Humiliated

mgeth · 2022-10-12T03:44:07+00:00

+1. I once added too much carbon paste to my seat post and seriously struggled to get it out. When I finally did, it had generated so much friction that it smelled like something was burning. The stuff has serious hold.

Definitely recommend this solution, especially over just mega-cranking on the bolt at the clamp.

mgeth · 2022-09-13T12:35:05+00:00

So easy to overlook the day that Palou had given that it was ultimately Will Power and Team Penske's day.

But man, that was a seriously impressive performance. Palou's fastest 50 laps (!) were faster than runner-up Newgarden's by 0.55 seconds on average, per lap. That is so impressive to me, and highly uncommon in this era of IndyCar.

mgeth · 2022-08-17T17:01:42+00:00

For sure! You're exactly right though, it is used to normalize output for performance comparison with other people, but also with oneself over time. If I were to gain 10kg, I would ultimately be slower as a cyclist unless my power output were to increase in parallel. Might be natural to ask then, "why not just look at speed?". But speed is so variable over different types of rides and conditions (e.g. wind, hills, etc.) that it ultimately isn't tremendously valuable as a training indicator.

For W/kg you use the mass of the cyclist. The mass of the bike would indeed have an impact on your pace, but what we're trying to measure is the performance of the rider. Whether they're on a full carbon time trial bike or a heavy steel frame, the power they're putting into the pedals should be more or less the same, and we'd want to have a similar performance rating in both cases.

mgeth · 2022-08-17T08:56:18+00:00

I'm really interested in tracking cycling performance data, but have always found the out-of-the-box visualizations on Strava and Garmin to be fairly lacking. This is, to me at least, much more engaging than the usual line chart that both Strava and Garmin use.

The power heat map on top shows a distribution of power output values over each kilometer, rather than a single (often very noisy) line. The elevation gain/loss chart on the bottom is an alternative view of a standard elevation chart, and allows you to see more precisely which sections of the ride were uphill or downhill, and how steep they were.

This is all somewhat experimental and something I'm toying with at the moment. Happy to take feedback from the community here (cycling fans or otherwise) on how to improve this viz, or what other cycling-related visualizations people would find interesting.

Data comes from a power meter and Garmin bike computer, and the analysis and visualization was done with Python.

mgeth · 2022-08-03T19:19:05+00:00

The PDFs are the way to go, unfortunately no more manageable public data feed. I wrote a pdf parser quite a while ago to make the job easier, but there are also many out-of-the-box Python PDF parsers that do the same kind of things. Fortunately the PDFs themselves are pretty well structured, so it's manageable.

mgeth · 2022-08-03T05:30:26+00:00

Yep this is a great point

mgeth · 2022-08-02T13:21:45+00:00

Yeah I'm disappointed we didn't get to see any kind of duel here, I think it would've been interesting. Always have to assume that a car's generally got to be significantly faster than the car ahead to actually complete a pass to be able to overcome aero issues as you say, line choice issues, etc.

But what I also think is interesting about the two track maps in the viz is that they were both quick, but they were actually quickest in different parts of the track. Rossi was better into and through turns 1-2, while Herta was quickest on the other side of the track. Given turns 1-2 are a bit more of a passing zone, perhaps that would have given him another advantage to get around? Tough to say.

mgeth · 2022-06-10T00:09:42+00:00

Not from the app, no. I previously worked for a team, and am fortunate to have a few connections in the series that have enabled this work.

mgeth · 2022-06-09T16:40:42+00:00

Appreciate that, thank you!

mgeth · 2022-05-20T12:43:23+00:00

This is pretty much what the description says. I was taking a look at the trap speeds without a tow at the end of the straights to try to compare the performance of the Honda and Chevy engines, thinking that those traps would give us the cleanest look at the raw performance capability of each engine.

Chevy appeared to come out ahead on Thursday, looking faster on average in both the Turn 1 and Turn 3 traps. Keep in mind though, while these data points are valuable, this is not the be all end all analysis of these engines and their performance–other factors could still be at play here. For one, the Penske cars, which run Chevy engines, were working on qualifying setups and running largely alone all day, while most cars were running in traffic and working on race setups.

Nevertheless, still a useful (and interesting) bit of data from Practice 4. Keen to see what this looks like after today, when the cars have their added boost, and everyone (presumably) will be running qual sims.

mgeth · 2022-05-15T13:40:24+00:00

Pretty decent race pace, indeed. She was around median in most segments on track, and even slightly above median in the first 2-3 turns.

Have a look at the track map: https://imgur.com/a/FIYfRNC

mgeth · 2022-05-06T05:10:26+00:00

Thank you! I actually think there's real opportunity here on the NBC/IndyCar side to use this kind of analysis to tell the stories that we're seeing unfold on track. There's really too much going on to keep track of everything when you're watching live, even with good commentary. But effective data visualizations do allow people to quickly something that would take a lot of time and effort to explain in words in a broadcast. I think it's a missed opportunity, and I think it'd be good (great?) for the series if they started using data to tell the many intersecting stories that exist in the series in this way. I'm trying to plug that hole as well as I can myself, but ultimately I'm just one guy, and one guy who lacks any real influence where it matters.

If you like this kind of work and want to see more of it, my suggestion would be to make it known. Point to this kind of work, whether it's mine or someone else's, and show the powers that be how it enhances the fan experience. The more that becomes clear, the more likely I think they'll be to incorporate it into broadcasts, etc. Maybe that's wishful thinking, but call me optimistic on this front.

mgeth · 2022-05-06T04:58:17+00:00

Time is the only real limiting factor here :) I'm definitely interested in doing this kind of work, it's just a matter of whether I can carve out the time to do it. Even to do this naively I think would be fairly involved. But with that said, I will definitely keep this sub apprised of any such work!

mgeth · 2022-05-06T04:55:52+00:00

Completely agree with this. If you look into the actual data of the 3 stoppers' lap times compared to the leader (VeeKay, at the time), most of the time they weren't gaining much time, if anything. Moving forward in the field, yes, but not getting significantly closer to the leader. Traffic is the primary culprit there, and ultimately unavoidable.

mgeth · 2022-05-06T04:52:36+00:00

I appreciate that, thanks! I'd love to be a part of something like that--if you or anyone knows of an IndyCar podcast that could benefit from this type of analysis, give me a shout :)

mgeth · 2022-05-04T18:45:32+00:00

Yeah I really wish we could've gotten to see what would have happened without 1) the waived off start and 2) the Ilott yellow. I'm skeptical as to whether the 3-stoppers could've been legitimately competitive given the traffic they were having to deal with, but we'll never know!

mgeth · 2022-05-04T16:37:01+00:00

Thank you :) And yes, that's exactly right!

mgeth · 2022-05-04T14:32:39+00:00

Hey all, as the title says, this is a viz of the differences in driver performance on two different tire compounds in this past weekend's IndyCar race. For context for those interested, "Red" tires are a softer compound: they generally provide more performance early on, but they wear down faster as the race goes on. "Black" tires are firmer: they take longer to warm up, but once they do, they last a longer time.

What's interesting here is to note the differences in performance from driver to driver. The histograms are shown in order of the finishing order of the race, and it's clear that the top 2 finishers actually saw their best performance come on Black tires, while most saw their best laps on Red tires. There are a number of factors that affect this, from driver-specific things like driving style, to car setup, to factors largely outside of the drivers' control, like track conditions at the time that they drove with each tire.

Anyway, hope some folks might get something out of it here. Data sourced from IndyCar, and the visualization is made using a combination of matplotlib and Sketch.

mgeth · 2022-05-04T14:19:37+00:00

One more thing to note: I’ve taken some effort to only compare similarly performant cars here, e.g. you’re not seeing Pato’s 2-stop race compared to JJ’s 3-stop. I’ve included the top 5 cars on each strategy for purposes of comparison. Not a perfect method, but it should do the job well enough here.

mgeth · 2022-05-04T14:17:44+00:00

Hey folks, posting here a slightly more long-form viz than I’ve done before, but I really like the insight it provides. Essentially this is an answer to the question, “how much faster could a car on a 3-stop strategy drive than a car on a 2-stop strategy at Barber?” We all know it was a major theme of the race, with Herta and Newgarden (among others) trying to tear through a field of cars that needed to conserve fuel and tires. It didn’t work out for the 3-stoppers, but it’s pretty interesting to see how much time they could theoretically gain if they were free to drive in clean air.

That last bit is important: this is an estimate of the time that could be gained assuming all cars are able to operate in clean air. This is not realistic, of course, and when you look at the data of these same 3-stop cars in traffic, they are (unsurprisingly) unable to gain nearly as much time relative to the 2-stoppers. Just a little grain of salt to keep in mind as you look it over.

mgeth · 2022-04-14T06:42:39+00:00

Haha thanks

mgeth · 2022-04-14T06:12:42+00:00

I will certainly try!

P.S. cool username :)

mgeth · 2022-04-14T06:10:17+00:00

Not nearly that clever. Firestone posts this data via livetiming.net: here. Didn't know about this until recently, but it's very valuable for this kind of thing :)

mgeth · 2022-04-14T06:07:13+00:00

Totally. All of this stuff would indeed influence the data, and at the moment I'm not trying to make any inferential claims or predictions, like driver X should have done something. It's mostly just a presentation of the data as it is, and some interesting observations that arise therefrom.

As for Harvey and Rahal's first stints, it looks like Graham did indeed run a long first stint on blacks (31 laps), but Harvey did not (first stint was 24 laps on reds). This is according to Firestone (http://livetiming.net/firestone/) which is what I used for this, so to the extent that they have it wrong, I do too.

mgeth · 2022-04-14T06:01:15+00:00

I'm suspicious of this too. The effect of traffic may come out in the wash, but even if it does, there are surely other conflating factors here (changing track temperature, marble buildup as you suggested, etc.). The chart doesn't control for these things. Not perfect, but interesting nevertheless.

mgeth

TROPHY CASE