Understanding Gaming Experience: Case Study (Griftlands)

Here I have collated the case study work I performed on Griftlands, using formal analysis, Calleja’s Player Involvement Model, and Costikyan’s uncertainty categories.

Hand size5 (starting size; cards can increase hand size in the following turn)
Deck size10 (starting size; can grow or shrink as players add or remove cards)
Deck update methodNew cards are awarded through successful encounters, as is the ability to upgrade valuable cards once or remove unwanted cards from your deck; cards gain ‘experience’ through being played, and can be upgraded this way once
Basic card interactionUse action points to play cards, with the aim of reducing your opponent’s Resolve to 0 while keeping your Resolve above 0. Resolve is stored in the ‘core argument’, representing your narrative intention in the encounter.Cards can cause two different types of damage (Hostility / Diplomacy), each of which can be multiplied by playing additional ‘arguments’, which each have their own resolveManipulation cards can add ‘composure’ (defence) to your core and additional ‘arguments’
Special card interactionsArguably too many to list, but here are some:Draw: Add a card from your draw pile to your hand.Replenish: When drawn, this card draws another card immediately.Improvise: Generate a set of random cards for the player to choose 1 of to add to their hand.Discard: Discarding a card adds it to your discard pile and allows it to be shuffled and redrawn once your deck runs out of cards.Expend: When played this card is removed from your deck until the end of battle.Destroy: When played this card is permanently removed from your deck.Incept: Create an argument/effect on your opponent’s field.Evoke: Play this card automatically once the condition is met from your hand (or deck if drawn mid turn).
Turn cycles/roundN/A
Rounds/encounterTypically 5-15 turn cycles / encounter
End of turn actionsUnused cards are discarded; player draws 5 new cards
End of round actionsNot an end of round action, but when you run out of cards to draw, your deck gets refreshed with your discard pile
Round win conditionN/A
Round loss conditionN/A
Encounter win conditionReduce opponent’s Resolve to 0
Encounter loss conditionConcede, or have your Resolve reduced to 0
InformationGenerally perfect (opponents telegraph the arguments they are targeting, as well as the damage their arguments will do – this is called ‘intent’); some opponent actions can obscure their intents
Average encounter length5 minutes
Player/partner narrative relationshipConfrontational
Narrative objectiveEither avoiding a threatened physical confrontation or convincing your opponent to do something they don’t want to do – eject someone from a bar, sell something for a bargain price, or aid the player in an upcoming confrontation
Narrative super-objectiveCampaign-dependent (there are 3 campaigns, each with a different protagonist); ‘revenge yourself on your enslaver’ is the initial campaign character’s super-objective
Dialogue triggersProcedural barks after every 1-2 cards; dialogue ‘scene’ after victory, concession or loss

Player Involvement Model

  • Spatial – encounters are presented in the third person, with an animated player character ‘in conversation’ with your opponent; observing your own character reacting increases narrative involvement, but perhaps lessens the sense of incorporation. A slight sense of exploration is achieved at the game-wide level, which increases narrative involvement.
  • Kinaesthetic – controls are compelling and accessible, with fewer than 3 clicks on average required to affect most actions; feedback is stylish, immediate and involving.
  • Ludic – most elements are directed towards ludic involvement. Actions have a clear impact on the game state, with numerical consequences previewed before action is taken. Progress towards the ludic/narrative goal, represented by the integer state of player and opponent resolve, is clear, if quite ‘gamey.’
  • Shared – negotiation encounters are locked to quest-specific NPCs, so the sense of social interaction with agents in the world feels limited; negotiations are competitive-only affairs.
  • Narrative – narrative progression bookends each encounter, with choice-less dialogue ‘scenes’ playing out depending on a victory or a loss; though player and opponent are expressively designed and animated, the integer representation of ‘resolve’ as the only indicator of in-encounter narrative progression leaves little room for narrative nuance within negotiations. Characters recur, and have ‘relationship statuses’ towards the player.
  • Affective – despite the explicitly emotive naming conventions of cards (describing various conversational ‘moves’), encounters mostly involve the player at the affective levels of tension and suspense (will I win or lose?).

Uncertainties

  • Performative uncertainty – fairly low; misclicking is possible but unlikely, actions do what they are expected to do 99% of the time.
  • Player uncertainty – medium; new cards with unique mechanics constantly introduced, opponents have access to unique, often unpredictable ‘arguments’ and abilities; resource-based gameplay makes indecision more likely. Failure is a significant barrier event, rendering the player unable to negotiate until they have refilled their ‘resolve’.
  • Solver’s uncertainty – low in-encounter (numbers go up, numbers go down); high when considering deck-building possibilities (winning, buying and removing cards, no upper deck limit).
  • Randomness – highly dependent on deck size, but the length of matches means previously played cards are reshuffled into a new deck, increasing randomness exponentially over time.
  • Analytic complexity – relatively high; players need to balance tactical considerations like action management, offense, defense, optional floating resources (‘influence’) and turn-sensitive opponent actions (abilities that increase in power over time, or trigger after X turns); the number of game-available cards and opponent abilities is almost ungrokkably high, such that a wiki or guide is handy for optimal deck-building.
  • Hidden information – low; players usually have perfect information about an opponent’s ‘intent’ for the next turn.
  • Narrative anticipation – often lost mid-encounter; dialogue is only meaningfully progressed post-encounter, in predetermined dialogue ‘scenes’; in-encounter procedural barks quickly become repetitive.