MDST – NFL Free Agency Value Prediction Competition Kick-Off – Nov. 9, 6pm

By | Data, Data sets, Educational, Events, Happenings, MDSTPosts, MDSTProjects, News

In this competition, student teams at the University of Michigan will use historical free agent data to predict the value of new contracts signed in the 2018 free agency period. These predictions will be evaluated against the actual contracts as they are signed. This competition is organized by the Michigan Data Science Team (MDST), in collaboration with the Baltimore Ravens and the Michigan Sports Analytics Society (MSAS).  Food will be provided. This is an initial kick-off meeting of the competition.


Date, Time

Thursday, November 9 at 6:00 PM EST to Thursday, November 9 at 7:00 PM EST
Add To Google Calendar | iCal/Outlook


Weiser Hall 10th Floor Auditorium
500 Church St, 48104, MI


Michigan Data Science Team



CSCAR provides walk-in support for new Flux users

By | Data, Educational, Flux, General Interest, HPC, News

CSCAR now provides walk-in support during business hours for students, faculty, and staff seeking assistance in getting started with the Flux computing environment.  CSCAR consultants can walk a researcher through the steps of applying for a Flux account, installing and configuring a terminal client, connecting to Flux, basic SSH and Unix command line, and obtaining or accessing allocations.  

In addition to walk-in support, CSCAR has several staff consultants with expertise in advanced and high performance computing who can work with clients on a variety of topics such as installing, optimizing, and profiling code.  

Support via email is also provided via  

CSCAR is located in room 3550 of the Rackham Building (915 E. Washington St.). Walk-in hours are from 9 a.m. – 5 p.m., Monday through Friday, except for noon – 1 p.m. on Tuesdays.

See the CSCAR web site ( for more information.

Info session: Consulting and computing resources for data science — Nov. 8

By | Data, Educational, Events, General Interest, Happenings, HPC

Advanced Research Computing at U-M (ARC) will host an information session for graduate students in all disciplines who are interested in new computing and data science resources and services available to U-M researchers.

Brief presentations from members of ARC Technology Services (ARC-TS) on computing infrastructure, and from Consulting for Statistics, Computing, and Analytics Research (CSCAR) on statistics, data science, and computing training and consulting will be followed by a Q&A session, and opportunities to interact individually with ARC and CSCAR staff.

ARC and CSCAR are interested in connecting with graduate students whose research would benefit from customized or innovative computational or analytic approaches, and can provide guidance for students aiming to do this. ARC and CSCAR are also interested in developing training and documentation materials for a diverse range of application areas, and would welcome input from student researchers on opportunities to tailor our training offerings to new areas.


  • Kerby Shedden, Director, CSCAR
  • Brock Palen, Director, ARC-TS


Wednesday, Nov. 8, 2017, 2 – 4 p.m., West Conference Room, 4th Floor, Rackham Building (915 E. Washington St.)

Add to Google Calendar

Real estate dataset available to researchers

By | Data, Data sets, Educational, General Interest, Happenings, News

The University of Michigan Library system and the Data Acquisition for Data Sciences program (DADS) of the U-M Data Science Initiative (DSI) have recently joined forces to license a major data resource capturing parcel-level information about the property market in the United States.  

The data were licensed from the Corelogic corporation, who have assimilated deed, tax and foreclosure information on nearly all properties in the entire US. Coverage dates vary by county, some county records go back fifty years. Coverage is more comprehensive from the 1990s to the present.

These data will support a variety of research efforts into regional economies, economic disparities, trends in land-use, housing market dynamics, and urban ecology, among many other areas.

The data are available on the Turbo Research Storage system for users of the U-M High Performance Computing infrastructure, and via the University of Michigan Library.

To access the data, researchers must first sign a MOU; contact Senior Associate Librarian Catherine Morse for more information, or visit

Mini-course: Introduction to Python — Sept. 11-14

By | Data, Educational, Events, General Interest, News

Asst. Prof. Emanuel Gull, Physics, is offering a mini-course introducing the Python programming language in a four-lecture series. Beginners without any programming experience as well as programmers who usually use other languages (C, C++, Fortran, Java, …) are encouraged to come; no prior knowledge of programming languages is required!

For the first two lectures we will mostly follow the book Learning Python. This book is available at our library. An earlier edition (with small differences, equivalent for all practical purposes) is available as an e-book. The second week will introduce some useful python libraries: numpyscipymatplotlib.

At the end of the first two weeks you will know enough about Python to use it for your grad class homework and your research.

Special meeting place: we will meet in 340 West Hall on Monday September 11 at 5 PM.

Please bring a laptop computer along to follow the exercises!

Syllabus (Dates & Location for Fall 2017)

  1. Monday September 11 5:00 – 6:30 PM: Welcome & Getting Started ( Location: 340 West Hall
  2. Tuesday September 12 5:00 – 6:30 PM: Numbers, Strings, Lists, Dictionaries, Tuples, Functions, Modules, Control flow. Location: 335 West Hall
  3. Wednesday September 13 5:00 – 6:30 PM: Useful Python libraries (part I): numpy, scipy, matplotlib. Location: 335 West Hall
  4. Thursday September 14 5:00 – 6:30 PM: Useful Python libraries (part 2): 3d plotting in matplotlib and exercises. Location: 335 West Hall

For more information:


Info sessions on graduate studies in computational and data sciences — Sept. 21 and 25

By | Educational, Events, General Interest, News, Research

Learn about graduate programs that will prepare you for success in computationally intensive fields — pizza and pop provided

  • The Ph.D. in Scientific Computing is open to all Ph.D. students who will make extensive use of large-scale computation, computational methods, or algorithms for advanced computer architectures in their studies. It is a joint degree program, with students earning a Ph.D. from their current departments, “… and Scientific Computing” — for example, “Ph.D. in Aerospace Engineering and Scientific Computing.”
  • The Graduate Certificate in Computational Discovery and Engineering trains graduate students in computationally intensive research so they can excel in interdisciplinary HPC-focused research and product development environments. The certificate is open to all students currently pursuing Master’s or Ph.D. degrees at the University of Michigan.
  • The Graduate Certificate in Data Science is focused on developing core proficiencies in data analytics:
    1) Modeling — Understanding of core data science principles, assumptions and applications;
    2) Technology — Knowledge of basic protocols for data management, processing, computation, information extraction, and visualization;
    3) Practice — Hands-on experience with real data, modeling tools, and technology resources.

Times / Locations:

Call for Proposals: Amazon Research Awards, deadline 9/15/17

By | Data, Educational, Funding Opportunities, News, Research

The Amazon Research Awards (ARA) program offers awards of up to $80,000 in cash and $20,000 in AWS promotional credits to faculty members at academic institutions in North America and Europe for research in these areas:

  • Computer vision
  • General AI
  • Knowledge management and data quality
  • Machine learning
  • Machine translation
  • Natural language understanding
  • Personalization
  • Robotics
  • Search and information retrieval
  • Security, privacy and abuse prevention
  • Speech

The ARA program funds projects conducted primarily by PhD students or post docs, under the supervision of the faculty member awarded the funds. To encourage collaboration and the sharing of insights, each funded proposal team is assigned an appropriate Amazon research contact. Amazon invites ARA recipients to speak at Amazon offices worldwide about their work, meet with Amazon research groups face-to-face, and encourages ARA recipients to publish their research outcome and commit related code to open-source code repositories.

Submissions are to be made online and details including rules and who may apply are located here.

MDST partners with Adamy Valuation for market analysis

By | Educational, General Interest, MDSTPosts, News

Authors: Michael Kovalcik, College of Engineering; Xinyu Tan, College of Engineering; Derek Chen, Ross School of Business.

Problem Overview

adamy-full-logo-rgb copyThe Michigan Data Science Team partnered with Adamy Valuation, a Grand Rapids-based valuation firm, to bring data-driven insights to business equity valuation.  Business valuation firms determine the market value of business interests in support of a variety of different types of transactions typically involving ownership interests in private businesses. Valuation firms, such as Adamy Valuation, deliver this assessment, which includes a detailed report explaining the reasons why they believe it to be fair.

Valuations are performed by expert financial analysts, who use their knowledge about the factors that influence value to manually assess the value of the equity. Shannon Pratt’s Valuing a Business suggests that there are two key factors in particular that influence value: risk and size. Risk is a measure of uncertainty relating to the company’s future  and can be assessed by looking at total debt and cash flows. Size refers to a company’s economic power. Larger companies will spend and make more than smaller ones. While these factors are quite informative, the degree to which they influence value varies a lot from industry to industry and even from company to company. Therefore, a valuation firm will often adjust their models manually to account for additional features, using knowledge gained from years of experience and industry expertise.

Our goals were to conduct a data-driven analysis of the valuation process and to build a predictive model that could learn to make value adjustments from historical data. A critical requirement of our approach was that the resulting model must be interpretable. An algorithm that is extremely accurate but offers no insight into how the prediction was made or what features it was based off of is of no use to Adamy Valuation because, at the end of the day, they must be able to validate the reasoning behind their assessment.

model_overview copyThe Data Pipeline

While our goal is to value private companies, data related to these companies is difficult to come by.  Business valuation analysts address this issue by using market data from public companies as guideline data points to inform the valuation of a private subject company.  To this end, we acquired a dataset of 400 publicly-traded companies along with 20 financial metrics that are commonly used during valuation. We cleaned this dataset to only contain features that are relevant to private companies so that the model learned on public companies could later be applied to value private companies.

We separate financial metrics into four categories: Size, Profitability, Growth, and Risk, as indicated by the colors in Fig. 1. Our goal was to determine which of the four categories, or more specifically, which features in these categories, contribute the most to:

tev-ebitdawhere TEV represents the Total Enterprise Value a measure of a company’s market value, adjusting for things like debt and cash on hand, and EBITDA stands for earnings before interest, tax, depreciation, and amortization. EBITDA allows analysts to focus on operating performance by minimizing the impact of non-operating decisions such as which tax rates they must pay and the degree to which their goods depreciate. In other words EBITDA gives a clearer value for head to head comparisons of company performance. Valuation firms typically examine the ratio of TEV and EBITDA instead of examining TEV or EBITDA directly, because the ratio standardizes for the size of the company, making it easier to make apples to apples comparisons with companies that may be much larger or smaller, but are otherwise similar.

To study how feature importance varied across industries, we categorized each public company into one of three separate sectors:

  • Consumer Discretionary refers to companies that provide goods and services that are considered nonessential to the consumer. For example, Bed Bath and Beyond, Ford Motor Company, and Panera Bread are all part of this category.
  • Consumer Staples provide essential products such as food, beverages, and household items. Companies like Campbell’s Soup, Coca Cola, and Kellogg are considered Consumer Staples.
  • Industrial Spending sector is a diverse category, which contains companies related to the manufacture and distribution of goods for industrial customers. In this dataset we see companies like Delta Airlines, Fedex, and Lockheed Martin.


Our goal is not just to accurately estimate value, but also to identify key relationships between a company’s observable metrics and its ratio of TEV to EBITDA.We study 17 financial metrics, many of which have complex relationships with the ratio of TEV and EBITDA. To identify these relationships, we model the problem as a regression task. We use two simple but widely-used frameworks: linear models and tree-based models because both methods offer insight into how the predictions are actually made.

After fitting our models to the data, we identified the most predictive features of company value across industries, and compared this to profit margin and size, the metrics most commonly used in Valuing a Business. For our linear models we used the coefficients in our regression equation to determine which features were most important. For our random forest model we used the feature importance metric which ranks features according to the information gained during the fitting process.

Comparison of MethodsResults

The figure to the right depicts the accuracy our models versus the market approach (also known as comparable approach), the method used by valuation firms. With the size of the dataset and the specificity of the market approach we are not surprised that it outperforms our models. Rather we are showing here that our models have a reasonable enough degree of accuracy to trust the interpretation of the features.

Import features across different sectorsAlso on the right we show the top 3 features, according to information gain, per industry as learned by our random forest model. The larger the bar the more insightful that variable was for predictions.The features we see turning up in our model are indicators of profitability and size which agree with the existing knowledge in the literature. It is interesting to note that return on assets shows up in each sector which intuitively means the market values those companies that get high returns regardless of the sector.

Explanation of Key Predictors

Remember our goal was to predict TEV/EBITDA, which is a measure of company’s total value after standardizing for things such as size, tax structure, and number of other factors. There were 5 distinct predictors that really stood out in our analysis.

Return on Assets is a measure of a company’s efficiency in generating profit.

Total Revenue is also known as total sales and is a measurement of how much a company receives from the sale of goods and services.

EBITDA 1 year growth: EBITDA is a measure of profitability and growing EBITDA means growing profit and increasing value of a company.

A Capital Expenditure(Capex) is the amount of money that a company invested in property and equipment. Capex is often linked to the expansion or contraction of a business and is therefore a measure of growth. Looking at Capex as percentage of revenue provides a normalized measurement for comparison.

EBITDA Margin serves as an indicator of a company’s operating profitability. Higher EBITDA margin means the company is getting more EBITDA for every dollar of revenue.


MSSISS or the Michigan Student Symposium for Interdisciplinary Statistical Sciences is an annual conference hosted by the University of Michigan. MSSISS brings together statistics works from a number of different fields including computer science, electrical engineering, statistics, biostatistics, and industrial operations. Our poster was particularly interesting as it was the only one with a financial application. The novelty of our project drew in a number of viewers and impressed the judges. A major component of our poster score was determined by our ability to communicate our results to people outside the field. We received a certificate of merit for our work and ability to communicate it to the other attendees at the conference.

adamy_mssiss (2) copy