Jump to content

Recommended Posts

Hi Everyone, totally new to the Champcar forums,

Made it out and help crew for Team Junction at the 14 Hours of Road Atlanta, needless to say, had an amazing time, but since the actual race is over, myself and my partner in crime were going through the live timing data for some post race analysis. Here is what we found. Shameless plugging myself so please visit our Facebook page which leads to the actual blog post.

 

Edited by ApexPE
  • Like 6
  • Thanks 4

Share this post


Link to post
Share on other sites

Excellent job!  Do you mind sharing how you were able to gather the data to perform this analysis?  I always have a rough time getting it out of Speedhive in a usable format.

  • Like 1

Share this post


Link to post
Share on other sites

That is some interesting data. Would love to see how individual teams that finished compare to the top three. Or have an app that did that for you.... 

Share this post


Link to post
Share on other sites
1 hour ago, Huggy said:

Excellent job!  Do you mind sharing how you were able to gather the data to perform this analysis?  I always have a rough time getting it out of Speedhive in a usable format.

JSON is programmer speak for "nerdy and damn proud of it."

 

*disclaimer: former C# guy

  • Haha 1

Share this post


Link to post
Share on other sites

Box Plot

We have used the box plot to visualize the distribution of the lap times, but this time we can observe the distribution for each outing.

Box Plot 1Box Plot 2

The difference in strategy and consistency is clear. 23 Van Winden Racing is more consistent between outings while Huggins (Pinkies Out) strived for raw lap time in some outings. Interestingly, 23 Van Winden Racing has a lower median lap time than Huggins (Pinkies Out). Although 23 Van Winden had the pace to be race winner, the race was ultimately won by managing the slower laps and pit stops.

 

 

@ApexPE  interesting analysis.  I can't help but wonder if the issue for Van Winden in the last two stints isn't one or both of the following:

  • according to the info in the broadcast, most of them were new to the track.  The last two stints were night stints, and from personal experience I can tell you that it is a lot easier to go fast at night if you have the track well-memorized.  Although RA isn't home for Huggins, their last two drivers both have prior experience at that track
  • their car may not have had a great lighting setup.  I have driven a really good setup and a marginal setup and it really makes a big difference.  The less familiar the track is, the more difference it makes

Now all of that being said, they may have just simply had a car problem (like so many others) that slowed them late in the race.

 

Also worth noting, the drive of stint #7 for Huggins was the same as for stint #2 and stint #5.  Since most of the drama that took out the prior leaders happened before stint #7, that driver would have been on a conservative "just don't screw up" strategy.  So even if Van Winden had brought more speed, the results may have been the same because Huggins would have likely reverted to lap times closer to what you see in stint #2 and #5 (since they have a pretty good lighting setup)

 

Thanks for all of the plots/graphs!

Share this post


Link to post
Share on other sites

I don't have anywhere near your skills, but one of your plots inspired the following:

 

This is the mph for the fast lap of cars that placed in 16th to 60th place (I don't use lap time because I can't figure out how to get excel to stop thinking it is time of day).  The mph is just track length divided by the lap time so it is the same info in a different format.

image.thumb.png.510d4632c914434b2c133ff3dab101d6.png

 

I had excel put a trendline in.  If we assume that the fast lap for a car is the best proxy for the performance potential of the car (which is very much an imperfect assumption, but stay with me for a minute), the trendline says the that difference in speed capability between 16th and 60th is only about 1 mph (as a trend, not as an absolute measurement).  Now if you challenge the imperfect assumption that I made, you could certainly argue that the higher finishing cars would likely have on average better drivers (good cars attract good drivers) so you could argue that the likelihood of the cars on the left hand end of the plot being closer to the full potential of the car is higher than the ones on the right hand end of the chart.  If you buy into that argument, this plot would suggest that the performance potential of the cars in the "midpack" (left off the super fast and super slow cars) is about as equal as it could possibly be.  So what are the keys to being #15 versus #60?

  • time off track:  Whether it is pitting for more fuel because you can't do 2 hours, or pitting for damage/failures, or pitting for black flags, etc.
  • driver skill:  The car is fast but some of the drivers aren't capable of getting anywhere near the speed of the fastest driver

 

Share this post


Link to post
Share on other sites
3 minutes ago, Racer28173 said:

I don't have anywhere near your skills, but one of your plots inspired the following:

 

This is the mph for the fast lap of cars that placed in 16th to 60th place (I don't use lap time because I can't figure out how to get excel to stop thinking it is time of day).  The mph is just track length divided by the lap time so it is the same info in a different format.

image.thumb.png.510d4632c914434b2c133ff3dab101d6.png

 

I had excel put a trendline in.  If we assume that the fast lap for a car is the best proxy for the performance potential of the car (which is very much an imperfect assumption, but stay with me for a minute), the trendline says the that difference in speed capability between 16th and 60th is only about 1 mph (as a trend, not as an absolute measurement).  Now if you challenge the imperfect assumption that I made, you could certainly argue that the higher finishing cars would likely have on average better drivers (good cars attract good drivers) so you could argue that the likelihood of the cars on the left hand end of the plot being closer to the full potential of the car is higher than the ones on the right hand end of the chart.  If you buy into that argument, this plot would suggest that the performance potential of the cars in the "midpack" (left off the super fast and super slow cars) is about as equal as it could possibly be.  So what are the keys to being #15 versus #60?

  • time off track:  Whether it is pitting for more fuel because you can't do 2 hours, or pitting for damage/failures, or pitting for black flags, etc.
  • driver skill:  The car is fast but some of the drivers aren't capable of getting anywhere near the speed of the fastest driver

 

 

The "pointy end" of the field did not exhibit that same characteristic.  The difference in speed capability between 1st and 15th had a trend value of 4 mph!

 

image.thumb.png.b074f0f4ab3f51bf7a954d016cd83472.png

Share this post


Link to post
Share on other sites

I think I'm just saying it a different way than above ^^ but,  this graph appears to me to further prove that point.  basically the top 30 are all within a couple seconds of each other in average and fast time.  Nothing groundbreaking here but here's hard data showing consistency on track and in the pits puts you in the hunt more so than anything else debated here regarding individual car performance.  Thanks for the breakdown @ApexPE!image.png.a1b2d486124b3a51cc7714c169cb86ff.png

Edited by BollingerChump

Share this post


Link to post
Share on other sites
8 minutes ago, BollingerChump said:

I think I'm just saying it a different way than above ^^ but,  this graph appears to me to further prove that point.  basically the top 30 are all within a couple seconds of each other in average and fast time.  Nothing groundbreaking here but here's hard data showing consistency on track and in the pits puts you in the hunt more so than anything else debated here regarding individual car performance.  Thanks for the breakdown @ApexPE!image.png.a1b2d486124b3a51cc7714c169cb86ff.png

yeah - that was the figure that prompted my work.  I was looking at it saying "it looks like the trend of the shorter cars is basically flat all of the way across", but I long ago learned that you really need to zoom that type of data in the vertical axis to make sure there isn't a subtle up trend or down trend.  Once I did that, I saw the strong trend in the top cars and the negligible trend in the mid-pack.  I didn't bother plotting below mid-pack because I assumed that major factor in finishing below 60th was time off track (the 60th place car was 186 laps down from first) and not speed potential.

Share this post


Link to post
Share on other sites
4 hours ago, Racer28173 said:

If you buy into that argument, this plot would suggest that the performance potential of the cars in the "midpack" (left off the super fast and super slow cars) is about as equal as it could possibly be.  So what are the keys to being #15 versus #60?

  • time off track:  Whether it is pitting for more fuel because you can't do 2 hours, or pitting for damage/failures, or pitting for black flags, etc.
  • driver skill:  The car is fast but some of the drivers aren't capable of getting anywhere near the speed of the fastest driver

 

That is a reasonable observation to make. We can always flip the narrative and look at the 'overall average' lap time but sorted in order of the team's FTD. If you stand far away from the graph and squint your eyes, sure you could argue the trend that is going upwards in that faster cars are faster on average, but there is ton of variance in the data that, in counter argument, far exceeds the magnitude of the trend. It makes sense that fast cars can go fast, but fast cars can also go really slow. Of course preferable to have the faster car, but I agree that the pit stops and driver skill do have a big impact on overall placing.

 

This is where the rising lap time plots are fun to look at. Where the lines cross over is when one team begins to outperform another, though its tough to really see how the pit stop battles play out - it's better at showing the differences in race pace. Just imagine a race where your laps had to be in order of ascending lap time... the race would end with teams waiting to get out of the pits. Kind of odd to think about.

 

xgOuGOX.png

 

The vertical axes can hide the subtleties in the plot, so good on you for catching that. Here is the plot as a percentage increase over the event FTD - simply a rescaled version of the plot above.

K2LelWl.png

That car you see on the far right side is about 21% slower than the FTD set by Not Banned Yet.

  • Like 3

Share this post


Link to post
Share on other sites

Interesting data, thanks for putting it together.  I always download laptimes and sort/compile the data for our team.  I throw out the high lap times somewhat arbitrarily figuring they are cautions, either partial or full course.  We usually average less than a second difference between drivers.  Unfortunately, one of our drivers always beats me by .25 to .5 seconds which really pisses me off.

 

No matter what the data yields, but I am sure it will back this up, there are some basic premises required to do well in these races:

  • Drivers have to run consistent lap times no matter what traffic they deal with.
  • You have to stay clean, no penalties.
  • You have to hit pit out with time left on the clock.
  • No mechanicals.
  • If the race length is an even number, you have to make 2 hour stints.
  • Fastest time of the day for your car usually has to be in the top 5 cars ftd.
  • Like 5

Share this post


Link to post
Share on other sites

I'd like to see the under/over-performed chart that has been posted after other races.

 

  • Like 1
  • Thanks 1

Share this post


Link to post
Share on other sites
14 hours ago, jakks said:

I'd like to see the under/over-performed chart that has been posted after other races.

 

 

Road Atlanta.jpg

  • Like 4
  • Thanks 3

Share this post


Link to post
Share on other sites

I'd like to see the above chart based on average lap for each team excluding obvious pit/caution laps.  (but I'm too lazy to do it myself)

Share this post


Link to post
Share on other sites
51 minutes ago, zack_280 said:

I'd like to see the above chart based on average lap for each team excluding obvious pit/caution laps.  (but I'm too lazy to do it myself)

Overall average lap times are going to directly correlate with finishing position, other than a few lucky yellow flag pits.

Share this post


Link to post
Share on other sites
Just now, morganf said:

Overall average lap times are going to directly correlate with finishing position, other than a few lucky yellow flag pits

It would be a better metric for judging overperformance and underperformance of a team than basing it on fast lap vs finishing position.   

Share this post


Link to post
Share on other sites
4 minutes ago, morganf said:

Overall average lap times are going to directly correlate with finishing position, other than a few lucky yellow flag pits.

Also, I'm guessing that Rbank would disagree with this.

Share this post


Link to post
Share on other sites

Not sure about all teams, but nailed two examples.  We (Burningham) were a relatively fast car that broke down. Bottom right. Our loosely connected other team (Rocketham) was relative to the field slower but very consistent. Top left. This really comes as no surprise that we came out on this graph this way. 

Share this post


Link to post
Share on other sites
1 hour ago, zack_280 said:

I'd like to see the above chart based on average lap for each team excluding obvious pit/caution laps.  (but I'm too lazy to do it myself)

Id be happy to do it If I had access to the average lap time for each team.  The only way to do this that I know if is manually copy-paste the data from speed hive for ~80 teams.  

Share this post


Link to post
Share on other sites

Yeah, That's why it's a PITA.  I was hoping you knew a way to download everything.  Even if you can do that, it's still a bit of work to filter out the obvious longer laps.

Share this post


Link to post
Share on other sites
33 minutes ago, zack_280 said:

Yeah, That's why it's a PITA.  I was hoping you knew a way to download everything.  Even if you can do that, it's still a bit of work to filter out the obvious longer laps.

 

Statistically filtering out the longer laps is easy.  If I can get the data, no issue.

 

According to the OP, you can use JSON to do it.  Thats about all I know about JSON though.  Paging Jason?

Share this post


Link to post
Share on other sites
18 hours ago, Huggy said:

Thats about all I know about JSON though.  Paging Jason?

From the internet:

JSON (JavaScript Object Notation) is a lightweight data-interchange format. It is easy for humans to read and write.

 

Assuming you are human, it should be easy to figure out.

Share this post


Link to post
Share on other sites

Speedhive and race monitor do all of their work server side. I haven't seen a JSON from either of those sources. @ApexPE would have to share their process if the data is exposed somewhere or if they have access to the API.

Share this post


Link to post
Share on other sites

  How do stops figure into this .. we are right on the line . Performing average ?   Finished 4th with 10 stops ..

Share this post


Link to post
Share on other sites

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!

Register a new account

Sign in

Already have an account? Sign in here.

Sign In Now

×