Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
Articles 1 - 6 of 6
Full-Text Articles in Entire DC Network
How Often Does The Best Team Win? A Unified Approach To Understanding Randomness In North American Sport, Michael J. Lopez, Matthews J. Gregory, Baumer S. Benjamin
How Often Does The Best Team Win? A Unified Approach To Understanding Randomness In North American Sport, Michael J. Lopez, Matthews J. Gregory, Baumer S. Benjamin
Mathematics
Statistical applications in sports have long centered on how to best separate signal (e.g. team talent) from random noise. However, most of this work has concentrated on a single sport, and the development of meaningful cross-sport comparisons has been impeded by the difficulty of translating luck from one sport to another. In this manuscript, we develop Bayesian state-space models using betting market data that can be uniformly applied across sporting organizations to better understand the role of randomness in game outcomes. These models can be used to extract estimates of team strength, the between-season, within-season, and game-to-game variability of team …
Estimation Of Causal Effects With Multiple Treatments: A Review And New Ideas, Michael J. Lopez, Roee Gutman
Estimation Of Causal Effects With Multiple Treatments: A Review And New Ideas, Michael J. Lopez, Roee Gutman
Mathematics
The propensity score is a common tool for estimating the causal effect of a binary treatment in observational data. In this setting, matching, subclassification, imputation, or inverse probability weighting on the propensity score can reduce the initial covariate bias between the treatment and control groups. With more than two treatment options, however, estimation of causal effects requires additional assumptions and techniques, the implementations of which have varied across disciplines. This paper reviews current methods, and it identifies and contrasts the treatment effects that each one estimates. Additionally, we propose possible matching techniques for use with multiple, nominal categorical treatments, and …
Backlash Against Gender Stereotype-Violating Preschool Children, Jessica Sullivan, Corinne Moss-Racusin, Michael J. Lopez, Katherine Williams
Backlash Against Gender Stereotype-Violating Preschool Children, Jessica Sullivan, Corinne Moss-Racusin, Michael J. Lopez, Katherine Williams
Mathematics
While there is substantial evidence that adults who violate gender stereotypes often face backlash (i.e. social and economic penalties), less is known about the nature of gender stereotypes for young children, and the penalties that children may face for violating them. We conducted three experiments, with over 2000 adults from the US, to better understand the content and consequences of adults’ gender stereotypes for young children. In Experiment 1, we tested which characteristics adults (N = 635) believed to be descriptive (i.e. typical), prescriptive (i.e. required), and proscriptive (i.e. forbidden) for preschool-aged boys and girls. Using the characteristics that were …
Building An Ncaa Men’S Basketball Predictive Model And Quantifying Its Success, Michael J. Lopez, Gregory Matthews
Building An Ncaa Men’S Basketball Predictive Model And Quantifying Its Success, Michael J. Lopez, Gregory Matthews
Mathematics
Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts than traditional statistical tools. In this manuscript, we provide evidence that the combination of modest statistical methods with informative data can meet or exceed the accuracy of more complex models when it comes to predicting the NCAA men's basketball tournament. First, we describe a prediction model that merges the point spreads set by Las Vegas sportsbooks with possession based team efficiency metrics by using logistic regressions. The set of probabilities generated from this model …
Consistency, Accuracy, And Fairness: A Study Of Discretionary Penalties In The Nfl, Kevin Snyder, Michael J. Lopez
Consistency, Accuracy, And Fairness: A Study Of Discretionary Penalties In The Nfl, Kevin Snyder, Michael J. Lopez
Mathematics
Prior studies of referee behavior focus on identifying a bias in when certain calls are made [Kovash, Kenneth, & Levitt, Steven (2009). "Professionals do not play minimax: evidence from Major League Baseball and the National Football League (No. w15347)." National Bureau of Economic Research; Rosen, Peter A. and Rick L. Wilson. 2007. "An Analysis of the Defense First Strategy in College Football Overtime Games." Journal of Quantitative Analysis in Sports 3(2):1-17; Alamar, Benjamin. 2010. "Measuring Risk in NFL Playcalling." Journal of Quantitative Analysis in Sports 6:11.]. We extend this research by evaluating the consistency of specific discretionary penalties in professional …
Biased Impartiality Among National Hockey League Referees, Michael J. Lopez, Kevin Snyder
Biased Impartiality Among National Hockey League Referees, Michael J. Lopez, Kevin Snyder
Mathematics
This paper builds an economic model of referee behavior in the National Hockey League using period-specific, in-game data. Recognizing that referees are influenced by a desire for perceived fairness, this model isolates situations where a referee is more likely to call a penalty on one team. While prior research has focused on a systematic bias in favor of the home team, we find that referee bias also depends upon game-specific conditions that incentivize an evening of penalty calls. Refereeing games in this fashion maintains the integrity of the game, thus benefiting spectator perceptions and opportunities for financial returns.