RJMAnalytics

• April 2024
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• April 2023
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• December 2022
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The RJMAnalytics Mission

NASCAR’s analytics community is a rather small one, which is both a bad thing and a good thing. It means it’s harder to attain credibility for those of us who view the sport on a deeper level than the casual fan, but it also means there is plenty of uncharted territory to explore. As an avid race fan and even more avid statistician, there’s few things I want more than to be part of the reason that advanced metrics in the NASCAR world can one day be viewed by fans, the media, and even race teams with just as much importance as the basic ones.

Furthermore, I personally believe that many of the names in the existing NASCAR analytics world are going about their missions the wrong way. Their metrics tend to stem from a basis of results, and are focused on rewarding drivers who maximize them. Mine are the opposite, using various areas of performance in order to detect sustainable trends that maximize probabilities for good results. Because results – especially in auto racing – are heavily dependent on external factors, and don’t tell anywhere near the full story of a driver’s pure, raw ability. So the goal of the RJMAnalytics model, in a nutshell, is to approximate that ability as precisely as possible.

The model was originally developed during the 2020 season, with one of its primary inspirations being that I, as an unapologetic Chase Elliott fan, was very frustrated with the way Kevin Harvick and Denny Hamlin were continuously racking up wins and garnering all of the attention as the title favorites, despite my perception that Elliott was performing just as well if not better. Sure enough, the performance metric I came up with – and coined as True Driver Rating – was able to reinforce that belief, and when Elliott went on to win the championship that season, it felt like the ultimate vindication. Luck tends to have a way of balancing out over time, and that season may be Exhibit A in recent NASCAR history.

In the time since, the RJMAnalytics model has evolved and grown from a fairly basic formula involving strictly Average Running Position and Pass Differential, into a much more sophisticated one that requires entire page widths’ worth of Excel commands in order to calculate. RJMAnalytics has also branched out to metrics in both college and professional basketball to assess coaching strength, though admittedly there’s far less time and effort put into those, as I view them more as just something fun to play around with rather than any sort of serious long-term project. The eventual goal is to complete my model in its most updated version, which will be used to discover underlying trends in not just the performance of current Cup Series stars, but also the potential of prospects who’ve yet to make it to the top level.

Perhaps one day this model will be considered some groundbreaking predictive tool that’s used by race teams in order to make hiring decisions. Much more realistically, it’ll just be some niche underground project run by a guy who has too much time on his hands, and if you happen to stumble upon it, then all the power to you. But I guess at the end of the day, all I really want is to help open up a new perspective on performance in racing and other sports. The reality we live in is only one outcome out of an infinite number of once possible outcomes, and that’s really what analytics are all about – not predicting every single outcome correctly, but rather determining which outcomes are likeliest.

RJMAnalytics is operated by Ryan McCafferty, a 24-year old self-described numbers nerd who lives in Fairfax, Virginia and has been a diehard sports fan for nearly twenty years.