Welcome!

Java IoT Authors: Elizabeth White, Liz McMillan, Pat Romanski, Yeshim Deniz, Zakia Bouachraoui

Related Topics: @DXWorldExpo, @CloudExpo, @ThingsExpo

@DXWorldExpo: Blog Feed Post

Golden State Warriors Analytics Exercise | @BigDataExpo #BigData #Analytics

Identifying and quantifying variables that might be better predictors of performance

For a recent University of San Francisco MBA class, I wanted to put my students in a challenging situation where they would be forced to make difficult data science trade-offs between gathering data, preparing the data and performing the actual analysis.

The purpose of the exercise was to test their ability to “think like a data scientist” with respect to identifying and quantifying variables that might be better predictors of performance. The exercise would require them to:

  • Set up a basic analytic environment
  • Gather and organize different data sources
  • Explore the data using different visualization techniques
  • Create and test composite metrics by grouping and transforming base metrics
  • Create a score or analytic model that supports their recommendations

I gave them the links to 10 Warrior games (5 regulation wins, 3 overtime losses and 2 regulation losses) as their starting data set.

I then put them in a time boxed situation (spend no more than 5 hours on the exercise) with the following scenario:

You have been hired by the Golden State Warriors coaching staff to review game performance data to identify and quantify metrics that predict a Warriors victory

Here were the key deliverables for the exercise:

  1. I wanted a single, easy-to-understand slide with in-game and/or player recommendations.
  2. I wanted a break out of how they spent their 5 hours across the following categories:
  • Setting up your analytic environment
  • Gathering and organizing the data
  • Visualizing and analyzing the data
  • Creating the analytic models and recommendations
  1. Finally, I wanted back-up information (data, visualizations and analytics) in order to defend their in-game and/or player recommendations.

Exercise Learnings
Here is what we learned from the exercise:

Lesson #1: It’s difficult to not spend too much time gathering and cleansing data. On average, the teams spent 50% to 80% of their time gathering and preparing the data. That only left 10% to 20% of their time for the actual analysis. It’s really hard to know when “good enough” is really “good enough” when it comes to gathering and preparing the data.

Lesson #2: Quick and dirty visualizations are critical in understanding what is happening in the data and establishing hypotheses to be tested. For example, the data visualization in Figure 1 quickly highlighted the importance of offensive rebounds and three-point shooting percentage in the Warriors’ overtime losses.

Figure 1: Use Quick Data Visualizations to Establish Hypotheses to Test

Lesson #3: Different teams came up with different sets of predictive variables. Team #1 came up with Total Rebounds, Three-Point Shooting %, Fast Break Points and Technical Fouls as the best predictors of performance. They tested a hypothesis that the more “aggressive” the Warriors played (as indicated by rebounding, fast break points and technical fouls), the more likely they were to win (see Figure 2).

Figure 2: Testing Potential Predictive Variables

Team #2 came up with the variables of Steals, Field Goal Percentage and Assists as the best predictors of performance (see Figure 3).

Figure 3: ANOVA Table for Team #2

Team #2 then tested their analytic models against two upcoming games: New Orleans and Houston. Their model accurately predicted not only the wins, but the margin of victory fell within their predicted ranges. For example in the game against New Orleans, their model predicted a win by 21 to 30 points, in which the Warriors actually won by 22 (see Figure 4).

Figure 4: Predicting Warriors versus New Orleans Winner

And then in the Houston game, their model predicted a win by 0 to 10 points (where 0 indicated an overtime game), and the Warriors actually won that game by 9 points (see Figure 5).

Figure 5: Predicting Warriors versus Houston Winner

I think I’m taking Team #2 with me next time I go to Vegas!

By the way, in case you want to run the exercise yourself, Appendix A lists the data sources that the teams used for the exercise. But be sure to operate under the same 5-hour constraint!

Summary
A few other learnings came out of the exercise, which I think are incredibly valuable for both new as well as experienced data scientists:

  • Don’t spend too much time trying to set up the perfect analytic environment. Sometimes a simple analytic environment (spreadsheet) can yield consider insights with little effort.
  • Start with small data sets (10 to 20GB). That way you’ll spend more time visualizing and analyzing the data and less time trying to gather and prepare the data. You’ll be able to develop and test hypotheses much more quickly with the smaller data sets running on your laptop, which one can stress test later using the full data set.
  • Make sure that your data science team collaborates closely with business subject matter experts. The teams that struggled in the exercise were the teams that didn’t have anyone who understood the game of basketball (not sure how that’s even possible, but oh well).

One of the many reasons why I love teaching is the ability to work with students who don’t yet know what they can’t accomplish. In their eyes, everything is possible. Their fresh perspectives can yield all sorts of learnings, and not just for them. And yes, you can teach an old dog like me new tricks!

Appendix A:  Exercise Data Sources
Extract “Team Stats” from the Warriors Game Results website: http://www.espn.com/nba/team/schedule/_/name/gs.  Listed below is a cross-section of games from which you may want to use to start your analysis.

Wins

Rockets 1/20/17: http://www.espn.com/nba/matchup?gameId=400900067

Thunder 1/18/17: http://www.espn.com/nba/matchup?gameId=400900055

Cavaliers 1/16/17: http://www.espn.com/nba/matchup?gameId=400900040

Raptors 11/16/16: http://www.espn.com/nba/matchup?gameId=400899615

Trailblazers 1/2/17:  http://www.espn.com/nba/matchup?gameId=400900139

Overtime (Losses)

Houston 12/1/16: http://www.espn.com/nba/matchup?gameId=400899436

Grizzles 1/6/17: http://www.espn.com/nba/matchup?gameId=400899971

Sacramento 2/4/17: http://www.espn.com/nba/matchup?gameId=400900169

Losses

Spurs 10/25/16: http://www.espn.com/nba/boxscore?gameId=400899377

Lakers 11/4/16: http://www.espn.com/nba/matchup?gameId=400899528

Cavaliers 12/25/16: http://www.espn.com/nba/matchup?gameId=400899899

Note: You are welcome to gather team and/or individual stats from any other games or websites that you wish.

The post Golden State Warriors Analytics Exercise appeared first on InFocus Blog | Dell EMC Services.

Read the original blog entry...

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

IoT & Smart Cities Stories
@DevOpsSummit at Cloud Expo, taking place November 12-13 in New York City, NY, is co-located with 22nd international CloudEXPO | first international DXWorldEXPO and will feature technical sessions from a rock star conference faculty and the leading industry players in the world. The widespread success of cloud computing is driving the DevOps revolution in enterprise IT. Now as never before, development teams must communicate and collaborate in a dynamic, 24/7/365 environment. There is no time t...
CloudEXPO New York 2018, colocated with DXWorldEXPO New York 2018 will be held November 11-13, 2018, in New York City and will bring together Cloud Computing, FinTech and Blockchain, Digital Transformation, Big Data, Internet of Things, DevOps, AI, Machine Learning and WebRTC to one location.
The Internet of Things will challenge the status quo of how IT and development organizations operate. Or will it? Certainly the fog layer of IoT requires special insights about data ontology, security and transactional integrity. But the developmental challenges are the same: People, Process and Platform and how we integrate our thinking to solve complicated problems. In his session at 19th Cloud Expo, Craig Sproule, CEO of Metavine, demonstrated how to move beyond today's coding paradigm and sh...
The best way to leverage your Cloud Expo presence as a sponsor and exhibitor is to plan your news announcements around our events. The press covering Cloud Expo and @ThingsExpo will have access to these releases and will amplify your news announcements. More than two dozen Cloud companies either set deals at our shows or have announced their mergers and acquisitions at Cloud Expo. Product announcements during our show provide your company with the most reach through our targeted audiences.
What are the new priorities for the connected business? First: businesses need to think differently about the types of connections they will need to make – these span well beyond the traditional app to app into more modern forms of integration including SaaS integrations, mobile integrations, APIs, device integration and Big Data integration. It’s important these are unified together vs. doing them all piecemeal. Second, these types of connections need to be simple to design, adapt and configure...
Cell networks have the advantage of long-range communications, reaching an estimated 90% of the world. But cell networks such as 2G, 3G and LTE consume lots of power and were designed for connecting people. They are not optimized for low- or battery-powered devices or for IoT applications with infrequently transmitted data. Cell IoT modules that support narrow-band IoT and 4G cell networks will enable cell connectivity, device management, and app enablement for low-power wide-area network IoT. B...
In his session at 21st Cloud Expo, Raju Shreewastava, founder of Big Data Trunk, provided a fun and simple way to introduce Machine Leaning to anyone and everyone. He solved a machine learning problem and demonstrated an easy way to be able to do machine learning without even coding. Raju Shreewastava is the founder of Big Data Trunk (www.BigDataTrunk.com), a Big Data Training and consulting firm with offices in the United States. He previously led the data warehouse/business intelligence and Bi...
Nicolas Fierro is CEO of MIMIR Blockchain Solutions. He is a programmer, technologist, and operations dev who has worked with Ethereum and blockchain since 2014. His knowledge in blockchain dates to when he performed dev ops services to the Ethereum Foundation as one the privileged few developers to work with the original core team in Switzerland.
Contextual Analytics of various threat data provides a deeper understanding of a given threat and enables identification of unknown threat vectors. In his session at @ThingsExpo, David Dufour, Head of Security Architecture, IoT, Webroot, Inc., discussed how through the use of Big Data analytics and deep data correlation across different threat types, it is possible to gain a better understanding of where, how and to what level of danger a malicious actor poses to an organization, and to determin...
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...