Are Veach’s Minimal Wins in Indy Lights a Cause for Concern?

Zach Veach sets up for Turn 12 during the Honda Indy Grand Prix of Alabama

Zach Veach will race in his first full season this year at Andretti Autosport after competing in just two races last season. His first IndyCar experience was the GP of Alabama where he was a replacement driver on short notice after Hildebrand got injured before the race. Veach also had a chance to run at the Indianapolis 500 for Andretti where he finished 26th after retiring because of a mechanical failure. Although his first two outings in the series weren’t what he would have hoped for, Veach showed promise in Alabama and it resulted in a full time ride for Andretti in 2018.

At the GP of Alabama, Veach finished where he started in 19th place. While this isn’t a good result, he did show those watching a few important things that might have helped him land this year’s ride. First, with no race experience before Alabama, he managed to stay on the lead lap which is an impressive feat on its own. The second and more important thing is that he showed he can learn the car and how to find speed quickly. His teammate Spencer Pigot was 1.5 seconds faster than him in the first practice of the weekend. By the time qualifying came around, Veach had gotten the gap to his teammate down to just 0.54 seconds. This is still a fairly large gap, yes, but compared to where he started it is a vast improvement.

This is just a one-off example of his ability to learn quickly, but it might have stuck out to some people at Andretti as a positive sign when evaluating Veach for a seat. Veach’s experience in Indy Lights is encouraging too, although it isn’t spectacular by any means.

In three seasons of Lights racing he had six wins and 18 podiums in 44 races (14% win percentage). His podium performances are impressive, but it is the lack of race wins that makes me concerned about his performance at the next level. Compared to some of the current IndyCar drivers who raced in the Lights series, Veach is lacking in race wins. Carlos Munoz won six of his 24 Lights races (25%), Josef Newgarden won five of his 14 (36%), and Spencer Pigot won six of his 16 (38%). Veach hasn’t been able to win many races throughout his three seasons — or do particularly well in the championship for that matter. His finishes of 7th, 3rd, and 4th are not indicative of someone who will be a top-tier IndyCar driver, especially since the drivers he came behind in the points — Karam, Chaves, Munoz — all have been mid to low-tier IndyCar drivers.

Indy Lights History

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  Season     Races     Wins     Podiums     Poles     Pts Pos     Avg St     Avg Fn  
2013
12
0
1
1
7
4.7
6.2
2014
14
3
9
4
3
3.6
3.6
2016
18
3
8
1
4
5.8
5.6

Veach isn’t a bad pick for Andretti as I think he’ll be a top-15 average finisher and maybe even come away with a result or two in the top-10. I see him as a temporary replacement for Sato unless he starts developing quickly in the IndyCar series. He’s shown he can learn quickly, but to stay on a team like Andretti he will need to be a driver that can consistently challenge for a top-15 spot at least. His three year contract could give him just the time he needs to develop, but time will tell.

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Photo courtesy of Joe Skibinski/IndyCar

by Drew

Transitioning From Indy Lights to IndyCar

About half of the field in last year’s IndyCar season competed in the Indy Lights series at some point. Many of them got their racing start in Lights and then moved to CART or the IndyCar series depending on when they came through the system. Recently I looked at how drivers perform once they are in the series through the use of aging curves. This helped me answer the question of how drivers age and how they perform year to year on average. Aging curves are useful for predicting performance once drivers are in the IndyCar series, but what about the rookies? Is there a way we can predict how a driver is likely to perform in their first year in IndyCar?

To answer this question, I decided to look at drivers who had competed in the Lights series full time (competed in at least half of the races) and then transitioned to a full time IndyCar ride in the first or second year after their last season in Lights. Originally I was just going to include drivers who had transitioned straight from Lights one year to IndyCar the next, but decided to expand it to a two year buffer to include drivers who raced in a couple IndyCar races one season and then became full time drivers the next. For these cases, I compared their last season in Lights to their first full season in IndyCar, not their partial season.

Using these criteria, I came up with 23 drivers to include in my data set. For each driver, I compared their last season in Indy Lights with their first season in IndyCar in terms of championship position, average finishing position, and average starting position. Here is what I found:

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  Champ Pos Change     Avg St Change     Avg Fn Change  
-12.04
-9.69
-9.00

Drivers lose about 12 places in the championship on average when coming to the IndyCar series. So if a driver comes to IndyCar after winning the Lights championship the year before, we’d predict them to finishing around 13th in the IndyCar championship. Their average finishing and average starting position both also decrease by about nine places. The typical Indy Lights driver finishes their first IndyCar season with a championship position of around 15th, and an average start and average finish of 14. 
These numbers are useful because they can help us predict how a driver is likely to do in their rookie season in the series. Aging curves help us predict how drivers do once they are in the series, but don’t tell us anything about expected rookie performance. Closer to the season’s start I will be posting my projections for the 2018 season using this information for rookies and the aging curves for the rest of the field. 
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Photo courtesy of Indianapolis Motor Speedway, LLC
by Drew

Aging Curves For IndyCar Drivers

Aging curves are very popular on baseball analytics sites like Fangraphs and Baseball Prospectus. They give a forecast as to how a typical player ages over time and how their batting average, WAR, or some other statistic might change with the seasons to come. They give a best guess as to how a player will age, so while it is by no means a perfect representation of player aging, it is helpful. The real benefit of aging curves are that they can help us forecast a player’s (or driver’s in our case) performance in future seasons based on how he’s performed so far. No one has really tried to do the same for IndyCar, though David at Motorsports Analytics has done something similar for NASCAR, so I thought I’d give it a try. 
The basic technique behind constructing an aging curve is this: look at back to back seasons for many different drivers and see how a statistic (for example average finishing position or AFP) changes between those years. Put these “changes” into a bucket representing the ages, find the average change from say age 32 to 33, and then do this for all of the age pairings in your data set. For example, in 2013 Scott Dixon was 32 years old and had an average finish of 8.2. The next season he was 33 and had an average finish of 8.3, so his change between ages 32 and 33 is +0.1 average finishing position. This would go into the general 32/33 bucket for every driver who raced in back to back seasons at those ages. Once all of the 32/33 changes are put into the bucket, the entire bucket is averaged together to get the average change for a typical driver between those ages. 
This is called the delta method for constructing an aging curve because it looks at the average change from one year to the next in back to back seasons. I created three different aging curves for IndyCar drivers. One for average starting position, one for average finishing position, and one for championship position. I looked at 43 different drivers. To be include in the data set, the driver had to race in at least two back to back seasons from 2002-2017 and race in at least five races each season. This provided a total of 273 seasons from which to develop the aging curves.
Here is a look at the aging curve for average finishing position. 
If you have ever seen an aging curve for baseball before, you might be a little confused by this. In baseball, players want to have a high batting average or WAR, but in racing and with the statistics we’re looking at, you actually want a lower number. So the aging curve looks flipped when compared to this one for baseball. The y-axis is the change in average finishing position from peak year. Moving down the y-axis indicates a decrease in averaging finishing position (ex: moving from an average finishing position of 17th to one of 11th). For example, when a driver is 23 years old, he’ll be projected to shave a little over four places off of his current average finishing position by the time he reaches age 28 — this is the peak age for average finishing position performance. So if a driver has an average finishing position of 14th when he’s 23, we’d expect him to be have an average finishing position of 10th in five years time, based on how a typical driver ages. This graph also shows the year to year change for different age couplets. Going from age 34 to 35, a typical driver’s average finishing position increases by 0.4 places, meaning he is less successful than the year before.

The key to remember when reading aging curves is that an increase in average finishing/starting/championship position is a bad thing (AFP of 14.5 going to 16.7) and a decrease is a good thing (AFP of 20.1 going to 14.2). It doesn’t sound quite right at first, but that’s just the nature of starting/finishing/championship position in IndyCar.

Overall, the average finish curve shows us that drivers drop a little over half a place off of their average finishing position per year until age 28. After that, it is a gradual increase as the driver comes out of their prime years and starts moving down the grid again. Drivers lose their abilities much slower than they gain them, with a fair number of drivers even having better seasons than expected as they get older because of this.

The aging curve for average starting position is read exactly the same way as average finishing position, with lower numbers meaning a driver is closer to the peak age performance.

What’s interesting about this aging curve is the dip that occurs when going from age 20 to 21, even though the peak age still turns out to be 28. This clearly isn’t what we’d expect to happen from a “typical” driver, and it’s caused by the relatively small sample size of drivers in that age group competing in back to back seasons. When using this aging curve for projections, I adjust for this anomaly by providing less weight to that couplet.

Drivers seem to lose their qualifying abilities quicker than their racing abilities: by age 38, a driver has lost about 4 places off of their peak average starting position compared to just 2 places off of their average finishing position at the same age. This speaks to the idea that being consistent and safe throughout a race can pay off in the long run. In qualifying, drivers only need to get through one quick lap to place high. In a race, you need consistent laps and to stay out of trouble for over an hour and a half of racing in order to finish in a good position. Drivers who have a lot of experience are able to maintain high average finishes for a long time because they know this better than the younger drivers who are new to the series. Younger drivers seem more willing to take risks which can pay off in high average starting positions (think of the dip at age 20/21), but these don’t always translate to high average finishing positions as we saw above. It’s much easier to get away with “on the edge” driving for one lap compared to 80.

And finally, we have the aging curve for championship finishing position.

The championship aging curve shows that the peak year for championship position is right around the age of 27. At first glance this might surprise you given that the average age of the champion has been close to year 30 years old for the years 2000-2017, but remember that this is an aging curve for all drivers, not just drivers that went on to win the championship. On average, drivers are achieving one of their best championship finishes around the age of 27. Early on in their careers, drivers experience a lot of variation in season to season performance in the championship. This is likely because a few good results — which might be the result of luck early on — can boost drivers up the standings a lot when they are near the back in points. After drivers hit 23 their performance becomes more predictable throughout the rest of their career. Drivers drop back roughly 0.75 places in the points standings each year after the age of 28.

As I mentioned before, aging curves are not a perfect forecast: they are just our best guess based on how typical drivers have aged. One of the main drawbacks of aging curves, and a problem with every sport, is called survivorship bias. This is the idea that only the best drivers will be included in the later age ranges because all of the worse performing drivers will have been let go by then. If a driver isn’t good, he won’t stay in the series for long and he won’t have many back to back seasons to use. This is especially a problem in the first few seasons as new drivers come and go having only raced in one or two seasons. After that things start to even out and the survivorship bias doesn’t matter as much because most of the drivers in the series are typical of the rest of the drivers who have made it that far too. This is something to keep in mind when looking at aging curves.

There is a lot that can be done with aging curves and a lot that can be learned from them — too much to include all in one article. Here are the main takeaways from the aging curves and what we’ve looked at in this post:

  1. The typical driver peaks in average finishing and average starting position around the age of 28. 
  2. Drivers lose their ability to have a high average starting position quicker than their ability to have a high average finishing position. This is possibly due to younger drivers’ willingness to take risks in qualifying and the better experience older drivers have in managing race situations.
  3. There is a lot of early volatility in how drivers perform in the championship early on in their career. This evens out as their career goes on.
  4. Survivorship bias is an important thing to remember when evaluating aging curves.

As the season gets closer and the rest of the vacant seats get filled, I will post my projections for the 2018 IndyCar season based partly on these aging curves for each driver’s average starting/finishing position and their championship position. Look out for those!

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by Drew

Don’t Try to Predict Where a Driver Will Finish Based on Where He’s Starting

The general consensus has always been that the higher up you start in the grid, the higher you’ll finish in the race. This makes sense. The fastest cars qualify at the front of the grid and we expect them to perform well in the race too. But what exactly is the relationship between starting position and finishing position? Can we tell a lot about where a driver is likely to finish based on where he starts? These are a couple of the questions I want to look at today.

Using data from 2012-2017, I looked at how starting position correlates with finishing position. Here’s a plot of finishing position vs. starting position for those years, with a trend line added.

A driver’s starting position explains only 12.9% of their variation in finishing position, meaning that a driver’s starting position isn’t very predictive of their finishing position. If you just know where a driver started, you can’t predict their finishing position with much accuracy. I was expecting this number to be a little higher, but the more I thought about it, the more it started to make sense. 
There are a lot more factors that go into where a driver ends up finishing a race than simply where he starts. Accidents, untimely cautions, and differences in strategy are just some of the things that can make high qualifying drivers perform poorly and let poor qualifying drivers sneak into the top half of the field. Qualifying position is only one part of the puzzle that determines the results of a race. And on top of that, the difference between say 12th and 13th place is usually down to more luck of the draw then driver skill, making the prediction of individual places difficult. 
But this leads us to another question: what are the factors that truly impact where a driver finishes a race. Are practice results predictive of race performance? Or how a driver has raced at that track in the past? Or is racing inherently subject to a lot of randomness and it can’t be predicted with much accuracy? These are all interesting questions that I would like to tackle in the future, but trying to determine how they all interact with each other would be too much for one article. I would like to look at these factors one by one in different articles in the future (starting with qualifying position in this one). Race prediction is obviously the ultimate goal, but before we can determine if it’s even a feasible goal, we need to see what the different factors are that go into determining where drivers end up in a race. 
What we do know now is one part of the bigger picture: qualifying position is a statistically significant predictor of finishing position, but it isn’t a very good one. It explains just around 13% of the variation in finishing position and has a standard error of 6.7 places. Qualifying is a good place to start our investigation into finishing position, and I plan on looking at the other aspects that go into race performance in future posts.
by Drew

Graph of the Day: Age of the IndyCar Champion Over Time

Graph of the Day is a short piece where I post an interesting graph for chart I came across while doing research. Today’s graph looks out how the age of the IndyCar champion has changed over the years. 
Before Newgarden’s title at the age of 26, it appeared the age of the champion was rising ever so slightly on average. Newgarden was the fifth youngest champion since the year 2000. 
It’ll be interesting to see how the younger drivers in the series (Newgarden, Rahal, Rossi) change this trend over time. As the veteran drivers start to retire, I’d expect the average age of the series champ to tick down a little.
by Drew