Artificial Intelligence in Sports Predictions: From Football to Cycling

In the world of sports, passion and unpredictability often go hand in hand. Every fan knows the thrill of watching a favourite athlete or team push beyond limits, defying odds and delivering unforgettable moments. But behind all that unpredictability lies a question that has fascinated fans, analysts, and even scientists for decades: can we really predict the outcomes of sports competitions?

With the rise of artificial intelligence (AI), the answer is shifting closer to “yes.” Football has been one of the earliest sports where AI-driven analysis made headlines, with platforms like NerdyTips leading the way in transforming raw data into actionable insights. But the applications of AI go beyond the pitch. Today, cycling – a sport that combines endurance, tactics, and individual resilience – is becoming the next frontier for predictive models.

In this article, we’ll explore how AI is already being used to forecast football results, and how similar methods can help anticipate winners and key performances in cycling competitions.

Photo Credit: Blake Dahlin/Tour de Bloom

AI and Football: The Proof of Concept

Football, known globally as “the beautiful game,” is an incredibly complex sport with dozens of variables affecting each match. Traditional analysts look at form, injuries, head-to-head history, and maybe a few tactical notes. But artificial intelligence has changed the game entirely.

By processing hundreds of thousands of data points, AI can calculate probabilities far beyond human intuition. NerdyTips, for instance, integrates factors like expected goals (xG), player fitness, pressing intensity, weather conditions, and even travel fatigue into its models. Fans who want to understand who is more likely to win don’t just rely on pundits anymore – they can turn to data-driven forecasts.

The key takeaway? AI has proven itself in football, producing consistent, high-accuracy predictions that make the sport more understandable and engaging. And if it works in football, one of the most unpredictable sports in the world, imagine what it could do in a sport like cycling, where strategy, physical data, and race dynamics play a central role.

Why Cycling Is a Perfect Candidate for AI Predictions

Cycling may look straightforward on the surface: riders, teams, and a finish line. But anyone who follows competitions like the Tour de France, Giro d’Italia, or the Vuelta a España knows that predicting outcomes is anything but simple.

The complexity comes from multiple variables:

• Course profiles: flat stages, mountain climbs, time trials, and cobblestones all favour different riders.
• Weather conditions: wind direction, rain, and heat drastically change race dynamics.
• Team strategies: domestiques (support riders) can decide the fate of a leader.
• Rider physiology: power output, recovery ability, weight-to-watt ratios.
• Form cycles: riders peak at different points in the season, depending on training blocks.

Because of these complexities, cycling predictions have traditionally been a mix of expert opinion and gut feeling. But artificial intelligence offers the possibility of moving beyond guesswork into a scientific, data-driven approach.

Photo Credit: SWPix.com

How AI Could Predict Cycling Competitions

1. Physiological Data Analysis
   Modern cycling is filled with measurable data: wattage, heart rate, VO2 max, and recovery metrics. Feeding these into AI algorithms allows models to evaluate how a rider’s condition stacks up against the demands of each stage.
2. Race Profile Matching
   AI can analyse historical performances on similar terrains. If Rider A excels in high-altitude climbs, the algorithm can assign them a higher probability of success on comparable mountain stages.
3. Weather Impact Models
   Crosswinds and rain often split pelotons or cause crashes. AI could factor in weather forecasts and simulate how conditions might influence tactics, breakaways, or sprint finishes.
4. Team Tactics Simulation
   In cycling, individual brilliance often depends on collective effort. AI can assess team compositions, comparing the strength of domestiques, sprinters, and climbers to calculate overall team advantage.
5. Historical Data Integration
   By combining past race results with current season form, AI can highlight riders who peak at the right time versus those who may struggle in specific conditions.

Photo Credit: LaPresse

Case Study: Predicting a Grand Tour

Imagine an AI system analysing the Tour de France. It would:

• Evaluate each rider’s power output trends from previous months.
• Compare those numbers against the demands of specific stages (flat sprints, time trials, mountain stages).
• Adjust probabilities based on team strength – for example, does the leader have a strong climber support crew?
• Factor in weather forecasts for crosswind-heavy stages where sprinters may falter.
• Simulate outcomes over thousands of possible race scenarios.

The result? A prediction model that doesn’t claim certainty but provides probabilities with scientific grounding. Instead of fans guessing whether Jonas Vingegaard, Tadej Pogačar, or Remco Evenepoel has the edge, they would have percentage chances for overall victory, stage wins, and podium finishes.

Photo Credit: Cor Vos

Football vs. Cycling: Similarities and Differences in AI Predictions

• Similarities: Both sports involve dynamic variables – form, injuries, weather, and tactics. AI thrives in this environment by processing complexity better than humans.
• Differences: Football predictions are usually binary (win, lose, draw), while cycling is multi-dimensional (stage wins, general classification, team roles). Cycling requires not just one prediction but layered forecasts across different race categories.

This makes cycling more challenging but also potentially more rewarding for AI models.

Photo Credit: Getty

Benefits for Fans and Teams

• For fans: AI predictions bring an extra layer of excitement. Watching a mountain stage while knowing the AI assigns a 65% chance of victory to one climber and only 10% to another makes the viewing experience richer.
• For teams: AI can be used internally to refine strategies. If data shows that attacking earlier in a stage statistically favours success, teams may adapt accordingly.

Just as football clubs already use data-driven tools to optimise tactics, cycling teams can integrate AI into training schedules, race-day strategies, and rider selection.

Photo Credit: Getty

The Future of AI in Cycling

We are only scratching the surface. The next steps for AI in cycling might include:

• Live race predictions updated in real time as breakaways form and peloton dynamics shift.
• Personalised fan dashboards, where supporters follow their favourite rider’s chances stage by stage.
• Enhanced broadcast experiences, with commentators integrating AI probabilities into race narratives.
• Virtual simulations, letting fans “play through” a race scenario based on real data.

The potential is limitless, and cycling stands to gain as much as, if not more than, football from AI’s analytical power.

Photo Credit: Alex Whitehead/SWpix.com

Conclusion

Artificial intelligence is reshaping the way we understand sports. In football, it has already proven its worth, helping fans, analysts, and even clubs make smarter decisions. Cycling, with its wealth of physiological, tactical, and environmental data, is the natural next step.

For fans who love both sports, AI opens a world of new possibilities. Whether you’re debating who will lift the Champions League trophy or who will conquer the next Alpine climb, predictive algorithms provide insights that make the experience richer and more informed.

The unpredictability of sport will never disappear – and we wouldn’t want it to. But thanks to AI, we can now see the probabilities behind the passion, and that makes every game, every stage, and every race even more fascinating.

👉 If you’re curious about how AI predictions already work in football, and want to follow the evolution of these tools, check out the latest picks here.