Big Data impacts the way we make decisions in business today. Never have I seen a better example of its use than in baseball.

1. “You cant manage
what you don’t
measure”
JUST AS IT’S BEING USED IN OTHER BUSINESSES, BIG DATA IS UTILIZED TO
MAKE BASEBALL TEAMS BETTER AND ORGANIZATIONS MORE PROFITABLE

2. How is Baseball using Big Data
Analytics?
https://www.youtube.com/watch?v=yGf6LNWY9AI
Sabermetrics=“Moneyball” is essentially the term
(stemming from the book written by Michael Lewis)
for using advanced statistics to determine how to
build a team, what strategies to use, and more.
“Our ability to know what’s going to happen, when
it’s going to happen, how much cash we’re going to
generate on the revenue side, allows us to plan
accordingly. That’s a tremendous value proposition
to ownership and executives.”

3. What is there to analyze?
 162 games per team per season (not including pre-
season, playoffs or minor league games) 162*30 mlb
teams=4860 regular season games.
 There are 85 common player stats measured (BA, OBP,
HR… ERA, W, L…) and then each of those stats is
broken down against teams, lefty vs righty, day vs night,
home vs away….
 All of these stats are kept in data banks for every single
pitch thrown, …700,000+ pitches in 2014... Also keep
data on fans, food, drinks, promotions…
 First 135 years of Baseball are a combined 2GB of data
Today each game has almost 1TB of data collected.
That’s a 10 Million fold increase in data collected. And
its predicted that upwards of 7TB per game will be
collected.

4. What do you do with the data?
 Win at an unfair game!
 2002 Oakland A’s
 103-59
 2014-15 Astros
 Shifts
 Predict injuries
 3D Snapshots 10-15 gigs of data
 $1.4 Billion in knee injuries for MLB in 2014
 https://www.youtube.com/watch?v=8avavYawsA8

5. Question??
 There are 4 types of analytics that companies can use
to aid their business:
1. Prescriptive- Takes data and reveals what actions
should be taken
2. Predictive- Takes data and gives an analysis of likely
scenarios of what might happen
3. Diagnostic- A look at past performance to determine
what happened and why
4. Descriptive- What is happening now based on
incoming data
 MLB uses all 4. What type of analytics does your
company use that helps it gain a competitive
advantage?

6. Tools
 Statcast by MLBAM
 Uses Amazon Web Services
 Captures on field data
 Quickly analyzes and codes
 Pitch Rx
 Tracks every pitch
 Uses Camera Triangulation
 Field Fx
 Records all field plays using camera feeds and
object-recognition software
 BaseRuns Estimator
 Estimates the number of runs a team should
score given their offensive statistics and the
number of runs a hitter or pitcher creates or
allows
Source: Whitman School of Business, Syracuse University
Data from the Player Tracking System (Statcast) overlaid on video
of the Panik-Hosmer play. The red section on the right shows that if
Hosmer had maintained his speed instead of diving to the bag,
he would have been safe by about a foot.

7. Tools in use
Cloud
EC2
Compute
Pwr behind
solution
Amazon S3
Storage
7Tb per
Game
Amazon
Elastic
Cache
Temp
Memory,
Fast
Retrieval
AWS Lambda
 Used for Raw Data
Manipulation to Create
“On the Fly” Metrics
 Creates More Insight in
to Plays
Amazon DynamoDB
 Allows for powerful
queries.
 Supports fast retrieval
of information
Dedicated
Connection

8. Discussion Question
Given the emergence of complex
technological tools, how can companies
with smaller budgets stay competitive with
companies that have deep pockets?

9. 3 Key Differences in
Data
• Volume- More data
across the internet
every second vs what
was stored on the
entire internet 20 years
ago
• Velocity-Real time
data i.e. cell phone
location data
• Variety-Large amounts
of data being created
on every topic of
business

10. Data Types
Structured Data
 G = games • Number of games a
player participated in (out of 162
games in a season)
 AB = at bats • Number of times a
batter was hitting and either got a
hit or got out (does not include
walks or reaching base on an
error)
 R = runs • Number of runs the
player scored
 H = hit • Number of times a player
hit the ball or got on base or hit a
home run (sum of 1B, 2B, 3B, HR)
Unstructured Data
 Social Media updates tied to
baseball games/players
 Video
 Photos
 Open ended surveys

11. What are some examples of how
Unstructured Data is used in your
company?
Online Reviews
Facebook “Likes”

12. http://www.marketingpilgrim.com/2013/08
/infographic-major-league-baseballs-top-
social-media-performers.html
Structured data and unstructured data can be
combined to gain insights into new categories.

13. Big Data Acquisition:
 Data harvest from meticulous record-keeping
 (on-base percentage, batting average, slugging/fastball percentages, RBIs, stolen bases, etc…)
 Employ analytics experts: utilize their skillset to build team, field, and manage players
 Expand use: ticketing, promotions, fan-team relationships, concessions and products
 Milwaukee Brewers analyze each email received by teams to better understand fans
 Analyze who the occasional attendees are and how to get them to buy tickets more often
 Boston Red Sox developing concessions heat-map (geo-locating proximity fans to hotdog stands)
 Tracks type, quantity, frequency, and locations of concession purchases
 2014 App “IdealSeat” allows fans to choose seats based upon likelihood of catching foul balls
 Adjust and re-target focus of data sets (player field positions, t-shirt prices) as needed
 Q: What other venues or industries could benefit by a similar depth of big-data analysis?

14. Big Data Governance: Organization
 Effective governance is equal parts: organization and security
 Historic Organization (Waterfall Model): Garbage-In / Garbage-
Out
 Integration of data as it arrives into repository for use
 Indiscriminate harvest; lack of profiling/prioritizing data lengthens
time to organize/use
 Without organizing, data mismatches can damage customer
relations
 (i.e. coupons for women’s shoes sent to male customers)
 Understanding data before to employment is key
 Effective Governance: beyond scrubbing and deletion, focus
on ensuring accuracy
 Identify custodians (who's accountable for data consistency,
accuracy, and archival)
 Develop criteria policies (standards and procedures for use,
purpose, and by who?)
 Enact policy controls and audit (enforcement of policies and
accountability for custodians)

15. Big Data Governance: Security
 Security Issues:
 Financial and Reputational
 Too much data with too many vulnerabilities can be
catastrophic
 2015 Breach at Office of Personnel Management:
 Personal Records, PII (names, addresses,
etc…), Security Clearance details of 21M
citizens
 5.6M sets of fingerprints stolen
 2014 Breach at Home Depot:
 46M credit cards hacked
 Big Data poses Big Risks:
 Big gains can be realized IF security risks are properly
mitigated AND the data harvest is properly organized

16. Conclusion
 Big data is utilized to make teams better and organizations
more profitable
 4 Types of Analytics
1. Prescriptive
2. Predictive
3. Diagnostic
4. Descriptive
 Many tools available to analyze data
 Statcast by MLBAM, Pitch Rx, Field Fx
 Data Types
 Structured & Unstructured
 Effective Governance can ensure accuracy

17. Questions