Predicting March Madness with IBM Watson Analytics

Mar 18, 20162 likes2,138 views

The document discusses using IBM Watson Analytics to predict the results of the 2016 March Madness college basketball tournament. It introduces Watson Analytics and describes how to prepare basketball tournament data by combining 15 seasons into a single data set. Predictive analysis is conducted on the data to identify the top predictors of teams that reach the Final Four and championship game. A comparison is shown of how accurately previous seasons were predicted. The analysis predicts that North Carolina will be the 2016 tournament winner.

© IBM 2015 1#WatsonAnalytics
Get started at
WatsonAnalytics.com
Ian Balina
WATSON ANALYTICS EVANGELIST
ibalina@us.ibm.com
Predicting March Madness
with IBM Watson Analytics

© IBM 2015 2#WatsonAnalytics
Agenda
• Intro to Watson Analytics
• Data Preparation
• Predictive Analysis
• Comparison of Predictive Model
• Predicting 2016 March Madness

© IBM 2015 #WatsonAnalytics 4
How does Watson Analytics fit into the family of solutions?
Watson Analytics leverages Watson’s unique cognitive capabilities
in three ways:
1. Provides semantic recognition of concepts in your data such
as time, place, and revenue.
2. Identifies recommended starting points for analysis and
targets for prediction.
3. Enables interaction in natural language allowing for the
exploration of data through simple questions like you would
ask a colleague.
Watson Analytics is a cloud based smart data discovery tool from IBM Analytics

© IBM 2015 #WatsonAnalytics 5
Data Preparation
• Source - http://bit.ly/1nPJqma
• Seasons – 2001-2015 men’s college basketball seasons
The hardest part of this analysis is finding the data and merging data sets
• Combine all 15 seasons into one SUPER data set
• Add two columns for Final Four and Championship teams for each year

© IBM 2015 #WatsonAnalytics 6
Predictive Analysis
Top Predictors for Final Four teams
I uploaded the SUPER data set into Watson Analytics and set Final Four and
Championship as the targets to predict
Top Predictors for Championship Teams

© IBM 2015 #WatsonAnalytics 7
Historical Comparison of Predictive Model

© IBM 2015 #WatsonAnalytics 8
Predicting 2016 March Madness
North Carolina
Predicted
Winner

© IBM 2015 9#WatsonAnalytics
WatsonAnalytics.com – get started today!

© IBM 2015 10#WatsonAnalytics
Explore
Continue to ask
questions about
your data using
natural language
Click on the new
insights provided by
Watson Analytics and
explore the text and
visual results

© IBM 2015 11#WatsonAnalytics
Explore
Share images
with your peers,
or email it to
your team

© IBM 2015 12#WatsonAnalytics
Explore
Add in data points
and see things
differently using the
visualization menu,
data filters, color
pallets and more

© IBM 2015 13#WatsonAnalytics
Predict
Additional
insights
discovered
for you
Pin your results
for use in
dashboards and
storytelling
Automated data
visualizations
Identified
predictors of
your target
analysis

© IBM 2015 14#WatsonAnalytics
Predict
Additional tabs for
further predictive
insights
Pinning for
dashboard and
storytelling
Top decision rule
determined for
highest
percentage of
Wins

© IBM 2015 15#WatsonAnalytics
Assemble
Include graphics,
corporate logos
and more to
personalize your
view
Create
dashboards and
infographics
from your pinned
objects
Select from a
wide array of
templates and
color schemes

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While working with Agile projects, we simply can't get away from tracking and showcasing the progress of the project. A typical Agile project would be working with estimates, story points, velocities, burn-up or burn-down charts. I have witnessed numerous sprint reviews and showcases where the business is only waiting to see those few slides of the presentation where there is the "actual" red worm, running against the "planned" green worm, trying to catch-up. If the red worm is ahead, I have seen a smile on the faces of the stakeholders. If it matches the green one, there is a sigh of relief. And as a development team you should just pray that the poor red guy is not falling behind the green one, lest it might lead to a lot of questions starting with why, how, what etc. There have also been times where there have been some unfortunate heated discussions that last forever on why did the team end up not claiming a few points that they had committed. What gets lost is what the team accomplished in the sprint that adds good value to the product. There have also been times where the estimates are being questioned by the product owner or account managers. If you are working in a distributed setup where the product owner is working out of a different country, the problem is even bigger. Let us think about a scenario where the project gets completed on time, budget and scope. Majority (or all) of estimates were correct. However, when the product went live to the market it failed big time. What is the use of building such a product? Are we focusing too much on numbers and points and overlooking the other important aspects of Agile software development such as producing software that delights the customers and looking for ways on how we can measure that? Are we measuring if we are creating a solid, robust and a scalable platform that is ready for future developments and enhancements? Are we measuring the outcomes of the time we are spending in the shoes of the people who will actually use the software? The objective of this presentation is to promote the thinking of measuring what matters for your project. To measure the goals that your software development wants to achieve. I don't plan to showcase an exhaustive list of measurements that can solve all your problems, however, I instead want to highlight some samples that I have used in my projects with the help of my team, that helped us to measure things that add value to the business and development v/S simply creating burn down charts. Majorly, I want to encourage thinking out of the box to identify what measurements will really matter for your projects. Perhaps from the eyes of the users and business and see what things if measured will add a lot more value than simply estimates, and will help in creating a valuable product that will truly delight the business and the users of the product.

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This document provides an introduction to web analytics. It discusses what web analytics is, why it is useful, and who uses web analytics. It explains that web analytics measures user interactions before, during and after visiting a website to understand business performance. It also outlines some key metrics and dimensions that are commonly analyzed, such as bounce rate, pages per visit, and time spent on site. Finally, it emphasizes that the goal of web analytics is to generate insights from data that can be used to optimize the online business.

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Predicting March Madness with IBM Watson Analytics

1. © IBM 2015 1#WatsonAnalytics Get started at WatsonAnalytics.com Ian Balina WATSON ANALYTICS EVANGELIST [email protected] Predicting March Madness with IBM Watson Analytics

2. © IBM 2015 2#WatsonAnalytics Agenda • Intro to Watson Analytics • Data Preparation • Predictive Analysis • Comparison of Predictive Model • Predicting 2016 March Madness

3. © IBM 2015 #WatsonAnalytics 3 IBM Watson To focus on bringing Watson to market, three key areas were identified: • Natural language processing • Hypothesis generation • Evidence-based learning Trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer, and in some ways, think. The Watson you know that won Jeopardy! is now an IBM brand for a next generation of cognitive computing solutions.

4. © IBM 2015 #WatsonAnalytics 4 How does Watson Analytics fit into the family of solutions? Watson Analytics leverages Watson’s unique cognitive capabilities in three ways: 1. Provides semantic recognition of concepts in your data such as time, place, and revenue. 2. Identifies recommended starting points for analysis and targets for prediction. 3. Enables interaction in natural language allowing for the exploration of data through simple questions like you would ask a colleague. Watson Analytics is a cloud based smart data discovery tool from IBM Analytics

5. © IBM 2015 #WatsonAnalytics 5 Data Preparation • Source - http://bit.ly/1nPJqma • Seasons – 2001-2015 men’s college basketball seasons The hardest part of this analysis is finding the data and merging data sets • Combine all 15 seasons into one SUPER data set • Add two columns for Final Four and Championship teams for each year

6. © IBM 2015 #WatsonAnalytics 6 Predictive Analysis Top Predictors for Final Four teams I uploaded the SUPER data set into Watson Analytics and set Final Four and Championship as the targets to predict Top Predictors for Championship Teams

10. © IBM 2015 10#WatsonAnalytics Explore Continue to ask questions about your data using natural language Click on the new insights provided by Watson Analytics and explore the text and visual results

12. © IBM 2015 12#WatsonAnalytics Explore Add in data points and see things differently using the visualization menu, data filters, color pallets and more

13. © IBM 2015 13#WatsonAnalytics Predict Additional insights discovered for you Pin your results for use in dashboards and storytelling Automated data visualizations Identified predictors of your target analysis

14. © IBM 2015 14#WatsonAnalytics Predict Additional tabs for further predictive insights Pinning for dashboard and storytelling Top decision rule determined for highest percentage of Wins

15. © IBM 2015 15#WatsonAnalytics Assemble Include graphics, corporate logos and more to personalize your view Create dashboards and infographics from your pinned objects Select from a wide array of templates and color schemes

Predicting March Madness with IBM Watson Analytics

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