For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Factorization machines are a new type of model that is well suited to very high-cardinality, sparsely observed transactional data. This paper presents the new FACTMAC procedure, which implements factorization machines in SAS^® Visual Data Mining and Machine Learning. This powerful and flexible model can be thought of as a low-rank approximation of a matrix or a tensor, and it can be efficiently estimated when most of the elements of that matrix or tensor are unknown. Thanks to a highly parallel stochastic gradient descent optimization solver, PROC FACTMAC can quickly handle data sets that contain tens of millions of rows. The paper includes examples that show you how to use PROC FACTMAC to recommend movies to users based on tens of millions of past ratings, predict whether fine food will be highly rated by connoisseurs, restore heavily damaged high-resolution images, and discover shot styles that best fit individual basketball players. ^®

In fantasy football, it is a relatively common strategy to rotate which team's defense a player uses based on some combination of favorable/unfavorable player matchups, recent performance, and projection of expected points. However, there is danger in this strategy because defensive scoring volatility is high, and any team has the possibility of turning in a statistically bad performance in a given week. This paper uses data mining techniques to identify which National Football League (NFL) teams give up high numbers of defensive penalties on a week-to-week basis, and to what degree those high-penalty games correlate with poor team defensive fantasy scores. Examining penalty count and penalty yards allowed totals, we can narrow down which teams are consistently hurt by poor technique and find correlation between games with high penalty totals to their respective fantasy football score. By doing so, we seek to find which teams should be avoided in fantasy football due to their likelihood of poor performance.

In today's instant information society, we want to know the most up-to-date information about everything, including what is happening with our favorite sports teams. In this paper, we explore some of the readily available sources of live sports data, and look at how SAS^® technologies, including SAS^® Event Stream Processing and SAS^® Visual Analytics, can be used to collect, store, process, and analyze the streamed data. A bibliography of sports data websites that were used in this paper is included, with emphasis on the free sources.

When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

Prince Niccolo Machiavelli said things on the order of, The promise given was a necessity of the past: the word broken is a necessity of the present. His utilitarian philosophy can be summed up by the phrase, The ends justify the means. As a personality trait, Machiavelianism is characterized by the drive to pursue one's own goals at the cost of others. In 1970, Richard Christie and Florence L. Geis created the MACH-IV test to assign a MACH score to an individual, using 20 Likert-scaled questions. The purpose of this study was to build a regression model that can be used to predict the MACH score of an individual using fewer factors. Such a model could be useful in screening processes where personality is considered, such as in job screening, offender profiling, or online dating. The research was conducted on a data set from an online personality test similar to the MACH-IV test. It was hypothesized that a statistically significant model exists that can predict an average MACH score for individuals with similar factors. This hypothesis was accepted.

Graphics are an excellent way to display results from multiple statistical analyses and get a visual message across to the correct audience. Scientific journals often have very precise requirements for graphs that are submitted with manuscripts. While authors often find themselves using tools other than SAS^® to create these graphs, the combination of the SGPLOT procedure and the Output Delivery System enables authors to create what they need in the same place as they conducted their analysis. This presentation focuses on two methods for creating a publication quality graphic in SAS^® 9.4 and provides solutions for some issues encountered when doing so.

Ensemble models have become increasingly popular in boosting prediction accuracy over the last several years. Stacked ensemble techniques combine predictions from multiple machine learning algorithms and use these predictions as inputs to a second level-learning algorithm. This paper shows how you can generate a diverse set of models by various methods (such as neural networks, extreme gradient boosting, and matrix factorizations) and then combine them with popular stacking ensemble techniques, including hill-climbing, generalized linear models, gradient boosted decision trees, and neural nets, by using both the SAS^® 9.4 and SAS^® Visual Data Mining and Machine Learning environments. The paper analyzes the application of these techniques to real-life big data problems and demonstrates how using stacked ensembles produces greater prediction accuracy than individual models and na ve ensembling techniques. In addition to training a large number of models, model stacking requires the proper use of cross validation to avoid overfitting, which makes the process even more computationally expensive. The paper shows how to deal with the computational expense and efficiently manage an ensemble workflow by using parallel computation in a distributed framework.

In the 2015-2016 season of the National Basketball Association (NBA), the Golden State Warriors achieved a record-breaking 73 regular-season wins. This accomplishment would not have been possible without their reigning Most Valuable Player (MVP) champion Stephen Curry and his historic shooting performance. Shattering his previous NBA record of 286 three-point shots made during the 2014-2015 regular season, he accrued an astounding 402 in the next season. With an increased emphasis on the advantages of the three-point shot and guard-heavy offenses in the NBA today, organizations are naturally eager to investigate player statistics related to shooting at long ranges, especially for the best of shooters. Furthermore, the addition of more advanced data-collecting entities such as SportVU creates an incredible opportunity for data analysis, moving beyond simply using aggregated box scores. This work uses quantile regression within SAS^® 9.4 to explore the relationships between the three-point shot and other relevant advanced statistics, including some SportVU player-tracking data, for the top percentile of three-point shooters from the 2015-2016 NBA regular season.

In the world of gambling, superstition drives behavior, which can be difficult to explain. Conflicting evidence suggests that slot machines, like BCLC's Cleopatra, perform well regardless of where they are placed on a casino floor. Other evidence disputes this, arguing that performance is driven by their strategic placement (for example, in high-traffic areas). We explore and quantify the location sensitivity of slot machines by leveraging SAS^® to develop robust models. We test various methodologies and data import techniques (such as casino CAD floor plans) to unlock some of the nebulous concepts of player behavior, product performance, and superstition. By demystifying location sensitivity, key drivers of performance can be identified to aid in optimizing the placement of slot machines.