[Event] Cape Town Cycle Tour 2019

[Long Wheel Base]

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.

I have absolutely no idea what all this means but it looks cool.

[HelloRyanFowler]

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.

 

Data, glorious data!

 

Here's the key to the groups:

 

$ = 1;    @ = 2;    & = 3;    # = 4;    1A = 5;    1B = 6;    1C = 7;    1D = 8;    1E = 9;    1F = 10;    1G = 11;    1H = 12;    1J = 13;    1K = 14;    2A = 15;    2B = 16;    2C = 17;    2D = 18;    2E = 19;    2F = 20;    2G = 21;    2H = 22;    Jh = 23;    Js = 24;    Ju = 25;    2K = 26;    3A = 27;    3B = 28;    3C = 29;    3D = 30;    3E = 31;    3F = 32;    3G = 33;    3H = 34;    3J = 35;    3K = 36;    4A = 37;    4B = 38;    4C = 39;    4D = 40;    4E = 41;    4F = 42;    4G = 43;    4H = 44;    4J = 45;    4K = 46;    5A = 47;    5B = 48;    5C = 49;    5D = 50;    5E = 51;    5F = 52;    5G = 53;    5H = 54;    5J = 55;    5K = 56;    6A = 57;    6B = 58;    6C = 59;    6D = 60;    6E = 61;    6F = 62;    6G = 63;    6H = 64;    6J = 65;    6K = 66;   

 

[Long Wheel Base]

 

Data, glorious data!

 

Here's the key to the groups:

 

$ = 1;    @ = 2;    & = 3;    # = 4;    1A = 5;    1B = 6;    1C = 7;    1D = 8;    1E = 9;    1F = 10;    1G = 11;    1H = 12;    1J = 13;    1K = 14;    2A = 15;    2B = 16;    2C = 17;    2D = 18;    2E = 19;    2F = 20;    2G = 21;    2H = 22;    Jh = 23;    Js = 24;    Ju = 25;    2K = 26;    3A = 27;    3B = 28;    3C = 29;    3D = 30;    3E = 31;    3F = 32;    3G = 33;    3H = 34;    3J = 35;    3K = 36;    4A = 37;    4B = 38;    4C = 39;    4D = 40;    4E = 41;    4F = 42;    4G = 43;    4H = 44;    4J = 45;    4K = 46;    5A = 47;    5B = 48;    5C = 49;    5D = 50;    5E = 51;    5F = 52;    5G = 53;    5H = 54;    5J = 55;    5K = 56;    6A = 57;    6B = 58;    6C = 59;    6D = 60;    6E = 61;    6F = 62;    6G = 63;    6H = 64;    6J = 65;    6K = 66;   

 

 

What was up with these groups?

Jh = 23;    Js = 24;    Ju = 25;

[LongDonkey]

Well results are gone from PPA and Racetec web sites. Guess an updated file is being uploaded or reviewed. 

[DieselnDust]

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

 

 

 

and your race time in seconds

[MudLark]

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

 

 

I also don't get the formula or how to correctly apply it. When I apply it, I get a result of 1.1641111... Which doesn't quite make sense to me.  How about running an example using a hypothetical race time so we can properly understand how to correctly apply it?

[MudLark]

and your race time in seconds

 

 

 

I get very nearly the same result, whether I use minutes or seconds. 1.168564...

 

What does it mean?

Edited March 14, 2019 by MudLark

[Hilton.]

Anyone know what BMW was being used as the lead vehicle?

 

I think I saw that its the new BMW 8 Series ?

[dannygirl]

What was up with these groups?

Jh = 23;    Js = 24;    Ju = 25;

 

I think they were the groups that pulled the buggies...

[Karman de Lange]

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

 

commentators on live stream said the wind actually dropped during the day.  there probably somewhere stats on the actual winds for the day .. just to lazy to google now

[Karman de Lange]

 

Data, glorious data!

 

Here's the key to the groups:

 

$ = 1;    @ = 2;    & = 3;    # = 4;    1A = 5;    1B = 6;    1C = 7;    1D = 8;    1E = 9;    1F = 10;    1G = 11;    1H = 12;    1J = 13;    1K = 14;    2A = 15;    2B = 16;    2C = 17;    2D = 18;    2E = 19;    2F = 20;    2G = 21;    2H = 22;    Jh = 23;    Js = 24;    Ju = 25;    2K = 26;    3A = 27;    3B = 28;    3C = 29;    3D = 30;    3E = 31;    3F = 32;    3G = 33;    3H = 34;    3J = 35;    3K = 36;    4A = 37;    4B = 38;    4C = 39;    4D = 40;    4E = 41;    4F = 42;    4G = 43;    4H = 44;    4J = 45;    4K = 46;    5A = 47;    5B = 48;    5C = 49;    5D = 50;    5E = 51;    5F = 52;    5G = 53;    5H = 54;    5J = 55;    5K = 56;    6A = 57;    6B = 58;    6C = 59;    6D = 60;    6E = 61;    6F = 62;    6G = 63;    6H = 64;    6J = 65;    6K = 66;   

 

 

 

 

Possible todo this for 2018 please, want to see what tail wind impact was on times (wheel suckers)

Edited March 14, 2019 by Karman de Lange

[Vetplant]

A few pages back we were discussing the crash where the rider's bike frame snapped - assumedly the result of wear and tear and then a rough road service on the route.

 

This got me thinking and I have a question to the technically informed:

Is there any way (which is obviously non-intrusive and would not cost an arm and a leg) to check for minute cracks / weak points on the frame of an older bike as a precaution?

 

My carbon ride is over 5 years old and has - for all practical reasons - driven halfway around the earth. I would not want to be the one to have a cracked frame fail at 60km/h. Thanks guys!     

https://www.andtc.com/

 

Seeing as you based in Pretoria, give Africa NDT a call and get a quote on xrays.

 

Obviously they will charge you per scan and I don't think it makes sense to scan the whole bike in one scan as the cracks you are looking for might not show up on such a large scan, but maybe check first with them what they can do and charge.

 

Try to read up where the most catastrophic failures occur on a bike and have them scan those first. Front fork below the head tube is the first place that comes to mind...

[dannygirl]

 

Data, glorious data!

 

Here's the key to the groups:

 

$ = 1;    @ = 2;    & = 3;    # = 4;    1A = 5;    1B = 6;    1C = 7;    1D = 8;    1E = 9;    1F = 10;    1G = 11;    1H = 12;    1J = 13;    1K = 14;    2A = 15;    2B = 16;    2C = 17;    2D = 18;    2E = 19;    2F = 20;    2G = 21;    2H = 22;    Jh = 23;    Js = 24;    Ju = 25;    2K = 26;    3A = 27;    3B = 28;    3C = 29;    3D = 30;    3E = 31;    3F = 32;    3G = 33;    3H = 34;    3J = 35;    3K = 36;    4A = 37;    4B = 38;    4C = 39;    4D = 40;    4E = 41;    4F = 42;    4G = 43;    4H = 44;    4J = 45;    4K = 46;    5A = 47;    5B = 48;    5C = 49;    5D = 50;    5E = 51;    5F = 52;    5G = 53;    5H = 54;    5J = 55;    5K = 56;    6A = 57;    6B = 58;    6C = 59;    6D = 60;    6E = 61;    6F = 62;    6G = 63;    6H = 64;    6J = 65;    6K = 66;   

 

 

 

My dot is sadly one of the outliers to the right - I can see exactly which one!  

 

Irony is, I would probably have done a better time, had I dropped back a good 10-15 start groups or so - as it was, I cycled solo all the way and was already knackered getting into Simon's Town...

 

Was seeded way out of my league (and no, I did not cheat, but got a outlier seeding based on an MTB event...)

[HelloRyanFowler]

I also don't get the formula or how to correctly apply it. When I apply it, I get a result of 1.1641111... Which doesn't quite make sense to me.  How about running an example using a hypothetical race time so we can properly understand how to correctly apply it?

 

I need to do some work! I'll pop the formula in a spread sheet and post it  this evening. Then you will be able to input your time and get a result under 2018 conditions.

[HelloRyanFowler]

What was up with these groups?

Jh = 23;    Js = 24;    Ju = 25;

 

Hand Cycles, Special, Unicycles

Edited March 14, 2019 by HelloRyanFowler

[Vetplant]

 

Data, glorious data!

 

Here's the key to the groups:

 

$ = 1;    @ = 2;    & = 3;    # = 4;    1A = 5;    1B = 6;    1C = 7;    1D = 8;    1E = 9;    1F = 10;    1G = 11;    1H = 12;    1J = 13;    1K = 14;    2A = 15;    2B = 16;    2C = 17;    2D = 18;    2E = 19;    2F = 20;    2G = 21;    2H = 22;    Jh = 23;    Js = 24;    Ju = 25;    2K = 26;    3A = 27;    3B = 28;    3C = 29;    3D = 30;    3E = 31;    3F = 32;    3G = 33;    3H = 34;    3J = 35;    3K = 36;    4A = 37;    4B = 38;    4C = 39;    4D = 40;    4E = 41;    4F = 42;    4G = 43;    4H = 44;    4J = 45;    4K = 46;    5A = 47;    5B = 48;    5C = 49;    5D = 50;    5E = 51;    5F = 52;    5G = 53;    5H = 54;    5J = 55;    5K = 56;    6A = 57;    6B = 58;    6C = 59;    6D = 60;    6E = 61;    6F = 62;    6G = 63;    6H = 64;    6J = 65;    6K = 66;   

 

 

Brilliant to see the times displayed like that. Each group is its own peloton stretched out chasing a breakaway or working together against the wind.

 

I love graphs!