Predicting Board Game Collections

ZeeGarcia’s Collection

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
ZeeGarcia	ever_owned	1891
ZeeGarcia	own	517
ZeeGarcia	rated	2449

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
ZeeGarcia	-3500-2020	train	24172	346
ZeeGarcia	2021-2022	valid	9804	93
ZeeGarcia	2023-2028	test	9021	78

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.

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.

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.

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.

Categories

Mechanics

Themes

Families

Publishers

Designers

Artists

Components

Mechanisms

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.

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
ZeeGarcia	glmnet	resamples	binary	0.057	0.889	0.164
ZeeGarcia	glmnet	test	binary	0.045	0.876	0.076
ZeeGarcia	glmnet	valid	binary	0.042	0.898	0.148

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).

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.

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?

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?

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.

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	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022
1	Call of Cthulhu: The Card Game	Mr. Jack in New York	Glen More	The Castles of Burgundy	Libertalia	Legacy: The Testament of Duke de Crecy	Imperial Settlers	7 Wonders Duel	Scythe	Circle the Wagons	Root	Cthulhu: Death May Die	Pandemic Legacy: Season 0	Arkham Horror: The Card Game (Revised Edition)	Libertalia: Winds of Galecrest
2	Roll Through the Ages: The Bronze Age	Dice Town	7 Wonders	Eminent Domain	Robinson Crusoe: Adventures on the Cursed Island	Lewis & Clark: The Expedition	Blue Moon Legends	Mission: Red Planet (Second Edition)	When I Dream	Pandemic Legacy: Season 2	Château Aventure	Wingspan	Dragomino	Night of the Living Dead: A Zombicide Game	Endless Winter: Paleoamericans
3	A Game of Thrones: The Card Game	Alea Iacta Est	Mr. Jack Pocket	Puerto Rico	Shadows over Camelot: The Card Game	Eight-Minute Empire: Legends	Madame Ching	Pandemic Legacy: Season 1	Conan	Gloomhaven	Micropolis	Ishtar: Gardens of Babylon	Gloomhaven: Jaws of the Lion	Agropolis	Long Shot: The Dice Game
4	Ghost Stories	Macao	Earth Reborn	Mundus Novus	Il Vecchio	Longhorn	Port Royal	Raptor	Arkham Horror: The Card Game	Stop Thief!	Rising Sun	Aftermath	Via Magica	Sobek: 2 Players	Frosthaven
5	Byzanz	Long Shot	51st State	Takenoko	Seasons	Bruges	Five Tribes: The Djinns of Naqala	Blood Rage	Hit Z Road	Pandemic: Rising Tide	Everdell	Deep Blue	Puerto Rico	Sleeping Gods	Sea Salt & Paper
6	Bushido: Der Weg des Kriegers	The Adventurers: The Temple of Chac	Merchants & Marauders	The Lord of the Rings: The Card Game	Eight-Minute Empire	Rise of Augustus	Pandemic: The Cure	Elysium	Lorenzo il Magnifico	My Little Scythe	Zombicide: Green Horde	Naga Raja	Hues and Cues	Flourish	1001 Islands
7	Witch of Salem	Chronicle	Grimoire	Friday	Okiya	Glass Road	Don't Mess with Cthulhu	Viticulture Essential Edition	Islebound	SOW	Fall of Rome	Marvel Champions: The Card Game	Viscounts of the West Kingdom	Zombicide: 2nd Edition	Knight Fall
8	Snow Tails	Endeavor	GOSU	Elder Sign	Ginkgopolis	Eldritch Horror	La Isla	Trambahn	Reign of Cthulhu	The Castles of Burgundy: The Dice Game	A Song of Ice & Fire: Tabletop Miniatures Game – Stark vs Lannister Starter Set	Herbaceous Sprouts	Detective: A Modern Crime Board Game – Season One	Run Run Run!	Hunch!
9	Pandemic	Arcana	DungeonQuest (Third Edition)	Ora et Labora	Antartik	Skull King	Desperados of Dice Town	Between Two Cities	Mansions of Madness: Second Edition	Charterstone	Finca	Conspiracy: Abyss Universe	Unmatched: Little Red Riding Hood vs. Beowulf	Ankh: Gods of Egypt	Amsterdam
10	Piece o' Cake	Small World	The Speicherstadt	Witty Pong	Descent: Journeys in the Dark (Second Edition)	SOS Titanic	Dragon Run	GEM	Dice Stars	Lovecraft Letter	Treasure Island	Draftosaurus	Endangered	Marvel United: X-Men	Tribes of the Wind
11	Senji	Shipyard	Mystery Express	PRRRT...	Keyflower	City of Iron	Legendary Encounters: An Alien Deck Building Game	Steampunk Rally	Kanagawa	First Martians: Adventures on the Red Planet	Space Park	Ice Team	Dune: Imperium	Cestou necestou	The Guild of Merchant Explorers
12	Stone Age	Cyclades	Tikal II: The Lost Temple	The Blue Lion	Mice and Mystics	Bora Bora	Artifacts, Inc.	Codenames	Pocket Madness	BOO	Sprawlopolis	Ohanami	Dwellings of Eldervale	Cascadia	Wildtails: A Pirate Legacy
13	Ticket to Ride: The Card Game	Martinique	Luna	Summoner Wars: Master Set	Neuroshima: Convoy	The Little Prince: Make Me a Planet	Abyss	Arboretum	A Game of Thrones: Hand of the King	Legendary Forests	Century: Eastern Wonders	Tapestry	Roland Wright: The Dice Game	Imperium: Classics	Marvel Zombies: Heroes' Resistance
14	Penguin Party	Vampires of the Night	Haggis	The City	Suburbia	Caverna: The Cave Farmers	Orongo	Mysterium	The Castles of Burgundy: The Card Game	LYNGK	Arkham Horror (Third Edition)	Queenz: To Bee or Not to Bee	Pendulum	Explorers	Wayfarers of the South Tigris
15	Red November	Dominion: Intrigue	Funfair	Dr. Shark	Escape: The Curse of the Temple	Cinque Terre	AquaSphere	The Little Prince: Rising to the Stars	Inis	Arkham Noir: Case #1 – The Witch Cult Murders	Decrypto	Fantastic Factories	Tidal Blades: Heroes of the Reef	Key Pai Sho	Gateway Island

Predictions

New and Upcoming Games

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

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?