HelloRyanFowler

I've been waxing my squeaky bits for years now, So quiet. So clean. Candle wax, parafin, a pot, a stove, and a fire extinguisher on standby. I first soak the chain in really hot water and then give it a wash before putting it into the wax. Keeps the wax clean. I keep a few bees on the side but I haven't tried beeswax (as recommended by someone earlier). If anyone is interested in trying, I am happy to sell any excess beeswax I have. Ryan: 076 664 4916.

Give me a call if this is yours. Ryan 076 664 4916

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My guess is that there will be a large number of CTCT regulars who will unofficially ride the course this year. I'd like to do that and I'm hoping that there will be lots of other riders out there to share the pulling. Anyone aware of any loosely coordinated efforts? Date? Time? Routes (assuming that the M3 is out)?

The intention of the model is very much to be a predictor of whether or not someone rode a better ride this year than last. If I've done a reasonable job with the maths, it should be a reasonable indicator - at least for most finishers. But the model is based on only three variables, and requires only one input to get a result, so there are bound to be inaccuracies somewhere along the line. And if you've got a handle on your NP, Ave W, TSS score and other such things that I had to Google, then you've probably already got a much more reliable indicator than a generic model will ever give you. This might need to become an annual exercise; possibly throwing that huge and beautiful data set at an artificial neural network capable of identifying and quantifying the relevant variables. Of course, even a perfect model is not going to satisfy everyone because we probably place a disproportionately high value on our own impression of our performance. That's been the hard part for me. * But if I factor in 7 minutes of stoppages to deal with a broken spoke, a buckled rim, and touching brakes (which had to be released for the second half of the race), then maybe I can consider my performance on par with last year!

My thinking is this: There are riders who put in a better effort this year than last, and riders who did the opposite. On the whole, they will balance each other out. Therefore, any average performance difference (across all riders who took part this year and last) can be attributed to external factors: wind, course changes, etc. The intent of the model (with all its flaws) is to give you a time that you can compare fairly with last year's time. It doesn't matter if you were fitter, faster, more focused, whatever, because that is what the model should show up. I did a sub 4:02 this year . The model tells me that this is equivalent to a 3:44 under 2018 conditions. In 2018, I did 3:38. Therefore I did better last year. That sucks.

3:06? Double-check that. I'm getting 3:19 off a 3:31...

Here we go... (Be strong little spreadsheet for you may have a rough time ahead.) Ryan Reckons CTCT 2019 - Rev 1.xlsx

I'm right on the edge of my depth here. My assessment is intuitive, and quite possibly not mathematically sound. I cut up and plotted the data in various ways, looking for any performance variation trends. The strongest correlation I spotted was variation against race time. Looking at a scatter of the full field, it's not very convincing. But looking at average variance for race times falling in the same minute, it starts to look more like a trend. See Chart 5. The straight trend line looks OK. The 3rd order polynomial (it's nice and curvy but I don't know much more about it) looks better. The formulas are a bit of a mystery to me. Some gave me dodgy results. Something to do with the time format? Comments welcome.

So this is also where I started scratching into the data. I lost 20 minutes but knew that the wind was to blame for much of that. Then I looked at position. I moved up the ranks by about 500 places. Great, But then I had to factor in that there were far fewer finishers. My relative position was slightly worse than in 2018. Bummer. But how many of these DNF riders would have come in before me under 2018 conditions? No idea. That's where I started looking for a better way to compare 2018 and 2019 results.

Ja, it was tedious. But I don't even really know what a script is. And I figured that several hours of copying and pasting would give me the quickest results. That was after I called up Racetec to ask if they could send me the data. It was a long shot. They wouldn't budge. Fair enough. They must have their hands quite full after the CTCT, what with all those cyclists phoning in to complain about that infernal rattle of the timing chip.

Copy. Paste. Copy. Paste. Copy. Paste. Copy. Paste. Copy. Paste... No wizardry. I had to delete duplicates because I have no way of differentiating between riders with the same names. And I didn't quite get that right so there are some wobblies in the data that I should sort out sometime. As it currently stands, 14341 data points for riders who rode both this year and last.

Hand Cycles, Special, Unicycles

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.

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;