[Event] Cape Town Cycle Tour 2019

[DieselnDust]

still haven't got an update to my seeding

[DieselnDust]

1.03

 

 

(seeding is out)

 

 

I can't believe the beta is so low. I suspect they will adjust it.

[Skubarra]

still haven't got an update to my seeding

 

Pedalpower website not racetec

[Karman de Lange]

I can't believe the beta is so low. I suspect they will adjust it.

Its Automated and ctct is base event.. highly unlikely it will change.. ctct is not place to improve your seeding :-)

[Butterbean]

thansk very much for this info. My next frame will be checked by them before I build up into a functioning bike. an Annual inspection thereafter may just save some skin.

Buy a steel one instead.

[HelloRyanFowler]

I’m not a statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less affected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

Edited March 13, 2019 by HelloRyanFowler

[Mamil]

Jislaaik - that's interesting.

 

Diagram one looks like the elite peleton.

Diagram 2 looks like the mixed 1 DEF mass mooch that I'm pleased to have survived.

 

 

I’m not statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less effected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

[The Ouzo]

I’m not statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less effected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

well I for one am an anomaly on your charts, I did a 6h30 in 2018 because I rode with my dad and was more than 2 hours quicker in 2019

Edited March 13, 2019 by ouzo

[billygoat0523]

This seeding mularkey is interesting. My sub 3 99er gave me 17.31. My 3.31 argus gave me 31. I'm probably halfway between these two numbers... on a good day... with fresh legs...

 

I certainly couldn't have gone harder on the tour but i had something left after the 99er.

 

Supports the view that ppa members get inflated seeding. Wish they wouldn't do that.

 

 

don't think that we get inflated seeding, it's just that the beta algorithm is so hard to predict.  

 

i had a torrid time at the 99er (not a PPA event) and a great race at TdPPA, but my seeding for the two races is exactly the same.

 

aim of the game is just do more races.

[The Ouzo]

but to answer your question, the stronger riders in each group are less affected because they are stronger.

And I'm sure you'll find the stronger guys, especially as you move to the lower groups, did less seeding races yet trained more and therefore outgrown the seeding group they were in

[Mamil]

Flashback to your knees hitting your ears as you cruised the group looking for a multitool.

 

 

don't think that we get inflated seeding, it's just that the beta algorithm is so hard to predict.  

 

i had a torrid time at the 99er (not a PPA event) and a great race at TdPPA, but my seeding for the two races is exactly the same.

 

aim of the game is just do more races.

[billygoat0523]

I’m not statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less effected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

 

you really do seem like a statistician.  

 

so...  do i divide my 2019 time by this -> (0.8556952984 x 2019 race time + 0.9321198196)... or is it only the bit in brackets?

[Pure Savage]

I’m not statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less effected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

I like it, I can even see my dot

 

Could you plot people finishing time per group. So each column has all the riders in $ & @ 1A etc, then time on the left axis. 

 

You can then use this to see who either fell back to ride with a mate and won the group, or cheated on their seeding rides to get into a group they should not have been in. As the groups should get slower after 1A ish...

 

They do this for marathons with qualifications to find cheats, really interesting. 

 

I know there was a johann in our start that cheated in a MTB race and used that seeding to start in &. He rode the ride on his own basically, did not stay with a single group and rode a time of 5 something. 

 

I think that graph would be interesting to see.

[mrcg]

I’m not statistician. Now that we have that cleared up, have a look at this data:

Chart 1 shows the performance of 2019 CTCT cyclists relative to their 2018 performance, across race times.

Fastest 2019 cyclists to the left; slowest to the right.

Cyclists who did better than last year to the bottom; cyclists who had a bad run this year, up you go; cyclists who held their 2018 time sit on the 1.0 line.

So bottom left is fast and chuffed, while top right is slow and disappointed.

The dotted line shows the trend: it slopes upwards, i.e. the slower the rider, the greater the toll that the wind took. The first riders home were somehow impervious to the wind. The slower riders really felt it.

Chart 2 is a detailed view of the area covered by the trend line in Chart 1.

Chart 3 shows performance variance per start group. On average, all groups got pretty much equally battered by the wind – the trend line is nearly flat.

Any theories on why the faster riders, not the earlier riders, were less effected by the wind?

I was hoping that this data would allow me to claim that, despite coming in 20 minutes later than last year, I had put in a better effort this year. Sadly, the data disagrees. I came to this conclusion by offsetting my 2019 time by the average variance for my finishing time. This gave me a number that I could compare directly with my 2018 result.

Here’s the formula: 2019 race time / (0.8556952984 x 2019 race time + 0.9321198196). The result is an indication of the time you could have expected had there been no wind.

I’ve worked on the assumption that the 2019 wind was more or less consistent throughout the day and that 2018 was a peach with little to no wind all day. Are these assumptions reasonably accurate?

I scratched a little into differences across ages and genders but I didn’t pick up anything striking.

Gaps / errors in my thinking? Let me know.

I would say the assumption of equal wind all day is incorrect. It is fact that as the day heats up/day progresses the wind picks up in strength, most notably the gusts too. I think this factor will have the greatest impact on the "weaker/slower" riders as they will sit or be hit by the wind more often and thus lose more power/strength over time. The better riders stick together, work better together and just have the strength and skill to not be as greatly impacted by the wind.

That's my theory in any case

[HelloRyanFowler]

you really do seem like a statistician.  

 

so...  do i divide my 2019 time by this -> (0.8556952984 x 2019 race time + 0.9321198196)... or is it only the bit in brackets?

 

You need to use the whole formula, not just the bit in brackets.

[SoloCyclist]

Me looking at those figures

 

Sent from my SM-A605F using Tapatalk