Predicting Board Game Collections

aboardgamebarrage’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
aboardgamebarrage ever_owned 457
aboardgamebarrage own 236
aboardgamebarrage rated 330

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
aboardgamebarrage -3500-2021 train 26170 192
aboardgamebarrage 2022-2023 valid 10267 41
aboardgamebarrage 2024-2028 test 8590 3

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
aboardgamebarrage glmnet resamples binary 0.034 0.894 0.111
aboardgamebarrage glmnet test binary 0.010 0.951 0.003
aboardgamebarrage glmnet valid binary 0.024 0.825 0.021

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 Dominion: Intrigue Innovation A Fake Artist Goes to New York The Resistance: Avalon Impulse Maskmen Mottainai Scythe Azul Cosmic Encounter: 42nd Anniversary Edition Dune Project L Brian Boru: High King of Ireland SPYBAM TerraFyte
2 Telestrations Earth Reborn Omen: A Reign of War Archipelago A Study in Emerald Nyakuza 7 Wonders Duel Insider Dungeon of Mandom VIII Mr. Face Noctiluca Nidavellir Kemet: Blood and Sand – Kickstarter Edition Cat in the Box: Deluxe Edition La Granja: Deluxe Master Set
3 Steam Dominion: Big Box Discworld: Ankh-Morpork Terra Mystica Hemloch: Vault of Darkness Spyfall The Game + The Game on Fire Omen: Edge of the Aegean WOO Brass: Birmingham Chocolate Factory Mezo For Sale Autorama Kongkang: The Wild Party The Big Crunch
4 Chaos in the Old World Politico: The Fall of Caesar The Castles of Burgundy Rex: Final Days of an Empire Kobayakawa Chimera Elysium Turin Market Merchants of Muziris Rising Sun SCOUT Omen: Heir to the Dunes Import / Export: Definitive Edition Make the Difference Rafter Five
5 Hansa Teutonica Time's Up! Family Vanuatu Tooth & Nail: Factions Francis Drake Red7 The King Is Dead Junk Art One Night Ultimate Alien Pencil Nose! Omen: Fires in the East Insider Black Kemet: Blood and Sand White Elephant: A Gift Exchange The Same Game
6 Samurai: The Card Game 7 Wonders Puerto Rico Machi Koro Five Cucumbers La Isla Trans-Siberian Railroad Reign of Cthulhu Startups TOKYO METRO Obscurio The Red Cathedral The Diamond Swap Revive CoGNaC
7 Greed Incorporated London A Game of Thrones: The Board Game (Second Edition) Targi Dominion: Special Edition Tricks & Deserts TROLL Game of Thrones: The Iron Throne The Quest for El Dorado The Mind Fafnir Hello Neighbor: The Secret Neighbor Party Game Bad Company Hunch! Avalonia
8 Jaipur In a Grove City Tycoon The Great Zimbabwe Relic Kingdom Builder: Big Box GEM Lorenzo il Magnifico Troika VOID Nanty Narking Hues and Cues Evil Corp 1877: Stockholm Tramways The White Castle
9 Time's Up! Academy Bhazum Friday Fleet Habe fertig The Nile Ran Red Rights Akua Breaking Bad: The Board Game Just One Detective: City of Angels Merv: The Heart of the Silk Road Lorenzo il Magnifico: Big Box Order Overload: Cafe Disrupt
10 Finca Irondale Artus Uchronia The Valkyrie Incident Power Grid Deluxe: Europe/North America Keep Neolithic Calimala Newton We Need to Talk The Cost Biblios: Quill and Parchment A Game of Thrones: B'Twixt Spears and Bones
11 The Resistance Runewars Takenoko Android: Infiltration Room 25 Spike Het Koninkrijk Dominion Let Them Eat Cake Innovation Deluxe Moneybags One Night Ultimate Super Heroes The Game of Fuzzy Logic Mercado de Lisboa Gateway Island Yarr!: Stranded Scoundrels
12 American Rails De Vulgari Eloquentia Mundus Novus Kemet Age of Assassins Port Royal One Night Ultimate Werewolf: Daybreak Hit Z Road Import / Export Captain Edition Everdell TOKYO GAME SHOW 13 Monsters Moon Adventure Desamparados: Stalingrado Craft my Agenda
13 Kuhhandel Master Mystery Express Pictomania Tzolk'in: The Mayan Calendar Glass Road Imperial Settlers Hordes of Grimoor One Night Ultimate Vampire The Game: Face to Face Decrypto Miskatonic University: The Restricted Collection Sacred Rites Dirge: The Rust Wars GridL Zombie Sniper
14 Automobile The Hobbit Tournay The Manhattan Project Amerigo Pandemic: Contagion Codenames Codenames: Deep Undercover Import / Export The Binding of Isaac: Four Souls The North Long Live the King: A Game of Secrecy and Subterfuge Dune: Betrayal The Middle Ages Doodle Heist
15 Filipino Fruit Market High Frontier Singapore Serenissima (Second Edition) Lewis & Clark: The Expedition The Battle at Kemble's Cascade Watson & Holmes SYNOD Cartouche Dynasties Camel Up (Second Edition) Mega Empires: The West TOKYO TSUKIJI MARKET Moving Pictures Blood on the Clocktower Pizzachef

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?