When I started FAT Karting League, the goal was simple: build a grassroots motorsport ecosystem that finds the next world-class drivers using rigorous, data driven methods.  The same methods that are used in F1 to analyse both car and driver performance. Democratising motorsport, not romanticising it. If you can drive, we want a system that can prove it and then open a real pathway.

This year we hit a new milestone: our first FKL World Finals, held at Willow Springs (12–14 December), bringing together the best talent from our three active championships: UK, Midwest US and California. Importantly, it forced us to answer the hardest question in our whole project:

A NOTE FROM ROB SMEDLEY:

HOW DO YOU CHOOSE WHO DESERVES THE NEXT STEP, WITH SO MUCH QUALITY WITHIN THE FIELD?

It’s important as the next step is not a trophy; it’s graduation to the FAT Racing programme. Specifically, the F4 FAT Racing Shootout, where one driver will earn a fully funded seat in British Formula 4 in 2026. That’s a genuine career opportunity, and it means selection can’t be vibes, reputation, or “I liked what I saw”. It has to be objective, data driven and not based on mine or anyone else’s opinion.  

TWO SEATS WERE CHOSEN FOR US. TWO WERE NOT

The FKL World Finals regulations give automatic shootout entry to the top two in Junior Light and Junior Heavy categories. That meant two drivers qualified on merit, no debate: 

- Ellis McKenzie - Junior Light

- Shea Aldrich - Junior Heavy

Then we had the hard part: selecting the remaining two wildcards in a way that was objective, consistent, and aligned with what we’re trying to build.

The approach: build the data analysis methodology first, then look at the names.

Here’s the rule I care about: you set the method before you see the outcome. Otherwise, you’re not analysing, you’re just justifying.

So, we built an algorithm around 10 data driven metrics, grouped into five performance areas, each with a weighting aligned to overall driver talent and performance. The weighting system was rigorously analysed and checked for sensitivity in order to ensure that we were getting a robust output.

The point wasn’t to find a headline. It was to find the most complete driver profiles for the jump to F4.

THE MODEL: WHAT WE MEASURED

We looked at five areas that, in our experience, actually translate to driver performance: how well the driver performed in their respective championships, how well they performed at the World Finals (which was arguably the highest pressure event of the season), how fast the driver is over a single lap, how consistent the driver is in terms of a race distance, and finally, a score of “racecraft”; how the driver overtakes and deals with adversity.   

AREA 1 - CHAMPIONSHIP PERFORMANCE

Average points per race, with three explicit correctors so we were comparing like with like. The algorithm takes into account the following:

- Number of races in the specific championship completed: Example: California Hub 3 runs 5 races instead of 9, so raw totals would distort reality.

- Number of drivers competing with the championship (grid size matters).

- Championship type. Regional and Pro are weighted differently.

This overall algorithm captures season-long performance taking into account the fact that the championship structure is different region to region.

AREA 2 - WORLD FINALS POINTS

Here we looked at the simple data metric of points scored in the World Finals at Willow Springs. We normalised for any external factors which were not in the drivers control such as reliability of the equipment, where applicable.

Simple by design: the World Finals are where the pressure peaks, and execution matters.

AREA 3 - SINGLE LAP SPEED

The consideration was to look at the best lap of the entire WF weekend, regardless of which session it was set in. All drivers concurrently at the same track with the same pool of karts.

The qualifying session can be heavily affected by traffic, timing, and plain bad luck. Using the fastest lap of the weekend gives a larger and more diverse sample and reduces the influence of a single compromised session. Taking all the sessions into account allowed us to take a more statistical and therefore rigorous approach to the best lap time.

We normalised for vehicle weight between the two Junior categories using our in-house laptime simulation platform as well as normalising for time of day where the track could be faster or slower, dependant on the conditions.  

The result was a robust, single outcome for how fast each driver was over a single lap. A true measure of ability and talent.

AREA 4 - RACE PACE

Race pace was built as a mix of three data points. The principle was to use relative measures, so changing track conditions across sessions didn’t unfairly distort comparisons. The algorithm takes into account the following: 

- Average gap to the leader (in sessions the driver participated in).

- Average gap to the best pace.

- Average gap to the mean pace.

We removed the first lap of the race to avoid distortion due to grid position and first-lap incidents/traffic.   

AREA 5 - RACECRAFT

For any race driver hoping to have a professional career, this is an important performance area as it captures who performs when racing actually happens, not when the track is empty. The algorithm takes into account the following: 

- Average finishing position across races. 

- Average positions gained during races, with deliberate weightings taking into account where the driver started on the grid. 

- Number of wins during the WF event. 

- Number of podiums during the WF event.

The algorithm definition stops the model over-rewarding relatively easy gains that are derived from a driver qualifying out of their natural position and making gains (think of Lewis Hamilton in the 2025 F1 World Championship, on occasion) rather than clinical execution from the front.  

CONVERTING MIXED DATA INTO ONE FAIR SCORE

Given that the raw data points are quite often output in a range of units (points, seconds, positions), we normalised each metric using percentile scoring with most performant receiving a score of 100% and the remainder of the field receiving a score scaled to this.  

That turns each data metric into a simple and comparative output. Only after that do the weights come into play and everything is combined into a single output.

STRESS TESTS: WE TRIED TO BREAK OUR OWN RESULT

Once the model gave us a clear picture, we asked the obvious question: how fragile is it? If one incident flips the answer, you’re not measuring performance, you’re measuring noise.

So we ran different scenarios of penalties, incidents, etc. None of these materially changed the outcome and we therefore had a rigorous and stable scoring system.

FINAL VALIDATION: THE JUDGES SCORE

We then did what you should always do in performance work: we sanity-checked the output with our coaches and staff asking about performance traits, strengths, weaknesses and how each driver operates under pressure.  

From the objective data Monde-Jnr Konini was a clear choice. Once we add in the judges subjective score, he remains the outright highest score, and we therefore locked in his selection. We were then left with having to select for the final slot from the remaining drivers.  

Jackson Wolny scored highly in his WF performance, Championship performance, and Race pace. These metrics combined meant that Jackson also scored highly. When we take into account the high judges score received by Jackson he was a clear choice for the final wildcard slot.

Aligned to the ethos of the FKL system , the analysis managed to pinpoint two champions which underlines the importance consistent championship performance in the appraisal process. Completing the whole championship and demonstrating a consistent and high performance is a clear indicator that a driver will perform well at higher levels.

THE WILDCARDS

On that basis, the two wildcards for the F4 FAT Racing Shootout go to:

- Jackson Wolny

- Monde-Jnr Konini

They join Ellis McKenzie and Shea Aldrich, who qualified automatically.

WELL DONE - BECAUSE THIS WAS EARNED

I want to finish with this: congratulations to every driver who made it to Willow Springs. The level was high, and that’s why this process mattered. Ellis and Shea earned their spots outright. Jackson and Monde earned theirs through a method designed to be fair, robust, and brutally honest.

The truly heartening point for myself and the team was that that there were many talented driver to choose from, a lot of whom are also great ambassadors for FAT Karting League. On this occasion a few missed out on such fine margins. Given the relative experience of some of our drivers, they can be very proud for getting this far. To get to the World Finals and subsequently to be considered in the analysis is a major achievement in itself. Let’s ensure we all play our part in supporting those that weren’t successful on this occasion and help to turn their disappointment into a positive outcome. I’d love to see them come back and compete for the coveted F4 drive in 2026.  

And a genuine thank you to the entire FKL team for making the World Finals happen. Pulling together an event of that scale - with three championships converging on one track - takes an enormous amount of work. Their dedication, teamwork, passion and belief in changing motorsport for the good of the many was on display for all to see across the entire event.

Now the shootout starts. Let’s all get behind our four young drivers as they battle it out for this game changing prize. Whatever role you’ve played in the journey, you’ve played a key one in getting us all this far.

Rob Smedley

CEO & Founder 

FAT Karting League & FAT Racing