Earning the most points throughout the season is the goal of IndyCar racing. Points are how a driver wins championships and every decision a driver and team make throughout the course of a season should be centered around how they can maximize the number of points they’ll earn at the next race. Using the stats I tracked this IndyCar season, we can look at how correlated different stats are with earning points over the course of the season. Correlation is a measure of how strong or weak a relationship is between two variables. It can range from [-1,1], with -1 being a perfect strong negative correlation, 1 being a perfect strong positive correlation, and 0 meaning there is no correlation between the variables. If the correlation between two variables is 1, that means when variable x goes up, so does variable y. If the correlation is -1, that means that when variable x goes up, variable y goes down.

For example, the correlation between points earned and average track position is -0.89. This is a very strong and negative correlation, meaning that when more points are earned, a driver’s average track position tends to go down — this makes sense. Since a lower value (closer to 1) for average track position means a driver runs races closer to the front, it follows that earning more points is negatively correlated with ATP.

To start off, here are the correlations that are the strongest over the course of the season:

- Points is negatively correlated (-0.87) with average finishing position
- Points is negatively correlated (-0.81) with average starting position
- Points is negatively correlated (-0.88) with average track position
- Points is negatively correlated (-0.84) with average track position 25
- Points is positively correlated (0.87) with top five percentage
- Points is positively correlated (0.78) with average extra positions

Having a lower value for AFP, ASP, ATP, and ATP25 is better, so their relationships to points earned are negative. As a driver’s ATP goes down, they are typically earning more points each race. AFP has a stronger correlation to points than ASP does, but with a value of -0.81, qualifying is still tremendously important each race weekend. A higher value for Top Five Percentage and AEP is better, so those relationships are strong and positive. AEP is the number of “extra” positions a driver achieves in a race based on how similar drivers have fared when starting from the same position on the grid. Being able to consistently outperform your starting position expectation is a key to earning good points on weekends when qualifying might not go your way.

And here are the correlations that are weaker:

- Points is positively correlated (0.34) with deviation of finishing position
- Points is negatively correlated (-0.22) with deviation of starting position
- Points is negatively correlated (-0.12) with passing efficiency
- Points is positively correlated (0.26) with running percentage
- Points is negatively correlated (-0.21) with start retention
- Points is negatively correlated (-0.17) with start plus-minus

There are a few interesting takeaways in this set, even if their correlations are weaker. To start, points being positively correlated with deviation in finishing position means that when more points are earned, a driver’s deviation in finishing position tends to be larger (less consistent). This could be because the top drivers are more likely to take extra risks which could put them at greater risk of crashing out of a race. It could also be because the drivers earning the most points consistently run at the top of the field, but when they have a DNF, it is so far off of their “normal” performance that it affects their standard deviation more so than it does for a driver who runs in the middle of the pack.

Deviation of starting position has the opposite effect. More points correlate with a lower deviation of starting position. The drivers who earn the most points are consistent in where they qualify for a race, and they are less likely to crash out of qualifying and harm their small standard deviation than they are to crash out of a race that naturally has so much more out of their control.

Negative correlations between points and passing efficiency/start retention/start plus-minus all likely have the same explanation. Drivers at the front of the field for most of the race have less chances to pass other cars over the course of the race, and less chances to pass cars on starts especially. The opposite is also true: they have more cars behind them that could pass them, making their passing efficiency and start retention lower. While its important to hold onto your starting position (four of the top five drivers in the final points standings had start retentions above 75%) and still maintain a high level of passing efficiency (45% to 50%), the nature of these stats mean their correlations to points isn’t as strong as one might initially suspect.

The stats with lower correlations to points earned over the season explain less of the variation in points earned than those with stronger correlations. While most of the stats with strong correlations match up to intuition, it’s beneficial to get a value for these intuitions to see exactly how correct they are. Comparing how starting deviation and finishing deviation both interact with points earned differently sheds some light on the importance of qualifying and how hard it is to consistently finish races near the same position, primarily due to drivers crashing out of races as they attempt to make big moves.

*Photo: Stephen King/IndyCar*