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Sploosh Kaboom FAQ

Sploosh Kaboom Solution Write-Up

What is Sploosh Kaboom?

Sploosh Kaboom is a minigame in Legend of Zelda: The Wind Waker similar to the classic board game Battleship. In it, the player is presented with an empty board within which three ships of varying length are hidden. A player can fire at a given grid location and will be presented with a KABOOM if a ship is hit, or a SPLOOSH on a miss. The object of the game is to hit and elimate all ships within 24 shots. A ship is elimiated if all grid spaces it occupies are fired upon.

Why is Sploosh Kaboom Required for Wind Waker 100%?

The Wind Waker 100% rules dictate that all Treasure Charts and Heart Pieces must be collected. Sploosh Kaboom grants a Piece of Heart on Link's first win and a treasure chart on his second. If Link wins in under twenty shots, he recieves another tresure chart. Due to these items, a 100% Speedrun of The Wind Waker must complete the Sploosh Kaboom minigame twice and win at least once in under twenty shots.

Solving Sploosh Kaboom

Sploosh Kaboom is a largely luck based game. If we list all the possible ship layouts of the game, we arrive at 604,584 valid board configurations. In Wind Waker, the position and orientation of the ships is determined randomly for each play of the game. How, then, can we consistently complete this mini-game in a time senitive context like a speedrun?

Examining the Statistics

Sploosh Kaboom play can be optimized by examining the statistical odds of ship positioning on the board. By generating every possible valid ship configuration we can perform statistically optimal play by use of a simple alogorithm:

  1. Generate every possible board configuration that results in a valid ship placement. This results in 604,584 possible boards. Initialize a board working set with all these boards.
  2. Determine the probability each board space contains a ship by checking what fraction of the working board set has a ship in that space.
  3. Fire upon whatever empy space has the highest statistcal odds of containing a ship.
  4. Based on the current game state (hits, misses, unchecked spaces, eliminated ship count), determine what subset of the board working set is consistent with the game state. This subset is the new board working set.
  5. repeat from step (2) until the game is complete

This statiscs based algorithm can be further refined by optimizing opening patterns to quickly find ships and elimiate board possibilities. This algorithm makes the most-likely choice at each step of the game, which won't necessarily make the best moves overall. However, it is known from analysis of Battleship that this type of algorithm is close to optimal.

Examining the Code

In order to exactly understand Sploosh Kaboom it is necessary to examine the code used to generate boards. This code can be obtained from the Wind Waker game binary by Reverse Engineering techniques. We can determine the section of code dedicated to the generation of Sploosh Kaboom boards by examining memory during gameplay. This can be accomplished using the Dolphin Emulator and a memory monitoring tool called Dolphin Memory Engine. Once the relevant segment of the code is determined, it can be reverse engineered from machine code into a C approximation using PowerPC reverse engineering tools. Ghidra was used to approximate the C code for the Sploosh Kaboom board generation algorithm and the Random Number Generator of Wind Waker. Pseudocode of the findings are as follows:

Board generation Algorithm

board = [8×8 integer grid]      // board[i][j] means the value at col i, row j

function generate(): // generates a board layout // empty the board for y from 0 to 8: for x from 0 to 8: board[y][x] = 0

// place the ships
place(0,2)  // first #0 of length 2
place(1,3)  // then #1 of length 3
place(2,4)  // then #2 of length 4

function place(shipNumber, shipLength): // places a single ship on the board // generate ships until one fits // rng() gives a uniformly "random" decimal 0 ≤ x < 1, increments the rng state infinite loop: orientation = floor(rng() * 1000) % 2 // vert. or horiz., 0 or 1, equally-likely x = floor(rng() * 8) // top/left squid's col, 0–7, equally-likely y = floor(rng() * 8) // top/left squid's row if fits(x,y,shipLength,orientation): exit loop // we've now determined x, y and orientation

// place ship
if orientation == 0:
    for j from 0 to shipLength:             // for each squid
        board[x][y+j] = 102 + shipNumber    // put 102/103/104 in relevant tile
    for i from 0 to shipLength:
        board[x+i][y] = 102 + index

function fits(x, y, shipLength, orientation): // would the ship fit? if orientation == 0: for j from 0 to shipLength: // for each tile if x > 7 or y+j > 7: // is it out-of-bounds? return False if board[x][y+j] > 100: // does it already have a squid in it? return False return True // we've checked every tile by now else: for i from 0 to shipLength: if x+i > 7 or y > 7: return False if board[x+i][y] > 100: return False return True

The full reverse engineered code can be found here.

RNG Algorithm

Wind Waker makes use of the Wichmann-Hill PRNG, presented in pseudocode as follows:

double rng() {
    static int s1 = 100, s2 = 100, s3 = 100;

s1 = (171 * s1) % 30269;
s2 = (172 * s2) % 30307;
s3 = (170 * s3) % 30323;

return fmod(s1/30269.0 + s2/30307.0 + s3/30323.0, 1.0);


This generator makes use of three linear congruental generators that are then combined to produce a distribution between zero and one. This generator is initialized on console reboot to

s1 = s2 = s3 = 100
, a fixed initial seed. The values of
(s1, s2, s3)
at any given time determine what the next value of the random number generator will be. We can call this the "state" of the random number generator. Each iteration of the RNG will advance the seed values and generate a new random return value. The first few steps of this process can be seen below:

| s1 | s2 | s3 | return value | | ----: | ----: | ---: | ---: | | 100.0 | 100.0 | 100.0 | 0.6930906199656834 | | 17100.0 | 17200.0 | 17000.0 | 0.5253911237999249 | | 18276.0 | 18621.0 | 9315.0 | 0.1491021216452075 | | 7489.0 | 20577.0 | 6754.0 | 0.9526796411193339 | | 9321.0 | 23632.0 | 26229.0 | 0.8229855100670485 | | 19903.0 | 3566.0 | 1449.0 | 0.8003992983171554 | | ... | ... | ... | ... |

We can then use the board generation algorithm above to generate a mapping of RNG states to the board it would generate in the game.

Solving the Game

Due to the fixed initial seed on console reboot, we can determine every random number that the PRNG algoritm will generate for use by the game. Since we know the exact algorithm used to generate a Sploosh Kaboom board configuration, we can determine what board configuration a given starting RNG state will create. Due to the fixed initial seed of the RNG algorithm detailed above, we can "play forward" the RNG and generate out all future return values for the RNG algorithm. During normal gameplay, the RNG is advanced by a variety of different game elements at a rate of about 10 - 1000 steps per frame. This means that in the first hour of gameplay, the RNG state is advanced on the order of 200 million times. We can generate a board state for each of those future values from the moment of console boot up out to some arbitrarily large maximum. We then have a mapping of

RNG State -> generated board

for an arbitrarily large number of RNG states proceeding from the moment the console is turned on. We can then generate a reverse mapping of

board ship placement
Possible RNG States

for every board that can occur. If we create a map of a sufficiently large amount of RNG steps, we can cover all possible RNG states for when Sploosh Kaboom is reached in a run. If the runner then completes a single game of sploosh, we index into the reverse map with the board of that attempt.

reverse_map[board1] -> rng_state_set

This gives us a set of possible RNG states the game could have been in at the time of board generation. We know approximately how long a game takes and approximately how many times the RNG algorithm is called during that game, so we can step each possible RNG state by that amount of calls. Due to uncertainty in how quickly the game was played and exactly how many times the RNG function was stepped, we create a window around each possible RNG state index and add those states in the window to the possible RNG state set.

foreach state in rng_state_set {

We now take all our set of RNG states and create a set of possible boards using our forward map.

possible_boards = []

foreach state in rng_state_set { possible_boards.add(forward_map[state]) }

Those boards can then be used to play optimally with the statistical algorithm. After each subsequent game is completed, the set of possible RNG states can be further narrowed based on which indices within the RNG state map are consistent with both known boards. Once sufficiently narrowed, the set of RNG states becomes small enough we can predict the ship positionings very accurately.

Worked Example

Say a runner arrives at sploosh-kaboom after approximately 45 minutes of gameplay.

  1. The RNG state will have advanced from the fixed seed of

    (100, 100, 100)
    on the order of 100 Million times
  2. The runner plays sploosh kaboom and enters the ship locations observed at the end of the game into the program

  3. The program determines the given board to be board number 157238 of the ~600,000 possible.

  4. The board can be used to look up

    board -> rng state set
    in the map. We now have a set of RNG states we may have been at the moment the board generation took place. This set consists of RNG states like
    (1256, 25792, 319), (256, 1020, 1557)...
  5. For each RNG state in this set, we move forward in the RNG sequence by approximately the amount of RNG cycles used during a Sploosh game (on the order of 1000 steps). Expand this set in either direction along the RNG sequence from each memeber of the set, for instance if the set contains RNG state numbers

    (1123456, 9484594, ....)
    expand it the set to
    (...1123455, 1123456, 1123457..., ...9484593, 9484594, 9484595, ...)
    . This margin for error must be large enough to account for variation in the RNG step rate and play time of a sploosh game.
  6. Generate a set of possible boards from the expanded set of possible RNG states.

  7. Use this set of boards as the working set used in the statiscal method used above with greatly improved win odds.

  8. After a second game has been completed, enter the ship positions observed.

  9. We now revisit our set of RNG states from step (4). The second board will likely be present in the margin of error states of a very small subset of the RNG state set, if not in exactly one state. We can then extend from the smaller subset of RNG states as we did in step (5).

  10. For the third game we now have an extremely small number of possible boards, so the statistical method detailed above will be able to predict where the squids are with very high accuracy.


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Want to discuss this tool further in depth? Join the Linkus7 Discord, and chat in the #sploosh-kaboom channel.

Temporary credit page

This is incomplete and just a random listing of those that have contributed in the #sploosh-kaboom channel:

  • Peter Schmidt-Nielsen
  • Cryze
  • ginkgo
  • TrogWW
  • Langufo
  • csunday95
  • shoutplenty
  • the NSA for the beautiful piece of software called Ghidra
  • aldelaro for Dolphin Memory Engine
  • the inimitable Dolphin Devs
  • Linkus7 for complaining about sploosh enough to summon an army

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