Bingo

At the long term care facility at which I used to volunteer, bingo is played every other week or so. In the room in which it is held, there are six tables (numbered 1-6 in the sketch below), each holding about four players. There are also some individual seats for extra players. Each player receives two bingo cards, and bingo is usually played according to normal rules, with the exception that getting four corners on one of the bingo cards counts as a bingo as well.

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Among other things, I was often responsible for distributing bingo cards to the players. I noticed that the two bingo cards I gave to a player typically carried a very similar series of numbers. I began to wonder if I could increase a player’s chances of winning if I gave them two cards with as minimal overlap as possible (and perhaps by doing so, decrease the number of balls the caller has to draw in order to reach bingo). I figured wins would occur more quickly with a diverse pair of cards, for the same reason that you gain no advantage in the lotto when having two identical sets of numbers.

Running the numbers
I made a simulation using Java, which I checked by comparing the numbers it generated with the probabilities calculated on this site. After verifying, I set up a game with thirty players, each having two randomly generated cards. I then ran 10,000 games to determine the most likely ways to reach bingo. The 10,000 games resulted in 12,051 bingos (more than one bingo can occur per game if two or more players reach bingo at the same time). The data is as follows:
running the numbers

Note the following:

  • 59% of bingos used the free space. This is especially notable since there are more ways to get a bingo without a free space (9) than there are with a free space (4). If we exclude from the analysis the numbers from four corners bingo, the percentage of bingos that did not use the free space falls from 41% to 32.5%.
  • Approximately 25% of games resulted in multiple bingos.

Is a lack of card overlap advantageous?
Next I made sure one of the thirty players had no overlap whatsoever between his cards (call him player X). All the other members had randomly generated cards, which almost always contained some overlap, usually 4 or 5 numbers overlapping between cards on average. I ran one million games. The percentage of winning bingos belonging to player X was, on average, 3.37%. To be more specific about the method, I ran 100,000 games at a time, with player X having the same pair of cards for all 100,000 games, and all other players getting randomly generated cards each match. I ran this ten times for a total of 1,000,000 games. The upshot of all this is that there is no practical benefit for a player to have no overlap between his cards, as any given player has a 1/30 = 3.33% chance of winning a game of chance involving 30 players.

Does card overlap increase time needed to reach bingo?
The above results suggest that card overlap has little effect on time to reach bingo. To confirm, I ran 100,000 games where all players had a purely randomly generated set of cards and another 100,000 where each player had zero overlap between his cards (though this doesn’t rule out the inevitable overlap among different players’ cards). The results showed no difference in number of balls drawn to reach bingo (approximately 18.0 turns in either case).

Conclusion
The answer to all my initial questions was “no.” Practically speaking, overlap on the cards has no effect on the outcome of the game. One reason for this is that, unlike the example of the lotto numbers, the location of the number matters. Having the same number on both cards is not always a mere repetition because the ‘value’ of a bingo number depends on the numbers around it and, also, an overlapped number may fall on a different spot in the two cards.

I think the simulation is solid, given that I verified it independently. The only possible limitation I can think of is that, in the real life situation, there are only a limited set of cards, whereas the simulation randomly generates each pair – but I do not see how that would affect my results. Another possible issue is that player X’s cards were not randomized every game, but, again, I do not see how this would be a big issue, since the balls were randomly drawn each game and all the other players had cards randomly generated each game. It does raise an interesting question though: when we want a random simulation, should we make all parts random, or is it enough to have just one part random? I can share the source code for the simulation with anyone who wants it.

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