The 2025 Tour de France looks set to be the fastest in history thanks to mind-blowing modelling and speed-seeking simulations used by the teams in search of a competitive advantage.
Where once pro cycling was all about riders flogging themselves and racing by feel, now it’s about slick aerodynamics, no junk miles and huge amounts of number crunching.
In fact, you could argue that road cycling at the top table is morphing into Formula 1.
We dig deep into the growing influence of performance engineers and data scientists. Strap in for a speedy ride…
Devil in the detail
A performance engineer’s role is all about the detail; of unearthing gains where few had looked before. Briton Dan Bigham is arguably the highest-profile engineer in the field.
The 33-year-old former hour-record holder worked with the Mercedes F1 team before moving to cycling.
He’s currently head of engineering at the Red Bull-BORA-hansgrohe WorldTour team.
Before Bigham upped sticks from Ineos Grenadiers in 2024, he told this writer how a performance engineer’s role is focused heavily on equipment selection and even product design.
And, of course, incredible attention to detail. Take wheel selection – no longer is it simply a case of shallow or deep rims.
“Wheel choice comes down to variables like a rider’s power-to-weight ratio, weight, potential climbing speed and aerodynamic profile,” Bigham revealed.
“What you need to know is the magnitude of the difference. So, you might say, ‘This wheel is 100g lighter but has a higher CdA [co-efficient of aerodynamic drag] by 0.02m2’. You need to know the detail.
“It’s a little like you coming to me and saying, ‘I have 10 buckets of flour – how many buckets of water does that need [to make bread]?’ I can’t answer that. I need to know the bucket size.”
He continued: “It’s all about energy. If you’re climbing, you know that 100g at a given velocity and a given slope results in a certain energetic cost.
“You then throw in the CdA and use the aero equation to figure out how much drag there is.
“It’s finding the tipping point where that amount of weight equates to that amount of CdA, and that depends on the gradient and the rider’s speed.
“You then say, ‘Right, these are my options’ – say, five wheelsets – and if you’ve done your homework regarding the mass and CdA of each wheelset, you’ll pick the one with total lowest energy demands for that speed.”
Comprende? We hope you’re keeping up at the back.
Bigham proceeded to reveal how wheel selection can mean the difference between sticking with or losing the rider in front, which can be the difference between victory and defeat.
“It’s the same with tyre selection and rolling resistance,” Bigham added.
“The rolling resistance coefficient of every cycling tyre in the world varies between 0.01 and 0.06, from track tubs to slow winter ones. When you look at the physics of rolling resistance and the physics of climbing, the coefficient of rolling resistance is equal to a gradient.
“If you have a tyre that’s 0.02 and a tyre that’s 0.03, the difference is 0.01, which is equal to a 0.1% gradient. That might not sound a lot, but riders who care about performance should care about the tyres they choose. High-performing tyres are a very good choice for climbers, and are often latex or tubeless clinchers.”
Rise of the data scientist
It’s this obsession with granular detail that explains why XDS-Astana brought in another former hour-record holder (and Brit) Alex Dowsett, who worked with the team on Project 35, which helped Mark Cavendish to his record-breaking 35th Tour stage win in 2024.
“Alex will focus on optimising rider positioning on the bike, testing technical equipment and preparing for time trials,” announced Alexandr Vinokurov, the team’s manager, on his recruitment in 2024. “I believe Alex is an invaluable addition.”
Morgan Saussine, a French data scientist who also worked on Project 35 in a freelance capacity, has proven his worth by obsessing over details in a different area.
He’s credited with turning around the Kazakh team’s fortunes. At the start of the 2025 season, they needed to score around 5,000 more points than fellow strugglers such as Cofidis and Arkéa-B&B Hotels in order to finish in the top 18 and avoid relegation.
At the time of writing, they’re fourth in the UCI rankings and on course to remain in the WorldTour.
“[Saussine] analyses the cycling calendar [and predicts] where we have the highest probability to score points with the riders we have, taking into consideration their characteristics,” DS Dario Cataldo recently told Jean-François Quénet.
“In a certain period of the year, he studies which race we should take part in and with which type of rider. It’s bringing excellent results.”
Saussine’s ability to block out the noise and model clear, applied answers is paying dividends, which is why data scientists are appearing in staff rosters, especially at the top teams.
“I’d say there’s at least one person devoted to model development in the best five or six squads,” says Andrea Zignoli.
“In other teams, data analysis is still relevant but someone might be doing the job on a part-time basis, or they’re not data analysts or engineers by background and they’ve adapted.”
Zignoli most certainly is a data scientist by trade, with academic palmares including a doctorate in modelling human sport performance from the University of Trento in Italy.
His professional career has included joining forces with Jumbo-Visma nutritionists when working for blood-glucose monitoring outfit Supersapiens.
Now, he’s a data scientist at AI coaching outfit Athletica, alongside an ongoing collaboration with Team VF Group-Bardiani CSF-Faizanè, an Italian ProTeam outfit.
“At Bardiani, I model every aspect of cycling – bioenergetics (how cyclists convert metabolic power into mechanical power), biomechanics (how the cyclist’s legs spin and co-ordinate to transmit mechanical power from the muscles to the pedals) and locomotion (how the rider-bike system moves in space),” he says.
“Modelling is primarily for two purposes – analysis (looking back at data to extract meaning) and simulation (predicting the future). Modelling and simulation are often cheaper than collecting data yourself.
"Thus, one can appreciate the contribution a new set of tyres can provide, as well as how much a bike change can benefit in the next time trial, without the need for specific data collection or wind-tunnel testing.”
As an example, Zignoli might segment the time-trial course into flat, climb and descent, using elevation and gradient data to tag sections that require a rider to dig deeper.
He’ll estimate the trade-off between pushing hard on the climbs, while easing back on the flats, all the while factoring in the wind (tail vs head) to identify where the greatest gains are to be had and biggest losses to be avoided.
Historical power data will be thrown into the mix for a pacing plan that can then be uploaded to the rider’s bike computer.
Zignoli’s role is very much in the background, working with the coaches, who can then disseminate what they feel is relevant to the rider.
“This works because sometimes the results of simulations don’t pan out in the real world,” he says.
“For instance, I might say that a given corner is faster if you hit an early apex, but the rider might refrain from following that advice because there’s no clear visibility of what lies after the corner.
“Or the model might suggest one tyre will be faster on a given stage, but the rider prefers a different model. At the end of the day, it’s always about the rider. And that’s not perfectly predictable.”
The Italian says modelling can also help the team assess the effect of a training regime, heat, caffeine and much more, including the impact of clothing choice on a rider’s outcome.
Formula for better aerodynamics
Clothing is the specialist subject of applied sport scientist Jamie Pringle, whose many roles include consultancy work for Vorteq Sports.
The British brand sits under the umbrella of computational fluid dynamics (CFD) experts TotalSim, the brainchild of former Secret Squirrel [member of the R&D department at British Cycling] Rob Lewis, whose background is in Formula 1.
Vorteq came to the public’s attention in 2022 when Simon Yates won stage two of the Giro d’Italia, a time trial around Budapest, wearing a Vorteq skinsuit branded as Alé (BikeExchange-Jayco’s clothing sponsors at the time).
The bespoke suit, the result of working with Yates at Silverstone’s wind tunnel, cost £2,750 to produce.
Pringle says Lewis is a visionary and brings with him not only aerodynamic know-how from Formula 1, but also its pace of prototype production.
“Once we map the rider with our scanning technology, we can create a 3D-printed mannequin so we can start assessing how different fabrics and designs work on their replica torso straightaway.
“Via CFD work, we can have a bespoke prototype suit ready to test the next morning. That in-house capability follows the rapid development process of Formula 1. Many teams need rapid turnover of customised suits. That’s what we deliver.”
I came across Lewis presenting at a recent Science & Cycling Conference.
As a snapshot of how Formula 1 minds influence professional cycling, Lewis said he thinks you should work on the 80-20 rule, where you leave 80% of the ‘thing’ alone and work on the 20% that affects 80% of the result, using the example that the late-2000s GB track team cut 3% of skinsuit drag ‘simply’ by moving the seam slightly.
Nailing aerodynamics on a bicycle is 10 times harder than on a car, he thinks, but it could further improve at WorldTour level if budgets continue to grow.
“At a race like the Tour, you have 21 different courses. The ideal would be 21 different skinsuits,” Lewis said.
“On stage one, you say, ‘Right, the wind’s coming from the north-east, let’s choose this suit. The second stage is uphill and slower so we’ll choose this suit’. It could be the same with helmets.”
We can already hear the WorldTour drivers lamenting: “We’re going to need a bigger bus!”
Measurable improvement
Jean-Paul Ballard is another person who made the move to cycling, after 14 years in Formula 1, including time at BMW-Sauber.
He then started up aerodynamic wheels brand Swiss Side, and has been collaborating with Van Rysel/Decathlon’s AG2R La Mondiale WorldTour Team for a couple of years.
“Not only do we supply the wheels for all their teams, from WorldTour down to junior and cyclocross, but we also provide performance-simulation and aerodynamic-optimisation services with both wind-tunnel and on-road testing,” he says.
“In fact, in May we were in the wind tunnel for two weeks undertaking work ahead of the Tour de France.”
Ballard suggests the increased speeds of the peloton stem from the objective, data-driven approach that’s now omnipresent.
“That’s come from applying the same principles and tools that are used in Formula 1, where we combine CFD wind-tunnel testing and on-road measurements to ensure we deliver performance in the real world,” he says.
Yet, while Ballard’s brought his know-how from the world of fast cars, he admits he’s had to adapt.
That’s not only due to airflow behaving differently over a surface when driving at 240kph compared to cycling at 50kph, but also the ‘bluff body’.
“That’s the name for the bike-rider system, which is basically an un-aerodynamic blob moving through the air. We’ve had a lot of learning to do and needed to adapt our F1-derived development techniques.”
Keeping it safe
As you can see, Formula 1’s influence is resulting in ever-lower drag. However, that not only brings with it ever-greater speeds, but also safety concerns.
It’s another area in which Ballard feels cycling could learn from motorsport.
“F1’s become faster for decades, but it’s also safer,” he points out.
“In F1, big advancements were made in crash protection, not only with the crash structures on the cars, but with driver protection in the form of better suits, stronger helmets and visors, plus car G-sensors for indicating to track marshals and race control when a driver has had a high G-force incident.”
He says similar things could be done easily in cycling to increase rider safety, such as building protection into the apparel, creating better head protection and using tech such as G-sensors in helmets to note if a rider has a potential concussion in a crash.
“Also, the implementation of a centralised race control that actively monitors the race, the riders and any incidents that occur, and which can react quickly, is a necessity. The UCI and FIA should talk,” he adds.
In 2016, I recall talking to Robby Ketchell, then of Garmin-Sharp and later of Team Sky, who developed cycling’s first big data analytics tool, called Platypus.
The app laid the foundations for Dan Martin’s memorable stage 9 victory at the 2013 Tour, where the English rider jumped clear of the peloton.
Since then, the appliance of data science has become increasingly prominent. With the advent of AI, that’s set to accelerate. Inevitably, that’ll result in faster racing.
But will that racing be exhilarating and liberated, or manacled by deep analysis?
We’re certain data played a part in Simon Yates’ recent Giro victory – arguably one of the most memorable wins of recent times. But as ever with the tech revolution, only time will tell.
