The question of the US election is currently of particular interest. Different types of datasets are being used to predict who the winner will be this November, such as tweets on the subject, or voter demographic information. Tweets are available to anyone, making them an accessible option for data input. However, only a subset of the voting population uses Twitter, likely introducing bias into the data set. Voter demographic information might be more difficult to find, but it should theoretically be a representation of the entire voting population. This would ensure that the AI is making reliable predictions, assuming that the data is current.

 

K-core Analytics is continuously analysing hashtags used in relevant tweets and the sentiment behind those tweets to make up-to-date daily predictions about who will win the election come November. They are currently predicting a win for Joe Biden, but only by a marginal amount. This article details the use of publicly available voter demographic information to make predictions about the election. It goes into more detail about the process used, such as the identification of important factors in the prediction. The writer comes to the conclusion that voter demographic and education are the largest factors in predicting who they choose to vote for. Biden is predicted to be the winner in this case as well, but again by only a thin margin. The writer acknowledges the limitations of their approach, mainly that voters are unpredictable given the mismatch between the predictions made in the 2016 election and the results. 

 

In casinos, operators are often interested in how changes will affect customer behaviour. AI can be used to perform behavioural forecasting. If we only ever know how our customers are currently feeling about their gaming experience, by the time we react, their opinion will likely have changed. The ability to predict the future sentiment of customers will boost successes in marketing, generating offers, or making decisions about casino content.

 

In behavioural forecasting, metrics like affinity, loyalty, and value are measured from collected data to make a prediction about how these metrics will change over time and in response to marketing or casino changes. Additionally, sentiment analysis can be performed on data scraped from review hosting websites using natural language processing to broadly determine how customers are feeling about your casino and their time spent there. Customers might be segmented in different ways based on demographic data or past behaviour such as spending habits to determine a unique marketing strategy to increase loyalty or affinity. AI can identify trends in the popularity of different machines on the slot floor, allowing operators to make preemptive decisions on changes to casino slot floor content based on how customers will react in the future.

 

Predicting the outcome of a sports game and predicting the next day’s stock prices are very similar problems. They are both possibilities that rely on several internal and external factors. AI has seen a lot of success in predicting the stock market, and it is seeing the same success in the world of sports betting. AI can process a continuous stream of data quickly and in real time to get analytics faster. However, AI methods are no replacement for human intuition. They are simply a tool allowing for increased efficiency and reliability. Human intuition can work to inform the AI model by narrowing the scope in which the AI searches or by setting initial parameters based on industry knowledge gained from human experience. A sportsbook that utilizes AI-driven data analytics methods will have an edge over one that only employs humans and classical computing methods. 

 

Sports games are events that follow a very strict set of rules, unlike stocks that can react to the whim of public opinion. This likely makes sports betting an even better candidate for the application of AI-driven prediction techniques. Just as in the stock market, neural networks are popular tools for analyzing sports data. One advantage of neural networks as applied to the fields of sports or finance is that they do not experience human biases. They will only be subject to biases inherent to the data they are working with and not to biases tied to human emotions.

 

In order for AI to effectively predict the future, a large and diverse data set is a necessity. Data should capture as many possible sets of circumstances that have previously occurred so that they can be identified by the AI program. This means that high fidelity data collection is of the utmost importance. Fortunately, the casino and sports industries are some of the richest in terms of data, making these prime candidates for the implementation of artificial intelligence computing techniques.

 

Of course, incorrect solutions may arise in extraneous circumstances, just as when humans try to predict the future. The environments of casinos and sports are complex and always changing. The good news is that as time goes on and more data is collected, the AI can learn from those new circumstances. Something else to consider is that unquantifiable or uncategorizable factors do have an impact and AI cannot operate on these types of factors the way humans can. This is why AI is not a replacement for humans, but a tool we can use to eliminate our biases and to make more informed and analytical decisions.