username | status | games |
---|---|---|
phenrickson | ever_owned | 154 |
phenrickson | own | 145 |
phenrickson | rated | 100 |
Predicting Board Game Collections
phenrickson’s Collection
About
This report details the results of training and evaluating a classification model for predicting games for a user’s boardgame collection.
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?
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.
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$username)
Partial Effects
What are the effects of individual features?
Use the buttons below to examine the effects different types of predictors had in predicting the user’s collection.
Assessment
How well did the model do in predicting the user’s collection?
This section contains a variety of visualizations and metrics for assessing the performance of the model(s). If you’re not particularly interested in predictive modeling, skip down further to the predictions from the model.
The following displays the model’s performance in resampling on a training set, a validation set, and a holdout set of upcoming games.
Show the code
|>
metrics mutate_if(is.numeric, round, 3) |>
pivot_wider(
names_from = c(".metric"),
values_from = c(".estimate")
|>
) ::gt() |>
gt::sub_missing() |>
gtgt_options()
username | wflow_id | type | .estimator | mn_log_loss | roc_auc | pr_auc |
---|---|---|---|---|---|---|
phenrickson | glmnet | resamples | binary | 0.019 | 0.961 | 0.210 |
phenrickson | glmnet | test | binary | 0.007 | 0.956 | 0.011 |
phenrickson | glmnet | valid | binary | 0.009 | 0.963 | 0.140 |
An easy way to visually examine the performance of classification model is to view a separation plot.
I plot the predicted probabilities from the model for every game (during resampling) from lowest to highest. I then overlay a blue line for any game that the user does own. A good classifier is one that is able to separate the blue (games owned by the user) from the white (games not owned by the user), with most of the blue occurring at the highest probabilities (left side of the chart).
Show the code
|>
preds filter(type %in% c("resamples", "valid")) |>
plot_separation(outcome = params$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?