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.

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.

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. 

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.