The Case for Data

Regardless of the pitfalls of the DOSS system (talked about in this blog’s most recent article), there still is a good use case for data and telemetry information. Knowing if a vehicle was faster on entry or had better proximity with hard data could lead to different results. In some cases, the answer to the question of “did this driver straighten out?” could be made automatically.

To begin to discuss and implement data metrics, two issues have to be solved:

The first issue is cost. NASCAR and F1 have HD quality on board cameras and live data overlays for the viewer while Formula D has, at best, a replay from a streaming drone at low resolution. Technology isn’t cheap and the cost/benefit analysis is murky enough that FD might be averse to investing the money. Since the original implementation of the DOSS system, technology has gotten much better and much cheaper. Theoretically, the hardware required for this type of data should be more accessible.

The second issue is how the data is used. DOSS pre chewed certain numbers and used a proprietary calculation to decide scores. Inherently, for this judged sport, this “kills” the spirit of drifting. Instead of using the numbers to calculate a score, the collected data could be used to inform judging more accurately. Each collected piece of data should be shown to the viewer and to the judges to show hard facts that can then be used to create a score.

Types of Possible Metrics

Proximity

One of the easier possible metrics is proximity. Distance sensors are placed on the same spot on both cars (between the front axles or mid roof line) and the distance between the two sensors are tracked during the run.

Possible metrics are:

  • Average proximity during the entire run
  • Farthest proximity – This can be used to define what is considered “inactive chase”
  • Closest proximity
  • Average proximity per zone

Speed(s)

Formula Drift has, in the past, used a radar gun to judge entry speeds. The reason why it was dropped and why it wasn’t viewable via the live stream isn’t certain. Regardless, it was a simple metric that helped determine outcomes. Modern GPS equipment can update about 10 times per second and can give extremely accurate speed data instead of a radar gun.

The main issue with this is likely removal of subjectivity. The downfall of the DOSS system is its heavy reliance on overall speed. Instead of using this information to populate a predetermined calculation, speed related data can be displayed for the judges and audience. Speed metrics can then be used to render a judgement. The audience has more information to see the judges’ calls, and drives up viewer engagement.

Possible metrics are:

  • Entry speeds
  • Total speed averaged across the whole run (also doable via a stop watch)
  • Average/highest speed through each zone
  • Judgments on decel zones. IE “parking it” when a driver shouldn’t or decel in an accel zone
  • Top speed
  • Average speed per run used to determine if someone is sandbagging and intentionally driving slow during competition

Clipping Point Proximity

Using the same proximity sensors as mentioned above, distance measurements can be done by clipping points and zones. Knowing for certain one driver was closer to a clipping point can help both qualifying and competition/tandem judgments. Because there are more sensors, cones get hit, and there’s likely calibration necessary, this metric may be difficult to implement and keep accurate.

Possible metrics are:

  • Average proximity on an outer zone
  • Distances to inner clips per run
  • General distances to help derive qualifying numbers
  • Visual candy for the audience

Metrics/data in General

These are three, generally cost effective, possibilities to enhance judging and sometimes viewer enjoyment. Many more metrics are possible but unlikely due to cost or complexity of implementation. One of the easier ways to advocate for metrics is to use it for entertainment instead of judging. The more information viewers have, the better their understanding and engagement.

Possible non judgement data for viewers:

  • Tire life
  • Tire temperature
  • Wheel speed(s)
  • Driver radio chatter
  • Live speed/angle/etc overlays
  • Vehicle metrics (engine temp/throttle/braking/etc)
  • In car video replays

Why no angle metrics?

For complexity reasons, drift angle has been purposefully left out of this article. Measurement of slip angle is highly debated around the world and there doesn’t appear to be a single agreed upon way to measure it. Four wheel drifting is still over steer and concepts like ackerman make it impossible to measure angle accurately from the front wheels alone. Without consistent measurement between cars, drift angle still isn’t worth using as judging criteria.

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