Racing and Strategy of Formula 1 Racing

Formula 1 (F1) racing is a global leviathan that gulps millions of viewership every season. As internationally acclaimed motorsport, F1 never ceases to satisfy and please the crowd – from the stunning, edgy single-seater automobile designs to top-notch engineering technology incorporated within each car. It is an enormous opportunity to witness such a thrilling sport; how much more to be able to take part in one?

F1 cockpit crew

For F1 teams, winning in a Grand Prix is everything. It does not only boosts the team’s pride and morale (after all, the trophy is their badge of honor), but it also helps the sponsoring company’s marketing in the long run. This makes the competition even harder, as each team tries different methods to best their contenders.

Unbeknownst to the spectators, there is certainly more at play than what we see on the television when we watch F1 races. So, what are we missing? Well, two words: racing strategy.


Race strategy plays an integral part in winning (or losing) the game. In car racing, technology undoubtedly made F1 operational researchers’ job a lot easier. It all started with a man named Neil Martin, who pioneered the use of Monte-Carlo and Game Theory as techniques in F1 racing strategy in 1998.

Monte-Carlo is an application that runs computational algorithms which allowed teams to predict possible outcomes based on given scenarios and data. This includes the number of cars in the race, safety cars probability, traffic patterns, conditions, and potential results of overtaking and other random events. This simulation helps the team visualize and plan ahead to avoid them or take advantage of them.

On the other hand, game theory is computer software that does what Monte-Carlo can’t – presenting possible solutions should problems arise because of the interaction between you and your contender. It provides teams with data about what other teams ‘would likely do’ should a certain situation arise and how their actions could affect the game strategy. Simply put, Game Theory indicates the behavioral tendency of the player within the game and how to exploit that data.


Redbull racing team planning

A good plan made beforehand gets the team bigger chances of succeeding. For F1 teams, planning takes a considerable amount of teamwork and cooperation. Team size entirely depends, but basically, two heads are better than one. For instance, the 2020 leading F1 team, Mercedes, mentions four key team members; McLaren’s head of race strategy, Randeep Singh, also mentioned four people in his team. These people divide the tasks of overseeing the overall strategy, being individual car strategists, and analyzing opponents.


During the early days of the season, team strategists find it best to stick around the track and do what they most wanted to do – influence the races in the early season. This is a perfect time to establish a strategy since there are more variables at play. Learning how other teams behave and carry out their own strategies gives the team a good vantage point to further enhance and/or alter its original strategy.


Predictions made soundly from the past and present data let the strategists make the call laps before a possible in-race situation. “What happens if this scenario happens?” “How many stops do we go for?” These questions must be answered before the race day to put the race strategy in place. Hence, days before the race are crucial times to learn about the overall race conditions such as tracks, weather, tire degradation, etc. They reevaluate the team’s strategies and rule out any errors to avoid miscalculations during the actual race.


McLaren’s head of race strategy said, “We can model a race really well, so long as the car is racing by itself – what makes race strategy difficult is other cars!” Indeed, no matter how much teams may have anticipated every possible scenario, the actual race can be quite unpredictable. There are times when unexpected things happen (an accident, for example), situations where there is little to no time for strategists to react, and the decision falls on the driver’s hands. This is where all the planning, simulating, calculating, and analyzing phases should pay off – and it’s the driver’s duty to abandon plan A and perform the standard operating procedure for these kinds of scenarios (as per the strategist’s instruction) and stick to it – unless another scenario pops out forcing him/her to commence plan B, which, when fails yet again, proceeds to plan C and so on until the winning scenario is secured. We can truly relate to Ruth Buscombe, Alfa Romeo’s senior strategy engineer, when she said, “Strategy is 98 percent preparation and not the high-speed chess you get to see on T.V., which is – as with most jobs – the tip of the iceberg.”


Post-races are still the best times for Formula 1 teams to continue improving their strategy for future references; knowing what went well and wrong enhances the team’s ability to strengthen its strategy. There is no better way than to criticize oneself, and in tough competitions like F1 racing, self-criticism is more of a valuable tool. You may want to know whether your race model and strategy played well in reality, and if not, what factors affected it? What could a better strategy have been employed? What strengths does the team have? Weaknesses? Threats? Opportunities? The key to success in F1 racing is the constant asking of these questions, apart from looking into the statistics and analyzing data.