The sports industry’s leading data scientists make up Stats Perform’s world-class AI team. Find out how Stats Perform continues revolutionising sports through technological advancements.
From the first broadcast-based player tracking solution to automated match previews and player bios, find out how we’re transforming fan engagement through AI.
Engaging, Personalised Fantasy Recapsa and predictive data are just two of the ways the betting industry is being reshaped by AI.
From enhancing business intelligence dashboards and internal performance reports to targeted content on an unprecedented scale, Automated Insights helps organisations make better decisions through the power of artificial intelligence and data-driven stories.
Through a self-service platform that enables complete narrative customisation, real-time content updates, and a powerful API for flexible publishing.
Our team members Panna Felson, Sujoy Ganguly, and Patrick Lucey’s presented our paper “Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders” at the 2018 European Conference on Computer Vision.
In our paper, we presented a technique using conditional variational auto-encoder which learns a model that “personalises” prediction to individual agent behaviour within a group representation. Given the volume of data available and its adversarial nature, we focused on the sport of basketball to show that our approach efficiently predicts context-specific agent motions. We found that our model generates results that are three times as accurate as previous state-of-the-art approaches (5.74 ft vs. 17.95 ft).
Each year Stats Perform’s AI team attends MIT’s Sloan Sports Conference to showcase our latest innovations in sports technology. Our 2017 entry from Panna Felson and Patrick Lucey titled ““BodyShots”: Analysing Shooting Styles in the NBA using 3D Body-Pose Information” developed a novel attribute-based representation of basketball player’s body pose during a three point shot.
Using Pearson’s Chi-squared test to quantify differences in attribute distributions for made and missed shots, we observed statistically significant differences in distributions of attributes describing the style of movement, e.g., walk, run, or hop, in various phases of the shot.
The paper includes a case study on Stephen Curry, where we observed that Curry moves much more than the average player in all phases of his shot and he takes a higher proportion of off-balance shots compared to the average player.
Late in the 2018 Champions League Final, a key substitution resulted in an 83rd-minute goal to give Real Madrid the victory over Liverpool. The match result prompted several world-record-breaking goalkeeper transfers across multiple European leagues.
The high-profile transfers prompted the question, “how can you compare the performance of different goalkeepers across teams and leagues, when they don’t have head-to-head matchups or common opponents?”
Our AI team utilised spatial and body-pose data to bring deep, objective insights to the conversation and determine the best fit for clubs.