Which NASCAR Tracks Are Most Accident Prone?

With Bristol coming up this weekend, thoughts turn naturally to carnage. Short track racing means accidents. Given the greater-than-usual level of aggression among drivers this year, we can expect at least a few collisions.

But does short-track mayhem hold a candle to Daytona and Talladega?

Let’s see.

Accident vs. Spin

Racing-reference.info (one of the best places on the web to find NASCAR statistical data) posts a chart like the one below for each race listing cautions, reasons, who was involved, how long the caution was and who got the free pass.

The cautions table from racing-reference.info

Nascar divides cautions into about ten different types: stage end, debris, oil/fluid on track, etc. They distinguish between spins and accidents.

The first yellow flag in the chart above was for the #20 spinning. The second yellow flag was also a single-car incident: Something in the 2-car broke and sent it into the wall. The distinction is:

  • A spin produces little, if any damage
  • An accident means there was contact.

We’ll only deal with the latter here.

How Many Accidents Per Season?

Here’s a chart showing the total number of accidents for the last five years, plus what we’ve accumulated so far this year.

Total Accidents per Season 2014-2019

I wrote elsewhere about why accidents went down in 2018 (spoiler: damaged vehicle policy plays a role). We’re up just a little (75 vs. 65 at this time last year), but we were were already over 100 accidents other years shown by this time in the season.

Are There More Accidents in the Playoffs than the Regular Season?

The stakes rise as the season continues and you might wonder if the numbers increase as the year goes on. I’ve separated accidents for the first 26 races vs. the last 10.

A column chart showing the percentage of accidents that happen in the regular season vs. the playoffs.

The regular season is 72.2% (26/26) of the entire season. The red line (at about 72%) shows you where the split should be if accidents happened at the same rate during the two periods.

While it varies from season to season, there is no indication that there are more accidents in the playoffs vs. the regular season.

How Many Accidents in a Typical NASCAR Race?

NASCAR doesn’t really have a ‘typical’ raced because the series visits a wide range of track types. But if we combine the data for the last five years (plus 2019 through the first 23 races), we get a very reasonable histogram that is almost Poisson-ish in nature.

A histogram of the number of accidents per race from 2014-2019 (2019 includes the first 23 races)
  • Out of the 203 races, about 5.9% (12 races) had no accidents. This year, we’ve already had three accident-free races.
  • The most likely number of accidents in a race is 2 or 3
  • The biggest number of accidents over these years is 15, which happened at Darlington in 2015.
  • Only 6.4% races (13) had 9 or more accidents. You’re about as likely to have a race with 9 or more accidents as you are to have a race with no accidents

Are All Years Similar?

Here’s the same data, but broken down by year.

A histogram of Accidents broken down by year from 2014-2019.
  • There is a very similar pattern each year.
  • We haven’t had more than 10 accidents in a race since 2016.
  • We’ve had at least three accident-free races in the last two years
  • That big red spike shows that 28% of the races (10/36) in 2018 had two cautions — and none had more than eight.

Which Tracks Have the Most Accidents per Race?

The question I posed at the start was whether the mayhem at Bristol was comparable to the chaos at plate tracks. The table below shows the five races with the most accidents in each of the last five years, plus 2019 to date. I highlighted the plate tracks in blue and the mile-and-a-halfs in yellow.

A table showing the five races with the most accidents per race from 2014-2019 (so far)
*The data for 2019 only includes the first 23 races.
  • Most accidents in a single race:
    • Darlington 2015: 15
    • Fall Richmond 2016: 14
    • Spring Bristol 2016: 13
    • Fall Martinsville 2015: 12
  • The plate track with the largest number of accidents is This year’s Daytona 500, with 8.

Surprisingly, plate tracks don’t make much of a showing in this chart. There are actually more mile-and-a-halfs than plate tracks.

A pie chart makes the variations in track type even clearer. This chart shows how many times each track appeared on the list of top-five tracks for accidents each year.

A pie chart showing the distributions of accidents per race that emphasizes the different types of tracks.
  • Plate tracks (green) made up 13.3% of the 30 tracks
  • Large tracks (orange): 6.7%
  • 1.5-milers (blue): 20.0%
  • Smaller tracks (shades of red): 60%

To be fair, we haven’t run Talladega this year, but we also haven’t run fall races at Bristol or Martinsville, both of which typically have larger numbers.

Does Race Length Matter?

Short tracks have more accidents than longer tracks, even though they often run shorter distances. Bristol is 266.5 miles while races at Daytona and Talladega are 400 or 500 miles. Charlotte has a 600-miler.

So let’s look at how many accidents there are per 100 miles, which evens out the length disparity. Here’s the analogous chart.

A chart showing the top five races with the most accidents per 100 miles.
*2019 data includes the first 23 races.

Now there’s only one plate track represented: This year’s Daytona 500 had 1.55 accidents/100 miles. Spring Bristol had 1.7 times more.

The highest rate is Spring Bristol in 2016 with almost five per hundred miles. If Spring Bristol that year had been 500 miles, there would have been 24 or 25 accidents.

This pie chart shows how many times each track is represented in the table.

A pie chart showing the top 5 tracks each season from 2014-2019 when you consider accidents per 100 miles.

We’ve got a different balance of responsibility using this metric.

  • Plate tracks (green): 3.3%
  • Large tracks (orange): 6.7%
  • 1.5-milers (blue): 16.7%
  • Rovals (red): 3.3%
  • Smaller tracks (shades of red): 70%

So Are Short Tracks More Dangerous?

There’s one more variable we should consider. The 2019 Daytona 500 only had 8 accidents. But here’s a chart showing how many each car was involved in.

A column chart showing the number of accidents each car had in the 2019 Daytona 500.
The red bars denote cars that DNF.

While there were only eight accidents, almost everyone was involved in at least one (and often more than one) of them. It’s not too surprising that the first and second-place cars were two of the four vehicles that avoided all the crashes.

Even with fewer accidents at plate tracks, you have a higher likelihood of being caught up in each one.

Car Count

The chart below tallies the number of accidents and the total number of cars involved. I only have three years worth of data available here.

A table showing the five tracks for each season 2017, 2018 and 2019 that involved the largest number of cars in accidents
* 2019 only covers the first 23 races.

You can see the tradeoff:

  • In 2019, the Daytona 500 only had eight accidents, but they involved 51 cars.
  • The Charlotte race (spring) had three more accidents than the Daytona 500, but only involved 19 cars.
  • For each of the last three years, a plate track took the prize for the most numbers of cars involved with 51, 51 and 46
  • So far this year, 190 cars have been involved in wrecks.
    • 95 of those cars (50.0%) were at the three plate races.
    • 79 (41.5%) were at Daytona.
    • 45 (23.7%) were at Bristol

But, of course, we still have to run Talladega this fall!

Which Tracks Eat Cars?

Here’s a pie chart showing how the 313 cars involved in accidents breaks down by track in 2018.

A pie chart showing where all the accidents in 2018 happened.
  • Plate tracks (green) : 35.5% of all cars in accidents
  • Large tracks (orange): 10.0%
  • 1.5-milers (blue): 21.7%
  • Rovals (red): 15.0%
  • Smaller tracks (shades of red): 17.8%

But note that Bristol hasn’t disappeared, even in this category.

  • Bristol had more cars involved in 2018 than the rest of the short tracks combined
  • Bristol even outdid Talladega.

NASCAR’s Most Accident-Prone Tracks Are…

To really determine which tracks are the most accident prone, you have to look at how many accidents they have per 100 miles and how many cars are typically involved in an accident.

As a first attempt at a figure of merit, let’s just multiply those two numbers together. The tracks that consistently end up with the largest numbers (2017 through 2019-to-date) are Daytona, Bristol and Talladega.

Track DLP Danger Index
Daytona 8.91
Charlotte Roval* 7.65
Bristol 6.38
Talladega 4.58
Indianapolis 4.35
Martinsville 3.41
Kansas 2.40
New Hampshire 2.12
Phoenix 2.11
Charlotte 1.82
Dover 1.53
Texas 1.43
Michigan 1.41
Richmond 1.33
Auto Club 1.33
Pocono 1.28
Las Vegas 1.23
Kentucky 1.22
Darlington 1.20
Sonoma 0.91
Homestead-Miami 0.87
Watkins Glen 0.60
Chicagoland 0.17
Atlanta 0.07

I’ve starred the Charlotte Roval because we only have one data point. You’ll notice that road courses in general rank very low in terms of propensity toward accidents. We’ll have to see what happens this year.

The list is imperfect because of the low numbers of data points. And remember that there’s variation from year to year.

  • Daytona’s DLP Danger Index ranges from a low of 6.18 to a high of 12.1.
  • Bristol’s DLP Danger Index ranges from 4.12 to 9.38
  • Sonoma’s Index is low because it went two years without accidents, but the one year there were accidents, its Danger Index was 2.74


Although plate tracks seem to have the market on accidents, Bristol holds its own.

NASCAR 2018: The Year in Charts and Graphs

This is the time of the year when everyone takes one final look back at the last year before turning to think about the new one. So, in this last blog of the year, I thought I’d summarize the season in charts and graphs.

Where and When We Raced

If we consider the top-three NASCAR series.

  • NASCAR ran 92 points-paying races in 2018
    • 36 Cup Races
    • 33 Xfinity Races
    • 23 Truck Races

In 2018, we

  • Were scheduled to run 13,979 miles (10279 laps) worth of races
  • We actually ran 13,741 miles (10,250 laps).

Thank you, weather.

Where We Raced

The star denotes Canadian Tire Motorsport Park in Bowmanville, ON. For some reason, the only way Excel lets you show Canada is if you include the entire Western Hemisphere.
  • NASCAR visited 23 states (46%) and one province.
    • Florida and Virginia hosted the most races: 8 each
    • Ontario (Canada) and Wisconsin hosted the smallest number of races: 1 each.
  • Although NASCAR is national, it’s base is still strongly in the Southeast
    • 38 races (41%) were run in the Southeast
    • 14 races (15%) were run in the Southwest
    • 14 races (15%) were run in the Midwest
    • 6 races (6.5%) were run in Texas

If we look at Cup races only:

  • NASCAR ran its customary 36 points-paying Cup races in 2018 in 20 of the 50 states (40%)
    • Virginia hosted the most races of any state with 4
    • Florida hosted the next-most races with 3
  • The Cup Series’ base is also still clearly the southeast.
    • 16 races (44%) were in the SouthEast
    • 6 races (17%) were in the West.
    • 4 races (10%) were in the Midwest
    • 2 races (5%) were in Texas

A Quick Flashback

I went back to look at where we raced 20 and 40 years ago. I put all of the graphs on the same color scale, which runs now from 1 to 6 because there were six races in North Carolina in 1978.

Note also that the number of races changed in that time from 30 to 36 as well.

When We Raced

Only 3 races were moved due to weather this year, which meant 8% of our races ended up being on Monday.

Three-quarters of the races were run on Sunday — and all three Monday races were scheduled for Sunday, which means that the original schedule was for 83% Sunday races. That leave 17% Saturday races.

While it sometimes seems like there are more and more night races, they actually only make up 22% of all Cup races

You can see the distinction if we look at start times, too, because Saturday races are all night races.

  • 28 races (77%) started in the afternoon.
  • 19 races (53%) started between 2:00 and 3:00 p.m.
  • 12 races (1/3) started between 2:00 and 2:30 p.m.

The Races

29 drivers completed all 36 races. Unsurprisingly, they were the top 29 drivers in the end-of-season rankings. You already know who has the most wins (Harvick and KyBu tied with 8 each), so let’s look at some lesser known stats.

Margins of Victory

In 2018, only one race (first Michigan) ended under caution. The margin of victory for the other 36 races varied a bit.

The average margin of victory was 2.1 seconds, but that’s heavily skewed by runaway victories at

  • Fontana (Truex, Jr.)
  • Sonoma (Truex, Jr.: when he was good, he was very, very good…)
  • Watkins Glen (Chase Elliott)
  • first Dover (Harvick).

Those four races were each won by more than 7 seconds.

However, 50% of the races were won by 1 second or less, and 88% of the races that ended under green were won by 4 seconds or less.

  • 1 race (2.7%) ended under caution
  • 4 (11%) races were won by 0.2 seconds or less
  • 10 (28%) races were won by 0.5 seconds or less
  • The smallest margin of victory was second Talladega (0.105 seconds)
  • The largest margin of victory was Truex, Jr. at Fontana (11.685 seconds)

Flashback to 1998 Again

In 1998, the average margin of victory was about the same (2.0 seconds), but…

  • There were 33 races
  • 5 races (15%) ended under caution
  • The smallest margin of victory was 0.051 seconds (Jeff Burton at Richmond)
  • The largest margin of victory was 13.117 seconds (Dale Jarrett at Dover, of all places. There were only four cars on the lead lap at the end of the race.)
  • 13 races (39%) were won by 1 second or less
  • 7 races (21%) were won by 0.5 seconds or less
  • 4 races (12%) were won by 0.2 seconds or less

If we make a histogram for 1998…

It’s really not so different from 2018, it it?

What About 1978?

I was going to give you an average for 1978, but I can’t because a number of those races don’t actually have time differences, they have lap differences. While Rockingham was won by 1.3 seconds, Martinsville was won by 3+ laps.

Every year, we’re going to have at least one, and probably two or three (or four) races every year where someone runs away with it. It’s the nature of sports.

Pole Speeds

I suppressed the zero on this graph because it made it easier to see the differences.

  • Qualifying was rained out three times.
  • The high and low pole speeds of the season happened in two consecutive races.
    • The low was 94.597 mph (Sonoma; Kyle Larson)
    • The high was 203.361 mph (Michigan; Kurt Busch)
    • Neither pole sitter went on to win the race.


We’ve talked in some detail this year about the fact that the cautions are down, due in part to stage racing and in part to the damaged vehicle policy.

  • We had 247 cautions for 1328 laps
    • The largest number of cautions in a race was spring Bristol (13) followed by fall Vegas (12)
    • The smallest number of cautions in a race was 3, which we saw at Sonoma, fall Richmond, and fall Kansas. In each of those races, two cautions were for stage ends, so they really only had one unplanned caution each.

Lead Changes

  • We saw 550 lead changes this season, but the number per race was pretty spread out.
  • The smallest number of lead changes (7) was at Darlington
  • The largest number of lead changes (25) was at
    • Daytona in July and
    • Talladega in the spring.
  • Daytona (February), Atlanta and Chicagoland had 24 lead changes each.
  • Plate tracks tend to have more lead changes, but this year, fall Talladega only had 15 lead changes.
  • The average number of lead changes per race is 15.3.


I did a whole blog on penalties up to the 34th race, but let’s add those last two in, just for the sake of being complete…

  • NASCAR officials levied 393 pit-road penalties. I’m counting them the way racing-reference.info does: They don’t count pitting too soon because that’s usually done intentionally as strategy.
  • Of the penalties you can pin on the driver (194/393 or 49%):
    • 155 were speeding on pit road
      • 92 were too fast entering
      • 63 were too fast exiting
    • 16 were commitment line violations
    • 10 were pitting out of the box
  • Of the penalties you can’t necessarily pin on the driver (52%)
    • 86 were tire violations
    • 49 were too many men over the wall
    • 29 were crew member over the wall to soon
  • The teams of Michael McDowell, Daniel Suárez and J.J Yeley should probably set their warning lights a little further from pit road speed next year.
    • McDowell had 19 penalties, 13 of those being speeding on pit road.
    • Suárez had 16 penalties, 9 being speeding on pit road.
    • Yeley had 10 penalties, 7 of them for speeding on pit road.
  • The least penalized full-time driver was Kurt Busch with 4 penalties

Laps Run

  • Out of 10250 laps,
    • Ryan Newman completed the most laps (10,077 or 98.31%)
    • Ricky Stenhouse, Jr. completed one less lap (10, 076 or 98.30%)
  • I put red boxes by our final four. There’s no obvious correlation between being in the final four and laps run.
    • Harvick was the lowest of the final four (9691 laps or 94.55%)
    • Kyle Busch was the highest of the final four (10,001 or 97.57%)
  • Even Michael McDowell, who completed the smallest number of laps of any full-time driver, ran 87.4% of all the laps in all the races, which comes out to 8964 laps.

Laps Led

Completing laps is good: Leading them is better.

  • Here, there is a very clear correlation with finishing position. There’s our final four in the first four spots.
    • Harvick led 19.4% of all laps run
    • Kyle Busch led 14.33% of all laps run
    • Truex, Jr. led 9.91% of all laps run
    • Logano led 9.11% of all laps run
  • However, you will notice that the champion is number four on the list! So there’s no correlation with the final four finishing positions.
  • All the full-time drivers led at least one lap with the exception of poor David Ragan (who is one of the nicest people on Earth).
  • The top four drivers led 52.77% of all laps run.
  • The remaining 25 drivers led 42.3% of the remaining laps
  • Part-time drivers lead 0.48% of the laps run.

Lead Lap Finishes

Lead Lap finishes provides a better sense of discrimination, which is a good thing in statistics. Some numbers don’t vary too much, which means them don’t tell you a lot between the highest driver on the list and the lowest driver on the list.

I didn’t show you the graph for running at finish because it just wasn’t that interesting. There was one interesting thing: William Byron was off track at the end of 25% of the races, which is lot more than even the next lowest (DiBenedetto and Wallace 19.5%.

Well, here’s a stat that varies a lot from highest to lowest: In what percentage of the races did the driver finish on the lead lap?

Again, you see that our top four are right up there in the top four positions. Kyle Busch finished on the lead lap in all but 5 races But look at the range. Bubba Wallace and Matt DiBenedetto only finished 6 races on the lead lap.

Thank You!

Thanks for following along in 2018. I wish everyone a happy, healthy 2019.

How NASCAR’s “Enhanced Schedule” Affects the Race Teams

NASCAR’s enhanced schedule compresses three days on on-track activity into two. It’s designed to save teams time and money, but how will it affect the racing?

What is an “Enhanced Schedule”?

If you’re wondering why NASCAR used the moniker ‘enhanced’ for compressing what used to be three days of Monster Cup activity into two, it’s because they’ve added additional events and opportunities for fans to meet drivers and enjoy other entertainment. Although Cup racing is all done in two days, tracks point out that they offer a full weekend (or more) of activity.  This week, Chicagoland had an ARCA race Thursday, Trucks race Friday and XFINITY races Saturday.  The enhanced schedule doesn’t give the drivers an extra day off, but it does shorten the weekends for crew members — which saves the teams money on hotels, meals and rental cars and gives the crew members one more night at home with friends and family.

NASCAR tested the ‘enhanced schedule‘ at four races in 2017.  Twelve races feature the ‘enhanced schedule’  in 2018. Ten out of twelve weekends on the enhanced plan have only two of the three main series running, which makes it a little easier to compact the schedule. The exceptions are Chicagoland and Kentucky.

The tracks that have enhanced schedules in 2018

Chicagoland is the fourth enhanced-schedule race this year and will be the first on a Saturday-Sunday schedule. Richmond and Kansas were Saturday night races and Martinsville was snow delayed to Monday. The enhanced schedule shifts qualifying from Friday to after the XFINITY race Saturday, which allows fans who might only have seen the XFINITY race to see the Cup drivers qualify.

While the changes on-track are minimal, the behind-the-scenes changes are not. To get a clearer idea of how the ‘enhanced schedule’ affects the teams, I made a graphic to compare the 2017 Chicagoland Cup schedule with this year’s.

How Will the Schedule Change Things?

Let’s break this down by looking at how the enhanced schedule changes each of the elements: practice time, garage time, overall time, and the impact on inspections

Practice Time

Teams are losing their Friday practice, so they’ve got 2 practices instead of 3; however, the Friday practice was 85 minutes and neither of the remaining two practices were lengthened.

Comparing minutes of practice time in the standard and enhanced schedules

Comparing minutes of practice time in standard and enhanced schedules

Teams have lost 46% of their practice time.

  • A team can’t test as many setups. If they’re having problems, they have much less time to try to figure out why
  • They no longer have a practice that can be dedicated to qualifying. They’ll have to prioritize: Is it more important at this track to get a good qualifying position or to tweak the race set-up?
  • Teams with less experienced drivers will be impacted more than teams with veteran drivers, especially given limited on-track testing opportunities.
    • Kevin Harvick, Matt Kenseth and Kurt Busch have 17 Chicago races each. Figuring 3 hours for the race and 185 minutes for practice, that’s over 100 hours of track time (not including any testing, which all of them must have done).
    • Suárez and Jones each have 1 race at Chicagoland, which corresponds to a little more than 6 hours of track time. The loss of 46% of their track time will impact them much more than the veteran drivers

Practice Time Doesn’t Matter — Because Computers

I’ve heard the opinion expressed this week that lack of practice time doesn’t matter because everyone relies on computer simulations (and the drivers on advanced racing simulators). 

Teams have invested in computer simulation programs, especially since NASCAR cut back on-track testing. I’ve talked about the advanced simulators drivers use to prepare for races, but engineers also rely heavily on computer simulations: vehicle dynamics, aerodynamics, engine and even tires. These are incredibly complex programs and they’ve come a long way, but people often give them much more credit than they deserve.

Simulations Are Not Magic

Cars are complex, nonlinear systems. And that’s without even considering the chaos that is turbulence. Here’s a diagram with just some of the parameters you need to describe the motion of a car.

A few of the forces acting on a car

These forces aren’t calculated by simple equations like y=mx+b. Oh no. You need differential equations. Below are the equations for two components of just one of the forces shown in the diagram above.

The differential equations used to calculate one of the forces on the race car

And each track presents its own unique quirks and challenges (bumps and seams). And everything is impacted by the weather (rain, heat) and how it’s worn the track. It’s very difficult to fold those types of high-variable, time-dependent elements into a computer program.


A computer simulation requires input data. The universal principle of GIGO (Garbage in = Garbage out) applies here.

Garbage in = garbage out applies to race car simulations, too.

So even if simulators were perfect, they would only be as good as the data being input. The input data comes from the racetrack. The reduction of on-track time impacts how accurate the simulations can be. The reduction of overall time at the track decreases how many times simulations can be run, thought about and analyzed.

Some of the input data is readily available, like the parameters of your shocks or the size of your springs. But simulations use feedback. You test out a set up and check the laptimes against what the simulation predicts and the simulation can then include the new information. Without actually comparing what the model says against the actual lap time, you can’t make the model better.

The simulators narrow down the parameters the crew chief can change at the track — the springs, shocks, caster, camber, toe., Ackerman, trackbar, weight distribution, etc. But they have to test out those parameters to see which ones are best.

Decreased practice time = decreased input data for the simulations.

The Unmodel-able Parameter

Here’s a diagram from a Ph.D. thesis on creating a program for modeling car dynamics. Focus on the leftmost blue box. The one labelled “DRIVER”. 

A flowchart showing some of the elements that must be considered in a simulation program

Drivers are human. (Yes, even Matt Kenseth.) They don’t behave reproducibly or sometimes even predictably. A driver who wrecked his primary car is going to have a different attitude than one who didn’t. A driver who’s P1 in practice will have a different attitude than the driver who is P36. A large part of practice time is the crew chief figuring out the driver: his mood, his receptivity for feedback, his attitude toward the track, his physical state, how the teething child is affecting his sleep. I’ve said it before, but it beats repeating: the crew chief’s jobs is just as much about managing the driver as it is managing the car.

Loosing practice time gives the crew chief less time to gather data about the driver and that affects everything from the ultimate set up he picks to how he calls the race.

Garage Time

Let’s compare the hours the garage is open.

Comparing the hours the garage is open 2017 vs. the enhanced schedule in 2018

While the team members get one more day home, they’re going to have a very, very long day Saturday. The garage will open at 7:30 Saturday morning and won’t close until 9:30 that night. The teams have to be back the next morning at 9:30 for a long, stressful race day that may conclude with a late airplane flight home.

The garage was open 24 hours last year vs. 18.5 this year, which is only a difference of three-and-a-half hours.

But that’s a little deceiving because cars are impounded after qualifying. Teams can perform only very limited adjustments (tape, tire pressures, etc.). So even though the garage is open, the actual time the team can work on the car is much shorter. NOTE: I’m counting up until 2 hours before the race start because cars have to pass inspection and get out on the grid.

Comparing the total time to work on the car in 2017 vs. the enhanced schedule in 2018

  • The crews have about 50% less time to work on the car
  • They have to multi-task because they have to worry about qualifying and race trim.
  • They lose the opportunity to work on the car after qualifying, as the next graph shows

Comparing the time to work on the car between qualifying and the race in 2017 vs. the enhanced schedule in 2018

Thinking Time

The compressed schedule also decreases the amount of time the team has to THINK about working on the car.

Don’t underestimate the importance of thinking. Answers don’t come easily to complicated problems.  When I’m stumped, I go for a walk or do something else and let my subconscious work on the problem. You can argue that sleeping time doesn’t count, but but when I was in graduate school, I often fell asleep thinking about a problem and woke up with, if not the answer, a new way of attacking the problem. So let’s look at the time from when the garage opens to the last time you can change something on the car. Again, I’ve counted only up to two hours before the race.

Comparing the total time to think about the car in 2017 vs. the enhanced schedule in 2018

The crew chief now has 17% of the time he used to have to ponder his car. Nine hours instead of 52

  • He’s got only an hour (instead of overnight) to digest the results of the first practice before there’s a second practice
  • The crew chief has to multitask (qualifying and race setup), so he’s thinking about more things during less time.
  • There’s a psychological toll. How frustrating will it be for the crew chief to realize Saturday night that changing a spring or tweaking a shock could solve all the problems they had in qualifying?

Expect More Unforced Errors

People make mistakes and they make more mistakes when they’re tired, hungry or in a rush. There will be few opportunities for crew members to take breaks on Saturday. How many times have we seen a driver get a pit road speeding penalty because someone screwed up the tachometer calculations? Or something fell off the car because a crew member was in a rush? The most-disciplined, most organized teams will have an advantage.


What’s not obvious from the schedule are the changes in inspection. Compacting the schedule required NASCAR to eliminate one inspection.

  • The usual ‘welcome to the track’ inspection remains the same. NASCAR checks all safety features, the engine and fuel system, the chassis and runs them through the OSS.
  • There is no pre-qualifying inspection in the enhanced schedule. The teams have only four hours between final practice and qualifying. There’s an XFINITY race during that time, the teams need to switch from practice setups to race set ups, and they won’t be able to touch the car after qualifying. It would be tough for the teams and for NASCAR to get all the cars inspected in the time available. So some time that would have been used up in 2017 going through inspection is now available for working on the car.
  • The pre-race inspection will be done immediately after qualifying and the cars impounded.
    • If you fail the post-qualifying inspection, you start at the rear
    • If you fail the post qualifying inspection twice, a crew member is ejected
    • If you fail the post-qualifying inspection three times, there’s a 10-point penalty


So there it is: A quantitative analysis of what changes under the enhanced schedule and my opinion on how it’s going to affect the teams.

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Early-Season Predictions Require Caution

Predictions: The Peril of the Early-Season Off-Week

I can post only when I have something to contribute. The professionals who cover NASCAR don’t have the luxury of waiting until there’s news. The content monster remains perpetually hungry. That need to fill space is probably why, every year about this time, we see predictions that try to draw conclusions about the entire season based on the first few races.

This is almost never a good idea. You wouldn’t, based on the first four races, predict that Austin Dillon would win nine races this years and Kevin Harvick the other 27 just because Dillon won one race and Harvick three, would you?

Remember back in 2012, when everyone was panicking because cautions were way down?  I wrote an post then (Why You Can’t Predict Anything Based on the First 10 Races) warning that the it was a mistake to make predictions based on 10 races, but particularly on the first 10 races. But here we are doing it again.

Will 2018 Set a Record for Least Cautions?

I have no idea.

Neither does anyone else.

Worries about an anomalously low caution rate are being sounded again this year, spurred in part by a Martinsville race that was called by Kyle Petty as

“the most non-aggressive Martinsville race I’ve probably ever seen in my life… And I’ve been going to Martinsville for a long, long time.”

Let’s look at the evidence.

  • NBCSports points out that, there have been an average of 5.3 cautions per race, the lowest since 1999
  • There were 32 cautions this season, which is the same number as in 1999, while there were 66 cautions in 2008.

Here’s the graphic NBC Sports showed.

You know me: I don’t take anyone’s word for anything. I made my own plot.

The first thing you’ll see is that the TV people omitted some data. When you’re only showing a graph for 15 seconds, you can’t show anything very complicated because there’s just too much for people to take in.

The problem is that omitting that data also omits some important information. The problem is made worse by drawing lines between data points, because that suggests that the data behave the way the line indicates. Now that you’ve seen the entire data set, you know that the data are not as well behaved as the graph would have you think.

  • This graph makes it look like the cautions went up for awhile and then went down starting in 2008.
    • While there is an overall trend toward lower numbers of cautions since 2008, it’s not monotonic (which means it gets smaller each time.)
    • There were the same number of cautions in 2012 as there were in 2008.
  • The tracks being run and the lengths of the races changed from year to year. If you run 400 miles at one track one year and 500 miles the next year, you’re going to have more cautions in the longer race. These numbers don’t take those factors into consideration.
  • At the end of 2017, people were wailing about cautions having gone way up. That larger number of cautions in 2017 is omitted from the graph.

Comparing Apples and Oranges — Or Giraffes

I’ve made the argument before that you cannot look at cautions in terms of something as simple as absolute numbers of cautions and suggested that the appropriate measure is cautions per 100 miles of racing. Here’s the graph for the first six races, followed by my justification of this metric.

The Number of Races Run per Season Varies

We’ve only been running 36 races per season since 2001, so comparing absolute numbers only works from 2001 on. I realize that doesn’t make a difference in graphs of the first six races, but you’re predicting the entire season and it does make a difference if you run 34 vs. 36 races.

The Lengths of the Races and the Tracks Run Varies

  • Even when there are the same number of races, the orders of the tracks changes
  • Some races are rained out; others go into overtime
  • The lengths of races have changed over the years

Compare the cautions per 100 miles graph and the absolute cautions graph and you’ll notice some differences. Look at 2010 and 2011, for example. The points change because the Fontana race changed from 500 to 400 miles and Atlanta was replaced by Phoenix. We’d run 300 miles less in 2011 after six races than we had in 2010. The table below lists the first six races those years.

2011 Track 2011 Miles 2010 Track 2010 Miles
Daytona 520 Daytona 520
Phoenix 312 Fontana 500
Las Vegas 401 Las Vegas 401
Bristol 267 Atlanta 525
Fontana 400 Bristol 267
Martinsville 263 Martinsville 267
Total 263 Total 2480

We’ve run the same tracks with the same race lengths since 2015. Before that, Bristol was one of the first six races and you can imagine that’s going to change the information for the first six races. So here’s the Cautions per 100 miles after six races.

What About Stage Racing?

As we discussed in another blog, the advent of stage racing has changed things. We have fewer debris cautions and more stage cautions. I’m ignoring that here, just because it’s hard to control for that.

But 2018 Does Have Record Low Cautions, so Far, Right?

Technically, no. If you correct for some of the above considerations, 2018 has 1.34 cautions per 100 miles while 1998 has 1.27 cautions per 100 miles. The next lowest is 2016 with 1.42 cautions per 100 miles.

But the number of cautions is definitely lower than it was last year and slightly lower than in 2016, so let’s look at that. When did that happen? Let’s start with looking at the cautions after one race.

That’s a very different graph, isn’t it? 2018 is nowhere near the lowest. Let’s look at some other stopping points.

So 2018 wasn’t anomalously low after 3 races or 5 races. In fact, 2018 only became the “lowest-caution season” after the sixth race. What we’re seeing isn’t indicative of the 2018 season, it’s indicative of an extraordinary Martinsville race.

Blame 2018 on Martinsville

Here’s a graph of cautions for Martinsville since 1980. I separated out the Spring and Fall races, mostly because it made a very pretty graph.

We can make a histogram of cautions to see how they behave.

You’ll notice from the top graph that there have been more cautions in the last few years, so I made the same graphs for just 2001-2018.

Looking just from 1980-2018

  • Martinsville has an average
    • 10.76 cautions for the Spring race
    • 12.50 cautions for the Fall race
  • The races with the lowest numbers of cautions are 4 (Spring 2018) and 5 (Fall 2016)
  • The races with the highest numbers of cautions are 18 (Spring 2008) and 21 (Fall 2007)
  • Martinsville has a really high standard deviation: 3.45 (Spring) and 3.83 (Fall).

The standard deviation gives you a range of the most likely (most likely being 68% of the time) number of cautions.

For the Spring race

  • About 68% of the races should have between 7 and 14 cautions.
  • About 95% of the races will be between 4 to 18 cautions.

I should note that the number of cautions has been higher in the later years. If you just look at 2001-2018, the average s 12.33 – but it’s nowhere near a normal distribution, so I didn’t want to try to apply standard deviations to those numbers.

People came up with all kinds of explanations for what was going on:

  • Drivers are better
  • It’s all due to stage cautions, which have made debris cautions go down.
  • Double-file restarts made the cautions go up
  • Cars are easier to drive because of downforce changes
  • Drivers have realized how important it is for the championship that they not crash out early
  • Drivers have just gotten less aggressive
  • Better tires, better engines, better brakes mean less parts failures

These things may be true; however, the reason 2018 has an abnormally low number of cautions is because the last Martinsville race was on the lowest edge of likely. If we’d had the average 12 cautions, there would be 40 cautions total, which is still on the low side, but is higher than it was in 2016. 2018 wasn’t anomalous after three or five races. The Martinsville race pushed it over.

So This is Just Because of Martinsville?

No. As I’ve pointed out before, the caution number change significantly over the first 10-15 races. Let’s look at the cautions as a function of race number. Here’s the numbers for 2015.

You see how the numbers change rapidly over the first 10 race, then settle in to what ends up being the final average around race 20 or so. Making any kind of prediction before that is a losing proposition.

The cautions don’t always go up, either. Here’s the 2011 data

So lets compare the first six races to the final values.

There’s some similarity between the two graphs, but you’ll notice that , for example, 2004 didn’t end up being as low as it was after 6 races; 2012 was high after 6 races, but ended up being very low.

The moral: Don’t try to extrapolate a whole season based on one extreme. Remember when we talked about the average age of the Daytona field and found that having one 66-year old in the field of 40 drivers increased the average age of the field by one whole year? It’s the exact same thing here. We’ve got one really anomalous race and it skewed the numbers.

Let’s see what the numbers look like after Bristol. All we need is one crash fest and we’ll be right back to normal.

This Stuff Actually Matters

Yes, it’s a minor irritation when NASCAR stats are misrepresented; however, people do this in all kinds of areas from economics to politics. Knowledge really is power. Don’t be swayed just because someone is throwing numbers around. Make sure those numbers mean something.



Data Sharing: Fact and Fiction

New for 2018: Data Sharing

NASCAR announced (in a somewhat roundabout way) they would share the data collected by each car during races, qualifying and practice with all teams in the 2018 season. This produced a mixed reaction in the garage. Newer drivers and those driving for smaller teams seemed to like the idea while more experienced drivers didn’t seem so happy.

“I’ve spent 13 years in this sport to figure out how to drive a racecar, make it go fast, do the things I do to win races and championships… Now you’re going to hand all that on a piece of paper to a young driver, they’re going to figure it out, as long as they know how to read it.”

— Kyle Busch via Tom Errington at Autosport

“I know a lot of veterans are more against it than younger drivers, just because it’s not what they’re used to.

— Ryan Blaney via Tom Errington at Autosport

“Any resource that you can have at this level, no matter what it is or how small, you have to be so perfect at everything at the Cup level that anything that we can get our hands on is going to benefit us, for sure,”

— Matt DiBenedetto via Kelly Crandall at Racer

While there were a surfeit of driver opinions in the press, there was much less information about what data was being shared, why the decision was made to share it, and what the impact was likely to be.

What’s Being Shared and Why?

Let’s start with why.

NASCAR has long made information about the car available to their television partners in real time. Back in 2012, folks from SportVision (the company that collected the data for TV and Raceview),about whether teams could intercept that data. They said that they acquire such a huge amount of data that analyzing it in real time is impossible.

That was six years ago.

Raceview provides the same real-time information as television shows: steering angle, speed, throttle and brake, all keyed to the car’s on-track position. Six years ago, it might have been impossible for a team to intercept and use the data from Raceview. Today, it’s not only possible, it’s common. They call it data scraping: Intercepting data intended for display and funneling it into a database or analysis program. Wikipedia calls data scraping ‘inelegant’ and ‘ad hoc’, because you aren’t getting a nice clean stream of data, but if that’s the only way to get the data, that’s what you do.

All the larger teams have been data scraping Raceview for some time. It’s not that complicated a task, but it takes time and money — which puts smaller teams at a disadvantage.

Some combination of NASCAR and the manufacturers decided it was silly for everyone to spend large amounts of money and time scraping data. Now NASCAR provides the information directly.

What Information is Being Shared and How?

The information being shared comes in real time — during practices, qualifying and the races, but only a small subset of data is shared. The shared data comes from two places: The Engine Control Unit (ECU) and the GPS unit on the car.

Charlie Sullivan, who handles ECU management for Earnhardt-Childress Racing Engines, explained that the McLaren ECU can record about a thousand channels. A channel is one piece of data; for example, brake pressure would be one channel, throttle another channel.

Out of a thousand possible channels, NASCAR limits the teams to collecting 200 channels. NASCAR mandates what you measure with 60 channels and you can measure whatever you want on the other 140 channels. Let’s look at this graphically:


Four channels out of 200 from the ECU are being shared. Nine channels come from the GPS data and locate the car’s position, speed and velocity. The number is a little misleading: It takes three channels to locate the car in space: latitude, longitude and altitude. Position is a vector, after all.

The data being shared is important, but only a small fraction of everything the teams collect. It’s also pretty much exactly the data people were scraping off Raceview. Now everyone has access to it, and it broadens the available data because Raceview is active during qualifying or practice.

Data Precision

NASCAR teams take a lot of data, especially in the ECU. You want to measure some things frequently and some things not so frequently. Logging rate is the frequency at which measurements are made. It’s measured in Hertz (Hz), which you can read as ‘per second’.

  • 1 Hz means once per second.
  • 5 Hz means five times per second or once every 0.2 seconds.
  • 50 Hz means 50 times per second or once every 0.02 seconds
  • 500 Hz means 500 times per second or once every 0.002 second.

The simple thing to do is just measure everything at the highest possible logging rate — except the McLaren ECU only has 64MB of storage. (For specialists: this is 16-bit info. We’re not storing integers here.) Each team has to decide which data they really want. Something like temperature may be measured at 5 Hz and something like steering or brake or throttle might be measured much more frequently.

But all the data NASCAR provides to the teams is at 5 Hz, just like Raceview.  This means that I can have a much more detailed trace of my own brake and throttle than the other teams are seeing.

So What’s the Impact?

There are two areas in which data sharing could affect competition: letting the drivers and teams see what other drivers are doing and in determining race strategy.


Andy Randolph pointed out that the data sharing program means that a team now has access to IT’S OWN DATA in real time. Before this change, the team couldn’t access the data until they could physically plug a laptop into the ECU and download it.

  • Everything is cloud connected. During practice, someone can be analyzing the driver’s performance. When the driver pulls into the garage after a practice run, the crew chief will have a tablet with the relevant graphs all ready to show the driver. This information might be his performance, or might be a comparison between his throttle trace and another driver’s
  • As Charlie Sullivan pointed out, not only can people at the track access all of the information immediately, so can everyone on their network. The people at the shop can focus on analysis and forward observations to the crew chief, who doesn’t have a lot of time during practice to look over data. You’re basically bringing more brainpower to the problem by distributing different performance questions to different people. (“Sometimes we’re looking at the data while they’re still drinking champagne in Victory Lane”, he said.)


  • Let’s say your competitor is beating you back to the throttle by 0.4 seconds coming out of turn 2. The driver probably already knows that, just from following the competitor around the track and it doesn’t help him to know that the differential is 0.413 seconds. He’s not waiting to get back on the throttle because he thinks it’s better strategy, or he doesn’t understand how racing works. He’s slower on the throttle because the car won’t let him be any faster.
  • Teams don’t have any information on other teams’ setups: suspensions, steering geometries or anything else. Before shared data, you knew the other guy was faster. The shared data let you figure out where the other guy is faster. But nothing tells you how to make your guy faster. If winning was as easy as choosing the right line, there wouldn’t be much to it.


  • Josh Browne, former Crew Chief for Elliott Sadler, pointed out that you can’t find speed if you aren’t looking for it. The data will tell the team that it is possible to be faster in a particle section of the track and allow the team to focus efforts. For example, if you were 4/10 of a second slower in turns 1/2 and 2/10 of a second slower in turns 3/4, the crew chief might prioritize changes that will affect turns 1/2 because the potential gain is greater. While the data doesn’t tell you how to fix it, they do allow you to prioritize the search for speed.
  • Josh also points out that even without knowing setup details, you can still learn things. Steering input, for example, depends on the steering configuration, but you can compare different laps for the same driver to look for changes and you can also learn how loose or tight another car is based on how much wheel-sawing is happening.

Can’t You Already Get All This from Dartfish?

Dartfish is a video tool that allows you to overlay your car with another car. As the video plays, you can see exactly where one car has the advantage relative to the other. As Andy Randolph pointed out, this is good for comparing a small number of laps. Sometimes, you don’t have the video you need to do the comparison. Also, you’re not going to go through 500 laps of a race comparing your driver with 39 other drivers. Dartfish is still a useful tool (especially for those uncomfortable with graphs and charts), but it provides different information than the shared data.

Is It Fair?

Senior drivers’ complaints seem to center on rookies having an advantage they didn’t have. Back in the day, they had to figure out all this for themselves. (I sympathize. I walked to school uphill both ways.) But let’s look at what else has changed since Kyle Busch and Ryan Newman were rookies.

  • In 2003, teams could pick five 2-day tests and four 1-day tests at tracks on the Cup schedule, plus Daytona pre-season testing and unlimited testing at tracks not on the Cup schedule
  • In 2013, Organizations (not teams) could choose four Cup-scheduled tracks for testing, plus Daytona pre-season testing and 15 NASCAR-sponsored tests
  • In 2015, there were 12 1-day NASCAR sponsored tests and NO private testing at any track
  • In 2018, there are four NASCAR-sponsored tests.

I suspect if you asked today’s rookie drivers whether they would prefer being able to test on a real track, test on a simulator, or be able to see their competitor’s steering input traces, they would all say ‘test on a real track’. Just because you see what your competitor is doing doesn’t mean you can do it. So maybe the rookies ought to be pointing out that it’s not fair they’re having to learn how to race at the Cup level without getting as much seat time as the older drivers had.


Race Strategy

Back in the day, the crew chief had a much smaller number of variables to monitor. As cars have gotten more complex, the number of things the crew chief must consider has increased. Teams compile historical data like the probability of there being a caution in the last 25 laps of a race at a given track. They look at their own records: how many times did taking two tires instead of four give us a better finishing position? They subscribe to weather services so that they can make the right call when it looks like rain may wipe out a race.

They gathered data in real-time, too. They listened in on other teams’ radios to try to anticipate their pit strategy and adjust their own accordingly.

This is a lot of stuff for one person to keep straight and it’s just getting worse. In 2018, teams now have the ability to not just monitor their own driver, but to monitor every other car on the track. We have 13 channels, each at 5 Hz. That’s 65 numbers per second. Over the course of a three-hour race, we’re talking tens of thousands of pieces of information — and those pieces of information aren’t exactly in immediately usable form.

NASCAR Data Science

Data Science is hot. There is high demand for people with the skills to extract information from large quantities of complex data and output actionable information in a way people can use to quickly make decisions. The average salary for a Data Scientist is about $120,000/year.

Rho AI is a Data Science company that includes Josh Browne (aforementioned former NASCAR Crew Chief), and more MIT Ph.D.s than should be allowed in any one place that isn’t MIT. If you follow this blog, you probably also are familiar with another team member: Andrew Maness a.k.a. NASCARnomics. Autoweek had a nice article on the Rho AI crew’s work in NASCAR.

Josh knows the problems Crew Chiefs face — and which ones might be solved using Data Science. Let’s take one of the most common: How many tires do we take on the next pit stop? A train of thought might go something like this:

Note the second bullet under the second point.  In order to make this decision, I need to know what every other team on track is likely to do (or at least the ones ahead of and near me.

Once you’ve decided the question you want to answer and what data you have available, you can apply the standard Data Science process. The graph below sweeps a lot under the rug in the ‘analyze’ step, but I’ll return to that.

The system constantly evolves because it evaluates every prediction against what actually happens and feeds all that back into the model so that the next time the question comes up, every bit of relevant data can be used in making the next (and hopefully more accurate) prediction.

To give you an idea of what the system they developed is capable of, they predict lap times for each car based on all the currently available information.  When it’s time for a pit stop, they can provide a prediction of what position you’ll end up in over the course of the next fuel run based on taking 4,2 or no tires — and that takes into account what everyone else on track is likely to do based on historical precedent.

The systems they use belong to the same general family as the programs that Amazon and Netflix use to suggest movies or books you might like based on all the information they have about you and everyone else in the world. And if you’ve ever seen one of their recommendations and thought “what the heck…?”, you know that the predictions aren’t always on the mark. The program is only as accurate as the data and the model.

The programs rely on something called Machine Learning, which is a subset of Artificial Intelligence (AI). They key to Machine Learning is that the program uses the new information to modify its models and make itself more accurate. The more the program learns about NASCAR, the more accurate it gets. Interestingly, Josh tells me that the most challenging type of race to predict is the restrictor-plate race.

Incidentally, I met the Rho AI guys at an ARPA-E (Advanced Research Projects Agency – Energy) conference. NASCAR is far from the only thing they do. They work with companies involved in water, waste and energy to save the companies money while helping the environment. They laughed that some of the other problems they work on are easy compared to NASCAR.

Is This The Future of NASCAR?

No. It’s the present. Rho AI have been working with Richard Childress Racing for some time now to develop the system. It’ll be in action this weekend. As far as I know, there isn’t any other Data Science company working in the NASCAR space, although some teams have started developing their own programs. The Rho AI guys emphasize that their work is a true collaboration. It’s not like buying a copy of Office or Adobe Photoshop. Because the program is constantly evolving, it is literally a continual work in progress

“Decisions during a race have to be made in seconds. Our strategy tools have played a key role in our wins in 2017 and have shown the power of analytics in making real-time decisions.”

—  Dr. Eric Warren, Vice President of Competition at RCR via rho.ai website

You might think a program like this would threaten crew chiefs, but the ones using it view it as one more tool they can draw upon to give them an advantage over the other teams. Steve Letarte told me once that his job as Crew Chief was not to have all the answers, but to know where to find the answers and then make decisions.

The program isn’t always right because it must face situations for which it doesn’t have enough data to make a prediction with high confidence. It must be right much more often than it’s wrong because RCR wouldn’t maintain a partnership that wasn’t working.

The crew chief doesn’t have to follow the program’s recommendation. There are things the program doesn’t consider, like whether driver A and driver B who ended up to each other on the restart had an incident in the last race or whether driver C’s contract is up for renewal and he’s in wreckers or checkers mode.

And the program isn’t going to tell them the answer to all their problems is a spring rubber and one and a half rounds of wedge in the car.

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