When we were kids, my siblings and I each had our own favorite properties when playing monopoly. My brother always went for the hotel on Boardwalk. My favorite was St. Charles Place. It didn’t cost as much to develop, and I was convinced that people tended to land on St. Charles place a disproportionate amount of the time. I’d be delighted when I managed to purchase St. Charles, and every time someone landed on that square it would reinforce my conviction that St. Charles was the most profitable property in Monopoly.

Armed with a college-level understanding of statistics, I thought I would test my childhood hypothesis that the probability of landing on St. Charles is higher than for other properties. The movement aspect of Monopoly can be modeled as a *Markov process*, which just means that the probability of landing on a square depends only on what square you were on last term. Let be a row vector of length 40 giving the probability that a player will land on each square after rolls. All players start on GO!, so

### Dice Movement

Now define transition probabilities for moving from one square to another. Ignoring things like chance cards, Monopoly uses the sum of two dice to move, so the probability of moving from one square to another is a triangle distribution with a peak 7 squares forward. For instance, the probability of moving to each square if you were previously on GO! is

Here’s some matlab code to generate the full transition matrix. `D[i][j]` gives the probability of moving from square `i` to `j` after rolling the dice.

% Initialize transition matrix for dice movement D = zeros(40); D(1,3:13) = [1:6, 5:-1:1]/36; for i = 2:40, D(i,:) = circshift(D(i-1,:),[0 1]); end

We can calculate the probability of being on a particular square after turns by repeatedly multiplying current state vector by the transition matrix.

Applying this for a few steps, it appears that all squares rapidly become equally probable. But would they eventually all have probability ? This can be checked by examining the eigenvectors of the transition matrix. The eigenvector corresponding to gives the probabilities of landing on each square after an infinite number of rolls.

%% Find steady state [V, l] = eigs(A',1,'lm'); xss = V'/sum(V); %x at steady state, eg t->infinity

Running this shows that all the squares do indeed have probability 1/40 at . So if all movement were determined by dice roll, all spaces would be equally likely to be landed upon.

### Jail and Chance cards

Now lets see what happens when all the detail of Monopoly are included in the model. There are a couple other ways players get moved around:

- The dreaded
**Go to Jail**space **Community Chest.**Most of the 16 community chest cards deal with money, but one sends you to jail.**Chance.**10 of the 16 chance cards move the player around the board.

This type of movement can be modeled as a second transition matrix, which get applied after each dice roll. Most squares leave the player where they ended up. Landing on ‘Go to Jail’ has a 100% probability of sending you to the jail spot. Community chest has a 1/16 chance of sending you to jail, and a 15/16 chance of remaining on the same spot. The Chance squares are complicated, but we can work out the transition probabilities from Chance squares too.

% Probabilities for non-dice movement. ND = eye(40); % Go directly to jail ND(31,:) = 0; % 'Go to Jail' is square 31 ND(31,11) = 1; $ 'Jail' is square 11 % Community Chest cards: 16 total % Cards are assumed to be drawn uniformly at random with replacement. % 15 Unchanged chestSquares = [3, 34]; ND(chestSquares,:) = ND(chestSquares,:)*15/16; % 1 Go directly to Jail ND(chestSquares,11) = ND(chestSquares,11)+1/16; % Chance card calculations-download code for details % A turn consists of a dice roll and then some non-dice movement A = D*ND;

By including non-dice movement, some squares become much more likely than others. For instance, it is impossible to end a turn on ‘Go to Jail’. Squares such as Railroads are more likely, since players who land on Chance could be sent there.

Rank | Square | Probability |
---|---|---|

1 | Jail | 0.0570 |

2 | Illinois | 0.0317 |

3 | B&O Railroad | 0.0304 |

4 | New York | 0.0301 |

5 | Reading Railroad | 0.0301 |

6 | Water Works | 0.0294 |

7 | Communtity Chest | 0.0289 |

8 | Tennessee | 0.0286 |

9 | Free Parking | 0.0282 |

10 | Kentucky | 0.0278 |

11 | St. Charles | 0.0275 |

### Conclusion

It turns out that St. Charles Place was not a particularly good space, since the probability of landing there is only 0.0275, slightly above average. I would have been much smarter to try to buy Illinois, which gets landed on significantly more often than average.

So what’s the optimum strategy for playing monopoly? Looking at steady state probabilities of landing on each square gives a hint to this, but doesn’t capture the full complexity of the game. It doesn’t factor in the costs and revenues for each property, nor can it provide advice on trading, selling, or improving properties. Finally, looking at steady state probabilities can be deceiving since the game starts far from steady state. For instance, Vermont Ave has a lower than average chance of being landed on. However, it is almost 50% more likely to be landed on 5 turns after starting from GO! than it would have been if the players began already spread out. Sometimes extra revenue early in the game can translate to a big advantage later on.

Now, who wants to play monopoly?

### Code

Calculating the full transition matrix has a lot of cases, so I didn’t include the code here. If you’re curious, check out the full code from bitbucket. It was tested on both Matlab and the free alternative, Octave. The main script is called ‘MCMonopoly.m’.