# Sports

Sports

Sports sees significant growth in analytics with pervasive statistics shifting to more sophisticated measures. We start with baseball as game is built around segments dominated by individuals where detailed (video/image) achievement measures including PITCHf/x and FIELDf/x are moving field into big data arena. There are interesting relationships between the economics of sports and big data analytics. We look at Wearables and consumer sports/recreation. The importance of spatial visualization is discussed. We look at other Sports: Soccer, Olympics, NFL Football, Basketball, Tennis and Horse Racing.

## Basic Sabermetrics

This unit discusses baseball starting with the movie Moneyball and the 2002-2003 Oakland Athletics. Unlike sports like basketball and soccer, most baseball action is built around individuals often interacting in pairs. This is much easier to quantify than many player phenomena in other sports. We discuss Performance-Dollar relationship including new stadiums and media/advertising. We look at classic baseball averages and sophisticated measures like Wins Above Replacement.

Overview (40)

### Introduction and Sabermetrics (Baseball Informatics) Lesson

Introduction to all Sports Informatics, Moneyball The 2002-2003 Oakland Athletics, Diamond Dollars economic model of baseball, Performance - Dollar relationship, Value of a Win.

Introduction and Sabermetrics (Baseball Informatics) Lesson (31:4)

### Basic Sabermetrics

Different Types of Baseball Data, Sabermetrics, Overview of all data, Details of some statistics based on basic data, OPS, wOBA, ERA, ERC, FIP, UZR.

Basic Sabermetrics (26:53)

### Wins Above Replacement

Wins above Replacement WAR, Discussion of Calculation, Examples, Comparisons of different methods, Coefficient of Determination, Another, Sabermetrics Example, Summary of Sabermetrics.

Wins Above Replacement (30:43)

This unit discusses ‘advanced sabermetrics’ covering advances possible from using video from PITCHf/X, FIELDf/X, HITf/X, COMMANDf/X and MLBAM.

Sporta II (41)

### Pitching Clustering

A Big Data Pitcher Clustering method introduced by Vince Gennaro, Data from Blog and video at 2013 SABR conference.

Pitching Clustering (20:59)

### Pitcher Quality

Results of optimizing match ups, Data from video at 2013 SABR conference.

Pitcher Quality (10:02)

## PITCHf/X

Examples of use of PITCHf/X.

PITCHf/X (10:39)

### Other Video Data Gathering in Baseball

FIELDf/X, MLBAM, HITf/X, COMMANDf/X.

Other Video Data Gathering in Baseball (18:5) Other Sports

We look at Wearables and consumer sports/recreation. The importance of spatial visualization is discussed. We look at other Sports: Soccer, Olympics, NFL Football, Basketball, Tennis and Horse Racing.

Sport Sports III (44)

### Wearables

Consumer Sports, Stake Holders, and Multiple Factors.

Wearables (22:2)

### Soccer and the Olympics

Soccer, Tracking Players and Balls, Olympics.

Soccer and the Olympics (8:28)

### Spatial Visualization in NFL and NBA

NFL, NBA, and Spatial Visualization.

Spatial Visualization in NFL and NBA (15:19)

### Tennis and Horse Racing

Tennis, Horse Racing, and Continued Emphasis on Spatial Visualization.

Tennis and Horse Racing (8:52)

### Resources

\TODO{These resources have not all been checked to see if they still exist this is currently in progress}