Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports

Neuausgabe 2014
ISBN: 978-1-4822-3701-6
Verlag: CRC PR INC

Buch, Englisch, 254 Seiten, Format (B × H): 155 mm x 236 mm, Gewicht: 567 g

The Most Useful Techniques for Analyzing Sports Data

One of the greatest changes in the sports world in the past 20 years has been the use of mathematical methods to analyze performances, recognize trends and patterns, and predict results. Analytic Methods in Sports: Using Mathematics and Statistics to Understand Data from Baseball, Football, Basketball, and Other Sports provides a concise yet thorough introduction to the analytic and statistical methods that are useful in studying sports.

The book gives you all the tools necessary to answer key questions in sports analysis. It explains how to apply the methods to sports data and interpret the results, demonstrating that the analysis of sports data is often different from standard statistical analysis. Requiring familiarity with mathematics but no previous background in statistics, the book integrates a large number of motivating sports examples throughout and offers guidance on computation and suggestions for further reading in each chapter.

Zielgruppe

Senior undergraduate and graduate students taking a sports analytics course; students taking a first or second course in statistics; data analysts working with sports data.

Autoren/Hrsg.

Severini, Thomas A.

Inhaltsverzeichnis

IntroductionAnalytic methodsOrganization of the bookDataComputation

Describing and Summarizing Sports DataIntroductionTypes of data encountered in sportsFrequency distributionsSummarizing results by a single number: mean and medianMeasuring the variation in sports dataSources of variation: comparing between-team and within-team variationMeasuring the variation in a qualitative variable such as pitch typeUsing transformations to improve measures of team and player performanceHome runs per at-bat or at-bats per home run?Computation

ProbabilityIntroductionApplying the rules of probability to sportsModeling the results of sporting events as random variablesSummarizing the distribution of a random variablePoint distributions and expected pointsRelationship between probability distributions and sports dataTailoring probability calculations to specific scenarios: conditional probability Relating unconditional and conditional probabilities: the law of total probabilityThe importance of scoring first in soccerWin probabilitiesUsing the law of total probability to adjust sports statisticsComparing NFL field goal kickersTwo important distributions for modeling sports data: the binomial and normal distributionsUsing Z-scores to compare top NFL season receiving performancesApplying probability theory to streaks in sportsUsing probability theory to evaluate "statistical oddities"Computation

Statistical MethodsIntroductionUsing the margin of error to quantify the variation in sports statisticsCalculating the margin of error of averages and related statisticsUsing simulation to measure the variation in more complicated statisticsThe margin of error of the NFL passer ratingComparison of teams and playersCould this result be due to chance? Understanding statistical significanceComparing the American and National LeaguesMargin of error and adjusted statisticsImportant considerations when applying statistical methods to sportsComputation

Using Correlation to Detect Statistical RelationshipsIntroductionLinear relationships: the correlation coefficientCan the "Pythagorean theorem" be used to predict a team’s second-half performance?Using rank correlation for certain types of nonlinear relationshipsThe importance of a top running back in the NFLRecognizing and removing the effect of a lurking variableThe relationship between ERA and LOBA for MLB pitchersUsing autocorrelation to detect patterns in sports dataQuantifying the effect of the NFL salary capMeasures of association for categorical variablesMeasuring the effect of pass rush on Brady’s performanceWhat does Nadal do better on clay?A caution on using team-level dataAre batters more successful if they see more pitches?Computation

Modeling Relationships Using Linear RegressionIntroductionModeling the relationship between two variables using simple linear regressionThe uncertainty in regression coefficients: margin of error and statistical significanceThe relationship between WAR and team winsRegression to the mean: why the best tend to get worse and the worst tend to get betterTrying to detect clutch hittingDo NFL coaches expire? A case of missing dataUsing polynomial regression to model nonlinear relationshipsThe relationship between passing and scoring in the EPLModels for variables with a multiplicative effect on performance using log transformationsAn issue to be aware of when using multi-year dataComputation

Regression Models with Several Predictor VariablesIntroductionMultiple regression analysisInterpreting the coefficients in a multiple regression modelModeling strikeout rate in terms of pitch velocity and movementAnother look at the relationship between passing and scoring in the EPLMultiple correlation and regressionMeasuring the offensive contribution of players in La LigaModels for variables with a synergistic or antagonistic effect on performance using interactionA model for 40-yard dash times in terms of weight and strengthInteraction in the model for strikeout rate in terms of pitch velocity and movementUsing categorical variables, such as league or position, as predictorsThe relationship between rebounding and scoring in the NBAIdentifying the factors that have the greatest effect on performance: the relative importance of predictorsFactors affecting the scores of PGA golfersChoosing the predictor variables: finding a model for team scoring in the NFLUsing regression models for adjustmentAdjusted goals-against average for NHL goaliesComputation

Descriptions of Available Datasets
References

Suggestions for further reading appear at the end of each chapter.

Über Autor(innen)

Thomas A. Severini is a professor of statistics at Northwestern University. He is a fellow of the American Statistical Association and the author of Likelihood Methods in Statistics and Elements of Distribution Theory. He received his PhD in statistics from the University of Chicago. His research areas include likelihood inference, nonparametric and semiparametric methods, and applications to econometrics.