In 2010, we started our journey by only covering the NBA from our reference website NBAstuffer for a little group of researchers who were in need of sports data analysis. Throughout the years, our users have encouraged us to extend to other sports. This is why we have rebuilt our platform under the BigDataBall brand in 2014. We have added WNBA and NFL in 2017, MLB in 2018, and NHL in 2019. If you want to vote for a new sport, please do so.
The Business Model
BigDataBall aims to help individuals who want to analyze sports data without coding skills. Given limited availability and lack of collection time, it has been a struggle to access easy-to-manipulate, cleaned-up, and enriched datasets. We run a crowd-sourced model where the collected data from partners are being moderated through a couple of processes in which we keep investing to develop in an effort to have contributing partners collaborate in the most efficient way possible.
Data is then getting enhanced with calculated metrics like usage/rest-days/schedule/lineups/efficiency. External datasets such as betting odds/movements, daily fantasy sports salaries/positions are also included to diversify the data points for our team/player/play-by-play, and DFS users who extract value, insight, and competitive edge from the sports data. All datasets are being pushed into the validation where the partner charting errors are corrected to assure the accuracy of the output.
As we tried to give a brief regarding how we work, it wouldn’t be fair not to mention our support service to ensure your peace of mind. You will get a response within a 12-hour window.