F1 2019 Calendar Download Outlook

WHY F1 CHOOSES AWS

With drivers hitting speeds as high as 230 mph, taking pit stops in under two seconds, and flying around corners with a force of 5G, FORMULA 1 (F1) needs a technology provider as fast as their sport is. F1 is a battle between the world's best drivers, but it's also a battle of some of the world's most innovative engineers. By using AWS, F1 is utilizing innovative technologies, such as machine learning (ML) models and high performance computing (HPC), to digitally transform the sport.

Here's how it works:

Transforming the Sport

AWS's broadest and deepest functionality and unmatched pace of innovation is changing how F1 collects, analyzes, and leverages data and content to make decisions. With 300 sensors on each F1 race car generating more than 1.1 million data points per second transmitted from the cars to the pit, F1 is a truly data-driven sport.

Increasing Action
on the Track

F1 and AWS are using data to improve the performance of both vehicle and driver. By using AWS high performance computing, F1 was able to run aerodynamic simulations to develop its next generation car 70% faster than ever before, creating a car that reduces downforce loss from 50% to 15%. This dramatic reduction offers the chasing driver a higher chance of overtaking and in doing so offers more wheel-to-wheel action for the fans. This next generation car will be introduced in the 2022 season. F1 is also exploring the use of machine learning in its simulation process, giving the organization new insights and into more than 550 million data points collected through more than 5,000 single and multicar simulations.

Engaging and
Delighting Fans

The fan experience is changing during a race weekend. With AWS, F1 has been able to turn millions of data points transmitted from cars and trackside into an engaging fan experience through its F1 Insights. F1 uses 70 years of historical race data stored on Amazon S3, analyzed by complex models and shared with fans as rich data insights that reveal the nuances of split-second decision making, and highlight performances through these advanced stats.

ENGAGING THE FANS

F1 Insights powered by AWS transforms the fan experience before, during, and after each race. By using distinct data points to inform each insight, F1 enables fans to understand how drivers make split-second decisions and how teams devise and implement race strategies in real time that impact the outcome of a race. Here are a few examples on how it all comes together.

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By using timing data, F1 is able to create visual insights that allow fans to objectively analyze individual team and driver performance, strategy and tactics that will impact the overall race outcome.

Battle Forecast

Using track history and projected driver pace, Battle Forecast will predict how many laps before the chasing car is within 'striking distance' of the car in front.
Pit Strategy Battle

The Pit Strategy Battle graphic provides fans with additional insight into how to assess how successful each driver's strategy will be in real-time. Fans will be able to track subtle strategy changes and see the impact on the final outcome.
Pit Window

Estimated pit stop windows based on tyre compound, lap times, and spread of cars. Viewers will see how a race can be altered based on race dynamics, including racing strategies of other teams, safety cars, and yellow flags.
Predicted Pit Stop Strategy

Historical data is used to calculate race strategy during the formation lap, comparing predicted tyre and race strategies. This insight allows viewers to see when a driver should strategically make his next pit stop.

Data analysis allows F1 to compare the performance of given cars, teams and drivers across any relevant parameter and rank them visually to educate fans.

Car Analysis/Car Development

This insight shows how teams develop their cars, how quickly they develop their cars, and what the on-track result is throughout the season. The development race both during the season and from year to year is the principal KPI for an F1 team, and this provides a unique insight into the inner workings of F1 and how the teams perform against each other in this area.

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Car Performance Scores

This insight isolates an individual car's performance and allows fans to compare its performance to that of different vehicles head-to-head comparing building blocks that make up car performance – namely cornering performance, straight line performance and car balance or handling.

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Driver Performance

Driver Performance highlights which drivers are pushing their car to the absolute limit of performance in comparison to their teammates and competitors. Calculating the forces generated by a car's tyres during a lap, and comparing it to the car's maximum capability, this will show how much of the car's potential performance is being extracted by the driver. Three parameters will be shown to highlight three key areas of driver performance that have a major effect on the ultimate goal - lap time: Acceleration, Braking, Corners.

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Driver Season Performance

This provides a breakdown of driver performance based on the most important subset of driving skills by analyzing a wealth of data across effects of the car, tyres, traffic, fuel, and more to a scored output of each driver's performance across the season against seven key metrics – Qualifying Pace, Race Starts, Race Lap 1, Race Pace, Tyre Management, Driver Pit Stop Skill, and Overtaking. These metrics are normalized using from a range of 0-10 to provide a 'score' style metric, and provide an insight for viewers, fans and teams alike into where a certain driver's strengths and weaknesses lie and how they compare to others in the field.

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Pit Lane Performance

Pit Lane Performance will provide fans and broadcasters an opportunity to dissect the whole of the pit stop event, to understand the time being lost and found from the moment the driver dives into the pit lane and exits the other end.

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Qualifying Pace

Historically a subjective session, this F1 Insight powered by AWS will use machine learning and an analytical methodology, taking the practice data and using historical data of how teams progress between Saturday and Sunday's races.

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Start Analysis

Gives fans a detailed view of how each driver is able to exploit performance (or not!) in the start or launch phase.

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F1 looks closely at aerodynamics, tyre performance, power unit, vehicle dynamics, and vehicle optimisation to offer insights that help fans interpret overall car performance.

Braking Performance

Braking Performance shows how a driver's braking style during a cornering maneuver can deliver an advantage coming out of the corner. This compares drivers' braking styles and performance by measuring how closely they approach the apex of a corner before braking and will show how the car and driver perform together when cornering, such as top speed on approach, speed decrease through braking, the braking power utilized, and the immense G-forces drivers undergo while cornering.

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Corner Analysis

The single most important area for performance for an F1 car and this offers great insight into how good cars compare against great ones. This breaks the corner down into the 4 principal sections – braking, turn in, mid corner, and exit – analyzing and comparing the performance through the principal sections of a corner via car telemetry data.

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Exit Speed

Analysis of cornering as determined by optimal braking and acceleration point around a specific (and crucial) corner, which is the area where each driver has the most to gain. This insight gives viewers a detailed understanding of the losses and gains on lap times and allows comparison between cars.
Tyre Performance

Using car data, namely car speed, longitudinal and lateral accelerations, and the Gyro, we are able to build an estimation of slip angles and then derive vehicle balance models for each car. This gives an output of tyre wear energy. (Note: tyre wear energy is not physical tyre wear but instead the energy transfer of the tyre contact patch sliding across the road surface.) The output gives us a tyre performance for each corner, which indicates how much the tyre has been used with respect to its ultimate performance life.

Fastest Driver

Using AWS machine learning technology this insight provides an objective, data-driven ranking of all F1 drivers from 1983 through present day, by removing the F1 car differential from the equation to determine an age-old question: Who is the fastest driver? Data scientists from F1 and the Amazon Machine Learning (ML) Solutions Lab have for the first time in history created a cross-era, objective, complex, data-driven ranking of driver speed.

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IT STARTS WITH THE DATA

Every F1 car contains 300 sensors which generate 1.1 million telemetry data points per second transmitted from the cars to the pits. This real-time data is combined with over 70 years of historical race data stored on Amazon S3 to extract rich insights that inform, educate, and enrich the fan experience and bring about more insight on the choice of race strategy that creates winning performances on the track.

SHIFT THE FAN EXPERIENCE INTO GEAR

By sourcing historical data and using it to teach Amazon SageMaker complex machine learning algorithms, F1 can predict race strategy outcomes with increasing accuracy for teams, cars, and drivers. These models are then able to predict future scenarios using refreshed realtime data as GRAND PRIX races unfold to deliver a rich and engaging fan experience.

MACHINE LEARNING WITH F1 DATA

BLOGS

ACCELERATING THE FAN EXPERIENCE

Want to go under the hood and see how it's done? Learn how AWS and F1 are using machine learning algorithms built with Amazon SageMaker that deliver new insights and increase action on the track, and how F1 is using AWS to design the next race car.

F1 2019 Calendar Download Outlook

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