#f1 rpf analysis

[tyrannosaurus-maxy]

State of the F1 RPF Fandom on AO3 (2023 update)

This is part of my F1 RPF Analysis based on a dataset of the almost 28k AO3 F1 Fics pulled on 1 Dec 2023. Fics were analysed based on date of last update.

Feel free to follow the tag #f1 rpf analysis for more, and let me know what else you’d like to see! 

[valyrfia]

Hi! I hope you are doing great. Can you please do a detailed reading about Charles Leclerc future spouse? Who is she? Her character, job, look etc. How they going to meet and their first impressions about each other? What charles' friends (especially close ones) and his family (especially his brothers and mother) will think about her? How they will confess their feelings to each other? What are fans and society going to think about their relationship? Will the relationship be successful? Thank you :)

Hm so I’m getting….blond-ish, blue eyed. They’ll meet through work. Character….well they could be a bit abrupt and brutally honest at first but their honesty is one of their best traits, they wear their heart on their sleeve. Charles’ family already loves them and has known them for a while. Their job is…..multiple F1 world champion. The relationship is giving written in the stars, casually embedded in the very fabric of the universe kind of thing.

[bottas-valtteri]

I am once again BEGGING people to tag their fics. By all means write and post them I have no problem with it but as a person who isn't interested in rpf please just tag it as f1 fic or f1 imagines or really anything.

[topnotchquark]

Sometimes I come across gifs of Sepang 2015 or leading up to it posted in real time by some now defunct account and it's literally the long term nuclear waste warning in my head legitimately just "This place is not a place of honor... no highly esteemed deed is commemorated here... nothing valued is here."

Even worse is when I see blogs from 2013-14 talking about how Marc looks at Vale or something literally 5 seconds before disaster.

i do love the extremely low rez 2013 rosquez gifs hashtag we are the daughters of the rosquez truthers you could not burn… they do always make me wonder how those absolute warriors reacted to sepang live. how did they cope. what was the climate in the group chat. were there bbc sherlock reaction gifs

[mecachrome]

landoscar ao3 stats — 2023 overview

notes

retrieved ~sometime in march 2024

methodology: scraped metadata for every fic in the landoscar tag and...... that's it. however one important constraint is that all temporal data is date updated (not posted), so the above timeline isn't exactly a true representation of fic growth but rather how many fics were last-updated at that time. of course this is still its own reflection of fandom health in a way since dead fandoms don't update old fic but well... it's just not quite the same!

this is just info about general trends, fic content, tags etc... so nothing about kudos/comments or any authors specifically

i decided to focus solely on fics last-updated in 2023 (unless otherwise mentioned) because i wanted a tidy set that i can maybe compare & contrast in a year's time, because i expect a lot of details to look different then (tho as stated above this set isn't exactly static... 🤷‍♀️)

ngl i had to re-scrape a bunch of times because i forgot about it for like 3 weeks and then there were 100 new fics 😭 so if there are some minor discrepancies across the post it's because of that halfskh.

also i wanted to include more global comparisons (aka how 814 stack up against the f1 rpf tag in general), but this is also considerably difficult in some contexts since i can't exactly scrape 31,000+ fics can i... or i didn't even want to entertain the thought of trying to do so!!!

why did i do this? who knows.

anyway here's some viz T__T

ship growth

as evidenced in the opening graph, landoscar have been a very fast-growing ship over the past year — although interestingly enough they didn't really start growing substantially until july / the ~better half~ of the 2023 season. here are two views showing their "growth" (by date updated) alongside two other ships on the fringes of the f1 rpf top 10 (sebchal & galex):

landoscar are very much on-track to surpass them and officially enter the top 10 soon, likely before mid-april ❗️ :o

ship characteristics

onto the ship content — another thing i was mildly curious about was how landoscar differs in certain areas from other f1 ships, or the f1 rpf "global" average you could say. for example, here's a breakdown of rating popularity in their ao3 tag:

seeing as explicit is their most common rating, and that i don't necessarily expect this to be true for all ships/fandoms, i compared these percentages with the general f1 rpf tag to see whether some ratings are more commonly represented in 814 fic than average, which produced interesting results:

do lando ships simply skew more HornyTM in general? is it oscar? a secret third thing??? who knows... actually i think it would be fun to do more analysis in this direction but that can wait for another time!!!

similarly i also wanted to see which ships are the most "public" on ao3, as in have the highest share of fic that isn't user-locked... i will refrain from peppering in my feelings about the 4th wall too heavy-handedly but i was curious to see whether some sort of perhaps... er, generational gap (?) of sorts between ships that are more public vs. not could be identified. however i don't pretend to have any takeaways from this LOL i conclude absolutely nothing. (for ref landoscar is currently 72% public, vs. a global avg of 63%)

note that this graph is current stats, not filtered for 2023

looking at relationship tags, i also wanted to know whether landoscar suffer noticeably from Second-Ship Syndrome, so i tallied the first-tagged ship of every fic to find out. i know this doesn't necessarily mean that it's always the "main" ship but it's a good enough approximation. the results were quite positive!

filtered to top ships with count of >1 only

i then also calculated the number of ships tagged for each fic to discern the profile of multi-shipping in 814 ficdom; i did have to do a little bit of string standardization (all instances of implied / background / hinted collapsed to hinted for simplicity's sake + removal of other redundancies), but otherwise i left everything mostly untouched.

as you can see, landoscar also have a fairly promising amount of OTP: TRUE fic:

by the time you get to the fics with 10+ ships tagged, landoscar are less likely to be the primary ship, which makes sense just on a basic statistical level... this is also a very small sample size though

i also lazily tallied the 10 most common ships that weren't NOR/PIA or NOR & PIA to diff their shares of the 814 tag vs. of the general f1 rpf tag, to see which other pairings are more represented in the 814 tag than on average (because lestappen are the most popular by pure count but this is also true of fandom in general, so it would be a misrepresentation to say that their popularity is out of the ordinary):

maxiel's gap isn't really that surprising since i think that, generationally, in terms of when both pairings were teammates there is quite a gap; with carlando—actually let me tally this again but including all instances of "implied" and "past" as being part of the same ship, since that's how ao3 tag-wrangles as well:

Aha ! obviously as a direct ship there is competition between 814 and other lando or oscar ships, but this difference is somewhat less pronounced once we include all formats. tbh none of this really means anything but i thought i'd add it anyway... (it's also very possible that there are several errors in this, in which case my b 😔)

before we move on to additional tags, there are a few more basic characteristics of 814 fic we can calculate. i realize i never offered an overview of Super Basic Stats, so here are a few:

plus, looking at word counts, here is a distribution of those in 2023-updated fic, which shows that a majority of 814 fics were under the 5k mark:

85% of landoscar fics were under 10k & nearly 97% under 25k

i don't really have any reason to believe that landoscar's wc stats differ significantly from average ? so this is kind of just Data To Have Data, and it most likely reflects normal ao3 trends in general... but i thought i'd include it anyway because i already made it lol. similarly, here are word count distributions but stratified by rating:

& same info but heat map view:

i feel like this is also probably something you'd find across fandom in general — that gen fic is likely to have a higher share of under 1k works, since Building Up to sexual content often takes... Literal & Metaphorical Foreplay ! and the longer a fic is the more opportunities an author has to include a sex scene or other explicit content (ofc, not necessarily just porn but also graphic violence & so on). but i thought this was fun to visualize haha

additional tags & aus?

back in my old f1 rpf stats post, i made a table comparing fluff/angst "ratios" (not exactly a direct ratio because of how tag wrangling works, but an approximation) of the most popular f1 ships, and now that landoscar are somewhat popular i thought i'd first do an update:

also current data, not 2023 to make things easier

just like before, simi are one of the most fluffy ships and brocedes are by far the most angsty, but it's interesting to see 814 also extremely high up on the charts, with far and away the lowest % of angst. will be exciting 2 see how that holds or changes as the seasons progress !

finally, i also wanted to do a bit of au/additional tag analysis because you can kind of see this when you use additional filters on ao3 but the previews are limited and get bogged down by the prevalence of *checks notes* Fluff, Angst, PWP, Anal Sex and what have you. which are nice stats to have and all but what of the rest !

disclaimer that the set for these tables is a biiiit outdated because by the time i'd wrangled everything i was like I Am Not Changing It Again. unfortunately i clean my data with shoddy queries and regex functions in googsheetz...

there were 48 tags with at least 10 instances from 2023 fics, shown below, with ones that are (some ~vaguely) nsfw in red just to kind of get a rough sense of which tags get commonly used in M/E fic:

getting a bit too much into small sample size / specific fic territory so if you're an author i sincerely apologize for that... do not mean 2 put u on blast... TT__TT but i also tried to tally the most popular aus people write for 814, which is a bit dubious because people tag in really different ways and i had to accommodate for a lot of string formats but ... it's close enough ! (?)

i feel like this is very little interesting info but idk what else to add so i will stop here for now... well!!! if you made it to the end i hope u learned something or even vaguely enjoyed reading T__T and most of all thank you :')

[39oa]

(nonsensical hrpf data exercise) degree centrality graphing & other archive insights

intro/prior work

hello 🙇‍♀️ i'm not sure this post will make any kind of sense at all, but i love analyzing ao3 data and i especially find it fascinating in the realm of sports/hrpf because of the amount of player- and team-related attributes that offer dimensionality to fandom analysis when examined in parallel with archive metrics. i've already kind of done hrpf overviews on two separate occasions over the past year or so, but my method of collection differed in each instance and also always gave me new things to chew on and potentially explore, such as expanding on the link between player talent and shippability and whether high-draft picks have more fic written for them on average.

i most recently examined player data based on aggregated relationship counts since 2022, but this was a limited snapshot meant to piece together recent ficdom trends (see top ships since 01/01/22) and not be representative of fandom overall. basically, things i want to visualize/talk about now are:

hockey is so widespread as a sports fandom because there are 32 teams in the league, which when compared to a community like f1 makes it difficult to succinctly summarize primary relationships for. there is no self-contained grid of 20 drivers that remains generally fixed within a season, where every move in/out of that "roster" is highly reported upon and instrumental to fandom makeup, but instead a more amorphous network of malleable rosters featuring high-variance cascading orders of character visibility; in short, the difference between the most and least popular driver in f1 fandom is not the same as the difference between sidney crosby and that one ahl lifer who was called up to your 4th line two months ago because your team is utterly decimated and gunning for bedard.

Still: because rosters are so malleable and trades happen with some amount of frequency, and because hockey is still an "insular" ecosystem in terms of geographic accessibility and junior-age development (for better or worse; mostly for worse, but that's neither here nor there), players intrinsically have a low degree of separation between one another, whether it be as teammates now or as friends growing up in the ohl, ntdp, etc. i therefore wanted to take that a step further and look at it through fic metrics especially: can we use a summary of ficdom's real, tangible output and visualize it through a similar network? (+ where and how does that network differ from player connections in practice?)

back to the impact of draft pick # and assessments of talent relative to popularity, i also wanted to look at the most "successful" ships in ficdom from this network and evaluate the different distributions and impacts of their respective attributes. are certain player positions more popular? which nationalities are the most commonly shipped?

etc. But let's just get into it.

process

getting any kind of information from a 60%-locked fandom on ao3 is a nightmare and introduces a myriad of data-collecting limitations, so i do feel it important to disclaim that what i present in this post functions more in the realm of Approximate Interpretation and Potential Correlation than any actual 100% objective representation of fandom metrics.

a perceived limitation i have with character tagging metrics on ao3 is that they don't exactly reflect shippability; that is, if q.hughes is tagged as a character in a n.hischier/j.hughes fic, it gets attributed to his character tag but doesn't actually say anything about how many Relationship Fics exist for him on a whole. my best solution for this was essentially uncovering most of a player's relationships and summing their individual fic counts to create an approximate # of "relationship fics" for each player. so any kind of shippability graph going forward will use that metric.

i used ao3's relationship tag search and filtered by canonical in the men's hockey rpf fandom and only pulled relationship* fics ("/" instead of "&") with a min. of 20 works. ao3's counts are... Not the most accurate, so my filtering may have fudged some things around or missed a few pairings on the cusp, which again is why all the visuals here are not meant to show Everything in the most exact manner but function more so as a "general overview" of ficdom. although i did doublecheck the ship counts so the numbers themselves are accurate as of time of collection.

(*i excluded wag ships, reader ships, threesomes to make my life easier although i know this affects numbers for certain players, hc/gm ships, and any otherwise non-NHL Player ship. for ex., this eliminated anna kasterova/evgeni malkin, tyler brown/tyler seguin, and kyle dubas/william nylander, just to name a few)

all ship data was collected march 16, 2023.

PART 1. SC87 ship networking

when i first began this exercise i tried graphing ships for all the first-overall picks from 2003-2022 because i wanted to get an overarching sense of their connections. however, doing so made me realize that sidney crosby was by and far the most-connected node in the graph (and basically all hrpf in general) with a degree of 11, and that he was centering one huge component to which only two ships failed to connect (op/kj and slaf/xhekaj). basically:

so then i was like, right! let me instead use sidney crosby as my sole starting node, map out all ships with 20+ works from him specifically, take the players he connects to and map out their corresponding ships (excluding sid) and just keep iterating until i basically reach a final child node. through this, i yielded 112 ships and 98 unique players, with my final connecting node coming 9 degrees of separation away through brady tkachuk ↔ tim stützle/quinn hughes. unfortunately i can't actually host this little code snippet anywhere lol but i also wrote an input to check the pathways between any two players which was kind of fun:

here is the actual network graph with colors from automatically generated clustering, which doesn't really mean much but i thought was one nice way of presenting it. the edge width refers to the sum of fics for each ship and the node size refers to the degree, or number of ships, for each player.

i then also joined my player set with a dataset that included draft year, drafted team, position, etc... and through that color-coded the graph with the team each player was originally drafted to (i always struggle between using current team and draft team because which one matters more is super contextual, but... using draft team made my life easier this time so i hope it's still interesting.) here i only included colors for 13 teams that had 3+ players each:

→ [full-size graph]

we can do a bit more analysis based on this specific sidcros network, like which players are the "most-shipped" or overviewing cross-team shipping tendencies:

but! of course, not ALL big hrpf ships lead back to sc87. using him as a central node essentially just helped me filter out excess "noise" when searching for relationship tags on ao3, because now i could exclude anyone connected to him at all (note: the relationship fics from my set equaled upward of 19,000 works, accounting for 60.4% of the entire men's hockey rpf archive) and hit other significant tags more efficiently.

through this method, i singled out a new set of 76 ships and 134 unique players (notice the significant decrease in overlap), which i then combined with my sid ships to create one massive set of Hockey Ships With Over 20+ Works On Ao3 that i could analyze holistically. no idea if this makes any sense but bear with me:

PART 2. general ship insights

i won't bore people with endlessly listing out ship rankings but here's the previous top chart with the new ships slotted in:

now for some overall player analysis!

first i wanted to look at how attributes like draft round, nationality, and position (F/D/G) are represented in the player set.

the nationality distribution is pretty close to all active nhl players this season, so there aren't major disparities there. however, the vast majority of players 1) were drafted in the first round and 2) are mostly forwards, with the forwards also seemingly reflecting the general philosophy of faster development/higher recent-round representation. we can take this overview a step further and actually examine the fic averages for each characteristic as a proxy for measuring shippability/ficdom popularity.

first, i scatterplotted all players by their draft pick and number of fic to (try and) show the heavy skew toward top picks (inspired by the gar draft pick value curve and other similar plots). this is... well, limited in many ways, and if i had an actually adequately large dataset i could specifically plot averages per distinct pick number and try to present something there, but the problem is that a lot of these later pick numbers only have like one player so there's way too much variance LOL.

but just for the sake of this exercise i excluded j.benn as an outlier and grouped fic averages by round (left below). again, noting the sample sizes, let's just say that first rounders on average seem to have the most fic written about them, even if it's not a particularly shocking insight. we can also try creating a histogram for "shippability" by draft year, binning here for every 2 years, to see which draft years appear to have had the most success (right below). note the peaks around 2005 and 2015, aka the sc87 and cm97 ~Generational Years~ 🤔

i've also been interested in figuring out which positions are commonly preferred—since centers are so often the faces of a franchise and are essentially the most sought-after position, and since goalies occupy a positionally static role/are less oriented toward contact (and the presumed homoeroticness thereof) in the way skaters are, is that reflected in the fic metrics as well? turns out: yes.

some ship analysis

learning more about player data in a vacuum is fun, but we also have all of this relationship data that lets us examine how different characteristics interact with each other, which is meaningful as well! for example, we know that forwards are heavily represented in the dataset, but is center4center the most common combination? or is there love for a franchise center and his beloved winger or the team's dependable 1d?

(fought for my life trying to figure out how to map this properly so please accept a horrible bar chart instead) as it turns out, the most common combination is centers/wingers, followed afterward by centers/centers. i don't know whether this really means much to me because i'd like to dissect the combos even further (is C/C more often 1C 2C or cross-team rivalry 1C shipping? are C/W usually linemates? etc.) but 🤷‍♀️ here's a graph.

i also distributed ships by their nationality combination, displaying to the surprise of no one a heavy preference (a whopping 66.4%!) for north american-exclusive shipping. i also thought stacking by "draft year" (= averaging the draft year between both players for each ship) offered some interesting insight into usa4usa shipping having slightly younger representation. also i do think usa/germany being singlehandedly driven up this chart by one family is remarkable and hilarious LOL.

also in the realm of draft year analysis, i wanted to look at draft year differences and whether fandom preferences seem to lie by way of same-age-ish pairings and In-Class Bicycling so to speak. graphing ships by these differences spanned a range of 20 years, with the oldest "age" (draft) difference being 20 years between zdeno chara and charlie mcavoy. overall, of 175 ships with a drafted player, 60.5% were drafted within 2 years of each other (18.2% in the same draft), and only 5% had a draft difference of 10 or more years.

then, of those 32 ships drafted within the same year, i distributed their counts by year to see which draft classes featured the biggest in-class clusters, leading us again to the Famed Class of 2015:

closing thoughts

i'll stop here :saluting_face: something else i had on the agenda that i don't really know how to adequately explore with this dataset is basically stanley cup champion stuff, e.g. looking at players and ships and fic counts from winning teams and how/whether a sort of "winning bias" has been trending down as of late (see the relative success of ships from teams like phi/ana compared to tbl/stl)—temporal data is so particular and difficult to wrangle with ao3 though so i'll have to let this one percolate a little bit.

finally, another thing (!) that i love examining is captaincy and how it often helps inform shippability; C/A/guyswithletters shipping obviously generously overlaps with being drafted early, high-impact players, some positional stuff like Young Star Center having the role foisted onto him, etc. and many of these aspects are immediately identifiable in top ships like 8771, 1634, 1386... just to name a few obvious ones. unfortch i don't really have the time or space to look at that here but it's something i'm still interested in maybe expanding on, and i also never ended up collecting actual skater *performance* data which would be super fun to eventually get to, e.g. mapping ficdom output to not just background identifiers like draft year/pick but also 1) actual tangible evaluations of player goals/points/(salary?!?)/etc. and 2) some dimension of draft outperformance/underperformance, which is pertinent for scenarios like late-round picks who have defied career expectations (see outlier jbenn having a shit ton of lifetime fic) AND early-round picks whose trajectories have not panned out as expected for whatever reason; often the ~tragic~ frustration of being a bust actually invites more narrative focus and scrutiny, but at the same time ficdom trends have pointed themselves to being attracted to many historic, talented, generational, and so on players, who more often than not are also winners, which potentially posits a need for some sustained line of access/visibility to high-expectation players significantly before they're regarded as "busts" in order to organically grow and generate initial interest that can survive the renewed reality of their situation. but who knows

again, i don't know whether any of this even makes sense or is interesting to literally anyone at all, but i personally enjoyed just dicking around graphing shit and getting to join a ton of tables together for absolutely no reason lol. that's all!

[tyrannosaurus-maxy]

F1 RPF on AO3 x Omegaverse

Just a quick look at fics tagging the dynamics of characters in the F1 RPF fandom over on AO3. Of the ~28k fics, about 1.6k (or 5.8%) were omegaverse. The % of omegaverse fics as a proportion of total fics have also gone up, from <1% in 2017 to 7% in 2023.

I went through and analyzed each drivers' tags and here are what the most common designations are (sorted by % alpha, % omega, % beta respectively) for the 20 drivers on the grid. This relies on authors tagging so it will miss designations not tagged!

This is part of my F1 RPF Analysis based on a dataset of the almost 28k AO3 F1 Fics pulled on 1 Dec 2023. Fics were analysed based on date of last update.

Feel free to follow the tag #f1 rpf analysis for more, and let me know what else you’d like to see! 

Edit 12 Jan: updated graphics to reflect accurate fic counts

[inchidentally]

https://www.tumblr.com/piastrisms/738775293993549824?source=share

I NEED YOUR ANALYSIS OF THIS VIDEO! PLEASE <3

okay listen this has spun me out into a whOLE thing so let's take another trip down a road I like to call Why is the Non-RPF Real Life Relationship between Oscar and Lando So Endearing and Boyish and Sweet:

so my absolute favorite thing about Lando's face when he's relaxed or in his natural element is how absolutely every. single. emotion. he's feeling is broadcast at equal volume. his vulnerability is a massive part of his charisma. but! he has to have the right habitat! streaming, Youtube, his lifelong friends - those are the right habitat.

F1 is not naturally the right habitat.

when Carlos found Lando on his proverbial driver's doorstep he did not know what to make of him and Lando was barely able to squeak out a few words around anyone new or when a camera was around. but! Carlos trained Lando into how to bounce a dynamic between the two of them just like he'd been doing with his last awkward baby, Max. interestingly Max was much more excitable and eager to please of the two and Lando's intense shyness took a LOT of work to get past. but once Carlos had gotten the drift of Lando's super silly sense of humor it was smooth sailing. and then with Daniel it was even easier because half the time Daniel knew he was expected to carry the conversation. it's interesting because Lando allowed a bit of that old shyness to come back and it definitely disarmed Daniel a bit in a satisfying way. but thanks to those big personalities, Lando found his F1 self and even started to deal back and lead occasionally. of course DTS and a lot of media pilloried him for this because apparently what Carlos and Daniel do naturally is seen as snottish and bratty for him to do. the Youtube/streamer personality where he felt so safe did not at all translate onto other platforms and media.

so it hasn't been smooth sailing for Little Lando Norris to know how to be as a person in F1.

cut to 2023 and with the advent of Oscar we've seen a slow dismantling of Lando's F1 PR personality completely in his content with Oscar over the season. their very first unboxeds Lando was still wearing his guarded PR face and assuming he should lead and carry all the content. it was still sort of around for the Jenga/Garden Games challenges but had started to soften around the Austin filming (including the Finish the Lyrics classics). at some point, Lando truly realized that Oscar would still be fond of him even when Lando was in a terrible, low blood sugar type mood (Tic Tac Toe etc) and oh wow!

their content could really just be Lando being whatever he was feeling that day/that moment and Oscar smiling and finding him funny/cute/fascinating! that was enough! he didn't even know that was allowed! (and maybe it wouldn't have been if Oscar wasn't there to bolster him)

and that's when we started getting unguarded, authentic Lando instead of entertaining Lando. and it's because Oscar was the person next to him representing all of us, trying to tell Lando that we just wanted to see him. we didn't care if he was 'on' or not. he's just an interesting outdoor cat we want to watch go about his life.

which is why we got Lando letting himself sit and stare right back at Oscar like this.

where we can see his expressions do that slow blossoming thing, and right in full view of just Oscar. it's the anti-DTS material because it isn't open to the camera and easy to manipulate. narrative television hates when two people go into quiet, subtle communication because it can't be made into a false dramatic arc. (trust me they'll invent one using chopped up footage and even more chopped up commentary lol they always do but it'll be uphill work)

but when you contrast this with the nonstop, quick back-and-forth Lando has with Carlos and Daniel it's where we pick up on the something that's so unique to Lando with Oscar. it's wrong and making way too big an assumption to say it's a closer friendship bc you can't quantify other people's friendships that way. but it's very, very different to those friendships. and the biggest difference that we on the outside can see is that Lando allows himself to determine exactly How He Will Be. and that might change from one minute to the next! and that doesn't always go down well with most people!

but every time, no matter what, Oscar smiles and laughs and everything Lando does is alright. he gets it. Lando means no harm and he's got a good, warm heart. if he likes you then that won't change just because his mood changes.

like their end of season message. Lando went from doing a great job summarizing his thoughts for the viewers, handed it over to Oscar and just... watched. didn't get bored and stare at the camera or off into space. I actually compiled just how often Lando spends staring openly at Oscar into one long gif lol:

he fully knows he's still on camera but he turns himself into a viewer instead of bouncing around and off of what Oscar is saying. Oscar gave him that, he can give it right back.

and there's no specific Lando-Oscar dynamic like there is Lando-Carlos and Lando-Daniel. hell, Lando's got a dynamic with just about anyone. except! Max F, Martin Garrix (and probably quite a few of his friendships that we're never actually even shown) and Oscar. with those people we see Lando be precisely whatever he's feeling at that moment because they'll either indulge it or enjoy it depending on how good or bad the mood is. if it's Lando, it's all good.

I feel like there's a commonality with those people of being quiet but strong as opposed to the big and bold of most of the F1 drivers on the grid. Max F absolutely has obvious similarities to Oscar (I still love how much he sided with Oscar when he watched the 'most likely to' video). I don't know a lot about Martin but it's literally a DJ's job to be enough apart from the crowd to read it and they set the energy passively through what they spin. Oscar is a fun guy who loves being around the people he cares about but he's never The Guy that it all turns around.

and for their own reasons, they find Lando inherently fascinating and lovable. whereas Lando has to inhabit Carlos', Daniel's, George's, etc etc worlds because they are in themselves The Guy Everyone's Watching just like Lando. Lando has to share. he has to figure it out. but guys like Max F and Oscar do not have the energy or interest in being The Guy. they'd choose privacy over popularity every single time if they were made to. and actually come to think of it, they have actively chosen privacy at the expense of popularity quite a few times.

because let's look at Oscar's face when Lando teasingly brings up Oscar's sprint win:

Oscar gives Lando that genuine, affectionate smile and ducks his head because he wants Lando to know that he appreciates it. in truth, that Sprint win was hailed by wider F1 community as being a massive achievement for a rookie in the Max V era of dominance. they both know that it is. but Oscar didn't posture or show off about it and for that Lando has made sure to bring it up on his behalf time and again. Oscar gave him that, Lando can give it back to him.

which is even sweeter going back to that post race video because Oscar gives Lando that same affectionate, private smile. he's had to throw the video's content over to Lando and Lando gave him that big affectionate smile first because this is how they do these videos. it's always awkward - especially if their results that day weren't great - but they know that together they can do these videos and share a laugh over how absurd it feels sometimes.

and the hint of a private joke between the two of them is there early on and you can just feel Lando relax into it.

and when you skip all the way to their last race media duties and this interview, it truly surprised me how much Lando kept checking in with Oscar - the rookie! -as he was answering. when he found himself giving boring PR answers he threw in a joke that he knew Oscar would crease up over. sure enough it loosened them both up.

and like, that's where the vulnerability and openness comes from now with Lando. he's got a teammate who is basically the same age, who gets him and who actively wants Lando to just be Lando. who clearly threw Lando at first by inadvertently foiling those attempts to establish a dynamic or a bromance. who Lando probably at first thought was just shy and awkward. but Oscar stayed true to who he is and kept that door open for Lando to eventually walk through.

so when it's the two of them, it's everyone else who's on the outside looking in. they're just being themselves. if that doesn't make everyone else feel entertained or happy they honestly don't care - and will probably share a secretive little smile about it.

it's also why they sometimes do that twinning thing and creep everyone out asfgjlaflsgjf

[mwebber]

the" look at you doing all that arty shit" marky mark flower photo could be another contender for the horse cock tag, less Explicit, however, Bulge must be considered in this scientific analysis

ON THE STUDY OF HORSE COCK: a Qualitative Analysis of Mark Webber's Bulge

mwebber [1]

Cite this article! mwebber. (2023). On the Study of Horse Cock: a Qualitative Analysis of Mark Webber's Bulge. Tumblr Asks, 9(11), 6-9. https://doi.org/10.420/horsecock

ABSTRACT Mark Webber, 2015 WEC world champion and retired F1 driver, has terrorized the online world with his dick bulge for all the years he has been photographed. Whether this is due to a lack of well-fitting pants, or simply because his cock is massive, remains to be answered. Building on previous academic work by scholars such as @cedobols and @f1rstyasfk, this paper uses grounded theory to analyze the work of Clive Rose, a friend and photographer of Webber. It takes into account primarily the draping of Webber's pants, the shadows, and his stance to examine whether the work should be added to the existing dialogue surrounding Webber horse cock. We conclude that, though the photo demonstrates bulge, it is unfortunately not obvious enough to be added to the horse cock tag specifically. That said, we suggest the work be added to a new academic collection on Webber's love handles.

KEYWORDS Grounded theory, Mark Webber, Women and Gender Studies

➽───────────────❥

[1] Faculty of Sports RPF, Tumblr University, Tumblr, Internet

Corresponding Author: User mwebber, Faculty of Sports RPF, Tumblr University. email @mwebber

➽───────────────❥

In 2016, Mark Webber announced his retirement from motor racing for good, but this announcement didn't mean the end of his relevance to the racing world. With his limited appearances on television, and without the need to wear team-mandated uniforms, Webber began to gain popularity in online circles who'd never heard of him for one thing: his incredible dick bulge. At least, this is how the history goes; scholars such as bom have pinpointed the phenomenon of what is now called "Mark Webber Horse Cock" as beginning in the late 2000s, or indeed earlier (motorkink, 2022). Dutiful academics have taken the time since to contribute to the contemporary discussion around Mark Webber's bulge, and it is in this context that this paper aims to add to the conversation with Clive Rose's recent photographic work from Singapore, 2023. In this paper, we will use grounded theory to examine the photograph from an aesthetic, qualitative angle to answer whether or not it can be added to the seminal works of the Horse Cock portfolio. We will first discuss the existing literature around Webber's bulge, then justify our use of grounded theory. We will then partake in a detailed, critical analysis of Clive Rose's work. Following the analysis, we will discuss our findings to conclude that though the photo demonstrates bulge, it is unfortunately not obvious enough to be added to the horse cock tag specifically. Finally, we suggest avenues for further study and review.

LITERATURE REVIEW

Perhaps one of the most obvious and encapsulating examples of Mark Webber Horse Cock academia comes from this post the author made in early 2022 (multi2-1). To our knowledge, it is currently the only work that has broken containment regarding Horse Cock and attracted a new wave of critical thinking about Mark Webber's penis. Other works exist in Horse Cock literature, namely by acclaimed scholar bom cedobols, who has contributed much to the movement. The author is too lazy to hack it with Tumblr's shitty search engine, so imagine that the rest of the substantial lit review is source: trust me bro.

This project uses grounded theory to examine Clive Rose's work photographing Mark Webber. Conceived in 1967 by Anselm Strauss and Barney Glaser, grounded theory is as much a theory as it is a methodology (Mediani, 2017). It was created to merge quantitative and qualitative research methods (Greon et al., 2017). It is prescriptive, meaning it generates new theories to account for patterns in a study rather than apply existing theories, thus avoiding the trappings and limitations of existing theories and conceptual frameworks (Mediani). There are several reasons why this study does not use pre-existing frameworks to guide the research. Firstly, grounded theory allows for a looser combination of quantitative and qualitative lens with which to examine the data. It allows for the examination of arbitrary things, such as the shadows around a cropped photo of Mark Webber's crotch. Secondly, though this study of horse cock is not new, the field of research studying the relationship between random photos and dick bulges is an emerging field that is only gaining prevalence with advancements in technology. Thus, avoiding the limitations on perspective that come with pre-existing theories is a benefit in providing insight specific to Mark Webber's horse cock that might otherwise be lost. 

Clive Rose's work can be found below. Rose has a prolific portfolio, most notably of beautiful Sebastian Vettel photos, and we thank him for his contribution to our cause (Rose, 2023).

ANALYSIS

First, it is important to isolate the most relevant part of Rose's photo. We have taken the liberty to do so here:

With the benefit of grounded theory, we have identified three key aspects to guide our analysis: the draping of Webber's pants, the shadows, and his stance. First, in examining the draping, we can see that he has pockets on either side. We can also see that the pants fit well; they leave ample breathing room and don't seem to hug his thighs, while simultaneously providing adequate definition to his dumpy, the likes of which we can extrapolate from the front.

Next, let us consider the shadows. They fall most prominently beneath the fold of his right pocket and in the dip towards his left thigh. They are also visible around where his bulge is, in the large crease leading to his right pocket.

Finally, Webber's stance is that of a man on the move, which we can deduce by focusing on his elevated right thigh. We can estimate, based on the rest of the photo, that Webber is walking, rather than running.

DISCUSSION

In our analysis, we highlighted how Webber's pants drape, how the shadows fall over and around his crotch, and his movement in the photo. Based on our analysis, we can reason our way logically through to a conclusive stance on whether or not Rose's photo should be added to the Horse Cock portfolio.

Webber's pockets, namely his right pocket, seems to take centre stage. The way the fabric of his pants drape, in addition to how the shadows fall, leads us to the notion that his pocket is folding as pants pockets do when one walks: outward. Assuming that Webber's pants have a zipper in the front, we can further presume that the front part of his pants are attached along its seam, thus creating the tension that causes his pocket to fold in such a manner.

The pocket seems to be the source of confusion. From a distance, or on a small screen such as a mobile device, the way it folds seems to suggest Mark's dick bulge. However, upon closer inspection, the bulge itself is barely present. As we are debating the merits of adding the photo to works under the horse cock tag, this is what Rose's case hinges on. Thus: we believe the photo should not be added.

CONCLUSION

This paper investigated Clive Rose's recent work from Singapore 2023 to answer the question of whether it should be considered among works demonstrating Mark Webber's dick bulge. It used grounded theory to identify three aspects of the photo we believed were significant: the draping of Webber's pants, how the shadows fell over his crotch, and his photographed stance. Upon analysis and discussion, it found that the photo should not be added to the horse cock tag on the basis of Webber's bulge not being obviously present, and cast blame on his pocket for inspiring hope.

It is a sinister illusion for Horse Cock theorists, but a significant one nonetheless. Negative results, as they say in the research world, are still results. That said, Rose's photo is not without merit, and we propose a new avenue of investigation where it may be relevant, out of the scope of this paper: Webber's love handles.

ACKNOWLEDGEMENTS

I would like to thank the anonymous asked who sent this to my inbox and prompted me to dig out one of my old papers from second year undergrad.

DECLARATION OF CONFLICTING INTERESTS

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. However, the author would like to request that it never sees the light of day outside Tumblr dot com.

FUNDING

The author(s) received no financial support for the research, authorship, and/or publication of this article. God, don't they wish they received funding for this shit.

REFERENCES

Ames [@multi2-1]. (2022, January 22). actually insane how mark webber walks around when his massive fucking schlong bounces... [Textpost]. Tumblr. https://www.tumblr.com/multi2-1/703853656379899904?source=share

Groen, C., Simmons, D. R., & McNair, L. D. (2017). An Introduction to Grounded Theory: Choosing and Implementing an Emergent Method. American Society for Engineering Education Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://doi.org/10.18260/1-2--27582

Mediani, H.S. (2017). An Introduction to Classical Grounded Theory. SOJ Nursing & Health Care, 3(3), 1-5. http://dx.doi.org/10.15226/2471-6529/3/3/00135

[@motorkink]. (2022, January 25). this needs to be included. [Textpost]. Tumblr. https://www.tumblr.com/multi2-1/703853656379899904?source=share

Rose, C. (2023). [Mark Webber walks in the Paddock prior to practice ahead of the 2023 Singapore Grand Prix] [Photograph]. Getty Images. https://www.gettyimages.ca/detail/news-photo/mark-webber-walks-in-the-paddock-prior-to-practice-ahead-of-news-photo/1682058315

[valyrfia]

okay i want to hit pause on the analysis and discourse for a bit i crave silly asks send me your most unhinged f1 rpf takes please

[stripedstarsblueflags]

is it too far?

okay, so, obviously logan posted. like a summer photo dump like EVERYONE ELSE has been doing and what the drivers are probably contractually obligated to do by 11:59 on some specific day like a google classroom assignment. but whatever.

the thing is... i could do an analysis/breakdown of that post. of some very key factors of that post.

but since this was like.. hours ago? the pictures from within the last week? that's... recent. very recent. i don't know why that changes it for me and i know fan content stays in fan spaces but i'm questioning whether or not an analysis of a post is too far compared to an analysis of a video. so let's ask the void.

[lil-shiro]

ੈ✩‧₊˚ Ann || she/her || Canada || LS18 + YT22 enthusiast • RPF, NSFW, minors DNI • F1 — sometimes figure skating and hockey • check featured tags for more — back up blog: @vietnamgp

˗ˏˋ self promo • my fics || my edits || my gifs || my memes

˗ˏˋ questions or concerns? • pinned info / longer bio • send a DM/ask

F1 Content archive below ˏˋ°•*⁀➷

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[charles-leclerc-official]

Hi I'm Luci, your resident Charles Leclerc insane Ferrari girl! I like talking non-stop about the silly men in the very fast cars 🏎️💨✨

Askbox is always open ❤️

You can find all my creations below 📝🎨📕📷

[ ao3 | edits | race analysis | F1 journal | fanart | text posts ]

"Tyre screeches" is my rambling tag bluesky | twitter

About Me and My Blog

I am an adult, and use she/they pronouns, in Pacific Timezone. Please no minors, I do post suggestive and nsfw content. I will block for not respecting this boundary.

1. My favorite drivers are: Charles, Max, Fernando, George, Lewis and Kmag. Also of course Kimi (my first love) and Seb <3 It should be evident that my ultimate loyalties lie with Charles and Ferrari(that's who I am cheering for in races)

2. Just because I like one driver it does not mean I hate another driver.

3. I don't do driver hate on my blog, any asks that are overly negative and hateful get deleted. I will block for any nasty driver hate in my tags. That does not mean I won't be critiquing drivers, but I try to be fair and analytical and not just hate for no reason. I tag any driver critique so that you can filter those tags if you don't want to see. The tag format is "DRIVER NAME critical" ex "Valtteri Bottas Critical" (neutral example)

4. I am a lestappen shipper, I know it's not real and it's just for fun. They are my favorite driver dynamic. I will also multi-ship for fun, but this is a lestappen supremacy garage.

5. Always feel free to tag me in posts, send me asks, dm about F1. I love talking about it, even if it's not about my top teams/drivers. If you want to have a Valtteri Bottas party in my asks I am down for that.

6. I am autistic and if you send me a joke there is a good chance I will not get it and take it literally. No problems, but you've been warned.

7. If you have any questions or concerns reach out, I will keep things private.

Tags

"Luci answers" for any ask "Lucis text post" for text posts made by me "Lucis edit" for photo edits made by me "Lucis race analysis" for my race analysis posts "lights out and away queue go" for queued items(I finally came up with a clever tag) "Lestappen lore" for posts about important events in the lestappen timeline "Predestined things" for posts waxing poetic about Charles "Lucis race analysis" for my race analysis posts "Race analysis tips" for posts with advice on race analysis "Analysis resources" for posts that have resources for race and car analysis "Technical talk" for posts about the technical elements of F1 cars "the rpf of it all" for asks discussing rpf dynamics among the grid

Queue set to post twice a day

[mecachrome]

loll thank u for confirming it to me re:kpoppie fan past!!! i think i remember it was you (?) who said aomething about landoscar coworkerisms and i was like EXACTLY! this is what makes them interesting to me. i get people who don’t really like 814 bc on the surface they are just teammates who have content together thus easier to ship but at the same time, that’s the beauty in them isn’t it <33 and lando’s comment about being contractually obligated to hang out together was Romance to me actually bc >coworkership.

alsooo once again i would just like 2 say i love your metas/analysis cause i think yours is such a fun pov to read from!! not sure if this is bcs of our commonality in fandom history but it’s truly like, a different perspective than if you were like on the 1d > f1 rpf pipeline or just a general sports rpf background. i’m not sure what makes kpop so unique in this ejduksdj maybe perhaps bcs we spend so much time dissecting idolsona and manufactured content or whatever 🙂 anyways! love your work and your gifs 💘💘💘 u are a godsend for the fandom!!

yesss anon coworkershipping is literally so important and real 2 me!!! ofc people are entitled to their own opinions and preferences and i will never force my silly little ships onto anyone but admittedly i am 100% the type of guy who thinks that sometimes Things that aren't romance are romance and sometimes Things that aren't sex are sex. so that's a me problem HKLSDFH

i think the ability to be self-aware about your coworkerisms and find humor in it or even show fondness for it is really sweet... like to me it's meaningful that oscar followed lando's career for so long and clearly rates him and just sincerely objectively LOVES being his teammate and is satisfied with that alone because again "i'll have you in whichever way you'll have me" is lesbianism to some. it's also interesting when they're regarded as a pr bait ship especially considering how much mclaren like... Don't bait them to us that much anymore and how simple and stripped bare the content they post nowadays is? i think there's just a general conflation of [shares hobbies off-track] + [likes each other as people] in that regard...

fandom history is definitely so interesting, i've been in 132402382834 fandoms so i reflect on this a lot !!! i got into hockey like 10 years ago so i think just wrt like, the sporting aspect of f1 then i view it through that lens primarily (and hockey is a sport that's a lot just like... less Sympathetic to individual athletes because it's very explicitly about systems which i think is why i'm less like. parasocially invested in flop careers halsdfh), but when it comes to the celebrity consumption and sheer global influence of f1 then there are genuinely a lot of parallels to k-pop, which adds an interesting dimension. the thing about many other sports/esports/etc. fandoms is that not all of them have the same tenuous 4th wall + measure of exclusivity that f1's genz marketability propels itself off, which i think is where this accusation of 1d/k-popification comes from because it's a less familiar space in quite a few sports. but there's a lot of nuance there of course...

so random but i actually had this discussion with someone earlier where i was like my "type" in k-pop is always pathetic gayboys with horrible personalities alsfdkhalksdfh but in sports i like really adjusted normie boring straightguys (my 2 favorite hockey players are also very much Polite Cats who have cute nicknames hehe :3c) so it's interesting how despite certain fandom similarities there are many cultural nuances that influence our investment.

also idk if you're familiar with bbb but i thought i'd share the hockey version my friend made of it :') it's all quite interesting to me because hockey is like, MUCH more accessible as a fandom product just because of how many players major sports leagues have and the fact that it's not as successful in north america as any other league save mls (lol) (actually idk if this is still true with messi we might fr be flops now), so you can spend $20 to go to a game in some markets and see your favorite player up close during warmups whereas that... is not possible in f1. there's just a lot of considerations for how personas are built up & managed and the space f1 occupies in that analysis !!!!!!!! but i don't want to talk your ear off too much aklsdfhldfh thank you so much for your kind words! 🥺💕

[39oa]

f1 rpf graphing & archive insights

intro & prior work

hello! if you're reading this, you may already be familiar with my previous post about graphing hockey rpf ships and visualizing some overarching archive insights (feel free to check it out if you aren't, or alternatively just stick around for this intro). i've been meaning to make an f1 version of that post for a while, especially since i've already done a decent amount of f1 rpf analysis in the past (i have a very rough post i wrote a year ago that can be read here, though fair warning that it really does not make any sense; while i've redone a few viz from it for this post i just figured i'd link it solely because there are other things i didn't bother to recalculate!)

f1 is quite different from many team sports because a large part of my process for hockey was discovering which ships exist in the first place—when there are thousands and thousands of players who have encountered one another at different phases of their careers, it's interesting to see how people are connected and it's what was personally interesting to me about making my hockey graphs. however, with f1's relative pursuit of "exclusivity," barriers to feeder success and a slower-to-change, restrictive grid of 20 drivers, it becomes generally expected that everyone has already interacted with one another in some fashion, or at least exists at most 2 degrees of separation from another driver. because of this, i was less interested in "what relationships between a large set of characters exist?" (as per my hockey post) and more so in "what do the relationships between a small set of characters look like?"

process

my methodology for collecting "ship fic" tries to answer the question: what does shippability really look like on ao3? (the following explanation is adapted from my hockey post:) a perceived limitation i have with character tagging numbers on ao3 is that they don’t exactly reflect holistic ship fic; that is, if lando is tagged as a character in a max/daniel fic, it gets attributed to his character tag but doesn’t actually say anything about how many Relationship Fics exist for him on a whole. my best solution for this was essentially uncovering most of a driver's relationships and summing their individual fic counts to create an approximate # of “relationship fics” for each player. so any kind of shippability graph going forward will use that metric.

i used ao3’s relationship tag search and filtered by canonical in the formula 1 rpf fandom and only pulled relationship* fics (“/” instead of “&”) with a min. of 5 works. ao3’s counts are… Not the most accurate, so my filtering may have fudged some things around or missed a few pairings on the cusp, which again is why all the visuals here are not meant to show everything in the most exact manner but function more so as a “general overview” of ficdom. although i did doublecheck the ship counts so the numbers themselves are accurate as of time of collection.

(*i excluded wag ships, reader ships, threesomes to make my life easier—although i know this affects numbers for certain drivers, team principal/trainer/engineer ships, and any otherwise non-driver ship. i left in a few ships with f2, fe, etc. drivers given that that one character was/is an f1 driver, but non-f1 drivers were obviously excluded from any viz about f1 driver details specifically. this filtering affected some big ships like felipe massa/rob smedley, ot3 combinations of twitch quartet and so on, which i recognize may lower the… accuracy? reliability??? of certain graphs, but i guess the real way to think of the "shippability metric" is as pertaining solely to ship fic with other drivers. although doing more analysis with engineers and principals later down the line could be cool)

also note that since i grouped and summed all fics for every single ship a driver has, and since one fic can be tagged as multiple ships, there will be inevitable overlap/inflation that also lessens the accuracy of the overall number. however, because there's no easy way to discern the presence and overlap of multiship fic for every single driver and every single ship they have, and attempting to do so for a stupid tumblr post would make this an even larger waste of time… just take everything here with a grain of salt!

data for archive overview viz was collected haphazardly over the past few days because i may have procrastinated finishing this post haha. but all ship data for section 2 was specifically collected april 22, 2023.

PART I. f1 rpf archive overview

before i get to ship graphing, here are a few overviews of f1 ficdom growth and where it measures relative to other sports fandoms, since i find the recent american marketability of f1 and its online fandom quite interesting.

first off, here's a graph that shows the cumulative growth of the top 8 sports rpf fandoms from 2011 until now (2023 is obviously incomplete since we're only in may). i've annotated it with some other details, but we can see that f1 experienced major growth after 2019, which is when the first episode of dts was released.

something that fascinated me when making this graph was the recent resurgence of men's football rpf in 2023; while the fandom has remained fairly consistent over the years, i had noticed that its yearly output was on the decline in my old post, and i was especially surprised to see it eclipse even f1 for 2023. turns out that a large driver behind these numbers is its c-fandom, and it reminded me that out of all the sports rpf fandoms, hockey rpf is fairly unpopular amongst chinese sports fans! i wanted to delve into this a little more and look at yearly output trends for the top sports fandoms since 2018, only this time filtered to exclusively english works (a poor approximation for "western" fandom, i know, but a majority of sports fandom on tumblr does create content in english).

another thing i've long been curious about with f1 specifically is—because of how accessible dts and f1 driver marketing are to fans online, does f1 rpf and shipping culture skew a bit more "public" than other fandoms? i'd initially graphed the ratio of public fic on ao3 for hockey because i also wanted to see whether it was on the rise (again, apologies for how many callbacks and references there are in this post to hockey rpf... it's just easy for me to contextualize two familiar sports ficdoms together *__*), but i was surprised to see that it's actually been steadily trending downward for many years now. f1 fic, on the other hand, has steadily been becoming more public since 2016.

another note is that c-ficdom follows different fic-posting etiquette on ao3, and thus chinese-heavy sports rpf fandoms (think table tennis and speed skating) will feature a majority public fic—here's another old graph. since f1 fandom has a relatively larger representation of chinese writers than hockey does, its public ratio falls a little bit if you filter to english-only works, but as of 2023 it remains significantly higher than hockey's!

anyway, onto the actual ship graphing.

my ship collection process yielded 164 ships with 57 drivers, 46 of which have been in f1. all 20 current active f1 drivers have at least one ship with min. 5 fics, though not all of them had a ship that connected them to the 2023 grid. specifically, nyck de vries' only ship at time of collection was with stoffel vandoorne at 56 works.

once again because f1 is so strongly connected, i initially struggled a lot with how i wanted to graph all the ships i'd aggregated—visualizing all of them was just a mess of a million different overlapping edges, not the sprawling tree that branched out more smoothly from players like in hockey. this made me wonder whether it even made sense to graph anything at all... and tbh the jury is still out on whether these are interesting, but regardless here's a visualization of how the current grid is connected (color-coded by team)! i graphed a circular layout and then a "grid-like" layout just for variety lol.

of course, i still wanted to explore how ships with ex-f1 drivers have branched out and show where they connect to drivers on the current grid, especially because not too long ago seb was very much the center of the ficdom ecosystem, and the (based purely on the numbers) segue to today's max/charles split didn't really come to fruition until the dts days. so here's a network of f1 ships with a minimum of 75 works on ao3:

before i go into ship breakdowns, i also have a quick overview of the most "shippable" drivers, aka the drivers with the highest sum of fic from all their respective ships. the second bar chart is color-coded by the count of their unique ships to encapsulate who is more prone to being multi-shipped.

PART II. ship insights

first let's take a look at the most popular f1 ships on ao3, again filtered to driver-only ships.

here's another graph filtered to the current grid only, and then one that shows the 15 ships where one driver isn't and has never been an f1 driver:

for this section, i ended up combining my ship data with a big f1 driver dataset that gave me information on each driver's birth year, points, wins, seasons in f1, nationality... etc., so that's what i'll be using in the rest of the post. disclaimer that i did have to tweak a few things and the data doesn't reflect the most recent races, so please note there might be some slight discrepancies in my visualizations.

anyway—in my hockey post i did a lot of set analysis because i was interested in figuring out what made the players who were part of the ship network different from the general population. with f1, since almost Every Driver has at least one ship and it's a much more representative group, doing a lot of set distributions wasn't that interesting and so i stuck more to pure ship analysis. still, the set isn't completely representative, which i noted by checking the ratios of driver nationalities in my dataset and then in the large database of f1 drivers i merged with (though filtered to debut year >= 2000 to maintain i guess the same "dimensions").

while british and german drivers have been the most common nationalities in f1 since 2000, both in general and in my ship data, it seems that ficdom slightly overrepresents/overships them and then underrepresents brazilian drivers. i was also curious to see the distribution of ships by nationality combination (which is actually quite diverse), and though it once again wasn't surprising that uk/germany was the most common combination given that we've just established the commonality of their driver groups, i found it somewhat interesting to realize just how many ships fall under this umbrella.

i then once again wanted to see what the distribution of age differences looked across ships. the ships i graphed yielded a range of 25 years, with the oldest age difference being 25 years between piastri and webber. tbh, something that's interesting to me about f1 ships is not just how connected current drivers are but also how there is a very strong aspect of cyclicality, wherein long careers in combination with well-established celebrity culture and post-retirement pivots to punditry & mentorship position drivers perfectly to still be easily shipped with any variety of upcoming drivers, hence why we encounter a relatively significant variety of age differences.

of the ships with two f1 drivers, 38% were within 2 years of each other, while 44% had an age difference of 5 years or more.

more experimentally (basically i wanted to use these performance metrics for something!), i tried graphing driver metrics against "shippability" to see whether i could uncover any trends, normalizing to percentile to make it more visually comprehensible.

one thing that was interesting to me is that there is a strong correlation between a driver's points per entry and their number of ship fic; really, this isn't surprising at all because it's basically a reflection of whether they've driven for a big 3 team, and we know that the most popular drivers are from big 3 teams, but then i guess it does become a bit of a chicken and egg question... which is something i'm continuously fascinated by when discussing success and talent in sports fandom, especially in a sport like f1 where there is so little parity and thus "points" do not always quantifiably translate to "talent," making it difficult to gauge why and when a driver's skill becomes consciously appealing to an audience. i don't know but here's that scatterplot.

similarly, i also wanted to look at years active vs. fic to gauge which drivers have a High Number Of Ship Fic relative to how long they've actually been in f1, basically a rough rework of the "shippability above expected" metric i'd tried exploring in my old f1 post haha. because the set i merged with attributed 1 "year active" to a driver just like, filling in as reserve for a single race, and it also included drivers who maybe raced one season and then never raced again, but then i still wanted to include current rookies in their first season to show where their Potential lies... i settled on filtering to drivers who were or have been active for at least 5 seasons OR who debuted recently and thus have a bit of rookie leeway. there's a decent amount of correlation here, which is again... in f1, the underlying argument for remaining active for many years is that you have to be good enough to keep your seat, so it's expected that if drivers stay on the grid for a long time they will eventually accrue more fandom interest and thus ship fic. still, we can see some drivers who underperform a little relative to their establishedness—bot and per, interestingly also below the trend line in the points/entry graph–and then those who overperform a decent amount, like nor and lec.

this is somewhat interesting to me because i'd tried to make a similar scatterplot with my hockey set and found that there was... basically nooo correlation at all, but i also had to make do with draft year and not gp which i think might move the needle a little bit. regardless, it's just interesting to think about these things in the context of league/grid exclusivity and then other further nuances like the possibilities of making your niche in, for example, the nhl as a 4th line grinder or f1 as a de facto but reliable #2 driver for years down the stretch, and then how all of that impacts or shapes your fandom stock and shippability.

moving on, here's a look at the current top 20 f1 ships and how much of their fic is tagged as fluff or angst! out of all their fic, kimi/seb have the highest fluff ratio at 38.44%, while lewis/nico hold the throne for angst at 34.74%.

lewis/nico are also the most "holistically" tragic ship when you subtract their fluff and angst percentages (by a large margin as well), while jenson/seb are the fluffiest with a difference of 17.38%. really makes you think.

and finally this is a dumb iteration from my old f1 post but i thought this was kind of funny haha so: basically what if teammate point share h2h but the points are their shippability on ao3.

closing thoughts

that's really all i have! again, i don't know whether any of these graphs make sense or are interesting to anyone, but i had fun trying to adapt some of my hockey methodology to f1 and also revisiting the old f1 graphs i'd made last year and getting to recalculate/design them. i know there's a lot more i could have done in examining drivers' old teams since many ships are based on drivers being ex-teammates and not the current grid matchups, but it would have been too much of a headache to figure out so... this is the best i've got. thanks for reading :)

[tyrannosaurus-maxy]

Just a quick update on the active ships that people are writing for in 2024! Landoscar is right up there 👀

Also I have updated the fandom pull to the Motorsports RPF group (of which F1 is part of) to allow us to track other drivers better!

This is part of my F1 RPF Analysis based on a dataset of the almost 36k AO3 Motorsport Fics pulled on 24 Apr 2024. Fics were analysed based on date of last update.

Feel free to follow the tag #f1 rpf analysis for more, and let me know what else you’d like to see!