Carnegie Mellon Sports Analytics Conference 2020

Poster Session:   Saturday 10/24: 12:50-2:15pm EDT;   Sunday 10/25: 1-2pm EDT


Projects
Sentiment Analysis and Video Assistant Referees in Premier League Soccer
Dylan Blechner, Syracuse University		 Poster Short Presentation Zoom
College Basketball Rating: A new body-of-work metric for NCAA tournament selection
Justin Stocks-Smith		 Poster Short Presentation Zoom
A Poisson Betting Model with a Kelly Criterion Element for European Football
Syracuse University Soccer Analytics Club		 Poster Short Presentation Zoom
Clustering and Analyzing 5v5 NHL Shot Location Data
Brendan Kumagai, McMaster University/Univ of Toronto		 Poster Short Presentation Zoom
Reproducibility in Cricket
James Thomson, Simon Fraser University; Robert Nguyen, UNSW Sydney		 Poster Short Presentation Zoom
The Quarterback and the Situation
Isaac Spear, University of Pennsylvania		 Poster Short Presentation Zoom
Learning Weakness and Strength Rules of Cricket Players using Association Rule Mining
Swarup Ranjan Behera, V. Vijaya Saradhi, Indian Institute of Technology Guwahati		 Poster Short Presentation Zoom
Examining Substitution Decision Making in the Canadian Premier League using Machine Learning
Jacob Klein, Rochester Institute of Technology		 Poster Zoom
Defining Offensive and Defensive Positions in College Basketball to Build Optimal Rosters for Maximum Tournament Success
Colin Krantz, Kushal Shah, Syracuse University		 Poster Short Presentation Zoom
The Big Ben Effect: An Analysis of How Injuries Impact Players in Fantasy Football
Bruce Liska, Syracuse University		 Poster Short Presentation Zoom
Optimum Pass/Run Ratio in NFL
David Schmerfeld		 Poster Short Presentation Zoom
The Problem with the Empty Stands at the NBA Bubble
Gustavo Garcia-Franceschini, Carnegie Mellon University, Samford University		 Poster Short Presentation Zoom
NFL3D: Adding Dimension to Football Tracking Data
Rishav Dutta, Carnegie Mellon University		 Poster Short Presentation Zoom
Predictive Plate Appearance Model
Kai Franke		 Poster Short Presentation Zoom
Video Data Do More, Tracking Data Do Much, Text Commentary Data Do Much More
Swarup Ranjan Behera, V. Vijaya Saradhi,Indian Institute of Technology Guwahati		 Poster Short Presentation Zoom
Poisson Modeling and Predicting English Premier League Goal Scoring
Quang Nguyen, Loyola University Chicago		 Poster Short Presentation Zoom
Quantifying Off-ball Offensive Movement in the NBA
Parth Athale, Indian Institute of Technology Kanpur		 Poster Short Presentation Zoom
Using Spatial Tracking Data to Better Evaluate Defensive Performance in the NHL
Matthew Raber		 Poster Short Presentation Zoom
Distributing xG: Giving Passes Due Credit
Kapil Khanal, Winona State University; Nathan Moss, Carnegie Mellon University; Jeremy Sanchez, University of Florida		 Poster Short Presentation Zoom
Advanced Real Plus Minus
Syracuse University Soccer Analytics Club		 Poster Short Presentation Zoom
Finding Determinants of NBA Shot Probability using Interpretable Machine Learning Methods
Avyay Varadarajan, Mission San Jose High School		 Poster Short Presentation Zoom

Statistics in Sports Research Group

One of the primary goals of Carnegie Mellon Sports Analytics is to push forward the sports analytics discipline through the dissemination of cutting edge research that also contributes to both applications and statistical methodology. Below are some of our most recent papers:

Open Source Sports Podcast

Ron Yurko and Kostas Pelechrinis host the Open Source Sports podcast as a public reading group for discussing the latest resaerch in sports analytics. Each episode focuses on a single paper feturing authors as guests, with discussions about the statistical methdology, relevance, and future directions of the research.

You can find it on Apple Podcasts , Spotify, Google Podcasts, and Anchor .

  • First episode: openWAR with Gregory J. Matthews (Loyola)

Carnegie Mellon Sports Analytics Conference

The Department of Statistics & Data Science and the Carnegie Mellon Sports Analytics Club are proud to host an annual (mid-fall) conference focusing on high-quality, cutting edge sports analytics research with a strong emphasis on community and supporting the next generation.

We look forward to having you join us!

Carnegie Mellon Sports Analytics Camp

Carnegie Mellon Sports Analytics Club

The CMU Sports Analytics Club is an undergraduate student-run sports statistics club which uses in-depth quantitative data to develop our members’ understanding of sports analytics, strategies, and management. The club actively seeks new members year round to research and write articles on a variety of sports, develop leadership skills, and participate in analytics competitions. If you’re interested in sports analytics, learning more about it – and potentially making a career out of it – contact cmusportsanalytics@gmail.com. Learn more here.

Tartan Sports Analytics

In 36-493: Sports Analytics, we partnered with the Carnegie Mellon Athletics Department on a set of ground-breaking projects that integrate previously unlinked, disparate data sets to build interactive applications and statistical models that can be used by coaches and staff to better understand and predict student-athlete performance.

Feel free to explore the current projects below.

Spring 2020 36-493 : Professor Rebecca Nugent

Projects
Golf Performance (with CMU Men's and Women's Golf)
Marc Edwards, Yedin Liu, Xinzhe Qi		 Poster   App 5-min Presentation Feedback
Carnegie Mellon Softball Pitcher Efficiency (with CMU Softball)
Gautam Goel, Scott Steinberg		 Poster   App 5-min Presentation Feedback
Swimming Top Time Trajectories (with CMU Women's Swimming)
Megan Christy, Julia Miraglia, Omkar Sakhawalkar, Shwetha Venkatesh		 Poster 5-min Presentation Feedback
Using Text Analysis to Evaluate Softball Run Expectancy (with CMU Women's Softball)
Sean Jin, Zachary Siegel, Anna Tan		 Poster   App 5-min Presentation Feedback
Basketball Logistics and Performance Indicator Analysis (with CMU Men's Basketball)
Violet Dong, Ryan Mahtab, Shurui Zeng		 Poster 5-min Presentation Feedback