[webbo]

Get him to tell you about Pete Gomersall and the Blue Scar routes.

[nik at work]

I'll ask when I next see him.

[Oldmanmatt]

I surrender:

https://instagram.com/p/BW011MVDvw0/


Sent from my iPhone using Tapatalk

[monkoffunk]

I like the list in principle but I can't get behind the rounding up the ages. It's been bugging me for days but thought would make me seem weird to say.

[monkoffunk]

Purely talking about the ages and ignoring the decade categories for a sec, this is how I see the data:



http://imgur.com/1LAZlJ9

[jwi]

Training link-ups don't count. Clearly.

Descente Lolita has pretty much the same grade as “Descente aux enfers” 8c/+ since it has a perfect rest after “Descente...” followed by an 8a-bit to the top [according to Pierre Trolliet which I think is the only one to have repeated the route]. (Also: “Descente en terre inconnue” which links all of “Descente aux enfers” into the hard part of “Tierra incognita”, 8c, is an easy 9a according to both the FA Lucien Martinez and 1st repeater Josef Savarino)

[duncan]

Beat Kammerlander "Kampfzone" (8c, 8a obl.) at 58.

[moose]

Purely talking about the ages and ignoring the decade categories for a sec, this is how I see the data:



http://imgur.com/1LAZlJ9

I find that really depressing - I've got to keep up my current standard for another 20 years before I can let myself go and blame my age for the decline!  20 more years before I feel I can justify spending the occasional weekend eating cake and drinking gin, and not feel duty bound to keep up a regime of midweek training and weekend missions to cold, damp crags (I would always keep up the home board climbing though.... woodie sessions are both fun and brutally satisfying).

By the way, love the old lady dead-lifting video - what makes it priceless is that after her lifts, she immediately reverts to resembling a slightly doddery granny (although that could be lactic acid and psyche induced staggering) - even down to looking momentarily confused when offered a high 5 by the trainer. 

[Muenchener]

90: Marcel Remy "Miroir de l'Argentine" 5b at 94

Father of Swiss new route machines Claude et Yves, I assume.

[mark s]

tim lowe had a good crack at shockhorror on sunday, didn't end too well for his heel bone though.

[Muenchener]

And Marcel Remy again, a few years previously:

80: Stars on Stage 6a, Kalymnos. (Source; photo on p.143 of the 2016 guidebook)

[andy popp]

Kind of on topic: blog from Jonathan Siegrist about his dad achieving a new personal best (5.13b) at 67 - http://blog.arcteryx.com/optimist-jonathan-siegrists-dad/

[galpinos]

Kind of on topic: blog from Jonathan Siegrist about his dad achieving a new personal best (5.13b) at 67 - http://blog.arcteryx.com/optimist-jonathan-siegrists-dad/

Very on topic, it gets him on the list.

[Duma]

be interested to see more data points in the seventies - shape of the curve from 8a to 6a

[tomtom]

I personally think the individual curve is interesting... rather than the individual performance (good as it is) of an 80 YO doing a 6a... (not dissing the thread - just pointing out my own interest in this)

So Haston and Moon have managed to sustain - if not increase their 'performance' (in terms of grade) through into their 50's...

I am of course interested as at the tender age of (ahem) 47 and 11/12ths, the line on my curve is still going up (slowly!) and I'm wondering how long I can sustain this general upward trajectory...

[SA Chris]

90: Marcel Remy "Miroir de l'Argentine" 5b at 94

Father of Swiss new route machines Claude et Yves, I assume.

"NW of" doesn't say actual route, there are about a dozen on it? I think "directe" goes at about that grade, brilliant climbing on a great crag.

[slackline]

Updated graphs...




A more usefully formatted dataset of what others have posted.  I've added Josune Bereziartu's and Ashima Shishari's ascents (still need more data, particularly in women, across all ages and I maintain that a 'benchmark' is not the term for the best performance for a given age).

Code: [Select]

name,age,grade,gender
"Chris Sharma",31,9b+,"male"
"Stevie Haston",52,9a,"male"
"Manolo",51,9a,"male"
"Francesco Marin",60,8b+,"male"
"Lee Sheftel",59,8b+,"male"
"Herman Gollner",70,7c+,"male"
"Lee Sheftel",68,8a,"male"
"Stimson Bullitt",83,6a,"male"
"Fred Beckey",93,4,"male"
"Adam Ondra",19,9b+,"male"
"Adam Ondra",24,9c,"male"
"Alex Megos",20,9a,"male"
"Beat Kammerlander",50,"8b+","male"
"Eneko Pou",40,"8c","male"
"Ben Moon",48,"9a","male"
"Bill Ramsey",54,"8c","male"
"Ethan Pringle",27,"9b","male"
"Seb Bouin",22,"9b","male"
"Paul Reeve",50,"8c","male"
"Dave Stainthorpe",65,"7c+","male"
"Steve McClure",46,"9b","male"
"Muriel Sarkany",43,"9a","female"
"Marcel Remy",80,"6a","male"
"Bob Siegrist",67,"8a","male"
"Josune Bereziartu",30,"9a","female"
"Josune Bereziartu",32,"9a+","female"
"Ashima Shiraishi",13,"9a/+","female"

...and the code in R for generating the above and fitting regression curves...

Code: [Select]

library(ggplot2)

## Read in the CSV                                                                                                                                                               
climbers <- read.csv("climber_age.csv",
                     header = TRUE)

## Define grade as a true factor variable                                                                                                                                       
climbers$grade <- factor(climbers$grade,
                         levels = c("4", "5",
                                    "6a", "6a+", "6b", "6b+", "6c", "6c+",
                                    "7a", "7a+", "7b", "7b+", "7c", "7c+",
                                    "8a", "8a+", "8b", "8b+", "8c", "8c+",
                                    "9a", "9a/+", "9a+", "9b", "9b/+", "9b+", "9c", "9c+"))
climbers$gender <- factor(climbers$gender,
                          levels = c("female", "male"))


## Regress age and gender onto grade                                                                                                                                             
##                                                                                                                                                                               
##                                                                                                                                                                               
## First we fit a linear relationship between age, gender and grade                                                                                                             
## accounting for the differences between gender.  This is                                                                                                                       
## quite crude, but serves as a starting point                                                                                                                                   
regress.linear <- lm(grade ~ age + gender,
                     data = climbers)
## Look at the results, the number underneath the age variable                                                                                                                   
## tells you how much of a decrease in grade occurs with an                                                                                                                     
## increase in age by 1 year.                                                                                                                                                   
regress.linear

## But the relationship might not be linear so now we fit                                                                                                                       
## a regression model using the Loess method which performs                                                                                                                     
## local regression (see http://en.wikipedia.org/wiki/Local_regression)                                                                                                         
regress.loess <- loess(as.numeric(grade) ~ age + as.numeric(gender),
                       data = climbers)


## Plot the Relationship                                                                                                                                                         
##                                                                                                                                                                               
## This is done twice, the first one fits a line that represents                                                                                                                 
## a linear relationship as fitted by the linear grade regress                                                                                                                   
png(filename = "age_grade_linear.png", width=1024, height=768)
plot.linear <- ggplot(climbers,
                      aes(age, as.numeric(grade),
                          shape = gender,
                          fill  = gender)) +
                 geom_point() + geom_smooth(method = lm,
                                            fullrange = FALSE) +
                 xlab("Age (Years)") + ylab("Grade (French Sport)") +
                 scale_shape_manual(values = c(1, 2)) +
                 scale_y_continuous(breaks = seq(1:length(levels(climbers$grade))), labels = levels(climbers$grade)) +
                 ggtitle("Climbers Age and Maximum Grade Climbed (Linear)") +
                 theme_bw()
plot.linear
dev.off()
## The second fits a curve by a method called Loess which performs                                                                                                               
## local regression (currently not yet fitting curves for both genders                                                                                                                                                             
png(filename = "age_grade_loess.png", width=1024, height=768)

plot.loess <- ggplot(climbers,
                     aes(age, as.numeric(grade),
                         label = name,
                         shape = gender,
                         fill  = gender)) +
                geom_point() + geom_smooth(method = "loess",
                                           fullrange = TRUE) +
                xlab("Age (Years)") + ylab("Grade (French Sport)") +
                scale_shape_manual(values = c(1, 2)) +
                scale_y_continuous(breaks = seq(1:length(levels(climbers$grade))), labels = levels(climbers$grade)) +
                ggtitle("Climbers Age and Maximum Grade Climbed (LOESS)") +
theme_bw()
## geom_text(size = 2, angle = 0, hjust=-0.5, vjust=0)                                                                                                           
plot.loess

dev.off()



I am of course interested as at the tender age of (ahem) 47 and 11/12ths, the line on my curve is still going up (slowly!) and I'm wondering how long I can sustain this general upward trajectory...

You can use the above R code to plot your own data if you format it correctly, call the filename the same thing (or adjust the code to reflect the location and name of your file) and then extrapolate as you choose (although its always somewhat folly to try and make predictions beyond the range of data).

[Muenchener]

Sorry to muck up the dataset, I should have specified that the Marcel Remy 6a in the "80" bucket was actually at 88. Which puts him bang on the curve, well done Marcel.

[T_B]

Only ascents that have been verified should be on the list. I know there's some doubt around some of the claims listed above. Just sayin'

(I must be feeling especially sceptical having seen 'Shadowplay' 8C with my own eyes this past weekend).

#climbers #fantasists #egos

[jwi]

 

Goes for at least two points of data.

[slackline]

Only ascents that have been verified should be on the list. I know there's some doubt around some of the claims listed above. Just sayin'

(I must be feeling especially sceptical having seen 'Shadowplay' 8C with my own eyes this past weekend).

#climbers #fantasists #egos

Goes for at least two points of data.

I don't know which are doubted (be it grade or ascent) so can not do so.

Feel free to take the data and code and re-run it, even if you don't have any inclination to use R you can plot it in whatever you are familiar with, its why I took the time to include it along with the code.

Sorry to muck up the dataset, I should have specified that the Marcel Remy 6a in the "80" bucket was actually at 88. Which puts him bang on the curve, well done Marcel.

That is one of the disadvantages of binning continuous data.  You can change the data and re-run too if you wish.

[Doylo]

Some 48 year old Italian fella has just done his first 9a.

[Ru]

Goes for at least two points of data.

Not sure which ones you're doubting apart from the grade of Descente Lolita. But Stevie also climbed Hugh, 9a, at 52.

[jwi]

Ah, that's true.

[teestub]

Patxi, moving towards veteran territory at 37, is about on the best fit line with Pachamama 9a+/b