Related Experiment Video
Updated: Jul 15, 2026
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out
Michael J Lopez1, Michael Schuckers2
1a Department of Mathematics , Skidmore College , Saratoga Springs , NY , USA.
Journal of Sports Sciences
|July 5, 2016
Summary
Hockey shoot-outs significantly impact playoff races, yet performance data is scarce. This study reveals small talent gaps in shooters and marginal differences in goalies, with few predictors beyond individual skill, and highlights coaching biases in player selection.
Area of Science:
- Sports Analytics
- Hockey Performance Analysis
- Statistical Modeling in Sports
Background:
- Approximately 14% of National Hockey League regular season games are decided by shoot-outs.
- Shoot-out point allocation influences playoff races annually.
- Limited published research exists on team or individual shoot-out performance.
Purpose of the Study:
- To analyze National Hockey League (NHL) shoot-out performance.
- To identify predictors of success in shoot-outs.
- To investigate potential biases in player selection for shoot-outs.
Main Methods:
- Development of generalized linear mixed models and Bayesian hierarchical models.
- Analysis of per-shot data from NHL regular season games.
- Application of a resampling strategy to assess selection bias.
Main Results:
- Statistically significant, albeit small, talent disparities exist among shooters.
- Goalie performance shows marginal differences.
- Few predictors of player success are identified beyond inherent talent.
- Coaches exhibit a selection bias, favoring skilled offensive players early in shoot-outs.
Conclusions:
- Individual talent is the primary driver of shoot-out success.
- Coaching strategies in player selection introduce bias.
- The study provides valuable data and code for future research on hockey shoot-outs.
Related Concept Videos
01:09Random Sampling Method
15.6K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.6K
01:14Probability in Statistics
24.7K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
24.7K
01:17Probability Histograms
13.6K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
13.6K
01:15Binomial Probability Distribution
16.4K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
16.4K
01:24Bootstrapping
905
The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is...
905
01:19Survival Tree
463
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
463