How data helped our 55 year old employee beat pro cyclists

She did. We kid you not. She did beat pros. Not on the Flèche Wallonne though, and the pros were not Van der Breggen, Van Vleuten or Marianne Vos, but still…
The stage race was the Giant Crit Crushers series on Zwift, an e-sports platform. The main opposition was the sublime Roxsolt Attaquer p/b Liv & SRAM, a UCI Women’s Continental Team with the likes of Peta Mullens (10th in the GC of the Tour Down Under 2020) and Justine Barrow (2nd in the Australian National Championship 2020, selected for the UCI World Championship e-sports for Australia). The race of course had some other fabulous female racers as well, like Jenny Pettenon, Illi Gardner, Carlee Taylor, Courtney Sherwell, Bree Wilson, new talents Neve Bradbury, Sophie Sutton, Stephanie Corset and so on…
Our employee never raced outdoors. She has no titles, no pedigree, no UCI team and, with her 55 years of age, could be the mom of most of the other girls out on the track. What she did have though, was data and the data arena.

Gauging the potential

First thing we did, of course, was looking at the potential and weaknesses of our employee. We measured as minutely as possible of course, using not only the data of the trainer (a Tacx Smart Neo and a Kickr for comparison), Ultegra power cranks and Assioma Duo pedals. She was weighed and height measured and during this period checked by ZADA, the e-sports anti-doping agency.
We got her data in Zwiftpower, a free data retrieving application to Zwift, and Today’s Plan and TrainingPeaks, two data crunching applications for athletes, and analysed it using the insights of Andrew Coggan and Stephen McGregor*.

Fran’s dashboard on Trainingpeaks

We worked with these numbers and tried to build her up, not by doing workouts, but by racing the specific powers she lacked. Since her sprints seemed to be worst, we trained that most, which – although still low according to the Coggan insights – seemed to become her best power quite fast on Zwiftpower where she could compare to other Zwift Racers, being in the top 94% on w/kg and 98% in pure watts. Her 20 minutes power only put her in the 72% on wkg and 81% in watts.

Choosing the race

With that data we could start looking for races that might suit her to exploit that power, without being hindered by her lower long range power. So we looked for short races, less than 20 minutes, that would probably end in a sprint. To heighten the probability of her ending near the podium we also figured we needed a series. More races would even out fabulous days and lesser days, hopefully getting her closer to the percentiles predicted on Zwiftpower.

Fran’s data on Zwiftpower

We also wanted to focus on the watts rather than the watts per kg – our employee is quite heavy compared to those young girls – and would thus look for a race series on a predominantly flatter terrain.

Understanding e-sports physics

Although e-sports takes place in a virtual and thus fictional world, the world builders did add laws of physics to it. They added road feel, gravity, wind resistance and so on. That world is not realistic at all – you could go down a mountain at 100 km per hour through hairpin turns without having to break or fearing of flying off the road – but it does have rules. Knowing those rules of physics helps you racing that world. Quite often Zwift racers beat pro racers on Zwift because the latter have less experience with these rules. This would not be the case here. The RXS girls are extremely well versed in the laws of Zwift physics.
However, it was our hope that those ladies, like most women on Zwift, would overestimate the importance of watts per kg, and would try to put the hurt in the competition by putting out enormous w/kg.

Zwiftpower data of one of the competing ladies

On the flats however the pure watts would be more important than the watts per kg. So they would mainly be hurting themselves, since Fran’s heavier body allowed her to go for a higher wattage.
Fran would have to try and save as much energy as possible on the flats to be able to follow the light girls uphill on the few slopes the terrains would undoubtedly present. So Fran had to draft. Drafting is staying in the slipstream of the others and conserving up to 30% of energy. On the occasional slope Fran would try to discourage attacks by semi-sprinting up herself, keeping the pace too high for others to fly away.

Knowing the competition and the courses

At the start of each race we would also analyse the powers of the competition. Who would have potentially attack where exactly? How should they use their team and personal potential on this course to their advantage? Figuring that out gave us idea how to respond to that. We knew from the data that it would never be in our favour to make the race as hard as possible, since Fran would possibly be one of the first to perish from it. Anticipating to the stronger girls and sucking wheels till the finish would be the way to go.
That does sound simple, but it means you need to be sucking the right wheels. If the person you are drafting from drops a gap, you are gapped as well. So Fran would have to be sure to understand where those gaps would probably appear, and who would be the ones who would open a gap, or get dropped.

Analysis in terms of tips of the 5th stage

Of course you have to know as well when you need to start sprinting and from what position. To divine that again you have to know your data. How long can Fran hold her sprint and how many meters does that mean on this course: where should she start, and how long should she wait? And what does it mean if others sprint in front of you? We knew that you can use other racers to get slingshot around them if you time it right, we knew that you can sprint longer if you surf other sprinters using the draft. We also knew that waiting for the optical cue of other sprinters to start sprinting was wrong, because that optical cue has a delay: the racer has started sprinting even earlier than that. Fran had to stick to the plan and go where we calculated she should.

Know the scoring systems, count the points

Last but not least we had to understand how the points were allocated. 20 points for the first one over the line, counting down from there. Ending up as high as possible in the first races was the first objective and from then on, we would start watching the competition. And: if you had no heart rate meter… you would not get any points at all. Which meant that, looking at the life data of the other racers, we could discard those without heart rate data, because they were only existing in terms of draft, but not as true competition. We would not have to chase those ladies.

Taking the lead in consistency

There were two stages a day and we soon noticed that Fran was weaker in the 2nd, that 2nd one counting over the 20 minute range. So we decided to try to gain points on the most important competition in the 1st stage, and try to hang on and control the gap on the 2nd stage. The competition noticed that as well after the 4th stage but by then Fran had a small advantage. Her results on the first 8 stages were: 2nd, 5th, 1st, 3rd, 2nd, 4th. At that point she had the lead in the race by ten points to Justine Barrow.

GC before stage 7

The finale

With two races to go, there were 40 points left to win, meaning Bree Wilson could still come up to Fran’s position if Fran got absolutely nothing… which could have actually happened since our employee managed to fall during exercise breaking both her trainer and her bike. Fortunately she managed to replace both in time for the race.
More realistically, the RXS ladies would play Justine’s card and not only use her as a decoy for Courtney or Neve. That would have been too great a risk. If we were RXS we would send in as many team members as possible and try to get them between Justine and Fran. The main objective was to let Justine and Jenny, nrs 2 and 3 in the GC, take as little points as possible on Fran while saving enough energy for race 2.

Stage 7. Penultimate stage. The RXS girls put the hamer down from the start of stage 7, but Fran got the perfect assistance of team members Katherine Mackay, Sam Smagas, Marylou Fitz, Julia Broom and Justine Clark to keep the group together in the first laps.

Watching data during stage 7. Fran’s heart rate looks quite ‘relaxed’, not in overdrive. Something wrong with Carlee’s heart or meter though. Annabel will not get points. The 5 second gap at that point was nothing to worry about, as Fran still had Justine C and Kath M nearby and Justine B by her side.

Fran was too far back in the group at the time of the sprint and started sprinting too late, but still managed to finish behind Justine who finished 2nd. Justine had closed in with one point.

The sprint: Illi wins with lengths ahead of orange-red Justine and grey haired Fran.

Stage 8. Final stage. A 9 points gap when there are only 20 points available may seem like a comfortable gap but nothing was further from the truth. The last stage offering a very hilly course the odds were very much against Fran. The finish was at the top of a 900 meter climb which meant the race would be decided with w/kg instead of pure watts. More than ever Fran had to be vigilant, because RXS would try to force a breakaway hoping to get a group of more than 9 people in, preferably without Fran.
That breakaway did form… But with the help of her teammate Justine C, Fran managed to bridge to that breakaway. Still not safe though, because that breakaway had 12 people in them and Justine B would quite probably be the strongest of that group in the uphill finish. Fran had to make sure not to end up off the back or on the tail. So whenever she dropped a bit on the climbs, she had to go deep to get back in there. And she did. She was at the rear end of that breakaway group at the start of last climb, but started overtaking ladies one by one going for that finish, dropping the sprint at 300 meter to go…

Fran still far right on the screen on the last 300 m… How many ladies seperate her from Barrow?

Justine Barrow did win, ahead of her team mate Courtney Sherwell, who must have hoped 7 more random ladies would be behind her. But Fran was there. Taking third, taking the overall win.

300 meter later: Fran taking third and the overall victory in stage 8.

Data for the win?

Did our employee only win because of the data? Of course not. She had to train as well. But the data did help us pick the right arena for her to win. Data can help you to pick your battles, point out the tipping points, gauge your opponents and take the most effective decision towards your goal, given your strengths and weaknesses and that of your opponents. And yes, you can defeat the bigger party.

*Allen, H. Coggan A(2010) Training and racing with a power meter. Chicago

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