1. Prospecting and Mining
MLB (Sports) Data
Ryan Elmore
rtelmore@gmail.com
Twitter: rtelmore
August 6, 2011
Rocky Mountain SABR Meeting

2. Data Science

3. ... And Hype
The Economist May 14-20,
2011: “Corporate chefs
are in demand again, office
rents are soaring and the
pay being offered to
talented folk in fashionable
fields like data science
is reaching Hollywood
levels.”

4. Not Your Typical Tech Talk

5. A Delayed Flight at DIA ...

6. Why Are The Games Boring?

7. Simmons Red Sox Data

8. Simmons Red Sox Data

9. A Few Thoughts ...
• The underlying data unit is in minutes. So?
• Why is he only looking at this particular set
of year?
• How do the Red Sox compare to the other
teams in MLB?
• Crap, that last point will require
downloading a lot of data ... and my flight
was boarding in 10 minutes!

10. Where Can We Get Data?

11. Where Can We Get Data?
http://www.baseball-reference.com/teams/COL/2010-schedule-scores.shtml

12. Where Can We Get Data?
http://www.baseball-reference.com/teams/COL/2010-schedule-scores.shtml
Just step through
all of the teams:
COL, BOS, etc.

13. Where Can We Get Data?
http://www.baseball-reference.com/teams/COL/2010-schedule-scores.shtml
Just step through and any years
all of the teams: that you are
COL, BOS, etc. interested in.

14. A Typical Website
The Golden Nugget

15. Data All Teams

16. Another Visual Representation

17. Boston vs. The Rest

18. Are The Games Getting Longer?
• I don’t know!
• I would say that the evidence supports an
increase up until 2000 and then it’s been
constant or slightly decreasing.
• This is not an exercise in statistical
inference; I was just mining the data and
looking for trends.
• thelogcabin.wordpress.com/
• github.com/rtelmore/MLB

19. Another Exercise
• In a conversation with Paul Parker, he asked
if the minimum number of pitches per (full)
inning (6 pitches) has ever been attained.
• This is a hard problem!
• Where do you find this sort of data?
• Back to baseball-reference.com ... the box
scores.
http://www.baseball-reference.com/boxes/COL/COL201104010.shtml

20. The Boxscore Website
The Golden Nugget

21. How Do We Proceed?
The most systematic way that I could find
was to break it down like this:
• 30 Teams
• 2005 - 2010
• Everyday from Apr 1 through Oct 31
• This is a little more than 78K URLs!
• My program took about 3 hrs 25 min.

22. Was That Minimum Attained?
• NO! Unless there is an error in my code.
• Did we learn something? Of course.
• Example: I should’ve stored everything in a
database while I was downloading and
processing the data. Why? I didn’t save any
of the data from the 3+ hrs of computing.
http://www.baseball-reference.com/boxes/COL/COL201104010.shtml

23. Using Google Trends Data
• http://www.google.com/trends
• You can put a search term in and it will
return a lot of historical statistics related to
your query (e.g., flu trends)
• There is an R package (RGoogleTrends)
that allows access to the GT API if you have
a google account (e.g., gmail).
• Use the getGTrends(“query”) function

24. GT “Colorado Rockies”

25. GTrends “Colorado Rockies”
World Series
Playoff Run

26. Google Trends “NFL”

27. Google Trends “NFL”

28. Google Trends “NFL”

29. Google Trends “NFL”

30. Conclusions/Discussion
• There is a lot of data available on the web!
• You can access this data from a browser;
however, you can access A LOT more data
if you let your computer do the work.
• Good tools for data mining: R, python,
perl, etc.
• Download data and see where you go

31. Resources
• thelogcabin.wordpress.com
• github.com/rtelmore
• baseball-reference.com, espn.com, Google
Trends, etc.
• Twitter (@rtelmore)
• www.r-project.org
• www.meetup.com/DenverRUG