- 1,085
- Italy
This is a statistical analysis of the overall result of 3 TTs, especially oriented to understand correlations between Dailies Ranks and TT Ranks, if any. I’ve chosen the last one (Catalunya Gr3) as a quite “standard” TT, but also I’ve chosen the Sardegna one (probably the most easy, looking at the percentage figures of gold medals) and Nordschleife (on the opposite side, don’t think I need to explain why 😊)
Here are some data together with an explanation. The latter one is obviously my explanation, not necessarily the right explanation.
Note: all the times have been normalized against the best time of the specific TT. Therefore 100 means the world record, 103 means the gold threshold, 105 silver threshold, 110 is bronze threshold.
First of all: how are min, average and max times spread across the different rankings?
Min: they are all quite close, no matter the rank. Looks like some good drivers don’t pay too much attention at Dailies
Average: here you start seeing some difference against the ranks. This difference is bigger when the challenge is harder
Max: this amplifies the difference seen in the average. Also here comes into the picture the abandon rate (also A or B drivers will score high lap times if don’t put too much effort in the challenge and maybe do just couple of laps) [Note: picture clips at 150]
Standard deviation: the worsts the raking the spreader the figures. The harder the challenge, the spreader the figures
By now the only surprise is maybe the min distribution. Seeing also lower ranked people pointing gold times was not expected to me.
Let’s go now to distributions: I was expecting some sort of gaussians, maybe overlapping a bit, but it’s not really the case.
Sardegna
Catalunya
Nordschleife
First of all, the distributions are not symmetrical. I think this is due to the fact the training on the TT lowers times. Therefore, the left-side of the distribution rise quicker that the right-side decrease. Note: data at the right is cut at 110
Aliens (rank S): they are not kind enough to provide enough data, and they are not kind enough to go slow, therefore I don’t want to talk about them.
Just kidding, these data shows consistency and performance at a very high degree. They are really great!
In these trends also the different difficulties of the TT is easy to spot.
Final note about shapes: it’s not a gaussian, it’s not symmetrical, but... what are the two humps seen in most of the trends? I think it’s evidence of the effort of the people. The gold and silver threshold is responsible for them. If PD doesn’t pay out starting from the medal, but just starting from the percentage, with non-step rules, I think we would not have seen them.
Here are some data together with an explanation. The latter one is obviously my explanation, not necessarily the right explanation.
Note: all the times have been normalized against the best time of the specific TT. Therefore 100 means the world record, 103 means the gold threshold, 105 silver threshold, 110 is bronze threshold.
First of all: how are min, average and max times spread across the different rankings?
Min: they are all quite close, no matter the rank. Looks like some good drivers don’t pay too much attention at Dailies
Average: here you start seeing some difference against the ranks. This difference is bigger when the challenge is harder
Max: this amplifies the difference seen in the average. Also here comes into the picture the abandon rate (also A or B drivers will score high lap times if don’t put too much effort in the challenge and maybe do just couple of laps) [Note: picture clips at 150]
Standard deviation: the worsts the raking the spreader the figures. The harder the challenge, the spreader the figures
By now the only surprise is maybe the min distribution. Seeing also lower ranked people pointing gold times was not expected to me.
Let’s go now to distributions: I was expecting some sort of gaussians, maybe overlapping a bit, but it’s not really the case.
Sardegna
Catalunya
Nordschleife
First of all, the distributions are not symmetrical. I think this is due to the fact the training on the TT lowers times. Therefore, the left-side of the distribution rise quicker that the right-side decrease. Note: data at the right is cut at 110
Aliens (rank S): they are not kind enough to provide enough data, and they are not kind enough to go slow, therefore I don’t want to talk about them.
Just kidding, these data shows consistency and performance at a very high degree. They are really great!
In these trends also the different difficulties of the TT is easy to spot.
Final note about shapes: it’s not a gaussian, it’s not symmetrical, but... what are the two humps seen in most of the trends? I think it’s evidence of the effort of the people. The gold and silver threshold is responsible for them. If PD doesn’t pay out starting from the medal, but just starting from the percentage, with non-step rules, I think we would not have seen them.
Big Thank You, to everyone that shares their tunes and post them here for us that strugle with tuning! 
