The Problem
Despite recent advances in artificial intelligence and machine learning, sports industries are ever striving to improve on five core areas of sports data and engagement.
Supply Problems
01
Volume
Sports organizations want certainty. The certainty that their product will sell, their team will win, and their players will perform. They look to forecasting, modeling, and predictions to gain this certainty.
However, accurate predictions and worthwhile insights rely on lots of data, and Big Data is slow to find its way to sport and often limited by the number of historical games played.
02
Accuracy
Though artificial intelligence is helping, sports data is still commonly gathered manually by humans. As a result, data quality can be inaccurate, inconsistent, and even subject to human bias.
In sports, minute measurable increments can offer massive gains, but inaccurate data is a basis for incorrect predictions, selections, and strategies.
03
Validation
The specific data needed to improve performance is not always known and may not always be collected. There is no standard data set, no absolute defined measure of what exact data is needed to achieve a gain.
It is often left to individual coaches and analysts to find the data gold dust in a mass of numbers.
Efficiency Problems
04
Speed
Speed of accurate data collection and distribution is critical to success in any aspect of the sports ecosystem, from high performance through to fan engagement.
Again, though artificial intelligence is aiding this space, human-led collection systems are still predominant, not fast or accurate enough, and subject to human error.
05
Engagement
Players, coaches, and fans’ behaviors have changed. How they chose to access content and an ascending desire to explore the intricacies of any given sport have led to a need for a new way to interact with sports data.
TEN14 has developed a successful and uniquely compelling way of connecting the data story to the audience.
How can we help?
TEN14 are exploring these problems using artificial intelligence and machine learning to create large volumes of accurate and validated sports data.
