Predicting Board Game Collections

rahdo’s Collection

Author

Phil Henrickson

Published

May 21, 2025

About

This report details the results of training and evaluating a classification model for predicting games for a user’s boardgame collection.

Note

To view games predicted by the model, go to Section 5.

Collection

The data in this project comes from BoardGameGeek.com. The data used is at the game level, where an individual observation contains features about a game, such as its publisher, categories, and playing time, among many others.

I train a classification model at the user level to learn the relationship between game features and games that a user owns - what predicts a user’s collection?

username status games
rahdo ever_owned 1776
rahdo own 290
rahdo rated 272

I evaluate the model’s performance on a training set of historical games via resampling, then validate the model’s performance on a set aside set of newer relases. I then refit the model on the training and validation in order and predict upcoming releases in order to find new games that the user is most likely to add to their collection.

username years type
Own
no yes
rahdo -3500-2021 train 26158 204
rahdo 2022-2023 valid 10251 57
rahdo 2024-2028 test 8564 29

Types of Games

What types of game does the user own? The following plot displays the most frequent publishers, mechanics, designers, artists, etc that appear in a user’s collection.

Show the code
collection |>
    filter(own == 1) |>
    collection_by_category(
        games = games_raw
    ) |>
    plot_collection_by_category() +
    ylab("feature")

The following plot shows the years in which games in the user’s collection were published. This can usually indicate when someone first entered the hobby.

Games in Collection

What games does the user currently have in their collection? The following table can be used to examine games the user owns, along with some helpful information for selecting the right game for a game night!

Use the filters above the table to sort/filter based on information about the game, such as year published, recommended player counts, or playing time.

Show the code
collection |>
    filter(own == 1) |>
    prep_collection_datatable(
        games = games_raw
    ) |>
    filter(!is.na(image)) |>
    collection_datatable()

Modeling

I’ll now the examine predictive models trained on the user’s collection.

For an individual user, I train a predictive model on their collection in order to predict whether a user owns a game. The outcome, in this case, is binary: does the user have a game listed in their collection or not? This is the setting for training a classification model, where the model aims to learn the probability that a user will add a game to their collection based on its observable features.

How does a model learn what a user is likely to own? The training process is a matter of examining historical games and finding patterns that exist between game features (designers, mechanics, playing time, etc) and games in the user’s collection.

I make use of many potential features for games, the vast majority of which are dummies indicating the presence or absence of the presence or absence of things such as a publisher/artist/designer. The “standard” BGG features for every game contain information that is typically listed on the box its playing time, player counts, or its recommended minimum age.

Note

I train models to predict whether a user owns a game based only on information that could be observed about the game at its release: playing time, player count, mechanics, categories, genres, and selected designers, artists, and publishers. I do not make use of BGG community information, such as its average rating, weight, or number of user ratings. This is to ensure the model can predict newly released games without relying on information from the BGG community.

What Predicts A Collection?

A predictive model gives us more than just predictions. We can also ask, what did the model learn from the data? What predicts the outcome? In the case of predicting a boardgame collection, what did the model find to be predictive of games a user has in their collection?

To answer this, I examine the coefficients from a model logistic regression with ridge regularization (which I will refer to as a penalized logistic regression).

Positive values indicate that a feature increases a user’s probability of owning/rating a game, while negative values indicate a feature decreases the probability. To be precise, the coefficients indicate the effect of a particular feature on the log-odds of a user owning a game.

The following visualization shows the path of each feature as it enters the model, with highly influential features tending to enter the model early with large positive or negative effects. The dotted line indicates the level of regularization that was selected during tuning.

Show the code
#|
model_glmnet |>
    pluck("wflow", 1) |>
    trace_plot.glmnet(max.overlaps = 30) +
    facet_wrap(~ params$username)

Partial Effects

What are the effects of individual features?

Use the buttons below to examine the effects different types of predictors had in predicting the user’s collection.

Assessment

How well did the model do in predicting the user’s collection?

This section contains a variety of visualizations and metrics for assessing the performance of the model(s). If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.

The following displays the model’s performance in resampling on a training set, a validation set, and a holdout set of upcoming games.

Show the code
metrics |>
    mutate_if(is.numeric, round, 3) |>
    pivot_wider(
        names_from = c(".metric"),
        values_from = c(".estimate")
    ) |>
    gt::gt() |>
    gt::sub_missing() |>
    gt_options()
username wflow_id type .estimator mn_log_loss roc_auc pr_auc
rahdo glmnet resamples binary 0.033 0.942 0.153
rahdo glmnet test binary 0.021 0.884 0.043
rahdo glmnet valid binary 0.026 0.944 0.134

An easy way to visually examine the performance of classification model is to view a separation plot.

I plot the predicted probabilities from the model for every game (during resampling) from lowest to highest. I then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (left side of the chart).

Show the code
preds |>
    filter(type %in% c("resamples", "valid")) |>
    plot_separation(outcome = params$outcome)

I can more formally assess how well each model did in resampling by looking at the area under the ROC curve (roc_auc). A perfect model would receive a score of 1, while a model that cannot predict the outcome will default to a score of 0.5. The extent to which something is a good score depends on the setting, but generally anything in the .8 to .9 range is very good while the .7 to .8 range is perfectly acceptable.

Show the code
preds |>
    nest(data = -c(username, wflow_id, type)) |>
    mutate(
        roc_curve = map(
            data,
            safely(~ .x |> safe_roc_curve(truth = params$outcome))
        )
    ) |>
    mutate(result = map(roc_curve, ~ .x |> pluck("result"))) |>
    select(username, wflow_id, type, result) |>
    unnest(result) |>
    plot_roc_curve()

Top Games in Training

What were the model’s top games in the training set?

Show the code
preds |>
    filter(type == "resamples") |>
    prep_predictions_datatable(
        games = games,
        outcome = params$outcome
    ) |>
    predictions_datatable(
        outcome = params$outcome,
        remove_description = T,
        remove_image = T,
        pagelength = 15
    )

Top Games in Validation

What were the model’s top games in the validation set?

Show the code
preds |>
    filter(type %in% c("valid")) |>
    prep_predictions_datatable(
        games = games,
        outcome = params$outcome
    ) |>
    predictions_datatable(
        outcome = params$outcome,
        remove_description = T,
        remove_image = T,
        pagelength = 15
    )

Top Games by Year

Displaying the model’s top games for individual years in recent years.

Show the code
preds |>
    filter(type %in% c("resamples", "valid")) |>
    top_n_preds(
        games = games,
        outcome = params$outcome,
        top_n = 15,
        n_years = 15
    ) |>
    gt_top_n(collection = collection |> prep_collection())
Rank 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
1 Shipyard 7 Wonders The Castles of Burgundy The Manhattan Project Lewis & Clark: The Expedition Port Royal Oh My Goods! Honshū The Castles of Burgundy: The Dice Game Newton The Magnificent Lost Ruins of Arnak Ark Nova Planet Unknown La Granja: Deluxe Master Set
2 Macao Innovation Puerto Rico Suburbia Amerigo La Granja 7 Wonders Duel Agricola (Revised Edition) NUT Underwater Cities The Castles of Burgundy Puerto Rico Boonlake Beer & Bread Expeditions
3 At the Gates of Loyang Luna Ora et Labora Tzolk'in: The Mayan Calendar Glass Road Imperial Settlers Viticulture Essential Edition Terraforming Mars Circle the Wagons Architects of the West Kingdom Wingspan Tekhenu: Obelisk of the Sun Lorenzo il Magnifico: Big Box Marrakesh Welcome To...: Collector's Edition
4 Dungeon Lords Merkator Mage Knight Board Game Robinson Crusoe: Adventures on the Cursed Island Bruges AquaSphere Tiny Epic Galaxies The Castles of Burgundy: The Card Game Spirit Island Everdell Maracaibo Gloomhaven: Jaws of the Lion Great Western Trail: Second Edition The Lord of the Rings: The Card Game – Revised Core Set Empire's End
5 Endeavor Troyes Eminent Domain Galaxy Trucker: Anniversary Edition Händler der Karibik Fields of Arle Mombasa Lorenzo il Magnifico Santa Maria Carpe Diem Era: Medieval Age Faiyum Cascadia Tiletum Shipyard (Second Edition)
6 Finca Dominion: Big Box Strasbourg Ginkgopolis Dominion: Special Edition Patchwork Between Two Cities Scythe Tybor the Builder Concordia Venus Tapestry Monster Expedition Agropolis Woodcraft: Roll and Write Art Society
7 Bunny Bunny Moose Moose Navegador Tournay Terra Mystica Suburbia + Inc. Orléans Through the Ages: A New Story of Civilization Broom Service: The Card Game Unfair New Frontiers Marco Polo II: In the Service of the Khan Bonfire Corrosion Woodcraft Night Flowers
8 Dominion: Intrigue Spiel mit Lukas: Dribbel-Fieber The New Era Keyflower Nations Viticulture: Complete Collector's Edition Pandemic Legacy: Season 1 Last Will Charterstone Sprawlopolis PARKS The Castles of Tuscany Llamaland Frostpunk: The Board Game Ticket to Ride Legacy: Legends of the West
9 The Pillars of the Earth: Builders Duel Glen More City Tycoon Agricola: All Creatures Big and Small Bora Bora Roll for the Galaxy The Voyages of Marco Polo Cottage Garden Anachrony Welcome To... Paladins of the West Kingdom Merlin: Big Box Terraforming Mars: Ares Expedition Carnegie Nucleum
10 Vasco da Gama 20th Century The City Il Vecchio Patchistory Isle of Trains Minerva The Oracle of Delphi Sagrada The Estates Fields of Arle: Big Box New York Zoo Rolling Realms Agricola 15 Astro Knights
11 Arena: Roma II Vinhos Walnut Grove Würfel Bohnanza Viticulture Fresco: Big Box Steampunk Rally Codenames: Pictures Gaia Project Railroad Ink: Blazing Red Edition Ragusa Furnace Galaxy Trucker (Second Edition) Endless Winter: Paleoamericans Cuzco
12 Alea Iacta Est The Speicherstadt PAX Wallenstein (Second Edition) Euphoria: Build a Better Dystopia Nations: The Dice Game Mombasa (Limited Edition) A Feast for Odin Altiplano Space Park Ultra Tiny Epic Galaxies Welcome to New Las Vegas Newton & Great Discoveries Hamburg The White Castle
13 Automobile It Happens.. Trajan Uchronia Caverna: The Cave Farmers La Isla Het Koninkrijk Dominion Kodama: The Tree Spirits In the Year of the Dragon: 10th Anniversary Between Two Castles of Mad King Ludwig Barrage Calico Corduba 27 a.C. Amsterdam The Castles of Burgundy: Special Edition
14 Homesteaders The Mines of Zavandor Pergamemnon Seasons Concordia Alchemists Broom Service Codenames: Deep Undercover Notre Dame: 10th Anniversary Orchard: 9 card solitaire game Clank! Legacy: Acquisitions Incorporated Pandemic Legacy: Season 0 Tumble Town Wayfarers of the South Tigris Aldebaran Duel
15 Alhambra: Big Box Funfair Friday Myrmes Cinque Terre Five Tribes: The Djinns of Naqala The Prodigals Club Jórvík Near and Far Hokkaido Tiny Towns Dune: Imperium Imperium: Classics New York City Faraway

Predictions

New and Upcoming Games

What were the model’s top predictions for new and upcoming board game releases?

Show the code
new_preds |>
    filter(type == "upcoming") |>
    # imposing a minimum threshold to filter out games with no info
    filter(usersrated >= 1) |>
    # removing this goddamn boxing game that has every mechanic listed
    filter(game_id != 420629) |>
    prep_predictions_datatable(
        games = games_new,
        outcome = params$outcome
    ) |>
    predictions_datatable(outcome = params$outcome)

Older Games

What were the model’s top predictions for older games?