Certificate Program in Sports Analytics

Big data in sports provide a unique and challenging opportunity for advanced statistical analysis.  Extensive data are being collected on athletes, teams, and opponents, and these data are being used in various ways, e.g.,

• For player evaluation and development
• To make strategic and tactical decisions
• To enhance fan experience

The goal of the sports analyst is to identify patterns from the data – to inform and optimize player and team strategy and to predict future outcomes in athletic performance.

The Certificate Program in Sports Analytics at Mizzou offers a unique opportunity for students to learn statistical methods and computational tools to perform analyses for sports. The program will help to prepare students for career opportunities in the exciting and growing industry of sports analytics. The Certificate in Sports Analytics can be obtained at both the undergraduate and graduate level.

Current MU students can register for certificate courses through myZou. Not currently an MU student? Mizzou Online can help you get started: Register Here

Program Details

The program consists of two courses in applied statistical models and two courses in methods specific to sports analytics. It can be completed in 9 months by taking two courses in both the fall and spring semesters, and is open to degree and non-degree seeking students through Mizzou Online. The program is available to both undergraduate and graduate students.

Program prerequisite: Any one of STAT 3500, STAT 4710, STAT 4760, STAT 7020, STAT 7070, STAT 7710, or STAT 7760

Undergraduate certificate curriculum:

• STAT 4330 Methods in Sports Analytics I
  • Listed as STAT 4006 for Fall 2019
• STAT 4340 Methods in Sports Analytics II
• STAT 4510 Applied Statistical Models I
• STAT 4520 Applied Statistical Models II

Graduate certificate curriculum:

• STAT 7330 Methods in Sports Analytics I
  • Listed as STAT 7006 for Fall 2019
• STAT 7340 Methods in Sports Analytics II
• STAT 7510 Applied Statistical Models I
• STAT 7520 Applied Statistical Models II

Course Descriptions

STAT 4330/7330: Methods in Sports Analytics I (fall)

Introductory course on collecting, processing, visualizing, and analyzing data in sports. Technologies used in data collection and processing will be explored, along with methods for measuring and comparing individual and team performance. (Prerequisite: any one of STAT 3500, STAT 4710, STAT 4760, STAT 7020, STAT 7070, STAT 7710, or STAT 7760)

STAT 4340/7340: Methods in Sports Analytics II (spring)

Advanced course in methods for analyzing individual and team based performance in sports and the use of data to drive strategy and tactics. Emphasis will be put on analytical methods to improve skills and optimize the performance of athletes. (Prerequisite: both STAT 4330/7330 and STAT 4510/7510)

STAT 4510/7510: Applied Statistical Models I (fall; spring)

Introduction to applied statistical models including regression and ANOVA, logistic regression, discriminant analysis, tree-based methods, semi-parametric regression, support vector machines, and unsupervised learning through principal component and clustering.  No credit toward a graduate degree in statistics. (Prerequisite: any one of STAT 3500, STAT 4710, STAT 4760, STAT 7020, STAT 7070, STAT 7710, or STAT 7760)

STAT 4520/7520:  Applied Statistical Models II (spring)

Advanced course in applied statistical modeling focusing on extensions of the linear model. Topics include generalized linear models, such as logistic and Poisson Regression. Random effects models will also be introduced, with emphasis on linear and generalized linear mixed models, repeated measures, and longitudinal data. These methods will extend to general models for dependent data, such as spatially-referenced data and time series. Lastly, nonlinear models through neural networks and deep learning will also be discussed. (Prerequisite: STAT 4510/7510)