A Statistic to Assess IndyCar Race Strategists

Photo: Chris Jones

In my opinion, there isn’t currently a good way to evaluate team strategy in IndyCar based off of the traditional statistics of starting and finishing position. If a car improves throughout a race, was it because of good strategy, great passes, or just being fast? It’s not particularly easy to tell and even if you watch the race, there’s too many cars and different things going on to get a good measure of what happened by the eye test.

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What is Expected Points and Why Does it Matter?

Photo: Chris Jones

New for 2019, I will be keeping track and updating Expected Points (xPts) for every race and for the season as a whole. xPts is the number of points we would expect to see a driver earn in a race given how he ran as judged by their average track position (ATP) and ATP25. The last 25% of the race is given extra weight as it is when the race is finally coming down to the wire and performance is more crucial. If two drivers both had an ATP of 5 but one had an ATP25 of 3 and then other an ATP25 of 18, while they both had good days from their general ATP, we would still expect the former to score more points than the latter.

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How Our Race Predictions Work

Single Seater will be “forecasting” each race of the 2016 IndyCar season, with win probabilities posted to the site after qualifying is over. These will be up either late Saturday or early Sunday morning — if a driver scratches from a race, an additional update will be posted.

How It Works
Our model is a simple one, only taking into account three variables. I chose a simple model for two main reasons:
  1. It’s easier to update and keep track of. There aren’t 20 different variables I have to gather together to make a prediction, so I can easily post them for each race.
  2. There is so much randomness in racing. Crashes, mechanical failures, caution flags — these can all have a drastic affect on the race, and they’re almost impossible to predict. Instead of trying to with a ton of variables, I just skip over it altogether. Also, it helps to distinguish the signal from the noise — data that isn’t very predictive. I’ve chosen variables that are historically very predictive of race success, and that’s it.
These variables are added together for each driver and a winning percentage is determined. 

Qualifying Position
I’ve done research before on qualifying position and how it relates to race win probability. These same values are used as the baseline win probability for a driver. These values are different for road/street courses and ovals, as the probability of winning from different places is different depending on the type of track.

Recent Wins
The number of wins a driver has over the past three years is the next variable. If a driver has won 20 percent of the races the last three years, you could say there’s a 20 percent chance he’ll win the next one.

Prior Performance At The Track
How a driver has fared at a track in the past is also included. If a driver has won two of the last three races at a track, most of the it means he’s pretty good there. This variable rewards drivers who have a “good” track and knocks down drivers who have never had a good result there.

That’s it. You can find the race predictions for each 2016 race here.