How Big Data Is Changing Board and Card Games

Big data is gradually changing everything. From marketing to education, sports to gaming, nearly every industry is exploring how to tap on the new phenomenon. Described as the field that analyzes and methodically analyzes large subsets of information, big data has also made significant influences to board and card games. Here are some of them.


Deep Blue, a computer that defeated chess icon, Garry Kasparov, in 1997, is credited for starting the big data revolution. Developed to use large data volumes to make strategic chess moves, Deep Blue lost one, drew three but ultimately defeated Kasparov in the fifth game.

Out of shock, Kasparov accused Deep Blue of cheating and abandoned the six-game competition immediately. The chess master later came up with a conspiracy theory accusing an unnamed grandmaster of controlling the IBM machine.

In reality, Deep Blue relied on a data-based strategy and made emotionless moves at every turn of the game. It didn’t even use AI, the increasingly popular technology Elon Musk and Mark Zuckerberg have invested millions worth of research on.


If you love board games, chances are you grew up playing the monopoly board game. You probably play it to date. Maybe you’ve tried some variations of the game like Monopoly Megaways, one the best Megaways slots online. The board game has influenced many more video slots and free to play arcade games.

Thanks to big data, scientists are learning strategies to play monopoly without losing. Their suggestions seem like common sense. But they are recommendations based on analyzing large subsets of information related to the game.

For example, the researchers recommend staying away from jail as one of the best ways to win the game consistently. They also suggest you choose streets wisely. The Yellow, Orange, and Red streets, precisely, offer the best chances to maximize your returns for investment.

Online Poker

In theory, winning poker is straightforward. All you need is to produce the winning hand. In practice, winning a multiplayer Texas Hold’em game against professional players can be challenging. The reason is simple. Poker is an information-based game.

In this big data and AI-driven world, however, it could become possible to beat the world’s best poker players even as a novice. Earlier in June, Facebook, in collaboration with a pair of scientists, developed an algorithm that relies on data and artificial intelligence to win Texas Hold’em.

Named Pluribus, the AI was so successful that it accrued an average of $1000 per hour. What’s more, it defeated a group of experienced poker stars consistently after 10,000 hands. Following its success, Pluribus developers have vowed to use it as a benchmark on how to improve cybersecurity and AI.


Similar to chess and poker, scientists have algorithms that use data and AI to defeat GO experts. In one study, a scientist gathered data from 13,000 games and use an algorithm to analyze every move players had made. 

After compiling his findings, the scientists successfully created a strategy to play and win GO consistently. In another research financed by Google, an algorithm that combines both AI and data analysis can beat the world’s best GO players with zero human interventions.

When first launched, Google fed its algorithm, Alpha Go Zero, data from 100,000 GO games to analyze and come up with a winning strategy. Similar to Pluribus, Zero also improved its skills by playing numerous copies of GO.

After playing the game millions of times within 40 days, the bot won 90% of games and even defeated world champion Lee Se-dol.


Tracing its origins in Germany, Bohnanza has sprouted into a hugely successful board game played both online and offline. For the uninitiated, the game involves a card of game with eleven types of beans. It’s played by 3-5 people who compete to plant and harvest seeds converted into treasure coins. 

Although still not as popular as monopoly or chess, Bohnanza is one of the many board games benefiting from the advancements of big data. By analyzing vast volumes of data from people playing the game, scientists have been successful in developing systems to win the game consistently. 

Ideally, some of the findings from big data confirm what many expert Bohnanza players already know. Buying multiple fields early is better than doing it late. Trading cards is a better option than donating while it’s never too late to change what’s not working. But by using data to analyze what successful players do, it’s easier to become a champion of the game. 


Similar to most board game champions, Scrabble experts haven’t been spared by computers that use data and AI to win. First introduced in 1986, the computer bot Maven beat Scrabble champions more than 75% of the time. 

Similar to Google’s Alpha Zero, Maven would play copies of the game thousands of times before going against world-class players. If it lost, it would harness its skills continuously until it became virtually unbeatable. 

Surprisingly, Maven didn’t need supercomputers that used thousands of gigabytes. One of its most successful algorithms, DAWG, sat on 0.5MBs of data. Its slightly bigger algorithm consisted of 2.5MB of data. The size of this data is essential because players intending to cheat could use it to defeat their online opponents unnoticed.


Bingo is highly reliant on chance, but that doesn’t make it a boring board game. Quite the contrary, it’s one of the most popular games in the world. At its core, bingo tasks players to mark numbers called out by an announcer. And if they mark numbers forming a row, they call out ‘Bingo’ to confirm they have a winning card.

Despite its significant reliance on chance, scientists have been working on algorithms that can increase the odds of winning at bingo. So far, no algorithm has been successful at winning bingo consistently. The element of chance is far too massive, and there are too many numbers to combine digits that could increase the odds of winning bingo.

Still, it wouldn’t be surprising if supercomputers successfully analyzed millions of data and formed patterns in bingo and other chance-based games. 

Nakoa Davis