What Does Good Look Like?

Back in March 2019, when I started racing again I had no idea how I would fare. At the end of that first evening despite finishing 14th, 11th and 10th out of 16 I was already looking forwards to returning the following month. Now the season is over I asked myself how do I know that I’ve had a good night and that my driving is improving? What goals should I set for next season? What does good look like for me?

At work, when we’re setting the success criteria for a piece of work we’re always looking to move one of our KPIs by a certain amount – maybe the time a customer spends on a page, reduce the amount of returns or increase engagement with an article. When the work is delivered we can take the measurement again, compare the new value to the original and see if our work had the intended effect. If it did – awesome! If not, we can take a look at why not. This is a really hot topic for us at the moment and I started to have a think about what KPIs I could come up with for my racing.

There are obvious things like fastest lap, qualifying & finishing positions etc. but I wanted to go a bit deeper than that. I was also aware that while I was new and learning the track, most of the other competitors had been racing there for years. While I was likely to be able to improve, they were unlikely to get significantly faster – this would give me a benchmark that I could measure myself against.

Qualifying.

I initially arrived at 2 KPIs here.

Average qualifying position is calculated based on each drivers average qualifying position across all 3 races for each meeting. You can see in the graph that I started with an average of 11.7 but finished with 4.3 – much better! Matt, who won the series started with an average of 2 and finished with 1 and was never worse than 4.7. Consistency obviously matters!

I also looked at average qualifying times but soon discounted these as a useful KPI because they varied so much from meeting to meeting. Even though all the meetings were dry the weather and conditions still made a difference.

After I’d discounted lap times, I started to look at the average gap to pole. This was a much more useful KPI for measuring progress. As you can see in the graph, I made big improvements quickly and then started to find the time more slowly. Interestingly, most drivers slowed down in round 5 (not just me) – I’ll have to go back and look into why that might be. At the end of my first meeting, my average gap to pole was a massive 2.6 seconds. Practically a year in racing terms! The 0.59 seconds I finished up with is much better but still leaves room for improvement. Now that I have my telemetry systems working I can start to dig into finding that time.

Race

When I started looking at the data for the races, I quickly arrived at 4 KPIs.

Total points scored per round was an easy one and is exactly what it says on the tin. Points are what win championships and you can see my line (light grey) increasing steadily throughout the season. There’s still plenty of work to do here but I already know that improving my qualifying will help me in the races a massive amount as will improving my racecraft and awareness of other drivers.

Although I’d discounted qualifying lap times as a valuable KPI I was curious what average fastest race laps told me when plotted in a graph. Interestingly, I’m consistently in the middle of the pack here. I can only surmise that while I can be quick I also make mistakes and let other drivers past me.

Avg. seconds behind the leader is a really interesting measurement, helping me to see how close I’m getting to the front. At the start of the season I was almost 30 seconds (half a lap!) behind, which I’d got down to 7 seconds by the end of the season. From round 4 this stayed fairly constant (other than round 5).

Avg. finishing position is another interesting one. I started with a score of 11 and finished with 4. This started to go down quickly and then started to improve more slowly. When looked at in tandem with the average seconds behind the leader it would suggest that the racing is actually pretty close at the front and if I’m able to improve my times, racecraft and consistency only a little it could make a big difference to the points I’m scoring.

Summary

Boiling my 1st season down to hard stats looks like this:

KPIRound 1 ValueRound 8 Value
Avg. Qualifying Position11.74.3
Avg. Gap to Pole2.600.59
Total Points Scored5462
Avg. Fastest Race Lap Time60.99859.902
Avg. Seconds Behind the Leader 28.437.54
Avg. Finishing Position114

As I go into next year I’ll be looking to improve on these and will be putting together a plan to move them over the winter.

Postscript

If you’re wondering where I got all this data from, it was all freely available from the alphatiming website. I did spend several rather tedious hours copying and pasting it into Excel but now I have a workbook I can reuse it should be much easier next year and I can quickly work out the KPIs for each meeting and use them to help me analyse the data.

Because of the amount of data, I only looked at the drivers who had finished above me in the championship and who had competed in 7 or more rounds. As the season progressed other drivers, some of who were very quick, started to join us and would ‘take points away’ from those of us who were driving the whole season. It would be interesting to see what the championship points would have been if those drivers were removed but that’s for a different time.

For all of these measurements, they’re taken based on a drivers performance across the whole meeting. Most of the meetings were 5 race events and each driver would compete in 3 races. I took the average or total of all 3 races and compared those to mine to obtain the final values. This allows me to measure myself against the whole pool of drivers in the same Karts on the same track under the same conditions. Some of the Karts were better than others but they’re broadly the same.