Speed is an important quotient whether it is a technology or a race. With information being a driving factor for each process, every industry has started using data to analyze and compare the outputs. The same happens when we watch a cricket match ranging from run rate to average speed, everything is measured using sensors. Later on, experts analyze the data and compare the performances of each and every player. That is when Big Data Analytics comes into the picture. Formula 1 racing is next in this row. As the entire game is about speed, the teams have moved to big data analytics to up the level of performance.
What concerns can be addressed by Big Data Analytics?
- Analyzing the relative speed of the car
- Machine/Engine failure or damage
- Predicting the outcome of a race
- Strengths and weaknesses of a car
- Communicating between the departments
And how can all these be overcome:
| Analysis of speed | By deploying sensors on the racing pit |
| Failure or damage recovery | Constant monitoring the machine status |
| Failure or damage recovery | By building connectivity between departments |
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How are they going to do it?
McLaren Group, the famous Formula 1 race team has moved on to use Data Analytics to determine the speed of the car on various points and curves to analyze the performance. A car’s speed depends on the driver as well as the machinery used. Here the turning point is that data analytics is going to be used not just to measure the speed of the car but the relative speed of the opponent as well. The analysts can predict the race before it even starts.
It is said that an F1 car racing with a speed of 200mph generates 1 GB of raw data each time using the sensors deployed on them. This gigantic data is transformed into predictive models and used to analyze the performance based on historical data. These sensors record each aspect of the performance whether it is tire pressure or fuel burn and hundreds of such sensors produce a huge amount of data to be analyzed and predicted.
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Looking at the above description you may wonder how such experts can rely upon such data for performance. However, there is another aspect attached to it, which is not just about increasing the performance but about optimizing the stability as well.
F1 race has strict rules about the number of engineers allowed on the pit, which is why at times the teams are unable to analyze the weak points of a car. The mechanics and aerodynamics of an F1 racing car are quite different and complicated from that of a normal car. Hence there is a need for in-depth analysis before and when it finally goes performed. Due to the restrictions posed on the number of engineers on the race circuit, communications becomes a major hurdle when there is a technical failure. However, with the help of big data analytics real-time strategic decision-making will be possible during the race and technical complications handling will be possible.
Do the foremost questions arise which technology may solve this problem? Apache Spark is an in-memory high-speed big data processing engine. Or a smart business intelligence tool like Tableau which will throw the output the moment data is fetched?
Till yet we have seen analytics be used in cricket, with the sensors deployed in the stumps and pitch, each and every movement with the intensity is captured and reflected in the graph. However, it will be interesting to see how real-time data analytics can keep the audience engaged with the sport.