Predicting Board Game Collections

phenrickson’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
phenrickson ever_owned 154
phenrickson own 145
phenrickson rated 100

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
phenrickson -3500-2021 train 26240 122
phenrickson 2022-2023 valid 10291 17
phenrickson 2024-2028 test 8587 6

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
phenrickson glmnet resamples binary 0.019 0.961 0.210
phenrickson glmnet test binary 0.007 0.956 0.011
phenrickson glmnet valid binary 0.009 0.963 0.140

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 Hansa Teutonica Mousquetaires du Roy Mansions of Madness Archipelago Lewis & Clark: The Expedition Orléans The Gallerist Star Wars: Rebellion Stop Thief! Brass: Birmingham Maracaibo Unmatched: Little Red Riding Hood vs. Beowulf Boonlake Frosthaven TerraFyte
2 Middle-Earth Quest Troyes Gears of War: The Board Game Keyflower Rococo Alchemists Blood Rage Sherlock Holmes Consulting Detective: Jack the Ripper & West End Adventures Azul Cosmic Encounter: 42nd Anniversary Edition Era: Medieval Age Unmatched: Cobble & Fog Unmatched: Battle of Legends, Volume Two Endless Winter: Paleoamericans Unmatched: Teen Spirit
3 Age of Conan: The Strategy Board Game Earth Reborn Rune Age Descent: Journeys in the Dark (Second Edition) Glass Road Nyakuza 7 Wonders Duel Agricola (Revised Edition) Gaia Project Century: Eastern Wonders Clank! Legacy: Acquisitions Incorporated Gloomhaven: Jaws of the Lion Imperial Steam Unmatched: Redemption Row Unmatched: For King and Country
4 The Adventurers: The Temple of Chac 7 Wonders Puerto Rico Wiz-War (Eighth Edition) Spyrium Pandemic: The Cure Food Chain Magnate Hit Z Road Pandemic Legacy: Season 2 Azul: Stained Glass of Sintra Dune Pandemic Legacy: Season 0 Railroad Ink Challenge: Shining Yellow Edition Unmatched: Hell's Kitchen La Granja: Deluxe Master Set
5 Greed Incorporated Glen More Tournay Terra Mystica Eldritch Horror Akrotiri Watson & Holmes When I Dream Century: Golem Edition Railroad Ink: Deep Blue Edition Blitzkrieg!: World War Two in 20 Minutes On Mars Ark Nova Planet Unknown Undaunted: Battle of Britain
6 Shipyard Dominant Species The Castles of Burgundy Agricola: All Creatures Big and Small Caverna: The Cave Farmers Port Royal Mombasa Junk Art Gloomhaven Newton Century: A New World New York Zoo Railroad Ink Challenge: Lush Green Edition Undaunted: Stalingrad The Witcher: Old World
7 American Rails Runewars A Game of Thrones: The Board Game (Second Edition) Shadowrift City of the Living AquaSphere Plums Scythe Sherlock Holmes Consulting Detective: Vanishing from Hyde Park Betrayal Legacy The Castles of Burgundy Century: Golem Edition – An Endless World Arkham Horror: The Card Game (Revised Edition) Foundations of Rome Arkeis
8 Small World Merkator Letters from Whitechapel Targi Gearworld: The Borderlands La Granja Oh My Goods! Terraforming Mars Century: Spice Road Railroad Ink: Blazing Red Edition Unmatched: Battle of Legends, Volume One My City Galaxy Trucker (Second Edition) Foundations of Rome (Emperor Edition) My Island
9 Cyclades The Mines of Zavandor Mage Knight Board Game Tzolk'in: The Mayan Calendar Concordia Arkwright Arboretum Reign of Cthulhu Spirit Island Architects of the West Kingdom Unmatched: Robin Hood vs. Bigfoot Sherlock Holmes Consulting Detective: An Irregular Meeting Cartographers Heroes Nemesis: Lockdown Deliverance
10 Jaipur Merchants & Marauders The Lord of the Rings: The Card Game Il Vecchio Eight-Minute Empire: Legends Nations: The Dice Game Pandemic Legacy: Season 1 Mansions of Madness: Second Edition Sherlock Holmes Consulting Detective: Carlton House & Queen's Park Root Silver & Gold Merv: The Heart of the Silk Road Corrosion アンドーンテッド:ノルマンディー・プラス (Undaunted: Normandy Plus) Welcome To...: Collector's Edition
11 Kuhhandel Master Innovation Eminent Domain Suburbia Nations Three Kingdoms Redux Elysium Iberia Twilight Imperium: Fourth Edition Rising Sun The King's Dilemma Hallertau Welcome to the Moon ISS Vanguard Fit to Print
12 Ubongo 3D Firenze Singapore Kemet Cube Quest Five Tribes: The Djinns of Naqala Steampunk Rally Perdition's Mouth: Abyssal Rift Iberian Gauge Coimbra Black Angel Sherlock Holmes Consulting Detective: The Baker Street Irregulars Kemet: Blood and Sand – Kickstarter Edition The Great Split Unmatched: Brains and Brawn
13 Endeavor Wars of the Roses: Lancaster vs. York A Few Acres of Snow Butterfly Garden Blueprints Artifacts, Inc. The King Is Dead Great Western Trail My Little Scythe Forbidden Sky Unmatched Game System Unmatched: Jurassic Park – InGen vs Raptors Bloodborne: The Board Game Carnegie Arkendom Conquista Starter Set
14 At the Gates of Loyang SNCF: France & Germany Eclipse: New Dawn for the Galaxy The Great Zimbabwe Forbidden Desert Roll for the Galaxy Mombasa (Limited Edition) Arkham Horror: The Card Game Bunny Kingdom Belfort: Edición Limitada Star Wars: Outer Rim Unmatched: Buffy the Vampire Slayer Great Western Trail: Second Edition Unmatched: Houdini vs. The Genie Dune: Imperium – Uprising
15 FITS Labyrinth: The War on Terror, 2001 – ? Risk Legacy Clash of Cultures Suburbia + Inc. Fields of Arle Codenames Sakura Arms Sagrada Agricola: All Creatures Big and Small – The Big Box Clinic: Deluxe Edition Rococo: Deluxe Edition Cubitos Return to Dark Tower Unmatched Adventures: Tales to Amaze

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?