Sports prediction is a fascinating process that is closely linked to the massive global industries of sports betting and fantasy sports leagues. Alongside the actual odds of a winning outcome, predictions and picks for sports like football, hockey, basketball and horse racing form the basis of the global sports betting industry. Bettors like to follow the predictions made by industry analysts, particularly when it comes to events contested between teams or opponents with a similar standing in their respective sports.
Taking the hugely-popular NFL for example, last December a crucial match was played out between the Eagles and the Giants, with the winner gaining a much needed foothold in the league standings. With each team receiving similar odds to win, the insights offered by experts like Oddschecker’s Ben Rolfe were invaluable to punters when it came to deciding who to place a bet on.
Naturally, as the sporting industries themselves have become more digitized, with matches and games streamed online via content sharing platforms and sports betting going mobile, for example, these days sports prediction involves a combination of technological advancement and human insight and analysis.
Let’s take a look at some of the main tech innovations that underpin this process.
Sports prediction algorithms
An algorithm is a mathematical formula that is used to solve complex problems or answer complex questions by organizing and evaluating data. Surprisingly, when it comes to sports prediction the algorithms used are actually still in their relatively early stages, but that doesn’t make them any less effective.
For the most part, analysts and betting specialists within the industry use algorithms to predict the outcomes of sporting events through the analysis of relevant data, such as player and team stats, terrain and environment. In order to be effective, sports prediction algorithms ideally need different circumstances to analyze, but the data itself should still be fairly straightforward.
As the digital information superhighway has developed over the past two decades, key information about sports teams, players and events is much more widely available. This in turn increases the efficacy of a prediction algorithm, since the most accurate ones combine both technology and up-to-date information. Looking at an algorithm for predicting NBA games, for example, the data it would need to process should be focused on team and player centered metrics such as win/loss percentages, points scored per game, injury count and so on.
Machine Learning/Artificial Intelligence (AI)
Machine learning takes the standard algorithm process further, by applying Artificial Intelligence to them. This creates a type of super-powered algorithm, one that is able to learn from previous events and improve itself over time.
Examples of machine learning can be seen across multiple industries and day to day situations, from the medical industry to financial services, online chatbots, the speech recognition system on your smartphone, and social media platforms. This form of AI is particularly useful to the sports prediction and sports betting industry, because making predictions is one of its primary applications. It can make independent decisions, predict outcomes, access and process the data it needs, and operate without any direct human involvement.
The most effective machine learning model for making sports predictions is a neural network. The algorithm in this network, as the name suggests, are organized in a similar way to neurons in the living brain, with each one receiving and processing inputs then firing off outputs that can be used by the next algorithms in the network.
Some prediction platforms are able to use neural networks to provide quantitative analysis into sporting events, turning seemingly unstructured data into accurate predictions and statistics.
As mentioned earlier, the algorithm, machine learning and artificial intelligence are still in their early stages, but as time progresses are sure to become much more advanced and adept at their functions. Despite this, however, it’s unlikely that any AI system alone will be able to be as effective at sports prediction as the powerful combination of advanced technology and human analysis.