The document discusses using data mining techniques to analyze baseball player and pitcher statistics from the 2010 regular season. Decision tree models identified several key stats that were associated with teams winning their divisions that year, such as keeping strikeouts high and walks low for pitchers. Counterintuitively, having too many home runs was linked to losing the division. Data mining provided more insights than traditional statistical analyses by revealing complex, nonlinear relationships between performance metrics.

1. Salford Systems
Dan Steinberg
Mikhail Golovnya

2.  Data mining is the search for patterns in data
using modern highly automated, computer
intensive methods
◦ Data mining may be best defined as the use of a specific
class of tools (data mining methods) in the analysis of
data
◦ The term “search” is key to this definition, as is
“automated”
 The literature often refers to finding hidden
information in data

3. Data Mining Data Mining Cont.
• Predictive • Statistics • OLAP
Analytics • Computer science • CART
• Machine Learning • Insurance • SVM
• Pattern • Finance • NN
Recognition • Marketing • CRISP-DM
• Artificial • Robotics • CRM
Intelligence
• Biotech • KDD
• Business
• Sports Analytics • Etc.
Intelligence
• Data Warehousing

4.  Data guides the analysis, it is the “Alpha and
Omega” of everything you do
 Analyst asks the right questions but makes
no assumptions
 The success of data mining solely depends on
the quality of available data
◦ Famous “Garbage In – Garbage Out” principle

5.  (Insert visual aid)
 In a nutshell: Use historical data to gain
insights and/or predictions on the new data

6.  Any game is the ultimate and unambiguous source of
the quality data
◦ This is very different from the data availability and quality
in other areas of research
 However, there is no universal agreement on the best
way of organizing and summarizing the results in a
numeric form
◦ Large number of various game statistics available
◦ Common sense and game rules are at the core
◦ Heated debates on which stats best describe the potential
for a future win

7.  (insert screenshot of Baseball-reference.com)
 Available from many sources, including the
Internet
 Player level: summarize performance in a season,
post season, and entire career
 Team level: wins and losses
 Game level: most detailed

8.  (insert Sean Lahman website screenshot)
 Widely known public database
 Gathers baseball stats all the way back to
1871
 Will use parts of it to illustrate the potential
of data mining

9.  Focusing on the 2010 regular season performance in
both leagues
 Have access to the player stats for the entire season
organized in a flat table
 Define a measure of the overall player success simply
by having the team winning its division
◦ Thus 6 out of 30 participating teams in 2010 are declared
as success
 Question: Which of the player stats are associated
with the team winning the division?

10. Core Stats Derived Stats
•AB-At Bats •AVG-Batting Average H/AB
•R-Runs •TB-Total Bases
•H-Hits B1+2x2B+3x3B+4xHR
•2B-Doubles •SLG-Slugging TB/AB
•3B-Triples •OBP-On Base Percentage
•HR-Home Runs (H+BB+HBP)/(AB+BB+SF+HBP)
•RBI-Runs Batted In •OPS-On Base Plus Slugging OBP+SLG
•SB-Stolen Bases •…-Many more exist
•CS-Caught Stealing
•BB-Base on Balls
•SO-Strikeouts
•SF-Sacrifice Flies
•HBP-Hit by pitch

11.  (insert scatter matrix)
 This is how the problem is usually attacked
 Each dot represents a single batter record for the whole
2010 season
 1245 overall records
 16 core stats
 Winning team batters are marked in red
 No obvious insights!

12.  Leo Breiman, Jerome Friedman, Richard Olshen and
Charles Stone
 Starting with CART in 1984, laid the foundation for tree-
based modeling techniques
 Conduct deep look into all available data
 Point out most relevant variables and features
 Automatically identify optimal transformations
 Capable of extracting complex patterns going way beyond
the traditional “single performance at a time” approach

13.  (insert graph)
 6 core batter stats were identified as most predictive
 About 20% of total variation can be directly associated with
the batter stats
 The single plots show non-linear nature of many of the
relationships
 Fine plot irregularities should be ignored
 Striking result: HR above 30 is associated with loosing the
division
 Proceed by digging into pair-wise contribution plots

14.  (insert images)
 The colored area within each plot shows pairs
that actually occur in the data
 Areas associated with contribution towards
team win are marked in red
 Contributions towards team defeat are
marked in blue

15.  (insert graphs)
 These two plots further highlight the rather
unusual HR finding
 It is a well-known fact that batters aiming at
a home run have higher number of strike-
outs
 However, in 2010 regular season the HR-
centered approach lead to a defeat!

16.  (insert graph)
 This plot represents two performance stats
plotted against each other taken “as is” from
the original data table
 Note the difficulty at discerning the identified
HR X SO pattern visually because of “shadow”
projections

17.  (Insert graphs)

18.  (Insert screen shot of Baseball-reference.com
and standard pitching chart)
 Similar to batting stats
 Large number of derived stats exists

19. Core Stats Derived Stats
•W-Wins •ERA-Earned Run Average
•L-Losses 9xER/InningsPitched
•H-Hits Allowed •DICE-Defense Independent
•BFP-Batters Faced Component 3.0+(13HR+3(BB+HBP)-
2SO)/IP
•R-Runs Allowed
•FIP-Fielding Independent Pitching
•HR-Home Runs Allowed
3.1+(13HR+3BB-2SO)/IP
•WP-Wild Pitches
•dERA-Defense Independent ERA 10-
•IPOUTS-Outs Pitched line algorithm
•SHO-Shutouts •CERA-Component ERA Long
•BB-Base on Balls convoluted equation
•SO-Strikeouts •…-Many more exist
•ER-Earned Runs
•HBP-Batters Hit by Pitch

20.  (Insert charts)
 Started by feeding a complete set of available
26 pitching stats for 2010 season
performance
 Using top variable elimination followed by
bottom variable elimination
technique, reduced the list to only 8
important stats

21.  (inset graphs)

22.  (insert graphs)
 Keep the strikeouts high and the base on
balls low to win the division!

23.  (insert graphs)
 Remember that these are pitchers not batters
 More wild pitches, more home runs
allowed, more strikeouts=>the division is
won!

24.  (insert graph)
 Conventional plot IGNORES other dimensions
which effectively project on top of each other
 As a result, there is a lot of confusion on the
plot, making it difficult to see any pattern
 In contrast, TN dependence plot shows the
given pair contribution AFTER the influence of
other dimensions has been eliminated

25.  (insert graphs)
 These plots represent the results of running
conventional linear regression (LR) on the
pitching data
 While the anomalous HR-effect is present, the
model fails at the identifying the fine local
nature of the phenomenon
 LR does not provide enough “resolution”

26.  It appears that in the 2010 regular season Home Run
driven strategy did not work!
 At least, this is what the data tells us, further
understanding will require experts in the field
 Core stats have good explaining potential once put into
true multivariate modeling framework
 Conventional statistics approaches do not have enough
“resolution” to see the real details
 Modern Data Mining helps identifying realized patterns
and allows quick and efficient check of the usefulness of
various performance measures available to a manager or
researcher

27.  NEVER FALL FOR THESE
 Absolute Powers- data mining will finally find and
explain everything
 Gold Rush- with the right tool one can rip the stock-
market or predict World-Series winner to become
obscenely rich
 Quest for the Holy Grail- search for an algorithm that
will always produce 100% accurate models
 Magic Wand- getting a complete solution from start
to finish with a single button push

28. The End