1. • Irfan Essa (Georgia Tech)
   

Title: Computational Video for Sports: Challenges for Large Scale Data Analysis

Abstract:

Video technologies have had a huge impact on sports. Most professional sports events are captured in video, are broadcast and are consumed by many. Video is also becoming a sensor used to measure and analyze athletic performances, overall games, and commonly used as an aide for judging calls made on the field. In this talk about, I will discuss some of the specific advances made in sports video analysis. Computer vision techniques are now used widely in sports analysis to extract data, insights, and inferences of much value. I will highlight some challenges as more and more of such data is becoming available, especially with growing pervasiveness of cameras. I will discuss how some foundational work in computer vision can be brought to bear on this growing problem of sports video analysis and showcase a few recent examples of our work on tracking, registration, and summarization. 

1. • Peter Carr (Disney Research)
   

Title: Automated Sports Broadcasting

Abstract:

In team sports, players move in complex but somewhat predictable ways.  With state-of-the-art computer vision and machine learning methods, it is now possible to collect large tracking datasets and learn patterns of how players move.  In this talk, I will describe methods developed at Disney Research to track players automatically using computer vision, as well as machine learning techniques to drive robotic cameras so that games can be recorded automatically.

1. • Joel Brooks (MIT)
   

Title: Recognizing and Analyzing Ball Screen Defense in the NBA

Abstract:

As the NBA’s go-to offensive play, determining how to defend the ball screen is among the most critical decisions faced by NBA coaching staffs. In this talk, we present the construction and application of a tool for automatically recognizing common defensive counters to ball screens. Using SportVU player tracking data and supervised machine learning techniques, we learn a classifier that labels ball screens according to how they were defended. Applied to data from five NBA seasons, our classifier identified the screen defense of over 300,000 screens in total. These labeled data enable novel analyses of defensive strategies. We present observations and trends at both the team and player levels. Our work is a step towards the construction of a coaching assistance tool for analyzing one of the game’s most important actions.

1. • Sarah Rudd (StatDNA)
   

Title: Making Strides in Quantifying and Understanding Soccer

Abstract:

Soccer has a rich history of people using data in an attempt to gain a better understanding of what happened in a game. However, due to its fluid nature, the sport is often assumed to be difficult to quantify and analyze.  This talk will highlight some of the progress that has been made in soccer analytics in recent years, including some of the advances being made thanks to rich, full-tracking datasets.

1. • (Blizzard Entertainment)
   

Title: Gameplay First: Data Science at Blizzard Entertainment

Abstract:

With a focus on gameplay first, Blizzard Entertainment is known for developing premium games like World of Warcraft, Starcraft, Diablo, Hearthstone, Heroes of the Storm and Overwatch.  Tens of millions of players login daily and interact with a variety of game features generating massive amounts of rich and diverse data streams.  In this talk, I will provide a general overview of data science challenges at Blizzard and discuss two challenges in the area of game design.  Specifically, I will discuss challenges and solutions for matchmaking in competitive games; and discuss how gameplay and player segmentation can be used to inform game balance.

1. • (tag.bio)
   

Title: Making an Idea Machine: Modular Architecture for a Scaleable Exploratory Data Analysis Platform in Genomics, Sports and Beyond

Abstract:

Exploratory data analysis has been a core facilitator of discovery in genomics research over the last 20 years. The critical advancement in the field has been to put data analysis tools in the hands of domain experts: non-programmers and non-statisticians that have the capacity to hypothesize and discover in their field. Jesse Paquette and tag.bio have identified key aspects from the discovery process in genomics and used them to develop a modular, scalable exploratory data analysis platform that can be configured for a wide variety of data domains. Case studies presented will focus on applications of the tag.bio platform for discovery in sports.