Press Releases - Researching Sponsors: Part 1: Genesis and Early Research

Researching Sponsors: Part 1: Genesis and Early Research

Have you ever wondered what impacts the sponsor offer and by how much? Have you ever hired another PR manager and then been disappointed at how little of an effect he had—or surprised at how large of an effect? Or wondered about how much of an effect Charisma has? Well, I’m here to end all of your worries and frustrations regarding the sponsor offer by revealing the exact formula used to calculate it.

But wait a minute, you say. You say I’m not a developer, and I don’t have access to the code, so it’s impossible for me to know the formula. My answer: let’s try SCIENCE! Or more specifically, the scientific method.

You see, in late January, I was wrapping up the career of my last driver after six seasons in Formula 1. I wasn’t really sure exactly how I wanted to approach a new driver yet, and my analytical mind is always curious about how things work, especially formulas. Four real-life years of observations had provided me with enough anecdotal evidence to suggest that the sponsor formula, specifically to determine the weekly payment, was based on a fixed formula, and that it was probably didn’t have any randomness to it. So, while figuring out what to do with my next career driver, I set out to research this formula in detail using the scientific method.

My goal was to systematically determine the effect of each parameter using controlled tests, where one parameter at a time is allowed to vary and all others are held constant. Such designed experiments were the only way to definitively derive the effect of each parameter.

Thus, rather than just dropping the formula here and running, I will first establish credibility by writing about this research and describing in detail my methodology along the way. Those who hang out in the United States forum have seen my thread dedicated to this research, where I have discussed much of these same things informally. This will be more of a formal presentation of my findings.

Upon retiring my F1 driver, my first experiment driver was a false start. I was still figuring out the best way to approach the project, and spring semester of college was ramping up and I was having a tough time in class, so I had to let the project slide for a few weeks. While a small amount of data from this driver would become useful in later stages of the research, most of it went in the garbage.

On Saturday, February 22, I began in earnest having figured out a full plan of attack. I had previously noticed that a new driver would receive their first sponsor offer the first time after creation that the minutes on the clock hit 20 (e.g. 3:20, 4:20, 5:20, etc.). I took advantage of this throughout the project to get one data point per hour on days where I had time to kill by repeatedly retiring a driver and creating a new one.

On this first day of serious testing, I went through nine drivers. Through these nine drivers, I ruled out any effect from any skill other than Charisma and Man Management and from age or total overall skill points. I also ruled out any effect from Man Management without employees.

The last few drivers of the day, along with twenty-one more over the following week, were used to collect data on Charisma. Data points were collected for every Charisma point from 50 through 60, then (to save time) in three-point increments from 63 to 96, and also for 98 (at the time, the maximum possible starting skill score) and 99 (via some quick training). Throughout these tests, all other variables were held constant, with a rating of 0.000, Man Management at 50, and no employees.

Early in this series of drivers, I noticed a pattern and was able to form a hypothesis on the effect of Charisma alone in the sponsor offer, specifically the weekly payment (on which all of the research was based). The remainder of the drivers confirmed this pattern. With no other contributing factors, the weekly payment was found to simply be the Charisma score multiplied by a constant. Additionally, one test enabled me to determine that partial skill points had no effect.

In early March, with Charisma figured out, I turned my attention to PR managers. Throughout all basic PR manager tests, other variables were again held constant. In particular, Charisma and Man Management were both kept at 50, the rating stayed at 0.000, and only one employee was hired at a time. Using 22 drivers over a one-week span in the early stages of COVID-19 quarantine, a wide variety of PR Manager skills were tested, ranging from 0 to 92 (higher skills were not tested at this time solely because they were not available on the market when I went to hire one).

Analysis of the resulting data determined a constant coefficient which, when multiplied by the PR Manager score, provided the exact difference between the actual sponsor offer and what the sponsor offer would have been without the employee; in other words, the “bonus” provided by the employee. In particular, it was dependent only on the employee’s PR Manager score, and not on whether the employee type was listed as PR Manager or not.

After that, I began testing two variables at once for the first time, testing the interaction between PR managers and Charisma while holding Man Management at 50 and rating at 0.000. To do this, I tested a matrix where the same Charisma score would be tested with three or four different PR Manager scores, and each of those PR Manager scores would be tested with three or four different Charisma scores. Various limitations prevented me from perfectly filling in every slot in this matrix, but I was able to fill in most of the slots, and certainly enough to enable useful analysis.

That analysis proved that the effect of PR managers was proportional to the Charisma score, i.e. if the Charisma score increased by 50%, the “bonus” provided by the PR managers also increased by 50%. I combined that with the coefficients previously found for Charisma alone and PR managers alone to create my first multi-variable formula, valid at the time only if Man Management was equal to 50 and rating was equal to 0.000. As such, it was not a useful formula in the general sense, but it was a basis on which my further research would be built.

My next step would be to test the interaction between PR managers and Man Management.

Author’s note: This is part 1 of a 4-part series on deriving the sponsor formula. Part 2 on Thursday will cover the remainder of the controlled research. Part 3 on Friday will cover the data analysis. Part 4 on Saturday will reveal and discuss the formula itself.

Reactions to this press release

Impressive but...

on 2020-06-29 23:10:25 by benob1 - Likes: 8 | Score: 13.738

I've played games like this where the formulas were all worked out and published and, to be honest, it sort of ruined the game. If you wanted to succeed it basically meant maintaining spreadsheets and working slavishly to the numbers. Eventually my f...

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