In the high-stakes world of professional cycling, marginal gains can spell the difference between victory and defeat. For Q36.5 Pro Cycling Team, data analytics has become the team’s “26th rider.” This invisible but essential teammate influences everything from rider performance and recruitment to logistical planning and real-time race strategy. The team’s use of data illustrates how professional sports increasingly rely on enterprise-grade technology to stay ahead of the competition. We spoke with General Manager Doug Ryder about the role of analytics in professional cycling.
Q36.5 operates with 25 professional riders on the roster. Their technology platform is embedded into every aspect of the organization. The team’s service course near Utrecht, Netherlands, acts as a central technology hub, coordinating global operations across 22 countries and 220 annual race days. With 23 vehicles on the road and 1,100 flights booked each season, this level of activity demands an efficient and connected infrastructure. Satellite internet with 250 Mbps download speed, made possible through Starlink, ensures continuous connectivity for data flows and communications.
In Ryder’s words, the difference between winning and losing often comes down to fractions of a second. The team’s analytics ecosystem processes millions of data points to identify where improvements can be made. It’s a philosophy based on the principle of marginal gains. While 90% of performance comes from effort and preparation, the final 10% consists of small but critical optimizations in training, equipment, nutrition, and mental focus. Those can truly unlock success, Ryder states.
Turning chaos into clarity
To manage the chaos of professional racing, Q36.5 relies on smart, connected devices that collect and analyze data across riders, equipment, and operations. Wearables track heart rate variability, training intensity, body weight changes, and sleep quality. The team knows when a rider wakes up, how well they slept, and even whether their smart scale at home was used by someone else. This information feeds into decisions that may seem minor, such as room assignments during stage races, but can impact overall recovery and performance.
“Some riders lose up to 5% of their body mass during intense stages,” Ryder explains. With scales in team buses measuring weight before and after each race, that data is immediately transmitted to team chefs, sometimes hundreds of kilometers away. By the time a rider arrives at the next hotel, nutritionists have adjusted meal plans to compensate for the loss. “Before the rider can even shower, there’s a conversation happening between the chef and nutritionist,” Ryder notes.
This real-time nutritional response ensures riders maintain optimal performance levels across multi-day events, where fatigue, poor recovery, or insufficient fueling can accumulate into performance decline.
Predicting outcomes, adapting equipment
Beyond recovery and nutrition, data analytics plays a pivotal role in shaping race-day decisions. The team combines structured data like power outputs, route elevation, and weather forecasts with unstructured data gathered from online posts by cycling enthusiasts. These data streams are fed into predictive models that anticipate race dynamics and help fine-tune equipment selection, down to tire pressure and aerodynamic adjustments.
Metrics like Training Stress Score (TSS) and Heart Rate Variability (HRV) help monitor each rider’s fatigue and readiness, ensuring that training plans are both challenging and sustainable. “We analyze how environmental conditions affect each rider’s output and recovery,” Ryder says. “It allows us to push when it matters and hold back when recovery is more important.”
The psychological dimension of performance isn’t overlooked either. Mood tracking and behavioral indicators complement physiological data to help coaches understand when a rider might be mentally fatigued or in need of additional support.
A mobile strategy engine
During races, Q36.5’s cars function as mobile command centers equipped with Qlik dashboards. Sports directors receive real-time updates on breakaway groups, track how rival riders performed in similar situations before, and adjust strategy accordingly. “Qlik is essentially our 26th rider,” says Ryder. “It’s always present, always learning, and always helping us make faster, better decisions.” In contrast to the unpredictable nature of open-road cycling, this data platform provides consistency and clarity.
The team’s data-driven strategy even extends to post-race analysis. At their hub, they evaluate power output, rider positioning, and performance variances. After a recent Giro d’Italia stage, data showed a sprinter delivered 1,405 watts (power output) in his final sprint, even though he’s capable of reaching 1,800 watts. This type of analysis helps identify tactical adjustments that might lead to better outcomes in future races.
Beyond the finish line
Q36.5’s analytical approach also reshapes how talent is scouted and signed. Each year, thousands of professional riders become available on the transfer market. Using Qlik, the team filters candidates based on age, specialization, and performance metrics, narrowing the pool to a dozen viable prospects. For the upcoming season, they selected and signed three riders through this process.
“I know more about the rider than they do,” Ryder states. With insights into weight patterns, historical performance data, and race-day behavior, contract negotiations become data-informed rather than speculative. This recruitment methodology enhances decision-making, while also ensuring that young riders join the team with clear developmental goals and established data benchmarks.
While Formula 1 is often cited as a leader in sports analytics, Ryder argues that professional cycling is no less complex. The difference lies in the unpredictability of the terrain. “Our stadiums are the open roads of the world,” he says. “Every race is different, and we often don’t know the routes until months before. It’s like buying a company based on the logo, without seeing the financials.”
This variability requires a different kind of modelling, one that can adapt to constantly changing conditions. While F1 teams optimize performance on fixed circuits, cycling teams must account for wind, weather, terrain, and strategy on roads they may never ride again.
Smarter, not harder
Behind Q36.5’s operations lies a digital backbone. All data is stored in the cloud and shared across integrated platforms. Every vehicle is equipped with high-speed satellite internet to guarantee real-time access. The network should also work in remote race locations.
With an annual budget of approximately €18 million, far below the €50 million enjoyed by top-tier teams, Q36.5 uses technology to stretch every euro. Their approach has already paid off, Ryder states, referring to the team’s achievement of over 10 victories this year, including several at major races.
Looking ahead, Ryder sees artificial intelligence playing a greater role. The team is exploring machine learning models that predict tactical behavior from opponents and identify when riders are close to burnout. Through conversational analytics in Qlik, they envision proactive alerts such as, “This rider may not be fit to race tomorrow,” based on cumulative stress and recovery data.
The team’s ethos is clear. Success doesn’t only come from racing harder. It comes from racing smarter. “We use Qlik to make more informed decisions quicker,” Ryder says. “It’s about intelligence, decision-making, and continuous optimization. The sport today isn’t just about passion, it’s about understanding how to use every tool available to perform better.”
Also read: Qlik extends agentic tools across entire data platform