CARNEGIE MELLON SPORTS ANALYTICS CONFERENCE

Dani Chu is a second year masters student in statistics at Simon Fraser University. Recently, he completed an internship at the NBA department of basketball strategy and analytics. At SFU, he's the co-president of the SFU Sports Analytics Club with Lucas Wu and Matthew Reyers. Along with Lucas, Matt and James Thomson, he was the winner of the College Division of the 2019 NFL Big Data Bowl and the 2018 Sacramento Kings Case Competition. Dani has also interned as a statistician at Best Buy Canada and Fraser Health Authority.

Running out of time: A hierarchical model for estimating foul trouble in the NBA

NBA coaches are tasked with managing the playing time for each of their players. Throughout the season they develop lineups and rotations to give players the rest they need while maximizing the team’s chance at winning. But how should coaches adapt when a player is in foul trouble? Threatening the established game plan. Moreover, how do we quantify when a player is at a risk to not play their usual minutes? Typically, coaches use the Q+1 rule that says that a player is at risk of fouling out of a game when they have 1 more foul than the current quarter. This generic foul trouble calculation treats all players equally, despite many players having known tendencies with respect to foul acquisition. Our objective is to outline player personalized guidelines for a coaching staff to handle in-game foul management. To do so we fit a Hierarchical Bayesian model with NBA play by play data to estimate the distribution of time to foul out for a given player with a given number of fouls. We build this into a framework that can later incorporate factors such as fatigue, defensive responsibilities, restrained performance and game situation. We find that this personalized and data driven method to understanding when a player will foul out is more useful than a catchall approach and lays the groundwork to a more nuanced discussion of player rotations.

Sameer is a post-doctoral researcher at MITs Computer Science and Artificial Intelligence Laboratory. He received his Ph.D. in Statistics from the Wharton School at the University of Pennsylvania in 2018. His methodological research primarily focuses on Bayesian model selection, clustering, and model averaging. In addition to studying the effects of playing football, he has previously worked on estimating how NBA players help their teams win games and on quantifying the uncertainty about the value of pitch framing in baseball. Outside of statistics, he is an avid sports fan, with particular affection for Dallas-based teams.

Expected Hypothetical Completion Probability: An Analysis from the 2019 NFL Big Data Bowl

Using high-resolution player tracking data made available by the National Football League (NFL) for their 2019 Big Data Bowl competition, we introduce the Expected Hypothetical Completion Probability (EHCP), a objective framework for evaluating plays. At the heart of EHCP is the question 'on a given passing play, did the quarterback throw the pass to the receiver who was most likely to catch it?' To answer this question, we first built a Bayesian non-parametric catch probability model that automatically accounts for complex interactions between inputs like the receiver's speed and distances to the ball and nearest defender. While building such a model is, in principle, straightforward, using it to reason about a hypothetical pass is challenging because many of the model inputs corresponding to a hypothetical are necessarily unobserved. To wit, it is impossible to observe how close an un-targeted receiver would be to his nearest defender had the pass been thrown to him instead of the receiver who was actually targeted. To overcome this fundamental difficulty, we propose imputing the unobservable inputs and averaging our model predictions across these imputations to derive EHCP. In this way, EHCP can track how the completion probability evolves for each receiver over the course of a play in a way that accounts for the uncertainty about missing.

Connor Gibbs is a second year PhD student in the department of Statistics at Colorado State University. He graduated summa cum laude from the University of Georgia in May of 2018 with a B.S. in Mathematics and a B.S. in Statistics. Apart from his work in causal inference, Connor enjoys hiking and traveling.

The causal effect of a timeout at stopping an opposing run in the NBA

In the summer of 2017, the NBA reduced the total number of timeouts, among other rule changes, to regulate the flow of the game. With these rule changes, it becomes increasingly important for coaches to effectively manage their timeouts. Understanding the utility of a timeout under various game scenarios, e.g. during an opposing team’s run, is of the utmost importance. There are two schools of thought when the opposition is on a run: (1) call a timeout and allow your team to rest and regroup, or (2) save a timeout and hope your team can make corrections on the fly. This talk investigates the credence of these tenets using the Rubin causal model framework to quantify the causal effect of a timeout in the presence of an opposing team’s run and provides coaches with the needed analytic justification for better in-game decision making.

Dr. Stephanie Kovalchik is a senior data scientist in the Game Insight Group at Tennis Australia and is currently a Research Fellow at the Institute for Health and Sport at Victoria University in Melbourne, Australia. Dr. Kovalchik earned her PhD and MS in Biostatistics from UCLA and her BS in Biology from Caltech. Her current research focuses on the use of statistical methods to understand performance, game strategy, and mentality in high-performance tennis.​

Vamos! Estimating Shot Value In Tennis with a Functional Spatiotemporal Gaussian Mixture Model

Every point in tennis is a battle for control over the space and time of a tennis ball. Despite this, performance statistics in tennis largely ignore the spatio-temporal features of points. Taking inspiration from several high-resolution models that have been developed to estimate the value of actions in professional team sports, the present work develops a framework for modeling the space-time evolution of shots that allows us to estimate expected shot value (ESV) continuously throughout a tennis point. Key to our approach is the encoding of shot and player trajectories into a lower-dimensional representation that retains all available space-time information. An infinite Bayesian Gaussian mixture model provides a generative distribution for the 3D ball and 2D player trajectories that is in good agreement with real tracking data. In this talk, I will describe this fundamental building block of the ESV approach and show how ESV can be used to gain deeper insights into player skill and decision-making.

Brian is currently the Director of Sports Analytics in the Stats & Information Group at ESPN. He was previously the Director of Hockey Analytics with the Florida Panthers Hockey Club, an Associate Professor in the Department of Mathematical Sciences at West Point, an Adjunct Professor in the Department of Management Science at the University of Miami, and an Adjunct Professor in Sports Analytics in the College of Business at Florida Atlantic University. He received a Bachelor of Science in Electrical Engineering from Lafayette College, Easton, PA, and a Master of Arts and a Ph.D. in Mathematics from Johns Hopkins University, Baltimore, MD.

A Bayesian hierarchical regression-based metric for NBA players

We present a Bayesian hierarchical regression model that estimates the value of box score statistics and player coefficients simultaneously, and provides an estimate of an NBA player’s contribution to his team’s on-court performance. We discuss how our approach differs from other regression-based metrics, provide visualizations of those differences over time as a way to highlight the characteristics of each, and discuss how this approach could be used in hockey, soccer, football, or eSports.

Katy McKeough is a fifth-year Ph.D. student in the Harvard University Statistics Department. Her research involves using advanced statistical models in applied settings including sports analytics and astrostatistics. She graduated from Carnegie Mellon University in 2015 with a degree in physics with a secondary major of statistics. She is a member of both the CHASC: Astrostatistics Group and the Sports Analytics Lab at Harvard.

Growth Curves for Predicting Athlete Ratings

It is often the goal of sports analysts, coaches and fans to predict athlete performance over time. Methods such as Elo, Glicko and Placket-Luce based ratings measure athlete skill based on results of competitions over time but have limited predictive strength on their own. Growth curves are often applied in the context of sports to predict future ability, but these curves are too simple to account for complex career trajectories. We propose a mixture of non-linear, mixed-effects growth curves to model the ratings as a function of athlete age and time. The mixture of growth curves allow for flexibility of the estimated shape of the career trajectories between athletes as well as between sports. We use the fitted growth curves to make predictions about the future career trajectory of an athlete. We apply this method to men's slalom results but it can be generalized to other sports.

Christopher J. Phillips is Associate Professor of History at Carnegie Mellon University. He received his PhD in History of Science from Harvard University and also taught at New York University before coming to CMU in 2015. Phillips’s research focuses on the history of statistics, and in particular, the supposed benefits of introducing numbers and analytics into new fields. He is the author of Scouting and Scoring: How We Know What We Know About Baseball (Princeton University Press) and also serves as an Associate Editor for the Harvard Data Science Review.

Scouting and Scoring: How We Know What We Know About Baseball

Amateur scouting and data-driven analytics are presumed to be two very different ways of measuring quality and success in baseball, one focused on qualitative and subjective evaluation of prospects’ bodies, the other on quantitative and objective evaluation of playing statistics. The two camps—scouts and scorers—are sometimes portrayed as opposed or irreconcilable. Historically, however, their practices were far more similar than different. Both relied on numbers, bureaucratic management, and human labor to make reliable judgments. By unearthing the history of official scorers and database creators, scouting reports and bureaus, we can come to understand a new history of data analytics and scouting in baseball.

Paul Sabin is a Senior Sports Analytics Specialist at ESPN. He received his PhD in statistics from Virginia Tech after receiving an MS in Statistics and a BS in statistics and French at BYU. Paul has built several publically facing ESPN metrics including the suite of CBB metrics (BPI, SOR), NBA Draft Prospect Projections, Soccer fantasy projections, the Allstate Playoff Predictor (CFB), and the Playstation Player Impact Rating (CFB). He is also a contributor to ESPN.com. Outside of ESPN, Paul researches hierarchical, multiscale, and Dirichlet Bayesian methods and TV ratings in sports. Paul is forever a hopeless BYU and DC sports fan plus a hopeful fan of all teams featuring Kylian Mbappe, whose play converted him to soccer fandom.

Estimating Player Value in Football Using Plus-Minus Models

To date calculating the value of football players on-field performance has been limited to scouting methods and quarterbacks. A popular method to calculate player value in other sports are Adjusted Plus-Minus (APM) models. These models have long been used in other sports, most notably basketball (Rosenbaum (2004), Kubatko et al. (2007), Winston (2009), Sill (2010)) to estimate each players value by accounting for those in the game at the same time. Football is the least amenable major American sport to APM models due to its few scoring events, few lineup changes, restrictive positioning, and small quantity of games relative to the number of teams. More recent methods have found ways to incorporate plus-minus models in sports such as Hockey (Macdonald (2011)) and Soccer (Schultze and Wellbrock (2018) and Matano et al. (2018)). These models are especially useful in coming up with results-oriented estimation of each player’s value. In American Football, it is difficult to estimate every player’s value since many positions such as offensive lineman have no recorded statistics. While player-tracking data in the NFL is allowing new analysis, such data does not exist in other levels of football such as the NCAA. Using player participation data available for college football and the NFL, I provide a model framework that solves many of the traditional issues APM models face in football. Results for these APM models are provided for both Collegiate and NFL football players. Additionally, this methodology allows the models to estimate the value of each position in each level of the sport.

As editor-in-chief of Hockey-Graphs, Asmae manages the day-to-day operations and oversees the editorial and creative process. In her role, she spearheaded a mentorship program pairing NHL data scientists and executives with underrepresented persons. She has hosted multiple workshops in data viz and modelling at MIT Sloan Sports Analytics Conferences. She also works for the Massachusetts General Hospital Institue of Technological Assessment as a Data Analyst.

FROM GRAPES AND PRUNES TO APPLES AND APPLES: USING MATCHED METHODS TO ESTIMATE OPTIMAL ZONE ENTRY DECISION-MAKING IN THE NATIONAL HOCKEY LEAGUE

Previous research in the National Hockey League has suggested that teams' decisions to gain the offensive zone with puck possession ("carry-ins") is preferred over dumping the puck in and chasing after it ("dump-ins"). However, standard comparisons of zone entry strategy are confounded by factors such as offensive and defensive talent, location on the ice, and shift time, each of which impact player choice. Indeed, contrasting carry-ins to dump-ins isn’t exactly an apples-to-apples comparison; instead, it is more like studying grapes versus prunes. Using two matching methods – propensity score matching and Bayesian additive regression trees – we leverage player-tracking data to estimate the causal benefits due to zone-entry decisions. Both approaches better account for the variables that affect entry choice. We also highlight the wide-ranging potential of the causal inference framework with player tracking data in sports while emphasizing the challenges of using standard statistical methods to inform decision-making in the presence of substantial confounding.

Meredith J. Wills, Ph.D., is a Data Scientist for SportsMEDIA Technology (SMT). She has a B.A. in Astronomy & Astrophysics from Harvard University, and an M.S. and Ph.D. in Physics from Montana State University—Bozeman. Dr. Wills joined SMT in 2018, where she works primarily with FIELDf/x, a baseball ball- and player-tracking system used by Minor League affiliates of Major League ballclubs, international leagues, and NCAA. She also writes for The Athletic, and her best-known independent research involves MLB baseball construction and its effect on the game.

The 2019 Home Run Surge: A Whole New Ballgame (Again)

In 2017, Major League Baseball saw an unprecedented increase in home runs. It was determined that this Home Run Surge was caused by a physical change to the ball. By disassembling a sample of baseballs and studying their construction, I found that the introduction of thicker laces ultimately produced a more aerodynamic ball. This past season, MLB’s home run rate soared even farther, and was once again related to changes in baseball construction. Using similar methods, I disassembled a sample of 2019 baseballs and compared their properties to those of earlier populations. This time, my findings showed that multiple aspects of the ball had changed, and that these differences could account for lower drag and a higher home run rate. Evidence suggests that the changes were due to manufacturing process modifications and better quality control, and that the extent of the ball’s aerodynamic improvement—while perhaps not unwelcome—was likely unexpected.