The post One NASCAR Rules Package for All Tracks? appeared first on The Building Speed Blog.

]]>Adam Stern reported this week that NASCAR is negotiating with teams about modifying the 2020 rules package for short-tracks. Although the modifications will most likely be bolt-on parts (splitter and spoiler), teams are concerned about having to invest in testing two different configurations of a car in its last year of competition.

This graph (from one of my two year-ending posts on tracks and drivers) shows the percent change in lead changes per 100 miles (LC_{100}). A value of 200% means that the lead changes per 100 miles in 2019 were double those for the same race in 2018. Blue bars denote increases and red bars denote decreases.

The results reflect the mixed results, but they also raise two important issues in understanding the success (or not) of the 2019 rules package.

First: race with a large number of cautions often have a lot of lead changes. How many of those lead changes are green-flag, get-your-heart-pumping lead changes and how many are staying out while everyone else pits?

Second, no two races are the same, even at the same track. Some tracks have wildly varying number for metrics like lead changes and cautions, while other tracks are more consistent. Are we getting the real story comparing 2019 with 2018 without knowing if the 2018 races were exceptional?

I separated green-flag and yellow-flag lead changes for all the races from 2010-2019 using a very basic metric: a driver listed on the race report as having taken the lead during a yellow-flag is credited for a yellow-flag lead change.

Last year’s Coca-Cola 600 at Charlotte Motor Speedway had a whopping 214% increase in lead changes per 100 miles. It also, however, featured **16** cautions for **80 **caution laps. That’s the most cautions *and* the most caution laps in a Charlotte race in the last 10 years.

The graph below breaks out yellow-flag and green-flag lead changes for each race. The codes below each bar show the year and the number the race was that year. The ’12s’ and ’13s’ are Coca-Cola 600s and the ’30s’ and ’31s’ are fall races.

I’ve shown the numbers of cautions and caution laps in red boxes for select races. This graph suggests that there’s a correlation between yellow-flag lead changes and cautions.

It’s not a simple because the length of each caution determines whether it’s possible to have more than one lead change as people pit. A quickie caution may not allow time for any lead changes. While you may one or two lead changes during a six-lap caution, that doesn’t mean you’ll have three or four during a 12-lap caution.

To test my hypothesis, I’ve plotted the number of yellow-flag lead changes as a function of the percentage of the race run under caution (which is essentially caution laps corrected for the track length). .

I plotted data for racetracks alphabetically up to Dover (excluding Daytona) and stopped there because I think it’s clear that there is a trend. More cautions mean more yellow-flag lead changes. Yellow-flag lead changes aren’t what we’d call ‘good racing’, so what we ought to be comparing are green-flag changes.

Here’s the same plot I started with, but considering only green-flag lead changes.

It’s mostly the same, but there are a few differences.

**Ignore superspeedways.**Pack racing means a lot of lead changes that are less meaningful than lead changes at other tracks. Talladega was coming off a 2018 fall race that had very few lead changes, so the 2019 race looks huge in comparison.**Larger tracks still did well**- Most
**mile-and-a-half tracks**had more green-flag lead changes than they did in 2018. - One
**Pocono**race had a 25% increase; the other had a 25% decrease. - Both
**Michigan**races were up, the first one by more than 200%

- Most
**Not all short tracks were down**- Spring
**Bristol**was flat, while fall Bristol was up 133% – but again, we’ll have to see whether 2018 was an exceptionally static race. **Martinsville**,**Phoenix**and**Richmond**showed significant decreases in both races.**Dover**looks better when we only compare green-flag lead changes. Dover had fewer yellow-flag passes in 2019 than 2018, but more green-flag passes.**New****Hampshire**experienced an overall 40% increase in lead changes, but a 33% decrease in green-flag lead changes.

- Spring

But we are still simply comparing 2019 with 2018. What happens if we broaden our view?

A few posts ago, I compared the race histories of Phoenix and Homestead to try to understand if we had to worry about Phoenix now being the last race. To do this, I had to introduce box plots. So, in case you’ve forgotten…

- The red (or yellow) line represents the median (the middle value).
- Fifty percent of all races fall within the box.
- The whiskers show you the full range of ‘typical’ values.
- Values far outside the norm are represented by dots.
- I’ve put the average (or mean, which is different than the median) in red at the bottom of each box plot.

**Average Green Flag Passes per Lap**are passes for position, averaged throughout a race**Green-Flag Lead Changes**and**Margin of Victory**are self-explanatory**Number of Leaders**is the number of drivers who led one or more laps during the race**Quality Leaders**excludes drivers who led only one or two laps at a time — which is usually because they stayed out while people were pitting.**Most Laps Led**is the most laps led*by a single driver*throughout the course of the race. The larger this number is, the more dominant one driver was.

Michigan had a 200%+ increase in green-flag lead changes in 2019 relative to 2018. That makes the new rules package look amazing. However, the 2018 race was way *below* average in terms of lead changes. Compare 2019 to the last decade at Michigan.

It’s clear from these metrics that 2019 was indeed an improvement not just over 2018, but over the typical Michigan race from the last ten years. The average green-flag passes per lap high and the green-flag lead changes are high. One margin of victory is just about at the median, while the other is among the smallest.

Let’s re-examine our results now.

All of the mile-and-a-half tracks showed improvements, although some were larger than others. Most of the improvements are significant enough that it’s fair to credit the rules package.

**Texas**- The average number of green-flag lead changes at Texas is
**14.8**. The two 2019 races were**22**and**21**. - The average green-flag passes per lap is
**9.5**. 2019’s were**10.6**and**11.4**(The 2018s were in the 6’s, making 2019 look really good compared to 2018, but just ‘better’ compared to the average.)

- The average number of green-flag lead changes at Texas is
**Las Vegas**- The average number of green-flag lead changes at Texas is
**14.1**. The two 2019 races were**17**and**21**. - The average green-flag passes per lap at Vegas is
**10.7**. 2019’s were**13.1**and**14.3**. - In its history, Las Vegas had never seen more than
**7**quality leaders. The spring race had**8**and the fall race had**10**.

- The average number of green-flag lead changes at Texas is
**Kentucky**- Kentucky had a record of
**6**quality leaders prior to 2019. In 2019, there were**9**quality leaders - The average green-flag lead change is
**8.7**per race, whereas the 2019 race had**11**. - The average green-flag passes per lap is
**8.9**. The 2019 race saw**12.9**green-flag passes per lap on average.

- Kentucky had a record of

The news for the short tracks is still mixed. Let’s look at the three tracks of concern: Martinsville, Richmond and Phoenix.

Here are the box plots for Martinsville covering 2010-2019

The 2019 Martinsville races are both below average when it comes to green-flag passing and well below average in green-flag lead changes. On the positive side, the margins of victory weren’t too far from the median.

Martinsville doesn’t look any better if we look at this set of metrics, either. We had low numbers of leaders and quality leaders and one dominant driver in each race. In case you’re thinking maybe one team figured out Martinsville, there were two different winners from two different manufacturers in those two races.

The 2019 package is the culmination of a number of experiments over the last few years, but if you look at the trends over time, they’re going the wrong way. This is a pretty big piece of evidence as to why NASCAR is going to make a separate rules package for short tracks in 2020.

The number of lead changes is down in 2019, but they were down in 2018 relative to 2014-2015. So the argument that there really are problems with racing at Martinsville holds, no matter how you look at it.

Sadly, we also can’t write off Richmond as looking bad compared to awesome 2018 race: The metrics are below average when compared to the whole decade, including the largest margin of victory in the period covered.

The second set of metrics isn’t any better, although there were more drivers leading laps here than at Martinsville,

As I pointed out in the earlier post, Phoenix wasn’t horrible. It was just on the low end of average for a Phoenix race.

Hopefully, the tweaks NASCAR makes to help the tracks that decidedly need it will all help tracks like Phoenix.

**Bristol** was average to better-than-average in both races when compared to the last ten years of races. Both races had higher than average numbers of leaders, less domination by one driver, and higher-than average green-flag passes per lap.

**New Hampshire**, like Phoenix, was simply on the lower end of average for its races.

**Homestead** was just weird. In the last four races, an average of 40% of the cars finished on the lead lap. In 2019, only 25% of the cars finished on the lead lap. The metrics are low-to-average, except the margin of victory was the highest we’ve seen in ten years. Given that the rules package performed well on the other 1.5-mile tracks, 2019 might just have been an anomaly. Of course, there’s no way to know for sure until 2020.

If there were any way to avoid introducing a separate rules package for short tracks in 2020, NASCAR would have found it. It will be extra work for everyone, but it’s to their credit that they didn’t decide to just suffer through the year with the hope that 2020 would be different. And this way, they can collect more data that can be used to improve the 2020 rules package.

The post One NASCAR Rules Package for All Tracks? appeared first on The Building Speed Blog.

]]>The post NASCAR Numbers 2019: Part 2 — The Drivers appeared first on The Building Speed Blog.

]]>**A note, first.** I’m not calling out drivers to pick on them, especially those starting out and/or working for struggling teams. This is more of a way to figure out what needs to worked on for 2020. And what I’m showing you here is just the tip of the statistics iceberg each team’s number crunchers are breaking down into manageable ice cubes before next season.

Unsurprisingly, the top-30-ranked drivers ran all 36 races, as did Landon Cassill. Here’s how many races the remaining part-time drivers ran.

- 11 drivers drove only one race
- 3 drivers drove two races
- Most drivers with part-time teams (and some with full-time teams) drove more than one car.
- Garrett Smithley drove
**5 different car numbers**in just 14 races.

- Garrett Smithley drove

I did a whole post on how dominance of the few is nothing new in NASCAR. The average number of distinct winners over the last ten years is 14.1. *There were 13 distinct winners in 2019*, which is lower than the ten-year average, but equal to the average from the last five years.

We had 248 caution flags, but in 20 of those cases, no one was in position to get the free pass. What about the other 228 times?

**The Most Free Passes:**- The 32 car, driven by Corey LaJoie, with
*11*. - Two cars had
*10*free passes- The 15 (Chastain/Smithley/Houff/Nemechek)
- The 36 (Tifft/Crafton/JH Nemechek)

- The 32 car, driven by Corey LaJoie, with
**Most Free Passes out of the Top 16**- Harvick, Keselowski and Almirola each had
*7*free passes

- Harvick, Keselowski and Almirola each had
**The Unlucky Dog:**The only full-time driver who didn’t get a free pass in 2019 was*Clint Bowyer*.

I plotted free passes against season-ending rank to see how often the top drivers used them compared to those who finished in the bottom half of the standings.

The back half of the field uses more free passes than the front, which is perhaps easier to see in a pie chart.

*26%*of free passes went to drivers who finished between 31^{st}and 40^{th}*31%*went to drivers who finished in 21^{st}thought 30^{th}*61%*went to drivers who finished in 21^{st}or higher- The top 16 drivers (40% of the field) got
*29%*of all free passes - The top 10 (25% of the field) drivers got
*17%*of all free passes

Finishing races is a pre-requisite for winning races, so let’s look and see how the drivers did here.

No driver ran all 10,255 laps this year, but some came awfully close. For reference, this is five more laps than last year.

I circled the lower y-axis label to emphasize that I expanded the scale. Full-time drivers are pretty good about getting their laps.

**Top Lappers**- Joey Logano ran the most: 99.67% of the laps (10,221)
- That’s more than last year’s top lapper: Ryan Newman, who ran 98.3% of all possible laps

- Paul Menard ran a close second with 99.63% (10,217)

- Joey Logano ran the most: 99.67% of the laps (10,221)
**No Slackers Here**- All full-time drivers ran more than 90% of all possible laps.

- The driver completing the fewest number of laps (Michael McDowell) still ran 91.33% of them

There is a correlation between how often you finish on the lead lap and your final ranking, as the graph below shows. I plotted it again as a function of final standing

**How the top 16 fared**- Unsurprisingly, Kyle Busch had the highest percentage of lead lap finishes, with 31 out of 36 races. (86.11%)
- Sort of surprisingly, Martin Truex, Jr, who finished second in the standings, was only fifth highest in lead lap finishes with 26/36 (72.2%)
- Harvick and Hamlin tied with 83.3%
- There is a noticeable gap between these four drivers, who are all 80% or better and the rest (excluding Truex, Jr.) who were all between 60% and 70%

- After about 15
^{th}place, the percentage of lead lap finishes goes down pretty quickly the higher you go

Here’s a colorful, complicated graph:

- Kyle Busch led the most laps with 1582 (15.4%)
- Truex, Jr. came in second with 1371 laps (13.4%)
- The top four lap-leading drivers (Ky Busch, Truex, Jr., Keselowski and Harvick) led almost half (48.7%) of all laps
- The top six lap-leading drivers (the above + Hamlin & Logano) led two-thirds of all laps.
- 13 drivers led 90% of all laps.

Not finishing a race will certainly hurt your final standing, but there’s no clear correlation between DNFs and season-ending rank. Here’s the numbers, listed in order of final ranking.

**Most DNFs**- Larson and Ragan tie at 8 (22.2% of all races)
- 7 of Ragan’s DNFs were due to crashes. He only had 2 DNFs last year
- 7 of Larson’s DNFs were due to crashes, too. He only had 4 DNFs last year

- Bowyer comes in next at 7
- Elliott and Preece each have 6 DNFs
- Elliott had two engine failures this year, which is increasingly rare. He had 4 DNFs last year

- Larson and Ragan tie at 8 (22.2% of all races)
- Among full-time drivers, only Logano and Ty Dillon completed every race.

This is what I mean by using this type of information in planning for next year. If DNFs are due to equipment failures, for example, that suggests one path, while DNFs due to just having a really bad run of luck at superspeedways suggests re-examining race strategy.

Here’s a summary, showing how many lead lap (green), non-lead lap (yellow) and DNFs (red) each full-time driver had this year. This is again in order of final standings.

Stages were introduced in 2017. There were 109 stages in the 2019 season.

There were 73 stage wins possible (two per race, plus an extra one for the extra stage in the Coca-Cola 600.) **Sixteen drivers won stages in 2019**.

- Kyle Busch won the most stages (12, or 16.4%)
- Logano was next with 11 stage wins.
- Only three drivers (Johnson and both Dillons) won stages and did not make the playoffs.
- Three drivers made the playoffs without winning any stages in 2019.
- Bowman and Jones won races
- Newman was the only driver not to have won a stage or a race and still make the playoffs.

18 drivers accumulated playoff points: 16 from stage wins and 2 from race wins.

- Earning the most playoff points isn’t enough to win the championship
- Earning enough playoff points to carry you through a losing drought is absolutely critical (See: Kyle Busch, Logano, Keselowski)

I combined spins and accidents on one graph. Note that this is being *involved* in an accident: the statistics don’t say who caused the accident. But given that some drivers don’t have a lot of accidents and others do, understanding why your driver is in the latter group is important for 2020.

**Accident Avoidance Awards**- The only car number without any accidents (or spins) was the 46 — but it only ran once race.
- The other two cars that only ran one race managed to be involved in an accident in that race.

- The full-time driver with the lowest numbers of accidents/spins was Landon Cassill.
- Ryan Blaney was involved in three accidents and one spin
- Ryan Newman was involved in four accidents.

- The only car number without any accidents (or spins) was the 46 — but it only ran once race.
**Most Involved**- Ricky Stenhouse, Jr. has the most accidents & spins — 21
- The next highest drivers are Preece and Larson with 16

- Ricky Stenhouse, Jr. has the most accidents — 19
- Preece and Larson again follow with 15 each

- Clint Bowyer has the most spins — 4
- Eric Jones has the second-most spins — 3

- Ricky Stenhouse, Jr. has the most accidents & spins — 21

There is no correlation between driving experience (i.e. races run) and the number of accidents you get into. The top drivers in all three categories are veterans, and second place in two categories is shared by the 6th place finisher and a rookie.

The Championship Four fall in the middle of the spectrum.

- Hamlin and Truex, Jr. were each involved in 10 accidents
- Kyle Busch in 8
- Harvick in 6.

The last section of our wrap-up is all about penalties. I’ve broken the in-race penalties into driver penalties (e.g. speeding on pit road) and non-driver penalties, which can cover anything from too many men over the wall to loose tires.

- There were 378 penalties in 2019, a 4% decrease from last year
- Non-driver penalties are down by about 12% from 2018 (199 vs 174.
- Driver penalties are up 5% (194 vs 204)

- Penalties have been significantly higher the last two years

Penalties late in a race can be much more costly than earlier penalties, which give you time to make up for the mistake. Here’s the breakdown of when penalties were assessed in 2019:

We’ll ignore stage *, which is only in the Coca-Cola 600 and only accounts for 2/378 penalties.

- Penalties increase from Stage 1 (23%) to Stage 2 (32%).
- Given that stages 1 and 2 tend to be about the same length, you might expect them to have about the same number of penalties.
- 45% of penalties occur in stage three.
- Given that stage 3 is about twice as long as stages 1 and 2, you might expect 50% of the penalties to occur in stage 3, but it’s slightly less than that.

Two-thirds of penalties happen under yellow flags — which makes sense because most in-race penalties happen in the pits.

Driver penalties are ascribable to the driver — although there are instances when it’s not his fault, like if his instrumentation to tell pit road speed is set up incorrectly, but that’s hard to pull out of the numbers.

In 2019, **drivers were responsible for 54% of penalties**. The remaining penalties can be broken into crew (38%) and misc (e.g disobeying a NASCAR request, being sent to pit road to repair damage or not maintaining minimum speed), which comprised 8%.

- Speeding on pit road in some form (entering, exit, or just plain speeding) is the largest source of penalties, accounting for 79% of driver penalties.
- A driver is just as likely to miss a chicane as drive through more than 3 pit boxes or pit out of the box.

As far as the crew penalties:

- The largest source of crew penalties in 2019 was tire violations with 40%. It’ll be interesting to see if that goes down next year given changes in the loose tire rule.
- The second-largest crew penalty was too many men over the wall with 35%.
- Combining too many men over the wall with crew member over the wall too soon accounts for more than half (53%) of crew penalties.

- On the Naughty List
- The most-penalized driver for 2019 was Corey LaJoie, with 19 total in-race penalties.
- He’s followed by Wallace (16) and Truex, Jr (15)
- Chastain, Hamlin, T. Dillon, McDowell, Hemric and Stenhouse, Jr., each had 13.

- On the Nice List
- Chris Buescher only had one penalty in 2019
- Erik Jones and Matt DiBenedetto each had 3

- Although LaJoie had the most penalties, only 4 (21%) were under green, well under the 33% average
- Bubba Wallace has the most penalties under green at 9 (or 56% of total penalties).
- On the lower end of the scale, we’ve got Buescher, DiBenedetto and Logan, who had no penalties under green.

Remembering that the average is 54% of penalties due to the driver, let’s look at some interesting trends.

**Penske Heads up the ‘Nice Teams’ List**- Logano and Keselowski had no driver-attributable penalties in 2019
- Ryan Blaney’s team only got four penalties, but again, all of them were driver-attributable.
- The three Penske drivers had 12 penalties total. Nine drivers had the same or more penalties than the entire Penske lineup.

- DiBenedetto and Harvick each had only one driver-attributable penalty.
- Erik Jones’s team only had three penalties total, but he was responsible for all of them.

Given that crews were on average responsible for 38% of penalties…

- The three Penske pit crews incurred a total of 5 penalties – which seven teams on their own managed to beat.
- Kevin Harvick’s crew had the largest percentage of penalties at 80% — but the team was only penalized five times total.
- Denny Hamlin’s crew had the next largest fraction of penalties with 66.7% — and they had 12 penalties total
- Martin Truex, Jr.’s team again ranked high in crew penalties with 53.3%. (Last year, Truex’s team was near the top in penalties and almost all were crew-attributable. This is likely to be one of MTJ’s new crew chief’s priorities.)

- Chase Elliott incurred five out of the six penalties of the season in stage 1. He only had one stage-two penalty and no stage 3 penalties.
- Denny Hamlin had 11/12 of his penalties happening in stages 2 and 3.
*Many drivers incurred the majority of their penalties in Stage 3*- Jimmie Johnson: 6/9 total penalties in Stage 3.
- Martin Truex, Jr.: 9/15
- Ryan Newman: 9/11
- Ricky Stenhouse, Jr. : 6/12
- Austin Dillon: 7/9
- Kevin Harvick: 5/6
- Daniel SuÃ¡rez: 7/11

Late penalties can make a big difference in your race finish, so the above-listed teams are probably reviewing their races to see what types of penalties they’re drawing and how they might minimize them next year.

It’s not too surprising that the top two penalty-getters are also high up on the free-pass list.

So that’s the 2019 season in numbers. Thanks for staying with me this year and we hope to have a couple new surprises for you in 2020.

The post NASCAR Numbers 2019: Part 2 — The Drivers appeared first on The Building Speed Blog.

]]>The post NASCAR Numbers 2019: Part 1 appeared first on The Building Speed Blog.

]]>The requisite 36 points-paying races at 24 different tracks weren’t that much different from last year, but let’s stop to remind ourselves how big the numbers are.

**10255:**The maximum number of laps a full-time driver had the opportunity to drive in 2019, including rain-shortened races and overtime. That’s 13,777 miles!**10221**: The maximum number of laps any single driver completed. Joey Logano ran all but thirty-four laps.**64:**The number of drivers who ran at least one race this year.**31**: The number of drivers who ran all 36 races

**47**: The number of teams that ran at least one race in 2019**37**teams ran full-time.**31**of those teams had one driver all season.

**10**teams ran part-time

**501,037**: The number of miles run by all drivers in all Cup-level races.

- The mean distance between the moon and the earth is 238,855 miles. You could drive to the moon and back and the around the Earth at the equator.
- Or just drive around the Earth’s circumference 20 times.

**28**: The number of races run on a Sunday**5**: The number of races run on a Saturday**3:**The number of races run on a Monday

This is similar to last year, where 75% of the races were Sunday, 8% were Monday and 17% were Saturday.

**75%:** The fraction of races that start between 2 and 4 PM Eastern time.

**25%:** The fraction of races that started between 6 and 8 PM Eastern time.

**2:** The number of races ending under caution this year. They’re indicated by yellow stars on the graph below. That’s one more than last year.

As is usual, there was a wide variety of margins of victory

**0.007 seconds**: The smallest MoV, at second Talladega. The blink of an eye is about a third of a second, for reference.**9.5 seconds:**The largest MoV was the Spring Dover race, where Martin Truex, Jr. ran away with the win.**6 races**(16.7%) were won with less than a 0.2 second MoV**15 races**(41.7%) were won with less than a 0.5 second MoV**19 races**(52.7%) were won with less than a 1 second MoV

You can see the distribution a little better in a pie chart.

Compared to 2018:

**1.6 seconds**: The average MoV for 2019, compared to 2.1 seconds in 2018.- The largest MoV in 2018 was 11.69 seconds, compared to 9.5 seconds in 2019
**One race**: was won by more than 7 seconds. In 2018,**four races**were won by more than 7 seconds.

NASCAR as a whole is getting less accident prone. (NASCAR classifies accidents and spins differently. It seems that the difference is that a spin is when a single car spins and can continue under its own power.)

**121:**The number of accidents in 2019, up slightly from 111 accidents in 2018**27:**The number of spins in 2019, up slightly again from 24 spins in 2018.

**56**(46.3%) The number of single-car accidents.**34**(28.1%) The number of two-car accidents.**74.4%**: The fraction of all accidents that involve one or two cars.**25.6%**: The fraction of accidents involving three or more cars.

Because there were so many one and two-car accidents, I zoomed in on the scale so you could actually see the other numbers. The first two bars go off the graph.

**21:**The most number of cars in one accident this year (Daytona 500)**51:**The most cars involved in accidents in one race (Also the Daytona 500. A number of cars were involved in more than one accident. Ricky Stenhouse, Jr. was involved in four different accidents.)**11:**The largest number of cars in an accident at a non-superspeedway. This was the Charlotte Coca-Cola 600, but I should point out that fall Talladega had*two separate 11-car*accidents.

Some tracks are just more conducive to accidents than others. You may remember the DLP Danger Index (or DDI), which ranked Daytona, the Charlotte Roval, Bristol, Talladega, Indy and Martinsville as the most dangerous tracks, taking into account the number accidents and the number of cars per accident.

It’s not a surprise that the largest number of accidents in a single race was at the Coca-Cola 600: It’s the longest race of the year. But let’s look at the number of accidents per 100 miles, which eliminates the lengths of the races.

- 4: The number of races with no accidents: Atlanta, Las Vegas (spring), Sonoma and Dover (fall).
- 2.62: The highest accident rate at any track (Bristol, spring)
- 2.20: The second highest accident rate (New Hampshire)

Bristol and New Hampshire are followed by the Charlotte Roval and the spring race at Phoenix. Note that the superspeedways are relatively far down on that list. They’ll make up for it in a moment.

But the DDI also requires us to look at the average number of cars in each accident, so here’s that data.

You’ll notice the ‘winners’ here are the four superspeedway races, each of which averaged more than five cars per accident

Multiplying these two quantities together gives us the Diandra Danger Index

**9.86 and 8.81:** The whopping DDIs for the Daytona 500 and the rain-shortened July race, respectively. This gives Daytona the award for the most accident-prone race *and* the most accident-prone track in 2019. The runner up was the Charlotte Roval, followed by the fall Talladega race and both Bristol races.

**627:** The number of changes at NASCAR races in 2019. That’s up quite a bit from 2018, which had 534 lead changes. If we compare that number since 2001 (which was when we started having 36 races per year).

**704: ** The average number of lead changes per season from 2001-2019. This year is larger than any of the previous three years, and not too far off from 2015.

Looking at the lead changes per 100 miles (so we correct for race different race lengths…)

**7.3:**The average number of lead changes per 100 miles.**6.0:**The average number of lead changes per 100 miles if you exclude superspeedway tracks. (I exclude them because they have large numbers of lead changes, many of which aren’t meaningful in the grand scale of things.)**9.2:**Maximum average number of lead changes per 100 miles (Talladega, fall)**1.1:**Minimum number of lead changes per 100 miles (Martinsville, both races.)

Now let’s compare the percentage change from 2018 to 2019. Increases are positive (blue) bars, while decreases are negative (red bars).

Keep in mind that there are usual year-to-year variations, but it is interesting that the tracks showing decreases are two road tracks, Martinsville and Richmond. The 1.5 mile tracks are up.

One more metric before I quit for this week.

**248:**the number of cautions in the 2019 season. That’s only one more than the 247 we had last year.**6.9**: The average number of cautions in a race, although the numbers run from 2 to 16**1292**: The number of caution laps run in 2019 (12.6%).**4.18**: The highest rate of cautions per 100 miles (Martinsville spring race).**0.50:**The lowest rate of cautions per 100 miles (the Las Vegas spring race, which had only the two stage-end cautions)

Some people feel that the caution laps between stages shouldn’t count in the race because they’re planned cautions, so I thought I’d take a look at how many caution laps we saw each season.

2019 is the lowest number of caution laps run since 2013 and the second lowest number since 2001. So if you think there are more caution laps now then there were ‘before’, you might want to check the numbers again.

I hope you’ve enjoyed our first foray into the numbers and statistics that summarize the 2019 NASCAR season. Stay tuned for more, including who used the most free passes, who got in the most accidents, and who spent the most time up front.

The post NASCAR Numbers 2019: Part 1 appeared first on The Building Speed Blog.

]]>The post Was Phoenix a ‘Bad Race’? appeared first on The Building Speed Blog.

]]>In comparison to what? The typical race at Phoenix? Our memories of races at Phoenix? Our expectations?

I argued before that it’s worth considering the numbers and the history in addition to our gut feelings. So let’s do it now.

Every race is different. Track conditions are different, weather is different, the cars (e.g. engine, tires, setup) are different and the competitors are certainly different in what they’ve learned since their last visit and the drivers’ emotional states.

Viewers are different, too. You see the race differently when your driver dominates than you do when he went out on lap four and the guy who crashed him is leading.

Fans and pundits like to pull out statistics to reinforce their perceptions about how ‘good’ the races were, but those statistics are often cherry picked and selected from what’s readily available. Before calling a race ‘bad’, we have to define what a ‘typical’ race is a Phoenix. To do that, I pulled the data for all 47 races at ISM/Phoenix International Raceway. I considered a range of parameters one could argue measure how competitive a race is.

Since 2005, NASCAR has reported the average number of green-flag passes per lap. This parameter provides different information than lead changes because it measures all passing, everywhere on the track. Here are the numbers from 2005 to last weekend

I’ve indicated the average over the years (**5.5 passes per green flag lap**) by the red line. The last Phoenix race had **4.8 passes per green flag lap**, but there are some caveats to that number

- The average over all years is higher because some early races had more than eight GF passes per lap — but we haven’t see 8 since 2015.
- The average over just the last five years is 4.8 green flag passes per lap — exactly what it was last week.
- I’ve not adjusted for the fact that there were 43 cars on track before 2016 and 40 (or fewer) afterward. The average GF passes per lap goes down when there are fewer cars. For example, not as many cars are lapped with a smaller field.

I was surprised last week by how few cars were on the lead lap, but it turns out that’s actually pretty common at Phoenix. Because the number of cars changed throughout the years, I compare the percentage of the starting field that finish the race on the lead lap.

- Over the last 10 years, only 45% of the field finished on the lead lap (shown by the red line). I used 10 years because the first few years were much lower than the rest of the data.
- Last week, 38% of the field finished on the lead lap.
- An additional piece of info: on average, about 85% of the cars finish the race. The lowest recent number was fall 2012, where only 65% of the cars finished. In spring 2018, 95% of the cars finished the race.

The number of cars on the lead lap last weekend was a little lower than average, but not abysmal. It’s certainly better than the fall race in 2011, when only 28% of the cars finished on the lead lap.

How do we bring all this information together to see how last weekend’s race compares to the ‘average race’ at Phoenix?

Some people call them ‘box and whisker‘ plots. They look simple, but convey a lot of information. Because they’re small, you can look at more variables at a time.

- The box itself shows you from the 25th percentile of data to the 75th percentile. In other words, the value of that parameter will be within the bounds of the box 50% of the time.
- The median, denoted by a red line in my plots, separates the upper half from the lower half of the data. You can think of it as a ‘typical’ value. I pinched my boxes because I thought it made it easier to see the median.
- The whiskers (the dashed lines) show the range of the lowest 25% and the highest 25% of the data.
- Outliers (which I show by green diamonds) are values so far away from the rest of the data that they can be considered exceptional.

Let’s compare the histogram and the box plot of the same average green-flag passes per lap.

- You can see here how the outliers are called out in the graph.
- The median of the data is 5.3. Half the races had more than 5.3 green flag passes per lap and half the time there were fewer.
- The middle two quartiles are bounded by 4.65 and 6.05, which means that 50% of the time, there will be between 4.65 and 6.05 green flag passes per lap.

Let’s start with three quantities that might be used to define a ‘good race’: average green flag passes per lap, margin of victory and % cars finishing on the lead lap.

I’ve denoted the last Phoenix race with a pink star and put the value for that race in pink next to it.

**Average Green Flag Passes per Lap (left): **Last weekend’s race was on the low side of typical. See again the caveat above about the number of cars in the field not being constant.

**% Cars on the Lead Lap (right):** Moving to the right-most plot, this value is much closer to the median and well within the typical values for a Phoenix race.

**Margin of victory (middle): **We’re outside the box here, but that’s good: The margin of victory was smaller than we usually get in a Phoenix race. In fact, last weekend’s race was the 7^{th} closest race at Phoenix in the history of NASCAR Cup-level racing.

Let’s look at three more statistics that might help us define a ‘good’ race.

**Quality Leaders** **(middle):** In addition to how many times the lead changes, the number of different drivers who lead gives you additional information. A larger number should imply a more competitive race, since more people have a chance to win.

I invented this quantity (named after NASCAR’s ‘quality passes’) to correct for lead changes during green-flag pit stops. I excluded drivers who led 1, 2 or 3 laps over the course of the whole race.

In 50% of the races, between 4 and 6 different drivers lead. There were four quality leaders last weekend in Phoenix, so that’s consistent with the lowest end of average.

**Most Laps Led (right):** This is the largest number of laps led by any single driver. One driver leading a large number of laps implies domination. Virtually all of the races at Phoenix ran the same distance (312 laps). Denny Hamlin, on his way to a win, led 143 laps, which is a little under the most-likely value.

**Lead Changes (left):** The number of lead changes (8 last weekend) is low. The lower line of the box is at 9.5. The argument about having a smaller field doesn’t hold because this stat only captures the leaders. But here’s where the histogram may help:

The average number of lead changes over all history is **13.3**, but the average over the last five years is 11.3. In fact, 5 of the last 11 (45%) had eight lead changes (and one had seven) So although the number is low, it’s the same (or higher than 50% of the races in the last five years.

This was solidly on the lower end of a normal Phoenix race. If you were expecting ‘better-than-average’ or ‘blow-your-socks-off, you were likely disappointed and thus perceive Phoenix to have been a “bad race”.

And if you went in convinced that the new package is ruining NASCAR, you no doubt thought found reasons to support your beliefs.

Humans tend to look for evidence that supports views they already hold and ignore evidence that counters those views. We all do it, usually unconsciously. Even when you’re aware of it, you may not be able to stop it. This is why the gold standard for medical experiments are double-blind: neither the patient nor the doctors know whether the patient is part of the study or the control group, so as to minimize the possibility that either doctor or patient beliefs impact the results.

Too bad there’s no equivalent to a double-blind experiment in NASCAR.

All of this isn’t to say that there is still work to be done on the current package and NASCAR is well aware of that. NASCAR wanted to go with two different packages, but that seems to have been vetoed by the RTA because of the cost of R&D on two different packages. The change could be as simple as a different spoiler and splitter. I do not believe in the existence of a Unified Theory of Racecars that would allow one car to provide superior racing at the varied types of tracks NASCAR runs.

We’ve gotten better racing at some tracks and worse racing at others, but without viewing those races in the context of what the ‘average’ race is at each track, it’s hard to blame (or credit) the rules package. This is an ongoing experiment and the results are (as we like to say in science) only preliminary at this point in time.

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]]>The post Do Cautions Breed Cautions? appeared first on The Building Speed Blog.

]]>But do cautions really breed cautions? Or is that one of those things we notice when it happens and infer that it happens all the time, like the idea that anyone can win at Talladega?

What the statement means, of course, is that once we’ve had one caution, more are sure to follow. But how close does that second caution have to be to the first one to qualify as a descendent of the first? Five laps? Ten? Twenty?

I analyzed all the caution data from 2018 (the last complete season) and then did the same for 2008, just to see if there were any trends over time. (2019 so far looks very much like 2018.)

We’re talking around 200 -300 cautions per season, plus the correlations between each of them, so there are a lot of numbers running around. I needed a way to get a look at the data so I could have some idea what to investigate.

I needed a graphic: Something that tells you more than a chart of numbers. The first thing I tried was a pseudo-heatmap. I colored

- Red squares are cautions within five laps of the last caution
- Orange squares are cautions that came within ten laps of the last caution
- Cautions within 20 laps are yellow

Don’t worry about the numbers: you’re looking for patterns. You can see pretty quickly that there are some races where there are longer strings of red and orange than others. Those are the races that had a lot of cautions closely spaced.

The July Daytona race, for example (denoted by 18-Day) has a couple runs: Three cautions close together, then two more at the end. Fall Vegas also has a long run of red and orange at the end.

But this still isn’t as useful, in part because stage breaks are a different breed of caution. They didn’t exist when the ‘cautions breed cautions’ maxim was invented. The above attempt at a heatmap doesn’t show that.

So I invented the Caution-O-Gram, which gives you an entire race in one picture. Here’s July Daytona:

- The white bars are stage breaks
- Red bars are accidents
- Orange bars are spins

You can easily see the runs of cautions from this and you see where the stage breaks are.

Now, if cautions really caused cautions, we’d have a situation because one caution would start a chain of cautions that wouldn’t end. What we actually see are groups of two and sometimes three cautions, then a period of green flag racing.

Below are two of the 2018 races with the largest number of closely-spaced cautions.

If you look at the ends of those races, it’s sure tempting to suggest that cautions breed cautions.

There’s an alternative hypothesis. Drivers are much more aggressive at the ends of races, when the trophy is on the line. They’re willing to make riskier moves and, thus, there are more accidents and spins toward the end of the race.

The Caution-O-Grams show that. Most of the cases in which there were two or more closely-spaced cautions happened near the ends of races. That was definitely the case at Kansas. A debris caution was followed in quick succession by two accidents.

You can’t prove that something always happens, but all you need is an instance of it **not** happening to put a hole in the theory. At Kansas last week, they had a caution in the middle of the race that was followed by 41 laps of racing to the stage end, then 87 laps of green flag.

Some races don’t have cautions at the end. Here’s Atlanta from 2018.

Kevin Harvick led 181 of 325 laps in the first of four consecutive wins that year.

The pattern for two races at the same track can be very different, depending on whether there’s a dominant car. Look at the spring Vegas race: Two accidents right next to each other, followed by 73 laps of green-flag racing.

**The question we’re really interested in isn’t whether cautions breed cautions, but “how often do cautions breed cautions?”** In other words, do they breed like rabbits or pandas?

Let’s look at the number of laps between cautions for the 2018 season. I’ve included stage breaks because it’s rare to have a caution right before a stage break: when cautions are close to stage breaks, the caution replaces the stage break. And heavens knows there’s a good share of cautions right after stage-break restarts.

I was surprised at how large the “less-than-five” category was. As I mentioned, a good number of those are wrecks on restarts. But, in total, **about 25% of the time (23.2% to be precise), a caution is followed by another caution within five laps.**

Using a** **cumulative percentage** **lets you see how likely it is to have a caution in the next x laps.

After a caution…

**23.2%**of the time, there was a second caution within 5 laps.**36.5%**of the time, a second caution happens within 10 laps**45.5%**of the time, a second caution within 15 laps**53.1%**of the time, a second caution within 20 laps

This isn’t a full study, but I pulled out the data for 2008. There were more overall cautions, but as percentages, this graph looks similar to the graph for 2018.

- 2008 has roughly the same probability of of having a caution within 5 laps of of caution:
**25.3%** - The probability of having a caution within the next 10 laps, however, is
at*higher***44.2%** - There’s also a higher probability of having a second caution within the next 15 laps:
**55.5%**

I suspect that the higher ratios for second cautions in 2008 (where, remember, there were no stage cautions) are due in part to damaged cars staying on track. Those cars often shed pieces or experience another failure that leads to another caution.

It would be interesting to look at the years just before the damaged vehicle policy and see if that’s the reason.

Not so fast. If that were true, we’d find a good number of cases in which there’s just continual cautions and that’s not what we see. So let’s look at the second-order correlations: That is, how likely is it to have two more cautions within x laps of the first?

The numbers drop off a bit. In 2018, there were only **six times out of 175 **with three crashes within 10 laps of each other.

In 2018, there was a:

**3.4%**chance of having a third caution within 10 laps of a first caution**16.0%**chance of having a third caution within 20 laps of a first caution**31.4%**chance of having a third caution within 30 laps of a first caution- It takes about 45 laps before you have a
**50%**chance of having a third caution.

Race teams use these statistics (and many other) to inform their choices. They consider things I haven’t considered here: like the probability of a second caution changes according to how far along in the race you are. Given how much data there is and the pressure of making a decision in real time, some teams are going to complex artificial intelligence programs to aid their decision making.

Cautions don’t cause cautions: Drivers cause cautions.

There are limits to ‘cautions breed cautions’. There is a good chance of a second caution within 20 laps of the first, but the probability that there’s a third caution within another 20 laps is much, much lower.

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]]>The post Does Qualifying Matter at Talladega? appeared first on The Building Speed Blog.

]]>What does that tell us about where Joey Logano — or any driver who needs to win to stay in the playoffs — must qualify?

The histogram below shows how many winners (vertical axis) started at each qualifying position (horizontal axis) in the100 NASCAR Cup-level Talladega races to date.

- 14% of the winners started on the pole
- 34% of the winners started on the front row
- 50% of the winners started in the front two rows
- 90% of the winners started from 18th place or better
- No one has ever won starting from higher than 36th
- Only 5% of the winners have qualified 27th or higher

This would suggest that teams that must “win to stay in” should focus all their efforts on qualifying well.

But, of course, that’s not the whole story.

A large number of data points gives your stronger conclusions and nicer-looking graphs. We’ve built up a lot of race data for most tracks. But remember that NASCAR is always changing, and changes are often hidden when you reduce the data too much.

Let’s look at the qualifying positions of Talladega winners over time.

I’ve highlighted the top five qualifying positions in a red (well, pink) box. Pick a range of years and then compare how many points are in that box to how many are outside. The ratio is very different if you’re looking before about 1998 vs. if you look after 1998.

We can quantify this difference by looking at averages over different periods of time.

- The winner’s average starting position from 1969-1998 was
**5.2**. - From 1998 to the present, it’s
**12.1**.

There’s not really a hard line: There’s nothing magical about 1998. The difference gets even more interesting when we look at average starting position by decade:

Before 2000, most of the winners came from the first three rows. More recently, winners have come from further back and being in the front row (or front two rows) isn’t as important.

The standard deviation is the amount of variance in a set of numbers.

- A small standard deviation means that the numbers are all close to each other.
- A large standard deviation means that there’s a lot of spread in the numbers.
- 68% of all the numbers within a set lie within one standard deviation of the average.

So here’s the data above, but plotted to include the standard deviation.

- In the 1970’s, 68% of the winners qualified between 1st and about 14th.
- The spread was even smaller in the 1980s: 68% of the winners qualified in the top 8.
- In later years, the spread has gotten even larger: winners are coming from further back in the field.

What’s really interesting here is that, **in the 2000s, winners were actually less likely to come from the front row.** In the 2010s, so far, winning from the pole makes you an outlier.

Why the big changes over time?

A lot has changed in the last two decades. Just to name the ones that come immediately to mind:

- Computational fluid dynamics and wind tunnels revolutionized the way teams built their car bodies.
- Remember when drivers had a dozen radio channels in their cars so they could strategize with other drivers?
- Tandem drafting changed the nature of racing so much that NASCAR mandated changes in the cooling system to discourage it.
- Some drivers adopted a strategy of hanging in the back to try to avoid the big wrecks
- Manufacturers mandated cooperation only with teams driving the same make.

There are many other changes, all of which are hard to quantify, but I think we can pin down at least one major change that’s made qualifying up front a little less important at Talladega.

**DATASET**: The following analysis is based on all Fall Talladega races from 1970-2018.

One thing I wanted to look at was the number of cars that didn’t finish the race, because we’ve seen a lot of times where front-running cars get taken out in a crash and I wondered if that had changed over time.

This is the percentage of the field that didn’t finish: I didn’t use absolute numbers of cars because the field size has changed over the years.

The DNF rate definitely decreased — with the exception of the wacko 2017 race, where 62.5% of the cars in the field didn’t see the checkers — but the decrease wasn’t as much as I had expected.

You can see the change a little more clearly if we break it down by decade.

Restrictor plates were instituted in 1988, so you might think that was the reasons for the decrease was fewer crashes.

You’d be wrong.

Over the 50 races I examined, NASCAR gave 42 different reasons for why cars didn’t finish a race. That’s what ‘s in the word cloud in the header.

They were a lot more specific back in the day, noting whether a failure was due to a valve, a valve spring or a crankshaft.

Some are more descriptive than others. In the 1994 fall race, the car driven by three-time ARCA champion Tim Steele is listed simply as “quit”.

I combined the 42 reasons (which accounted for 708 DNFs) into five groups.

I split out engine failures from mechanical failures. The last two categories barely contribute to the total and I won’t do anything further with them.

This graph shows the percentage of cars that start the race, but DNF due to mechanical failure. It is *not* the % of DNFs, but the percent of cars in the field that DNF due to mechanical failure.

In the 70’s and most of the 80’s, it was not unusual for a third of the cars to drop out of the race due to mechanical failure. There were four races where 50% of the cars DNF-ed due to the car failing.

We just don’t see that anymore. In the last 10 years, we haven’t even reached 10% of the cars DNF-ing due to mechanical failures. The majority of those failures are engines giving up, but even that has become much less common in recent years.

So where do the rest of the DNFs come from?

Again, this is the % of cars that started the race and didn’t finish due to being involved in a crash.

On average, the number of DNFs due to crashes has increased over time, even if you toss out 2017 as a fluke. While we tend to think of Talladega as a track with a lot of crashes, it hasn’t always been that way.

The probability of exiting a race due to a mechanical failure has gone down, but probability of getting knocked out by a crash has gone up. That’s played havoc with the relationship between where you start and where you finish.

Look at how so many of the data points lie on a straight line for the highest finishers in 1978. (I drew the line to make it easier for you to see.) Most of the drivers at the later finishing positions (the ones off the line) didn’t finish the race.

In contrast, you could draw a straight line for the 2018 race, but the data points would be much further away from it. There’s less correlation between how well you start and how well you finish today.

We’ve changed from being knocked out by something within your control (i.e. your car) to something often outside of your control at Talladega: your ability to stay out of crashes.

If I’m a crew chief, I want to make sure we qualify decently — within the top 15 or so — but I’m not going to stress out over the difference between first or fourth. In the end, it probably won’t make a whole lot of difference.

I’m also going to have my spotter(s) keep an eye out for potential trouble spots to try to help him avoid a crash, which is a much bigger threat to remaining in the playoffs than not getting the pole.

This is all the DNF data from the discussion above put into one giant, colorful graph.

This graph actually has a purpose. You see, scientists love graphs because, if a picture is worth a thousand words, a graph is worth ten thousand words. Because we speak “graph”, if I were writing this for a scientific audience, I wouldn’t break all the data down, but just put this up there.

The more you learn how to understand graphs, the easier it is to understand the data for yourself — and not have to rely on someone else, who may not telling you what you need to know, or may be trying to slant the data to support their own position.

- Sonoma Raceway picture in header via ZappaOMati [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)]
- Bristol scoring pylon picture in header via Kim Phillips [CC BY 2.0 (https://creativecommons.org/licenses/by/2.0)]
- Data obtained from racing-reference.info

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]]>The post Dominance of the Few Isn’t New in NASCAR appeared first on The Building Speed Blog.

]]>But it turns out that having a small number of drivers responsible for a large number of the wins isn’t anything new.

In mid-July last year, only seven drivers had won races. Three of those seven drivers had won 14 out of 19 races. The trend was more obvious in 2018 because Harvick won races 2-4 and Kyle Busch won races 7-9.

We went on to have 12 distinct winners in 2018, which is on the low side, but not record-setting if you look at data over the last 30 years.

- On average,
**13.7 distinct drivers win each season** - The low was in 1993, when 10 drivers won 30 races
- The high was in 2001, when 19 different drivers won 36 races

We’re at 11 distinct winners right now, but don’t forget that one (or more) of the playoff drivers will likely win in the remaining races.

The number of races per season changed from 1990-2001, so I’ve scaled the numbers to represent how many winners there would have been *if* all seasons had 36 races. This raises the numbers for the earlier seasons because they were up to 16% shorter than the later seasons.

- An average of
**14.4 distinct drivers win each season** - The low was in 1999, when 11.6 (equivalent) drivers won
- The high was still in 2001, when 19 different drivers won

The number of distinct winners may change a lot from season to season, but when you look at the averages, there isn’t a lot of change.

The number of average winners is down by one in this decade compared to the last, but both are slightly higher than the 1990s when you take into account the changing number of races.

The number of distinct drivers doesn’t really tell you about dominance. For example, 1990 and 1991 both had 14 distinct winners and 29 races — but the wins were distributed differently.

- In 1990
- The winningest driver won 9 races
- The second-winningest driver won 3 races

- In 1991
- The winningest driver won 5 races
- The second-winningest driver also won 5 races

Those are two very different situations. In 1990, there was a single dominant driver who won 31% of the races. In 1991, three drivers split almost half the wins.

To take the differing number of races into account, we’ll look at the percentage of races won in a season by the top-5 winningest drivers. Note that these may not be (and often aren’t) the drivers who finished in places 1-5 in the championship standings.

- In three years, over 80% of the races were won by five drivers
- In 1993, 83.3% of all races were won by five drivers
- 1997 and 1998 are the only other years that five drivers won more than 80% of the races
- All three of these seasons are prior to this century

- In this century:
- 2019 is second only to 2008 in terms of dominance by the top five winningest drivers
- Five drivers won 77.8% of the races in 2008
- This year is at about 77%

- 2019 is second only to 2008 in terms of dominance by the top five winningest drivers
**The top-five winningest drivers have never won less than 50% of the races.**

That last one sort of surprised me. A small number of drivers — a different group each season — have *always* dominated NASCAR.

The numbers cycles through the years, but if we group these numbers by averages over a decade:

The dominance within the NASCAR Cup series has actually decreased from the 1990s to now by almost 10%. Although we have five dominant drivers, they are less dominant than they were in the 1990’s.

In 1993, 83.3% of all races were won by five drivers — but the fifth of those drivers only won one race. So you can guess what I looked at next.

**In the last thirty years, three drivers have always won at least one-third of the races in a seasons and sometimes more than two-thirds.**

- As with the top 5, the numbers are higher in the 1990s.
- In 1993, 70% of all races were won by three drivers

- 1998 came close, with three drivers winning 69.7% of the races

- In this century:
- 2008 is the highest percentage of races won by three drivers, with 66.7%

- The top-five winningest drivers have never won less than 1/3 of the races.

While the Big Three last year was definitely a thing, it was far from the first time three drivers have dominated a season. And 2018’s Big three weren’t as dominant as trios in the past have been. Again, looking at averages over the decades:

The percentage of races won by the top 3 winningest drivers has gone down since the 1990’s by 12%. Today, we should expect that three drivers will win at least 12 races each year and — on average — about 16 races of a 36-race season.

While having three or five drivers in close competition is good, the sport becomes less competitive when one driver wins a huge majority of races.

Competition now is higher than in the 1990, as demonstrated by the inability of one driver to run away with wins.

- Single-driver dominance was at its maximum in the 1990s
- In five seasons out of the ten one driver won more than 30% of the races
- This trend culminated with Jeff Gordon winning 13 races (almost 40% of the races) in 1998. We have not seen a season like that since
- The next highest was 1993, when Rusty Wallace won 10 races (out of 30) — and finished 2nd in the championship standings

- In this century:
- In 2007, Jimmie Johnson won 10 races (and the championship)
- In 2008, Carl Edwards won 9 races

- The winningest drivers has never won less than 13.9% of the races (5 races)

If we look again at decade averages:

Single-driver domination is down by about 9% now relative to the 1990s.

There have always been cyclical variations in dominance within the Cup series, but in every year since 1990:

- The top-5 winningest drivers have won at least 50% (18/36) of the races each season
- The top-3 winningest drivers have won at least 33% (12/36) of the races
- The winningest driver has won at least 13.9% (5/36) of the races

Competition has increased relative to the 1990s. Wins are spread around more, even when there are a smaller number of distinct winners each season.

Here’s all the data from above in a single graph. The dark blue bars are wins by the winningest driver, light blue is the 2nd winningest driver, red is the third winningest driver. The 4th and 5th winningest drivers are combined in the orange bars and the green bars show the wins by everyone else.

The post Dominance of the Few Isn’t New in NASCAR appeared first on The Building Speed Blog.

]]>The post Do Winless NASCAR Drivers Win in the Playoffs? appeared first on The Building Speed Blog.

]]>I was also pulling for Daniel SuÃ¡rez to make the playoffs because he doesn’t have a contract for next year yet, and also for Clint Bowyer because he came so close in 2012 and (also) doesn’t have a contract for next year yet…

But it was not to be for Johnson or SuÃ¡rez. Both drivers said all the requisite things afterward: There are still ten races left and we’re gonna focus on winning them.

But how likely is that for drivers who didn’t make the playoffs? And, for that matter, what about the six drivers entering the playoffs without any wins?

Let’s start by looking at last year because we were working with the same rules. Everyone who won a race was in the playoffs, plus six drivers who got in on points.

Here’s a pie chart that sums up when drivers won — or didn’t

This is a little confusing: I know because I confused myself a couple times. The sixteen drivers break down into four groups.

**No Wins: Four drivers**(**25%**of the drivers in the playoffs) got in on points**and**didn’t win any of the 10 playoff races.**Wins Everywhere:****Five drivers**(**31%**) drivers won regular season (RS)*and*playoff (PO) races.**RS****Wins, but No PO Wins:**Another**five drivers**(**31%**) won in the regular season, but didn’t win during the playoffs.**No RS****Wins, but PO Wins:**This is the one we’re really interested in. Only**two drivers**got into the playoffs on points, and then went on to win races during the playoffs. (That’s**13%**)

Since we’re interested in how likely it is that a driver wins a race, we need to look not just as what the drivers do, but how many races each wins — or doesn’t. This will end up getting a little confusing, so I hope my color code helps make it clearer.

**BLUE**colors denote regular season (RS) races**RED/ORANGE**colors denote playoff (PO) races

So here’s what 2018 looked like, graphically.

- Of the 26 regular-season races
- Drivers who won both RS and PO races won
**17 races** - Drivers who didn’t win in the POs won
**9 races**

- Drivers who won both RS and PO races won
- Of the 10 regular-season races
- Drivers who won both RS and PO races won
**8 races** - Drivers who didn’t win in the RS won
**2 races**

- Drivers who won both RS and PO races won

This data implies that those drivers who haven’t won this year have about a **20% **chance of winning a post-season race.

Yes. It is, and good for you for noticing. But here’s the problem: NASCAR keeps changing the rules. I’m not complaining: they’re trying to make the sport more competitive. But that just wreaks havoc with data analysis.

Just as a reminder, so you have some context, there have been (to date) five different schemes for The Chase/the playoffs.

- The Chase/playoffs began in 2004 and involved all drivers in the top 10 in points, plus any drivers within 400 points of the leader.
- In 2007, the field expanded to the top 12 drivers and they got rid of the ‘within 400 points of the leader’.
- 2011 brought another switch: 10 drivers + two wild-cards: the drivers who had the most wins, but were outside the top 10.
- Starting in 2014, NASCAR introduced win-and-you’re-in, with 16 drivers in the Chase/playoff system, plus elimination stages.
- It wasn’t until 2017 that we got the current playoff scheme with stages.

If we limit ourselves to 2017-2018 (where the playoff rules were the same as they are this year), we have **two drivers** in 2018 who only won in the playoffs and **one driver** in 2017, so the effective probability is 3/20 = **15%**.

Fifteen percent isn’t bad.

But is it a valid number?

Let’s look at the previous three years, where we had everything the same in the playoff scheme except for stages.

For three years in a row, **no one** won in the postseason who hadn’t won in the regular season. Putting the last five years of data together…

If we use the last five seasons on the argument that they are most similar to 2019, then drivers we were winless going into the playoffs won **6%** of playoff races.

But this graph also raises another issue that will shortly become even more important. See that hashed section in 2017? That’s Joey Logano, who won a regular season race, but his win was encumbered. So I needed to make another distinction in my graphs.

**SOLIDS**indicate races won by drivers who finished in the top 16 (i.e. made the playoffs).**PATTERNS**indicate races won by drivers finishing outside the top 16.

So…

- A blue hash = a driver who finished outside the top 16, but won a regular season race.
- A red/orange hash = a driver who finished outside the top 16, but won a playoff race.

I went ten years back, with the caveat, again, that we’re getting into different playoff schemes, so the data may be less relevant.

In 2011, Tony Stewart barely made The Chase, but had no regular season wins. He then went on to win five Chase races — and the championship.

Drivers who had won in the regular season only won three of the final 10 races that year. Drivers who **hadn’t** won in the regular season won seven races: Stewart’s five, plus one each from Clint Bowyer and Kasey Kahne.

Well, let’s look at the number of postseason races won by new winners for all ten of the last years.

It’s not too surprising that the number dropped once NASCAR introduced win-and-you’re-in. This graph represents 20 drivers over ten seasons, which amounts to 20% of the playoff races won by drivers who hadn’t won in the regular season.

But given that those larger numbers come from a different set of rules, I’m more comfortable with the 6% figure.

Well, not exactly.

The graph above fails to make an important distinction if we’re going to talk about the chances of, say, Jimmie Johnson or Daniel SuÃ¡rez winning a race vs. Ryan Blaney or Clint Bowyer.

The last three non-RS winners have all been from within the final 16. So do drivers from outside the top-16 ever win playoff races?

Here’s ten years of data. We’re looking for a winner not in the top 16 at the end of the year, which would be represented by a hatched area at the far right of the bar.

Do you see them? They’re small (1 race each), but they’re definitely there: one in 2013 and one in 2009. And the results aren’t very encouraging for those drivers sitting in positions 17 and up.

**Over the ten most recent seasons, drivers outside the top sixteen won only two playoff races (2%).**

Actually, no. Let’s look at that 2013 point, which is Denny Hamlin.

Hamlin missed four races in 2013 due to medical issues. That was before the exceptions and exemptions we have today. Hamlin finished 23rd that year. So that data point *sort of* doesn’t count because if Hamlin hadn’t missed those races, he very well might have finished in the top 16.

That leaves us with only the one race in 2009. Before we get excited, let’s check and make sure it’s a legit data point.

It is! Jamie McMurray finished 23rd in the standings that year, but managed to win his first race in the fourth-to-the-last race of the year.

So the actual number is 1%, but that comes from data a long time ago in a playoff system far, far away.

Unfortunately for you 41, 48, 21, etc. fans out there, the reality is that the probability of your driver winning one of the next ten races is pretty doggone low.

But that doesn’t mean it’s impossible. Because who predicted this year’s list of winners would include Justin Haley? One percent means that the event would happen once in a hundred times. There’s nothing saying that Las Vegas this weekend isn’t that one time.

And if one of those drivers does win, this analysis shows that the win ought to be even more respected because it’s a pretty amazing feat.

**Road Courses and Plate Tracks:** Blaney won the Charlotte Road Course and Almirola won Talladega last year. Our solitary data point (McMurray in 2009) won at Talladega. My analysis shows that these two types of tracks are wild cards and represent a much better chance of winning for less-likely-to-win drivers. So look to Talladega, or the Charlotte Roval.

Johnson almost won last year at the Roval, so that’s looking like #1 on my list of highest-probability tracks for him. But remember that Blaney (in the playoffs this year) won the Roval last year.

Johnsons’ best finishes in the regular season are at mile-and-a halfs, so you might look for him to do well this week, too.

**THANK YOU** to the great folks at racing-reference.info for all the info they provide and keep up to date. Their resources make doing this infinitely easier and faster.

The post Do Winless NASCAR Drivers Win in the Playoffs? appeared first on The Building Speed Blog.

]]>The post NASCAR Throwback: Are Things Easier for Rookie Drivers Now Than in the 90’s? appeared first on The Building Speed Blog.

]]>Let’s get some context by looking at who we’re talking about. Here are the top Rookie-of-the-Year contenders for the first five years of each decade. I’ve listed the winner first.

The Rookies of the Year (ROTYs) from 2010-2012 might not be familiar to you. They didn’t run full-time and most haven’t continued in NASCAR. Kevin Conway, for example, ran 28/36 races in 2010 and only ran 3 more races in NASCAR after that. I was glad to see Stephen Leicht pop up this year in the XFINITY series.

That’s not to say this didn’t also happen in the 90’s. It was common that not everyone would run all the races and sometimes even the season champion would have missed a race.

There were 33 races in 1998. Every race had the maximum 43 cars but **only 17 drivers ran all 33 races**. Another 10 ran 32 races — many because they DNQ’ed once. Compare this with 2018, where 29 drivers ran all 36 races.

Here’s the next five years of rookies.

Interesting Note:

- The average age of the Rookie of the Year from 1995-1999 was 31.6 years.
- The average age of the Rookie of the Year from 2015-2019 is 22.2 years

Let’s look at the records of the rookies relative to the veterans.

Surprisingly, there are only three drivers in these decades who won races in their rookie years:

- Tony Stewart won in 1999
- Trevor Bayne won in 2011 running a part-time season
- Chris Buescher won in 2016

Prior to Tony, only five drivers won races their rookie year:

- Earl Ross (1974) — his first and only win
- Dale Earnhardt, Sr. (1979)
- Ron Bouchard (1981)
- Morgan Shepherd (1987)
- Davey Allison (1987)

That’s six people in 25 years, which might make you think that winning a race your rookie year is almost impossible.

Except check out the ‘aughts’.

- 2000: Kenseth, Earnhardt, Jr.
- 2001: Harvick
- 2002: McMurray, Newman, Johnson
- 2003: Biffle
- 2005: Kyle Busch
- 2006: Hamlin
- 2007: Montoya
- 2009: Logano, Kezelowski

That’s **twelve** drivers who won races in their rookie years in the 2000’s, compared with three in the 80’s, one in the 90’s and two in the 2010’s. Today’s drivers compete against a class of exceptional winners when it comes to rookie-year performance.

Verdict: The numbers are low: one rookie winner in the 1990s and two in the 2010s. It looks to me like it’s about equal — but both decades are way below the 2000’s.

Our current rookies are competing against those exceptional win-your-rookie-year drivers from the 2000’s, but was the competition any less in the 1990’s? Here’s the drivers in the Top 5 at the end of the season for each of the first five years of each decade.

I bolded drivers who have won championships and starred Hall of Fame members. The latter is totally unfair because many of those on the right-hand side aren’t even eligible yet. But you can guess that there are at least five on the right-hand side.

Here’s the second halves of the decades.

This is a ‘how many angels can dance on the head of a pin?’ question. Do you want to argue that one group is better than the other? If I count correctly, in the first half of the decades there are 12 championships on the left side and 15 (and counting) on the right. In the second, but I count 20 championships on the left and 17 on the right, but we’ve got two more years to go before we can finalize this comparison.

I’m calling this one even.

Most drivers in the top five at the end of the season have at least five years of Cup-level experience. Considering only the 1990’s and the 2010’s:

- 1990’s: Tony Stewart made his first appearance in the top 5 in his rookie year.
- 2010’s: Chase Elliott made his first appearance in the top 5 in his second year at the Cup level.

If we look at all the drivers who made it into the top five within five years of their rookie season:

**In the 1990’s**decade, only Davey Allison (4 years), Jeff Burton (4 years), Jeff Gordon (3 years) and Tony Stewart (1st year) made it into the top five.**In the 2010’s**, we have Brad Keselowski (3 years) and Chase Elliott (2 years).

Interestingly, the average number of years in Cup for the top 5 didn’t vary that much between the two decades. It’s hard for younger drivers to break into the top five in any decade, but slightly harder in the 2010’s than it was in the 1990’s.

We can sum up the differences in the decades as follows:

- The 90’s were a time of individual domination. Out of the 10 championships, two people won seven of them.
- The 2010’s, on the other hand, came on the tail end of Jimmy Johnson’s championship streak. Out of nine championships, we’ve had seven champions.

1990’s-era drivers had to contend with champions with long winning streaks, while 2010-decade drivers have a much broader range of competition. Either way, it’s still a challenge for new drivers to get a foothold in NASCAR.

There’s one area with a demonstrable difference in terms of young drivers having it tougher than their counterparts and that’s in getting on the track in the first place.

NASCAR has changed in the last 20 years and today’s drivers have much different and formidable challenges than those in the 1990’s did.

There were 29 races on the schedule in 1990. By the end of the decade, there were 36 races.

The car count expanded, also: The 29 races in 1990 each had between 32 to 43 cars. In the 2000’s, almost all of the 36 races featured the maximum number of 43 cars.

NASCAR’s expansion was good in a lot of ways (more attention, more fans) and not-so-good in other ways (George Gillette, Bobby Ginn and others who got into NASCAR as a way to make a buck, rather than for the love of racing.) As with most businesses, NASCAR contracted in the 2000’s.

So where are we now?

A driver needs a car. The number of owners has gone down as the costs of doing business have gone up.

The number of full-time owners (owners who run at least one car full-time) is about 2/3 what was. The majority of that decline is among Ford teams.

The decrease is even larger when you look at teams that don’t run the entire whole season.

If you’re wondering how I got fractions here, it’s because some teams ran part of the season with one manufacturer and the rest with another.

Many part-time teams have multiple drivers, often choosing their driver by what he brings to the table in terms of publicity or sponsorship. That doesn’t happen with full-time cars. Fewer part-time teams means fewer opportunities for drivers.

You will point out (quite rightly) that the multi-car team was rare in the 1990’s. By 2006, NASCAR had to impose a four-car limit on teams. So the decrease in number of owners might have been compensated for by the increase of multi-car teams.

But we have to add in two more factors

- The
**number of races**increased significantly from 1990-2001, meaning more seats. - The
**maximum number of cars per race changed**, first rising, then falling.

Define the number of opportunities as the number of cars that race in a season. We know that number, and can project it for 2019 by scaling the data for the season thus far.

The number of opportunities increased significantly from 1990 to 2001, from 1112 in the early 1990’s when a season was 29 races, to 1548, which corresponds to 36 races, each with 43 cars.

The magnitude of the recent drop surprised enough to re-re-check my calculations. We went from 43 cars to a maximum of 40, which eliminates 108 opportunities each season. A number of races this year had only 36 cars, so the size of the drop starts to make sense.

So does this mean drivers are back where they were in 1997? No, because there’s one more factor to consider.

With the advent of the XFINITY and Truck series as proving grounds for drivers, there are fewer opportunities for ‘tryouts’. Drivers are signed to season-long (if not multi-season long) contracts right off the bat. That trend is much higher today than it was in the 1990s.

The competition on the track might be a little tougher today than it was in the 1990’s, but not enough to make much of a difference.

The real challenge for young drivers is off-track, because industry contraction and consolidation makes finding a ride is harder now than it was in the 1990’s.

The post NASCAR Throwback: Are Things Easier for Rookie Drivers Now Than in the 90’s? appeared first on The Building Speed Blog.

]]>But does short-track mayhem hold a candle to Daytona and Talladega? Let's see. [...]

The post Which NASCAR Tracks Are Most Accident Prone? appeared first on The Building Speed Blog.

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

Let’s see.

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.

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.

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

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.

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.

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.

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.

- 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

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

- 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.

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.

- 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.

- 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.

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.

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.

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%

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.

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.

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.

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!

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

- 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.

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.

The post Which NASCAR Tracks Are Most Accident Prone? appeared first on The Building Speed Blog.

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