Reach, Height, and Age Barely Predict a UFC Winner

Knowing who’s taller, has more reach, or is younger adds almost nothing beyond what the betting market already reflects. Across thousands of UFC fights, its implied odds closely match actual outcomes.

Physical attributes like reach, height, and age barely outperform a coin flip at predicting UFC fight winners, while the betting market’s implied odds are remarkably well-calibrated across thousands of fights. Win rates use a corner-independent advantage definition with Wilson confidence intervals; calibration is checked by probability bucket. Built in R with ggplot2, patchwork, and binom.

Figure 1: Two-panel chart titled “Reach, Height, and Age Barely Predict a UFC Winner.” Left panel shows win rates for the fighter with a physical advantage: reach (48.1%), height (48.0%), and youth (50.1%), all clustered near a dashed 50% coin-flip line with confidence intervals. Right panel plots betting-market implied probability against actual win rate across five buckets from heavy underdog to heavy favorite, with points falling almost exactly on the diagonal reference line and an average calibration error of about 1 percentage point. Data from the fightr R package (UFC athlete profiles, UFCStats, Kaggle, Octagon API).

Steps to Create this Graphic

1. Load Packages & Setup

```{r}
#| label: load
#| warning: false
#| message: false      
#| results: "hide"     

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
if (!require("pacman")) install.packages("pacman")
pacman::p_load(
    tidyverse, ggtext, showtext, janitor, ggrepel,      
    scales, glue, skimr, binom, patchwork
    )
})

### |- figure size ----          
camcorder::gg_record(
  dir    = here::here("temp_plots"),
    device = "png",
    width  = 12,
    height = 6.5,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false

## 2. READ IN THE DATA ----
tt <- tidytuesdayR::tt_load(2026, week = 27)
ultimate_ufc_dataset <- tt$ultimate_ufc_dataset |> clean_names()
rm(tt)
```

3. Examine the Data

```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(ultimate_ufc_dataset)
```

4. Tidy Data

```{r}
#| label: tidy
#| warning: false

## |- panel A: physical-advantage win rates (corner-assignment-proof) ----
## Reach is systematically confounded with corner (Red is the betting
## favorite ~62% of the time), so advantage is defined as "did the
## fighter WITH more of the trait win," independent of corner.

adv_data <- ultimate_ufc_dataset |>
  filter(!is.na(winner)) |>
  mutate(
    reach_adv_won = case_when(
      reach_dif > 0 ~ winner == "Red",
      reach_dif < 0 ~ winner == "Blue", TRUE ~ NA
    ),
    height_adv_won = case_when(
      height_dif > 0 ~ winner == "Red",
      height_dif < 0 ~ winner == "Blue", TRUE ~ NA
    ),
    younger_won = case_when(
      age_dif < 0 ~ winner == "Red",
      age_dif > 0 ~ winner == "Blue", TRUE ~ NA
    )
  )

wilson_summary <- function(x, label) {
  x <- x[!is.na(x)]
  ci <- binom.wilson(sum(x), length(x))
  tibble(trait = label, mean = ci$mean, lower = ci$lower, upper = ci$upper, n = ci$n)
}

advantage_summary <- bind_rows(
  wilson_summary(adv_data$reach_adv_won, "Reach advantage"),
  wilson_summary(adv_data$height_adv_won, "Height advantage"),
  wilson_summary(adv_data$younger_won, "Younger fighter")
) |>
  mutate(trait = fct_reorder(trait, mean))

### |- panel B: betting-market calibration curve ----
implied_prob <- function(odds) {
  if_else(odds < 0, -odds / (-odds + 100), 100 / (odds + 100))
}

calibration_data <- ultimate_ufc_dataset |>
  filter(!is.na(r_odds), !is.na(winner)) |>
  mutate(
    r_implied = implied_prob(r_odds),
    red_won = winner == "Red",
    implied_bucket = case_when(
      r_implied < 0.30 ~ "Heavy\nunderdog",
      r_implied < 0.45 ~ "Underdog",
      r_implied < 0.55 ~ "Close to\neven",
      r_implied < 0.70 ~ "Favorite",
      TRUE ~ "Heavy\nfavorite"
    ),
    implied_bucket = fct_reorder(implied_bucket, r_implied)
  ) |>
  group_by(implied_bucket) |>
  summarise(
    n = n(),
    mean_implied = mean(r_implied),
    actual_win_rate = mean(red_won),
    .groups = "drop"
  )

avg_calibration_error <- calibration_data |>
  summarise(avg_abs_gap = mean(abs(actual_win_rate - mean_implied))) |>
  pull(avg_abs_gap)
```

5. Visualization Parameters

```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
clrs <- get_theme_colors(
  palette = c("highlight" = "#722F37", "secondary" = "gray70")
)

### |- titles and caption ----

title_text <- str_glue("Reach, Height, and Age Barely Predict a UFC Winner")

subtitle_text <- str_glue(
  "Knowing who's **taller**, has more **reach**, or is **younger**<br>",
  "adds almost nothing beyond what the betting market already reflects.<br>",
  "Across thousands of UFC fights, its implied odds closely match actual outcomes."
)

caption_text <- create_social_caption(
  tt_year = 2026,
  tt_week = 27,
  source_text = "{fightr} R package (UFC athlete profiles, UFCStats, Kaggle, Octagon API)"
)

caption_text <- str_glue(
  "{caption_text}<br>",
  "Note: advantage defined as the fighter with more of the trait, independent of corner assignment."
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----
base_theme <- create_base_theme(clrs)

weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      face = "bold", family = fonts$title_1, size = rel(1.4), margin = margin(b = 6)
    ),
    plot.subtitle = element_markdown(
      family = fonts$subtitle, size = rel(0.95), lineheight = 1.2, margin = margin(b = 16)
    ),
    panel.grid.major.y = element_line(color = "gray90", linewidth = 0.3),
    panel.grid.major.x = element_blank(),
    panel.grid.minor = element_blank(),
    axis.ticks = element_blank(),
    strip.text = element_text(face = "bold", family = fonts$title, size = rel(0.9))
  )
)

theme_set(weekly_theme)
```

6. Plot

```{r}
#| label: plot
#| warning: false

### |- plot ----

### |- Panel A: physical advantage win rates ----
p_advantage <- ggplot(advantage_summary, aes(x = mean, y = trait)) +
  geom_vline(xintercept = 0.5, linetype = "dashed", color = "gray30", linewidth = 0.6) +
  geom_pointrange(
    aes(xmin = lower, xmax = upper),
    color = "gray45", fill = "gray45",
    size = 0.9, linewidth = 1.1, shape = 21
  ) +
  geom_text(
    aes(label = percent(mean, accuracy = 0.1)),
    vjust = -1.6, family = fonts$text, fontface = "bold", size = 3.6
  ) +
  scale_x_continuous(
    labels = c("45%", "50%\n(coin flip)"),
    limits = c(0.44, 0.545),
    breaks = c(0.45, 0.50)
  ) +
  coord_cartesian(clip = "off") +
  labs(
    title = "What fans debate before the fight",
    subtitle = "Win rate for the fighter with the advantage",
    x = NULL, y = NULL
  ) +
  theme(
    plot.title = element_text(size = rel(0.95), face = "bold", family = fonts$title_1),
    plot.subtitle = element_markdown(size = rel(0.7), color = "gray30", family = 'sans'),
    axis.text.y = element_text(size = rel(0.9), face = "bold")
  )

### |- Panel B: calibration curve ----
p_calibration <- ggplot(calibration_data, aes(x = mean_implied, y = actual_win_rate)) +
  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "gray40", linewidth = 0.6) +
  geom_line(color = "#722F37", linewidth = 0.6, alpha = 0.45) +
  geom_point(aes(size = n), color = "#722F37") +
  geom_text(
    aes(label = implied_bucket),
    vjust = -1.5, family = fonts$text, size = 3, lineheight = 0.85, color = "gray30"
  ) +
  annotate(
    "text",
    x = 0.30, y = 0.87,
    label = str_glue("Average error: ~{percent(avg_calibration_error, accuracy = 1)}"),
    family = fonts$text, fontface = "bold", size = 3.4, color = "gray30", hjust = 0
  ) +
  scale_x_continuous(labels = percent_format(accuracy = 1), limits = c(0.15, 0.95)) +
  scale_y_continuous(labels = percent_format(accuracy = 1), limits = c(0.15, 0.95)) +
  scale_size_continuous(range = c(2, 7), guide = "none") +
  coord_fixed(clip = "off") +
  labs(
    title = "What the betting market already captures",
    subtitle = "Market-implied probability vs. actual win rate  ·  point size = fights per bucket",
    x = "Betting market implied probability",
    y = "Actual win rate"
  ) +
  theme(
    plot.title = element_text(size = rel(0.95), face = "bold", family = fonts$title_1),
    plot.subtitle = element_markdown(size = rel(0.7), color = "gray30", family = 'sans'),
    axis.title = element_text(size = rel(0.8))
  )

### |- combine plots ----
combined_plot <- p_advantage + p_calibration +
  plot_layout(widths = c(0.72, 1.28)) +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        face = "bold", family = fonts$title_1, size = rel(1.6),
        margin = margin(b = 6)
      ),
      plot.subtitle = element_markdown(
        family = fonts$subtitle, size = rel(0.8), lineheight = 1.3,
        margin = margin(b = 18)
      ),
      plot.caption = element_markdown(
        family = 'sans', size = rel(0.55), color = "gray40",
        hjust = 0, margin = margin(t = 14), lineheight = 1.15
      )
    )
  )
```

7. Save

```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plot, 
  type = "tidytuesday", 
  year = 2026, 
  week = 27, 
  width  = 12,
  height = 6.5
  )
```

8. Session Info

TipExpand for Session Info

R version 4.5.3 (2026-03-11 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)

Matrix products: default
  LAPACK version 3.12.1

locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] here_1.0.2      patchwork_1.3.2 binom_1.1-1.1   skimr_2.2.2    
 [5] glue_1.8.0      scales_1.4.0    ggrepel_0.9.8   janitor_2.2.1  
 [9] showtext_0.9-8  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
[13] lubridate_1.9.5 forcats_1.0.1   stringr_1.6.0   dplyr_1.2.1    
[17] purrr_1.2.2     readr_2.2.0     tidyr_1.3.2     tibble_3.3.1   
[21] ggplot2_4.0.3   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1   farver_2.1.2       S7_0.2.1           fastmap_1.2.0     
 [5] gh_1.5.0           digest_0.6.39      timechange_0.4.0   lifecycle_1.0.5   
 [9] rsvg_2.7.0         magrittr_2.0.5     compiler_4.5.3     rlang_1.2.0       
[13] tools_4.5.3        yaml_2.3.12        knitr_1.51         labeling_0.4.3    
[17] htmlwidgets_1.6.4  bit_4.6.0          curl_7.0.0         xml2_1.5.2        
[21] camcorder_0.1.0    repr_1.1.7         RColorBrewer_1.1-3 tidytuesdayR_1.3.2
[25] withr_3.0.2        grid_4.5.3         gitcreds_0.1.2     cli_3.6.6         
[29] rmarkdown_2.31     crayon_1.5.3       generics_0.1.4     otel_0.2.0        
[33] rstudioapi_0.18.0  tzdb_0.5.0         commonmark_2.0.0   parallel_4.5.3    
[37] ggplotify_0.1.3    base64enc_0.1-6    vctrs_0.7.3        yulab.utils_0.2.4 
[41] jsonlite_2.0.0     litedown_0.9       gridGraphics_0.5-1 hms_1.1.4         
[45] bit64_4.6.0-1      systemfonts_1.3.2  magick_2.9.1       gifski_1.32.0-2   
[49] codetools_0.2-20   stringi_1.8.7      gtable_0.3.6       pillar_1.11.1     
[53] rappdirs_0.3.4     htmltools_0.5.9    R6_2.6.1           httr2_1.2.2       
[57] textshaping_1.0.5  rprojroot_2.1.1    vroom_1.7.1        evaluate_1.0.5    
[61] markdown_2.0       gridtext_0.1.6     snakecase_0.11.1   Rcpp_1.1.1        
[65] svglite_2.2.2      xfun_0.57          fs_2.0.1           pkgconfig_2.0.3   

9. GitHub Repository

TipExpand for GitHub Repo

The complete code for this analysis is available in tt_2026_27.qmd.

For the full repository, click here.

10. References

TipExpand for References

1. Data Source:
   • TidyTuesday 2026 Week 27: UFC Athletes and Fight Data

11. Custom Functions Documentation

Note📦 Custom Helper Functions

This analysis uses custom functions from my personal module library for efficiency and consistency across projects.

Functions Used:

• fonts.R: setup_fonts(), get_font_families() - Font management with showtext
• social_icons.R: create_social_caption() - Generates formatted social media captions
• image_utils.R: save_plot() - Consistent plot saving with naming conventions
• base_theme.R: create_base_theme(), extend_weekly_theme(), get_theme_colors() - Custom ggplot2 themes

Why custom functions?
These utilities standardize theming, fonts, and output across all my data visualizations. The core analysis (data tidying and visualization logic) uses only standard tidyverse packages.

Source Code:
View all custom functions → GitHub: R/utils