Quick and dirty analysis of 10mila 2025 results

data analysis
data
orienteering
web scraping
suunnistus
R
dataviz
10mila
Tekijä

Markus Kainu

Julkaistu

4. toukokuuta 2025

A Swedish spring classic in orienteering 10mila was raced this weeked in Finspång in Skåne. Similar to 2023, NTNUI won the mens 10 leg overnight relay. In women’s relay IFK Göteborg won with a massive eight minute difference to second team, that happened to be IFK Göteborg 2.

I took a little stab at their results system at online.10mila.se/, pulled out the data and did two graphs. You can download the excel data with first sheet containing the results and thereafter a sheet per leg:

I included the source code in R in case someone is interested in it.

Top ten teams at each exchange

Here are the positions for top 10 teams at each exchange!

Men

Women

Positions at each split point at each leg

And below a very tall images with positions at each split point at each leg.

Men

Women

Source code

Men

[/kode] 
library(httr)
library(rvest)
library(dplyr)
library(ggplot2)
library(hrbrthemes)
library(viridis)


osuus <- 10
looptbl <- tibble(
  leg = 1:10,
  splits = list(list(1,2,3,4), # 1
                list(1,2,3,4), # 2
                list(1,2,3), # 3
                list(1,2,3,4), # 4
                list(1,2,3), # 5
                list(1,2,3), # 6
                list(1,2,3), # 7
                list(1,2,3,4), # 8
                list(1,2,3), # 9
                list(1,2,3,4)) # 10
)

# **********************************************************
# vaihtoajat
d_leg <- tibble()
for (leg in 1:osuus){
# for (leg in 1:10){
  read_html(paste0("http://online.10mila.se/index2.php?classId=1&legNo=",leg)) %>% 
    html_table(fill = TRUE) %>% 
    .[[5]] %>% 
    .[-1:-2,] %>% 
    as_tibble() -> tmp
  names(tmp) <- c("place","name","club","time","diff")
  tmp$leg <- leg
  d_leg <- bind_rows(d_leg,tmp)
}
d_leg %>% 
  mutate(
  min = as.integer(sub(":.+$", "", time)),
  s   = as.integer(sub("^.+:", "", time)),
  secs = min * 60 + s,
  diff_min = as.integer(sub(":.+$", "", diff)),
  diff_s   = as.integer(sub("^.+:", "", diff)),
  diff_secs = diff_min * 60 + diff_s
  ) -> d_leg2

leg_info <- tibble(leg = 1:10)
leg_info %>% 
  mutate(length = case_when(
  leg == 1 ~ 8.9,
  leg == 2 ~ 7.7,
  leg == 3 ~ 8.2,
  leg == 4 ~ 16.3,
  leg == 5 ~ 6.8,
  leg == 6 ~ 6.8,
  leg == 7 ~ 5.8,
  leg == 8 ~ 11.8,
  leg == 9 ~ 13.9,
  leg == 10 ~ 15.1
), 
length_cum = cumsum(length)) -> leg_info

d_leg22 <- left_join(d_leg2,leg_info)

d_leg_0 <- d_leg22 %>% 
  filter(leg == osuus) %>% 
  mutate(length_cum = 0, 
         diff_secs = 0,
         leg = 0)
d_leg_11 <- bind_rows(d_leg_0,
                      d_leg22)

all_legs <- d_leg_11 # for excel

top10 <- d_leg2 %>% filter(leg == osuus) %>% 
  slice(1:10) %>% 
  pull(club)
d_leg_111 <- d_leg_11 %>% filter(club %in% top10)

ipsum_palette <- c("#d18975", "#8fd175", "#3f2d54", "#75b8d1", "#2d543d", "#c9d175", "#d1ab75", "#d175b8", "#758bd1")
ipsum_palette <- c(ipsum_palette,ipsum_palette,ipsum_palette)

ggplot(d_leg_111, aes(x = length_cum, y = diff_secs/60, color = club, fill = club)) + 
  geom_line(alpha = .8) + geom_point(shape = 21, color = "white", alpha = .8, size = 4) + 
  # scale_x_continuous(breaks = leg_info$length_cum , labels = leg_info$leg) + 
  scale_y_reverse() +
  geom_text(aes(label = place), color = "white", family = "Roboto Condensed", size = 2.5, fontface= "bold") +
  theme_ipsum_rc() + 
  ggrepel::geom_text_repel(data = d_leg_111 %>% filter(leg == max(leg)), aes(label = club), nudge_y = -.2, size = 2.7, family = "Roboto Condensed") + 
  theme(legend.position = "none") + 
  # scale_fill_ipsum() + scale_color_ipsum() +
  scale_fill_manual(values = c(rev(ipsum_palette),"black")) + scale_color_manual(values = c(rev(ipsum_palette),"black")) +
   labs(title = "Top 25 teams in 10mila 2025",
        subtitle = "Time difference to lead in each exchange",
        caption = paste0("Data: online.10mila.se\n",Sys.time()),
          x = "distance (km)", y = "minutes behind the lead") -> plot
ggsave(filename = "./kuvat/10mila2025_top_ten_legs_twitter.png", plot, width = 8, heigh = 6, dpi = 120)


# **********************************************************
# väliajat

d_splits <- tibble()
for (leg in 1:osuus){
# for (leg in 1:10){
  splits <- unlist(looptbl$splits[leg])
  split_dat <- tibble()
  for (split1 in splits){
  read_html(paste0("http://online.10mila.se/index2.php?classId=1&legNo=",leg,"&splitNo=",split1)) %>% 
    html_table(fill = TRUE) %>% 
    .[[5]] %>% 
    .[-1:-2,] %>% 
    as_tibble() -> tmp
  names(tmp) <- c("place","name","club","time","diff")
  tmp$leg <- leg
  tmp$split1 <- split1
  split_dat <- bind_rows(split_dat,tmp)
}
  d_splits <- bind_rows(d_splits,split_dat)
}
d_splits %>% 
  mutate(
    min = as.integer(sub(":.+$", "", time)),
    s   = as.integer(sub("^.+:", "", time)),
    secs = min * 60 + s,
    diff_min = as.integer(sub(":.+$", "", diff)),
    diff_s   = as.integer(sub("^.+:", "", diff)),
    diff_secs = diff_min * 60 + diff_s
  ) -> d_splits2

split_info <- d_splits2 %>% count(leg,split1) %>% select(-n)
split_info %>% 
  mutate(length = case_when(
    leg == 1 & split1 ==  1 ~ 2.8,
    leg == 1 & split1 ==  2 ~ 4.9,
    leg == 1 & split1 ==  3 ~ 7.9,
    leg == 1 & split1 ==  4 ~ 8.4,

    leg == 2 & split1 == 1 ~ 2.4,
    leg == 2 & split1 == 2 ~ 4.5,
    leg == 2 & split1 == 3 ~ 6.3,
    leg == 2 & split1 == 4 ~ 7.7,

    leg == 3 & split1 == 1 ~ 2.4,
    leg == 3 & split1 == 2 ~ 4.5,
    leg == 3 & split1 == 3 ~ 7.7,

    leg == 4 & split1 == 1 ~ 6.3,
    leg == 4 & split1 == 2 ~ 8.8,
    leg == 4 & split1 == 3 ~ 11.0,
    leg == 4 & split1 == 4 ~ 15.8,

    leg == 5 & split1 == 1 ~ 1.8,
    leg == 5 & split1 == 2 ~ 4.5,
    leg == 5 & split1 == 3 ~ 6.3,

    leg == 6 & split1 == 1 ~ 1.8,
    leg == 6 & split1 == 2 ~ 4.5,
    leg == 6 & split1 == 3 ~ 6.3,

    leg == 7 & split1 == 1 ~ 1.8,
    leg == 7 & split1 == 2 ~ 3.9,
    leg == 7 & split1 == 3 ~ 5.3,

    leg == 8 & split1 == 1 ~ 3.0,
    leg == 8 & split1 == 2 ~ 6.0,
    leg == 8 & split1 == 3 ~ 8.5,
    leg == 8 & split1 == 4 ~ 11.3,

    leg == 9 & split1 == 1 ~ 5.7,
    leg == 9 & split1 == 2 ~ 9.8,
    leg == 9 & split1 == 2 ~ 13.4,

    leg == 10 & split1 == 1 ~ 5.7,
    leg == 10 & split1 == 2 ~ 7.8,
    leg == 10 & split1 == 3 ~ 10.9,
    leg == 10 & split1 == 4 ~ 14.6,

  )) %>% 
  group_by(leg) %>% 
  mutate(length_cum = cumsum(length)) -> split_info

d_splits_10 <- left_join(d_splits2,split_info)

d_leg0 <- all_legs %>% 
  filter(leg %in% 1:osuus) %>% 
  mutate(length = 0, 
         diff_secs = 0,
         leg = 1)
if (exists("d_leg_10")){
d_leg_11 <- bind_rows(all_legs,
                      d_leg_10)  
} else {
d_leg_11 <- all_legs  
}

d_leg_11_0 <- d_leg_11 %>% 
  mutate(length = 0, leg = leg + 1)

legdata <- bind_rows(d_leg_11,d_leg_11_0)

d_splits_10 <- bind_rows(d_splits_10,legdata)

d_splits_10 <- d_splits_10 %>% filter(leg != 0, leg != 11)

d_splits_10$leg2 <- paste0("Leg ", d_splits_10$leg)
d_splits_10$leg2 <- factor(d_splits_10$leg2, levels = c("Leg 1","Leg 2","Leg 3","Leg 4","Leg 5","Leg 6","Leg 7","Leg 8","Leg 9","Leg 10"))

all_splits <- d_splits_10 # exceliin


d_splits_10 <- d_splits_10 %>% filter(club %in% top10)

d_splits_10 <- d_splits_10 %>% filter(leg %in% 1:osuus)

ggplot(d_splits_10, aes(x = length, 
                        y = diff_secs/60,
                        # y = diff_secs, 
                        color = club, 
                        fill = club)) + 
  geom_line(alpha = .8) + geom_point(shape = 21, color = "white", alpha = .8, size = 4) + 
  ggrepel::geom_text_repel(data = d_splits_10 %>% 
                             group_by(leg2) %>% 
                             filter(length == max(length)), 
                           aes(label = paste0(name,"\n",club)), 
                           nudge_y = -.2, 
                           nudge_x = 1, 
                           size = 2.6, 
                           lineheight = .8, 
                           family = "Roboto Condensed") + 
  facet_wrap(~leg2, ncol = 1, scales = "free") +
  # scale_x_continuous(breaks = 0:18, labels = 0:18, limits = c(0,18)) +
  scale_x_continuous(breaks = 0:20, labels = 0:20, limits = c(0,20)) +
  scale_y_reverse() + 
  geom_text(aes(label = place), color = "white", family = "Roboto Condensed", size = 2.5, fontface= "bold") +
  theme_ipsum_rc() + 
  theme(legend.position = "none") + 
  scale_fill_manual(values = c(rev(ipsum_palette),"black")) + scale_color_manual(values = c(rev(ipsum_palette),"black")) +
  # theme(plot.margin = unit(0,0,0,0)) +
  # theme(margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")) +
  labs(title = glue::glue("Top 10 teams in 10mila 2025 in legs 1-{osuus}"),
       subtitle = "Time difference to lead in each split",
       caption = paste0("Data: online.10mila.se\n",Sys.time()),
       x = "distance from exchange (km)", y = "minutes behind the lead") -> splitspic

ggsave(filename = glue::glue("./kuvat/10mila2025_splits_{osuus}.png"), splitspic, width = 12, height = 5+2.3*osuus, dpi = 120)

# excel_file
lista <- list()
legs <- unique(all_splits$leg2)
lista[["results"]] <- all_legs
for (l in seq(legs)){
  lista[[paste0("leg",l)]] <- all_splits[all_splits$leg2 == legs[l], ]
}
writexl::write_xlsx(lista, "10mila2025_results.xlsx")

Women

[/kode] 
library(httr)
library(rvest)
library(dplyr)
library(ggplot2)
library(hrbrthemes)
library(viridis)


osuus <- 6
looptbl <- tibble(
  leg = 1:6,
  splits = list(list(1,2,3,4), # 1
                list(1,2,3), # 2
                list(1,2,3,4), # 3
                list(1,2), # 4
                list(1,2,3), # 5
                list(1,2,3)) # 6
)

# **********************************************************
# vaihtoajat
d_leg <- tibble()
for (leg in 1:osuus){
  # for (leg in 1:10){
  read_html(paste0("http://online.10mila.se/index2.php?classId=2&legNo=",leg)) %>% 
    html_table(fill = TRUE) %>% 
    .[[5]] %>% 
    .[-1:-2,] %>% 
    as_tibble() -> tmp
  names(tmp) <- c("place","name","club","time","diff")
  tmp$leg <- leg
  d_leg <- bind_rows(d_leg,tmp)
}
d_leg %>% 
  mutate(
    min = as.integer(sub(":.+$", "", time)),
    s   = as.integer(sub("^.+:", "", time)),
    secs = min * 60 + s,
    diff_min = as.integer(sub(":.+$", "", diff)),
    diff_s   = as.integer(sub("^.+:", "", diff)),
    diff_secs = diff_min * 60 + diff_s
  ) -> d_leg2

leg_info <- tibble(leg = 1:6)
leg_info %>% 
  mutate(length = case_when(
    leg == 1 ~ 7.7,
    leg == 2 ~ 6.6,
    leg == 3 ~ 12.6,
    leg == 4 ~ 5.1,
    leg == 5 ~ 7.1,
    leg == 6 ~ 8.3
  ), 
  length_cum = cumsum(length)) -> leg_info

d_leg22 <- left_join(d_leg2,leg_info)

d_leg_0 <- d_leg22 %>% 
  filter(leg == osuus) %>% 
  mutate(length_cum = 0, 
         diff_secs = 0,
         leg = 0)
d_leg_11 <- bind_rows(d_leg_0,
                      d_leg22)

all_legs <- d_leg_11 # for excel

top10 <- d_leg2 %>% filter(leg == osuus) %>% 
  slice(1:10) %>% 
  pull(club)
d_leg_111 <- d_leg_11 %>% filter(club %in% top10)

ipsum_palette <- c("#d18975", "#8fd175", "#3f2d54", "#75b8d1", "#2d543d", "#c9d175", "#d1ab75", "#d175b8", "#758bd1")
ipsum_palette <- c(ipsum_palette,ipsum_palette,ipsum_palette)

ggplot(d_leg_111, aes(x = length_cum, y = diff_secs/60, color = club, fill = club)) + 
  geom_line(alpha = .8) + geom_point(shape = 21, color = "white", alpha = .8, size = 4) + 
  # scale_x_continuous(breaks = leg_info$length_cum , labels = leg_info$leg) + 
  scale_y_reverse() +
  geom_text(aes(label = place), color = "white", family = "Roboto Condensed", size = 2.5, fontface= "bold") +
  theme_ipsum_rc() + 
  ggrepel::geom_text_repel(data = d_leg_111 %>% filter(leg == max(leg)), aes(label = club), nudge_y = -.2, size = 2.7, family = "Roboto Condensed") + 
  theme(legend.position = "none") + 
  # scale_fill_ipsum() + scale_color_ipsum() +
  scale_fill_manual(values = c(rev(ipsum_palette),"black")) + scale_color_manual(values = c(rev(ipsum_palette),"black")) +
  labs(title = "Top 25 teams in Womens 10mila relay 2025",
       subtitle = "Time difference to lead in each exchange",
       caption = paste0("Data: online.10mila.se\n",Sys.time()),
       x = "distance (km)", y = "minutes behind the lead") -> plot
ggsave(filename = "./kuvat/10mila2025_top_ten_legs_twitter_women.png", plot, width = 8, heigh = 6, dpi = 120)


# **********************************************************
# väliajat

d_splits <- tibble()
for (leg in 1:osuus){
  # for (leg in 1:10){
  splits <- unlist(looptbl$splits[leg])
  split_dat <- tibble()
  for (split1 in splits){
    read_html(paste0("http://online.10mila.se/index2.php?classId=2&legNo=",leg,"&splitNo=",split1)) %>% 
      html_table(fill = TRUE) %>% 
      .[[5]] %>% 
      .[-1:-2,] %>% 
      as_tibble() -> tmp
    names(tmp) <- c("place","name","club","time","diff")
    tmp$leg <- leg
    tmp$split1 <- split1
    split_dat <- bind_rows(split_dat,tmp)
  }
  d_splits <- bind_rows(d_splits,split_dat)
}
d_splits %>% 
  mutate(
    min = as.integer(sub(":.+$", "", time)),
    s   = as.integer(sub("^.+:", "", time)),
    secs = min * 60 + s,
    diff_min = as.integer(sub(":.+$", "", diff)),
    diff_s   = as.integer(sub("^.+:", "", diff)),
    diff_secs = diff_min * 60 + diff_s
  ) -> d_splits2

split_info <- d_splits2 %>% count(leg,split1) %>% select(-n)
split_info %>% 
  mutate(length = case_when(
    leg == 1 & split1 ==  1 ~ 2.7,
    leg == 1 & split1 ==  2 ~ 4.9,
    leg == 1 & split1 ==  3 ~ 6.6,
    leg == 1 & split1 ==  4 ~ 7.2,
    
    leg == 2 & split1 == 1 ~ 2.3,
    leg == 2 & split1 == 2 ~ 4.4,
    leg == 2 & split1 == 3 ~ 6.1,

    leg == 3 & split1 == 1 ~ 4.8,
    leg == 3 & split1 == 2 ~ 7.3,
    leg == 3 & split1 == 3 ~ 11.2,
    leg == 3 & split1 == 4 ~ 12.1,
    
    leg == 4 & split1 == 1 ~ 2.1,
    leg == 4 & split1 == 2 ~ 4.6,

    leg == 5 & split1 == 1 ~ 3.4,
    leg == 5 & split1 == 2 ~ 6.0,
    leg == 5 & split1 == 3 ~ 6.6,
    
    leg == 6 & split1 == 1 ~ 2.7,
    leg == 6 & split1 == 2 ~ 4.1,
    leg == 6 & split1 == 3 ~ 7.8
    
  )) %>% 
  group_by(leg) %>% 
  mutate(length_cum = cumsum(length)) -> split_info

d_splits_10 <- left_join(d_splits2,split_info)

d_leg0 <- all_legs %>% 
  filter(leg %in% 1:osuus) %>% 
  mutate(length = 0, 
         diff_secs = 0,
         leg = 1)
if (exists("d_leg_10")){
  d_leg_11 <- bind_rows(all_legs,
                        d_leg_10)  
} else {
  d_leg_11 <- all_legs  
}

d_leg_11_0 <- d_leg_11 %>% 
  mutate(length = 0, leg = leg + 1)

legdata <- bind_rows(d_leg_11,d_leg_11_0)

d_splits_10 <- bind_rows(d_splits_10,legdata)

d_splits_10 <- d_splits_10 %>% filter(leg != 0, leg != 7)

d_splits_10$leg2 <- paste0("Leg ", d_splits_10$leg)
d_splits_10$leg2 <- factor(d_splits_10$leg2, levels = c("Leg 1","Leg 2","Leg 3","Leg 4","Leg 5","Leg 6"))

all_splits <- d_splits_10 # exceliin


d_splits_10 <- d_splits_10 %>% filter(club %in% top10)

d_splits_10 <- d_splits_10 %>% filter(leg %in% 1:osuus)

ggplot(d_splits_10, aes(x = length, 
                        y = diff_secs/60,
                        # y = diff_secs, 
                        color = club, 
                        fill = club)) + 
  geom_line(alpha = .8) + geom_point(shape = 21, color = "white", alpha = .8, size = 4) + 
  ggrepel::geom_text_repel(data = d_splits_10 %>% 
                             group_by(leg2) %>% 
                             filter(length == max(length)), 
                           aes(label = paste0(name,"\n",club)), 
                           nudge_y = -.2, 
                           nudge_x = 1, 
                           size = 2.6, 
                           lineheight = .8, 
                           family = "Roboto Condensed") + 
  facet_wrap(~leg2, ncol = 1, scales = "free") +
  # scale_x_continuous(breaks = 0:18, labels = 0:18, limits = c(0,18)) +
  scale_x_continuous(breaks = 0:20, labels = 0:20, limits = c(0,20)) +
  scale_y_reverse() + 
  geom_text(aes(label = place), color = "white", family = "Roboto Condensed", size = 2.5, fontface= "bold") +
  theme_ipsum_rc() + 
  theme(legend.position = "none") + 
  scale_fill_manual(values = c(rev(ipsum_palette),"black")) + scale_color_manual(values = c(rev(ipsum_palette),"black")) +
  # theme(plot.margin = unit(0,0,0,0)) +
  # theme(margin(t = 0, r = 0, b = 0, l = 0, unit = "pt")) +
  labs(title = glue::glue("Top 10 teams in 10mila 2025 in legs 1-{osuus}"),
       subtitle = "Time difference to lead in each split",
       caption = paste0("Data: online.10mila.se\n",Sys.time()),
       x = "distance from exchange (km)", y = "minutes behind the lead") -> splitspic

ggsave(filename = glue::glue("./kuvat/10mila2025_splits_{osuus}_women.png"), splitspic, width = 12, height = 5+2.3*osuus, dpi = 120)

# excel_file
lista <- list()
legs <- unique(all_splits$leg2)
lista[["results"]] <- all_legs
for (l in seq(legs)){
  lista[[paste0("leg",l)]] <- all_splits[all_splits$leg2 == legs[l], ]
}
writexl::write_xlsx(lista, "10mila2025_results_women.xlsx")

Uudelleenkäyttö

Viittaus

BibTeX-viittaus:
@online{kainu2025,
  author = {Kainu, Markus},
  title = {Quick and dirty analysis of 10mila 2025 results},
  date = {2025-05-04},
  url = {https://markuskainu.fi/posts/2025-05-04-10-mila-finspang/},
  langid = {fi}
}
Viitatkaa tähän teokseen seuraavasti:
Kainu, Markus. 2025. “Quick and dirty analysis of 10mila 2025 results.” May 4, 2025. https://markuskainu.fi/posts/2025-05-04-10-mila-finspang/.