Analytic methods in sports : using mathematics and statistics to understand data from baseball, football, basketball, and other sports /: using mathematics and statistics to understand data from baseball, football, basketball, and other sports. (2014)

• Record Type: Book
• Further Information: Note: Thomas A. Severini.
• Authors: Severini, Thomas A (Thomas Alan), 1959-
• Contents: Introduction; Analytic methods; Organization of the book; Data; Computation Describing and Summarizing Sports Data ; Introduction; Types of data encountered in sports; Frequency distributions; Summarizing results by a single number: mean and median; Measuring the variation in sports data; Sources of variation: comparing between-team and within-team variation; Measuring the variation in a qualitative variable such as pitch type; Using transformations to improve measures of team and player performance; Home runs per at-bat or at-bats per home run?; Computation Probability ; Introduction; Applying the rules of probability to sports; Modeling the results of sporting events as random variables; Summarizing the distribution of a random variable; Point distributions and expected points; Relationship between probability distributions and sports data; Tailoring probability calculations to specific scenarios: conditional probability; Relating unconditional and conditional probabilities: the law of total probability; The importance of scoring first in soccer; Win probabilities; Using the law of total probability to adjust sports statistics; Comparing NFL field goal kickers; Two important distributions for modeling sports data: the binomial and normal distributions; Using Z-scores to compare top NFL season receiving performances; Applying probability theory to streaks in sports; Using probability theory to evaluate "statistical oddities"; Computation Statistical Methods ; Introduction; Using the margin of error to quantify the variation in sports statistics; Calculating the margin of error of averages and related statistics; Using simulation to measure the variation in more complicated statistics; The margin of error of the NFL passer rating; Comparison of teams and players; Could this result be due to chance? Understanding statistical significance; Comparing the American and National Leagues; Margin of error and adjusted statistics; Important considerations when applying statistical methods to sports; Computation Using Correlation to Detect Statistical Relationships ; Introduction; Linear relationships: the correlation coefficient; Can the "Pythagorean theorem" be used to predict a team’s second-half performance?; Using rank correlation for certain types of nonlinear relationships; The importance of a top running back in the NFL; Recognizing and removing the effect of a lurking variable; The relationship between ERA and LOBA for MLB pitchers; Using autocorrelation to detect patterns in sports data; Quantifying the effect of the NFL salary cap; Measures of association for categorical variables; Measuring the effect of pass rush on Brady’s performance; What does Nadal do better on clay?; A caution on using team-level data; Are batters more successful if they see more pitches?; Computation Modeling Relationships Using Linear Regression ; Introduction; Modeling the relationship between two variables using simple linear regression; The uncertainty in regression coefficients: margin of error and statistical significance; The relationship between WAR and team wins; Regression to the mean: why the best tend to get worse and the worst tend to get better; Trying to detect clutch hitting; Do NFL coaches expire? A case of missing data; Using polynomial regression to model nonlinear relationships; The relationship between passing and scoring in the EPL; Models for variables with a multiplicative effect on performance using log transformations; An issue to be aware of when using multi-year data; Computation Regression Models with Several Predictor Variables ; Introduction; Multiple regression analysis; Interpreting the coefficients in a multiple regression model; Modeling strikeout rate in terms of pitch velocity and movement; Another look at the relationship between passing and scoring in the EPL; Multiple correlation and regression; Measuring the offensive contribution of players in La Liga; Models for variables with a synergistic or antagonistic effect on performance using interaction; A model for 40-yard dash times in terms of weight and strength; Interaction in the model for strikeout rate in terms of pitch velocity and movement; Using categorical variables, such as league or position, as predictors; The relationship between rebounding and scoring in the NBA; Identifying the factors that have the greatest effect on performance: the relative importance of predictors; Factors affecting the scores of PGA golfers; Choosing the predictor variables: finding a model for team scoring in the NFL; Using regression models for adjustment; Adjusted goals-against average for NHL goalies; Computation Descriptions of Available Datasets References Suggestions for further reading appear at the end of each chapter.
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2014
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 796.021; Sports -- Data processing; Sports -- Mathematical models; Sports -- Statistics
• Languages: English
• ISBNs: 9781482237023
• Related ISBNs: 9781482237016
• Notes: Note: Includes bibliographical references and index.; Note: Description based on CIP data; item not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.