Mechanics Over Time

November 9, 2017
board games R statistics


The goal of this post is to look at the trends in board game mechanics over time. I’ll be using the copy of the boardgamegeek database that I created in this post.



## Used by not read in:
# tibble


To prep the data for analysis, I’m turning the year published into a date, rather than a character variable. Boardgamegeek has games published as far back as… hm… “0000-01-01”, so I’m going to be focusing on the period between 1950 and today.

games <- readRDS("~/Desktop/boardgamegeek/gamesDf.RDS")

gamesDf <- games %>%
    yearpublished = as.Date(paste(yearpublished, '01', '01', sep = '-'))
  ) %>%
    yearpublished > '1950-01-01',
    yearpublished < '2018-01-01'

Mechanics Over Time

I’ll start by using dplyr to summarize the data by yearpublished and boardgamemechanic. Then, I’m calculating the percent of total games per year in each mechanic.

mechanicDensities <-  gamesDf %>%
  filter(! %>%
  group_by(yearpublished, boardgamemechanic) %>%
    count = n()
  ) %>%
    percent = count/sum(count)

To make the plot easier to read, I’m changing the default ordering of boardgamemechanic from the default (alphabetical) to be by overall count. Then I made a couple more swaps by hand.

mechanicOrder <-
  gamesDf %>%
  filter(! %>%
  group_by(boardgamemechanic) %>%
  summarize(count = n()) %>%
  arrange(count) %>%

mechanicOrder[c(51,50)] <- mechanicOrder[c(50,51)]
mechanicOrder[c(47,49)] <- mechanicOrder[c(49,47)]

mechanicDensities$boardgamemechanic <- factor(mechanicDensities$boardgamemechanic, 
                                              levels = mechanicOrder)

Finally, I’m using the ggridge package to draw a ridge plot. I found that to be the easiest and best way to show this many time series in a way that was easy to interpret.

ggplot(data = mechanicDensities, aes(x = yearpublished, y = boardgamemechanic, 
                                     group = boardgamemechanic, height = percent)) +
  geom_ridgeline(stat = 'identity', scale = 5) +


A few big things jump out immediately:

  • Roll and move mechanics were pretty much the only mechanic for a long time, but are not common now.
  • Dice rolling has been a consistently popular mechanic through the time period shown.
  • We can see the rise and fall in popularity of war gaming in the 1970’s in the trend of the hex-and-counter mechanic.
  • A few mechanics have received a jump in prevalence very recently, including:
    • Action point systems,
    • Cooperative play, and
    • Area Control


In the raw bgg data, games can have many mechanics, but I am only taking the first listed. It is possible that the mechanic listed first is random, or that it tends to be the/a main mechanic. If it’s random, then I can consider this to be a random sample of mechanics from each year, so my inferences here should be appliciable to the population. I’m not doing anything formal here to test that, but maybe a later post. If the first mechanic is the “main” mechanic, that changes some of the interpretation of the data, but not the overall idea.

Publishing Trends

November 17, 2017
board games R trends

Getting boardgamegeek Data

October 30, 2017
R web scraping boardgames