CASSIS

Program

The program consists of a plenary discussion, a panel, and 10 technical presentations. Throughout the program, posters will be on display. Note the exact ordering and times below are subject to change.

Schedule
• 8:30am: Registration
• 9:00am: Introduction
• 9:05am: Session 1: Advances with Tracking Data
• Peter Tea and Tim Swartz
  The Analysis of Serve Decisions in Tennis using Bayesian Hierarchical Models
• Jacob Mortensen
  Detecting Soccer Formations in Partially-Observed Broadcast Tracking Data
• Lucas Wu and Tim Swartz
  Evaluation of Off-the-Ball Actions in Soccer
• Ryker Moreau, Brendan Kumagai, Elijah Cavan, and Robyn Ritchie
  Evaluating and improving punt returners’ decision making through optimal path estimation
• 10:45am: Coffee/Poster Break
• 11:15am: Session 2: Plenary
• Moneyball Then and Now: A Conversation with Billy Beane (virtual session)
• 12:15pm: Lunch
• 1:30pm: Session 3: Hockey Analytics
• Jonathan Arsenault, Margaret Cunniff, Eric Tulsky, and James Richard Forbes
  Spatial Roles in Hockey Special Teams
• Michael Schuckers, Michael Lopez, and Brian Macdonald
  What does not get observed can be used to make age curves stronger: estimating player age curves using regression and imputation
• Andrew Thomas
  Real-Time Data Science Insights In The Broadcast Space: Case Studies in NHL Hockey
• 2:45pm: Coffee/Poster Break
• 3:15pm: Session 4: Panel Discussion
• "Been there, done that, got the quarter-zip"
  w/ Diana Ma (ex-Lakers), Dan Cervone (ex-Dodgers), and Ivana Seric (ex-76ers)
• 4:15pm: Session 5: Other Sports
• Quang Nguyen, Hannah Butler, and Gregory J. Matthews
  An Examination of Olympic Sport Climbing Competition Format and Scoring System
• Victor Chan
  MSE-optimal K-factor of the Elo rating system for round-robin tournaments
• Ryan Brill, Sameer Deshpande, and Abraham Wyner
  A Bayesian Analysis of the Time Through the Order Penalty in Baseball

Posters

• Nathan de Lara, Tyrel Stokes, Russell Steele, Ian Shrier
  Effect of Injury on Performance
• Rory Breslin
  Predicting Game Results Using a Combination of Machine Learning and Human Evaluation; A Case Study Using Soccer Event Data & PFF Shooting Grades
• Eric Gerber, Nelson Guirado
  Competing Risks Analysis of MLB Draft Data
• Shinpei Nakamura-Sakai, Brian Macdonald
  Estimating the age-conditioned average treatment effects curves: An application on assessing load-management strategies in the NBA
• David Clarke, Rachel Cheng, Ryan Brodie
  The intensity multiplying factor explains the strengths of associations and disparities between common training-load metrics
• Ryan Danczak:
  Canonical Correlation Analysis of NFL Wide Receiver on-field production and physical attributes
• Brendan Kumagai, Kimberly Kroetch, Ryker Moreau
  Predicting the NHL Draft with Rank-Ordered Logit Models
• Elijah Cavan
  Trade Evaluation Using Asset Pricing and Portfolio Allocation
• Divya Parmar
  Forecasting NBA Games: A model and feature set comparison
• Hassaan Inayatali
  Judgement Under Uncertainty: An Empirical Evaluation of NHL Draft Picks
• Nökkvi Dan Elliðason, Brian Macdonald
  Evaluating Favorable Shot Outcomes in Team Sports
• Abdolreza Mirzaei , Oliver Schulte, Mohammad Bahrami, Masoud Mousavi
  Sports Match Outcome Prediction with Spatio-Temporal Graph Representation Learning
• Jay Li
  Rush B? Bayesian Predictions of Tactics in Competitive Esports
• Scott Johnson, Will Thomson, Will Morgan, Dinesh Vatvani
  Decoupling positioning and shot-stopping in GK performance metrics
• Claire Dudley, Michael Schuckers
  Quantifying Short-Term Winners and Losers of the NHL Trade Deadline from 2016-2022
• Andrew Swift, Andrew Tew
  Forecasting the College World Series using a Ratio-based Method
• Kevin Jarvis
  Exploratory Feature Detection in NHL Tracking Data