time taken to make the stops in the first place. Imagine a car making two stops over the course of a race to change tires, taking a total of 50 seconds. Your fresher tires then get you to the chequered flag 51 seconds before you otherwise would have. Despite stopping two times, you've reached the finish-line one second earlier, potentially wining the race.

Formula One is such big business, with so much resting on victory that the decision of when to stop can no longer be left to gut instinct like 30 years ago. Operational researchers build mathematical models which can crunch data on relative track positions, fuel consumption, tire wear and weather conditions, along with a host of other factors. These computer models can run millions of scenarios simultaneously and find the strategies most likely to succeed. They can even say how likely they are to work. This allows team bosses to understand the relative risks associated with each strategy and decide which course to take. The group of people responsible for that is composed of a balanced mix of mathematical modeling and software engineering experts.

In "F1 magazine" (February 2015) there was an interview with Pat Symonds, Williams Formula 1 team Chief Technical Officer, and he was talking about racing strategies:

Q: "What sort of computer-based techniques do you use to assist in tactical decision making?
A: If the parameters that affect the outcome were all deterministic, we could just use mathematical and statistical techniques to guide us to the best outcome. The primary method we use is one known as a "Monte Carlo simulation". This is a long-established statistical technique whereby a large number of Virtual races are run, each with different parameters, and pitstop laps are applied to all the cars. The many thousands of results are then analyzed to determine the probability of a given outcome for a particular set of decisions. If we relied on this alone, it is likely that everyone would come up with similar answers, so we also apply a technique known as "Game Theory". This covers many mathematical techniques but, for example, takes the knowledge that people will deviate from a deterministic optimum to take advantage of the undercut.
With this assumption we modify our tactics to try to counter their move."

So, what is "Monte Carlo simulation" and "Game Theory" he was talking about?



Monte Carlo simulation

When computers entered Formula 1, strategy software was a standalone application which was run pre-race by race engineers at trackside. The engineers had the result like this:

"A one stop is quicker than a two stop".

During the time numerous enhancements were incorporated, however the big breakthrough was the use of Monte Carlo techniques in 1998. This allowed teams to consider all cars in the race, traffic patterns, probability of safety cars, overtaking and other random events. The results were with outputs like this:

"If we make a two-stop strategy we give ourselves approximately 20 per cent exposure to finishing 1st or 2nd, but if we get stuck in traffic, we will most likely finish 7th or below; however if we undertake a one-stop strategy we will most likely finish 3rd or 4th position, but unlikely to finish as high as 1st or as low as 7th".

So for the first time from the simulation team could visualize the risk associated with each option, and therefore a risk could be taken into consideration. All of the development was still offline and analysis was carried out pre-race. There were no data feeds which they were able to use to run real time, other than manually typing in the time gap to the car of interest on each lap to determine whether car is ahead of or behind predicted schedule.
The next leap was in 2001 when the FOM first provided live electronic timing data for all cars. This enabled in-race strategy to be undertaken in a more real-time way. Further, it allowed teams to run real time Monte Carlo simulations to answer questions like:

"Given the race information to the current lap, what is the best action to undertake if our competitor executes a three stop strategy?"

or

"What is the best strategy for our competitors to use?"

The number of permutations during formula 1 race is so vast that not even the world's fastest supercomputer could crank through them all for each race. This is where the Monte Carlo simulation comes in.

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Monte Carlo simulation is a technique that uses randomly generated numbers in order to approximate the outcome. This is a Stochastic Model where we have many random variables - rather than one simple function - and need to obtain a range of results. This method can be used to describe any technique that approximates solutions to quantitative problems through statistical sampling. The idea behind simulation is to imitate a real-world situation mathematically, to study its properties and operating characteristics, to draw conclusions and make action decisions based on the results of the simulation. Hence, it is a type of simulation that explicitly and quantitatively represents uncertainties by specifying inputs as probability distributions.
Whereas the result of a single simulation of an uncertain system is a qualified statement:

"With 3 pitstops we will finish 3rd"

the result of a probabilistic (Monte Carlo) simulation is a quantified probability:

"With 3 pitstops, there is a 20% chance that we will finish 3rd"

Such a result (in this case, quantifying the race position) is typically much more useful to decision-makers who utilize the simulation results. Monte Carlo simulation is perhaps the most common technique for propagating the uncertainty in the various aspects of a system (system is in our case a race itself) to the predicted performance. In these simulations, the entire system is simulated millions of times. Each simulation is equally likely to happened, referred to as a realization of the system.

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Monte-Carlo simulation runs over these input values and gives us different possibilities the project can end up

For each realization, all of the uncertain parameters are sampled (i.e., a single random value is selected from the specified distribution describing each parameter). The system is then simulated through time (given the particular set of input parameters) so that the performance of the system can be computed. This result is a large number of separate and independent results, each representing a possible "future" for the system (i.e., one possible path the system may follow through time). The results of the independent system realizations are assembled into probability distributions of possible outcomes. As a result, the outputs are not single values, but probability distributions.

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Instead of plodding through each scenario, race strategists capture its essence using a probability distribution, a set of values reflecting the relative odds of any given event taking place. They then dip into this set of values at random - just like a roulette ball in Monte Carlo - and combine the result with random values taken from all the other probability distributions. Repeated millions of times, the end result is a realistic simulation of the race and the strategy giving the best odds of winning.
Developed in the 1940s by scientists working on thermonuclear weapons, Monte Carlo simulation in F1 was pioneered during the 1990s by Neil Martin, who joined McLaren as a mathematics graduate from Southampton University. Martin's brilliance as a race strategist made headlines following the 2005 Monaco Grand Prix, when a crash prompted rival teams to make pit-stops. Refusing to follow the pack, Martin advised McLaren's driver Kimi Raikkonen to keep driving - ensuring he kept his lead, and won the race.
While teams typically run millions of Monte Carlo simulations for each race, they can still be caught out by totally unexpected events. In such cases, there is nothing else to do but to run new simulations while the race is still in progress. As speed is of the essence, the teams at the race venue keep fast data-links open to their engineering HQs, where supercomputers crunch through the new data.
The problem is when the new circuit has never hosted a grand prix race before, there's no historical data to work with. Yet without solid, reliable input, the Monte Carlo simulations cannot work their magic.



Game Theory

Monte Carlo simulation can give you a probable outcome of race. But this simulation can't take into account interaction between different parties. Computer scientists and mathematicians are increasingly interested in resolving the problems in situations where multiple self-interested parties interact. To act optimally in such situation, each player must take into account how the other players are likely to act. Game theory studies how this should be done, and it provides various solution concepts which prescribe how players should act when the actions of other players affect their game or strategy. In this way, Game theory software will review these concepts, as well as operationalize them by providing algorithms to compute the solutions.

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The primary use of game theory is to describe and model how human populations behave. Game theory is the mathematical model of decision making which study the conflict, strategic interaction and cooperation between intelligent rational decision-makers, between two or more players in a situation containing set of rules and outcomes, in which interacting choices of game playing produce outcomes with respect to the preferences (or utilities) of those players, where the outcomes in question might have been intended by none of the participant.
An alternative name suggested as a more descriptive name for the discipline is "interactive decision theory". While used in a number of disciplines, game theory is most notably used as a tool within the study of economics. The economic application of game theory can be a valuable tool to aide in the fundamental analysis of industries, sectors and any strategic interaction between two or more firms. Recently, however, evolutionary game theory has become of increased interest to sociologists, anthropologists and social scientists in general, as well as sport strategy prediction.
Any time we have a situation with two or more players that involves known payouts or quantifiable consequences, we can use game theory to help determine the most likely outcomes.

Let's start out by defining a few terms commonly used in the study of game theory:

• Game: Any set of circumstances that has a result dependent on the actions of two of more decision makers ("players")
• Players: A strategic decision maker within the context of the game
• Strategy: A complete plan of action a player will take given the set of circumstances that might arise within the game
• Payoff: The payout a player receives from arriving at a particular outcome. The payout can be in any quantifiable form, from dollars or utility to first place in race.
• Information Set: The information available at a given point in the game. The term information set is most usually applied when the game has a sequential component.
• Equilibrium: The point in a game where both players have made their decisions and an outcome is reached.

The games studied in game theory are well-defined mathematical objects. To be fully defined and to deduce a set of equilibrium, a game must specify the following essential elements: the players of the game, the information and actions available to each player at each decision point, and the payoffs for each outcome. When a game is presented in normal form, it is presumed that each player acts simultaneously or, at least, without knowing the actions of the other. If players have some information about the choices of other players, the game is usually presented in extensive form.

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Game theory and decision making in tic - tac - toe game. If players have some information about the choices of other players, the game is usually presented in extensive form.

It is assumed that players within the game are rational and will strive to maximize their payoffs in the game. The number of players in a game can theoretically be infinite, but most games will be put into the context of two players. By using simple methods of game theory, we can solve for what would be a confusing array of outcomes in a real-world situation.

The modern mathematical theory of games was invented by John von Neumann and Oskar Morgenstern 1944, but the first known discussion of game theory occurred in a letter written by James Waldegrave in 1713. Limitations in their mathematical framework initially made the theory applicable only under special and limited conditions. This situation has dramatically changed over the past, as the framework has been deepened and generalized. However, since at least the late 1970s it has been possible to say with confidence that game theory is the most important and useful tool for the analyst's.

 

Absolutely Tony!

 

In game theory, the most common strategy is min-max - i.e., assume other players will always optimize their reactions depending on the conditions, and then act to put them in the conditions where this optimization is minimal. In case of a complex environment like a real world race, some parameters are not known (because some conditions cannot be definitely predicted), so there should be some assumptions and some balanced assessments. That's where luck - or insight - can make a difference...

 

You stay classy Miami

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Somehow, this country keeps finding new ways to embarrass me. I'm reminded of the time Clarkson said, "I do believe that, in certain parts of America, people have started to mate with vegetables."

 

In countries that keep taking away punishments and accountability, people continue to push the boundaries of how bad they can behave.

 

A fascinating read. It makes sense that F1 teams have such insanely complicated strategy software, and it could explain a couple of things.

1) A strategy is only as good as the data/values entered into the software. Ferrari might suck at this.

2) Relying on a computer to kick out strategy means the human touch is lost. This might be why Ferrari have ZERO clue what to do with strategy changes on the fly.

 

Why doesn't Lewis race in that outfit?

 

After seeing numerous post on Twitter I can assure you educational level is very low in France too!
This is what happens when traditional structures implodes and people are just good to buy items.

 

Teams work hard to make a race for all fans and get this reception.

Hi Miami race promoter!! Unacceptable to say the least. TEAMS deserve better fans!!

 

For me I think its grossly over used and whilst it may seem to bring a sense of stability or even comfort to the weekends event, I wouldnt want it shoved in my face as a Team Principal, information overload!. Does it try to add a sense of intelligence to the game?

We have all seen computer models on lots of things and the predictions / outcomes are wildly out.

Whatever happened to gut feeling, sadly these qualities are grossly lacking in the modern team principal and they are not exposed to making the right call and gaining that all important - experience. Horner has it and cannot think of many others who can compete at his level. I guess the human brain can react to changing conditions much faster than any computer. The super computers at HQ does not know smell, temperature, humidity, skill of drivers, tyre wear, lockups, skidding and even driver temperament on the day. The list is endless in all reality but a key one which I fail to see how it can be integrated into any mathematical model is outbluff your opponent. ie false tyre stops winding up your opponent so he makes the wrong decision for the wrong reason.etc

Personally its way over used for its accuracy however I will use gut feeling and predict SF will have a bad season come 4th, there, and no maths involved at all!!

Ferrari would be best to save the precious budget money spent on it and fix the car first.

 

If that's of any consolation, vulgarity is gaining ground as standard behaviour in many countries.

 

Because it needs to be fire---PROOF...not just fire...like he thinks it is...

sjd

 

everyone's catching on to the new low end f1:

https://www.planetf1.com/news/christian-horner-lewis-hamilton-f1-kardashians-show/

as i said, the appropriate sponsors for miami and las vegas are the cocaine cartels......

 

I remember going into an all-hands meeting where the company president told us all that we would have to all put in 60 hours a week. All the engineers looked at each other breathed a sigh of relief as we were all already working 80 hours a week !!!

My guess is that cracking the whip will not work.......

 

The problem is that people in America can live a good and productive life without even having the intelligence of a turnip !

 

LOL

 

ME ami!!!! Has to be the most shallow, self absorbed, entitled city in the country. The diamond encrusted morality bar there is set so low an ant couldn't limbo under it.

Although I wouldn't consider it America anymore IMO. It's just an international play ground for trust find babies and d bags from all around the world.