Data Mining in Sports Analytics - Salford Systemsmedia.salford-systems.com/Salford_Analytics_and_Data_Mining...Data in Sports Analytics áAny game is the ultimate and unambiguous source

Data Mining in

Sports Analytics

Data Mining in

Sports Analytics

Salford Systems

Dan Steinberg

Mikhail Golovnya

2 © Salford Systems, 2012

Data Mining Defined Data Mining Defined

áData 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

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áThe literature often refers to finding hidden

information in data

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Uses of Data Mining Uses of Data Mining

DATA MINING

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Long Live the King

=Your Data=

Long Live the King

=Your Data=

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áAnalyst asks the right questions but

makes no assumptions

© Salford Systems, 2012 4

áThe success of data mining solely depends on the

quality of available data

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The Essence of Machine Learning The Essence of Machine Learning

á In a nutshell: Use historical data to gain insights

and/or make predictions on the new data


Data in Sports Analytics Data in Sports Analytics

áAny 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

áHowever, there is no universal agreement on the

best way of organizing and summarizing the

results in a numeric form

Large number or 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 © Salford Systems, 2012 6

Baseball Stats Baseball Stats á Available from many sources,

including the Internet

á Player level: summarize

performance in a season, post

season, and entire career

á Team level: wins and losses

á Game level: most detailed


Baseball Databases Baseball Databases

á Widely

known public

database

á Gathers

baseball

stats all the

way back to

1871

á Will use

parts of it to

illustrate the

potential of

data mining


Typical DM Problem Typical DM Problem

áFocus on the 2010 versus 2011

regular season performance in both leagues

áHave access to the player stats for the entire

season organized in a flat table

áDefine 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

áQuestion: Which of the player stats were

associated with the team winning the division?


Batting Stats Batting Stats Name Description

AB At Bats

R Runs

H Hits

2B Doubles

3B Triples

HR Home Runs

RBI Runs Batted In

SB Stolen Bases

CS Caught Stealing

BB Base on Balls

SO Strikeouts

SF Sacrifice Flies

HBP Hit by pitch


Core Stats

Name Description

AVG Batting Average

H/AB

TB Total Bases

B1 + 2x2B + 3x3B + 4xHR

SLG Slugging

TB/AB

OBP On Base Percentage

(H+BB+HBP)/(AB+BB+SF+HBP)

OPS On Base Plus Slugging

OBP + SLG

È Many more exist

Derived Stats

Conventional Statistical Approaches Conventional Statistical Approaches

á This is how the

problem is usually

attacked

á Each dot represents

a single batter record

for the whole 2010

season

á 1245 overall records

á 16 core stats

á Winning team

batters are marked in

red

á No obvious insights!


Unique Personalities Ï

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Unique Personalities Ï

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á Starting with CART in 1984,

laid the foundation for tree-

based modeling techniques

á Conduct deep look into all

available data

á Point out most relevant

variables and features

á Automatically identify optimal

transformations

á Capable of extracting complex

patterns going way beyond the

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TreeNet Model on Core Stats TreeNet Model on Core Stats


Key Findings Key Findings

á 6 core batter stats were identified as most predictive

á 15-20% of total variation can be directly associated with the batter

stats

á The single plots show non-linear nature of many of the relationships

Fine plot irregularities should be ignored

á Striking result: In 2010 season HR above 30 is associated with

loosing the division!

á 2011 season looks fine

á Proceed by digging into pair-wise contribution plots


Surprise: 2010 HR Leads to Division Loss! Surprise: 2010 HR Leads to Division Loss!


Comments on Batting Comments on Batting

á3D dependency plots further highlight the rather

unusual HR finding for the 2010 season

á It is a well-known fact that batters aiming at a

home run have higher number of strike-outs

This is supported by both graphs

áHowever, in 2010 regular season the HR-centered

approach lead to a defeat!


Compare with Conventional Plot Compare with Conventional Plot

á This plot represents

two performance

stats plotted against

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á Note the difficulty at

discerning the

identified HR x SO

pattern visually

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Pitching Stats Pitching Stats

á Similar to batting stats

á Large number of derived stats

exists


Pitching Stats Pitching Stats Name Description

W Wins

L Losses

H Hits Allowed

BFP Batters Faced

R Runs Allowed

HR Home Runs Allowed

WP Wild Pitches

IPOUTS Outs Pitched

SHO Shutouts

BB Base on Balls

SO Strikeouts

ER Earned Runs

HBP Batters Hit by Pitch


Core Stats

Name Description

ERA Earned Run Average

9xER/InningsPitched

DICE Defense Independent Component

3.0+(13HR+3(BB+HBP)-2SO)/IP

FIP Fielding Independent Pitching

3.1+(13HR+3BB-2SO)/IP

dERA Defense Independent ERA

10-line algorithm

CERA Component ERA

Long convoluted equation

È Many more exist

Derived Stats

Modeling Steps Modeling Steps

á Started by feeding a

complete set of

available 26 pitching

stats for 2010 season

performance

á Using top variable

elimination followed by

bottom variable

elimination technique,

reduced the list to only

7 important stats


One-Variable Contributions One-Variable Contributions


á 2011 season

agrees with what

is normally

expected

á 2010 season

surprises with

higher HR values

working in favor of

pitchers!

á This is further

supported by the

SO dependency

One-Variable Contributions One-Variable Contributions


á BB and R stats

agree with

expectations in

both seasons

á 2010 season

surprises with

higher WP values

working in favor of

pitchers!

á This could be

related to previous

findings

Two-Variable Contributions Two-Variable Contributions


á 2010 surprise: Keep the strikeouts high and the base on balls low

to win the division!

Two-Variable Contributions Two-Variable Contributions


á 2010 surprise: More wild pitches, more home runs allowed, more

strikeouts => the division is won!

Compare with Conventional Plot Compare with Conventional Plot

á Conventional plot for 2010

season IGNORES other

dimensions which effectively

project on top of each other

á As a result, there is a lot of

confusion on the plot making

it difficult to see any pattern

á In contrast, TN dependence

plot shows the given pair

contribution AFTER the

influence of other dimensions

has been eliminated


Compare with Conventional Regression Compare with Conventional Regression

á These plots represent

the results of running

conventional linear

regression (LR) on the

pitching data

á While the anomalous

HR-effect is present,

the model fails at

identifying the fine local

nature of the

phenomenon

á LR does not provide

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What Have We Learnt What Have We Learnt

á It appears that in the 2010 regular season

Home Run driven strategy did not work!

á At least, this is what the data tells us, further

understanding will require experts in the field

á Core stats have good explaining potential once

put into true multivariate modeling framework

á Conventional statistics approaches do not have

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á 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 © Salford Systems, 2012 27


Data Mining Mythology Data Mining Mythology

á NEVER FALL FOR THESE

á Absolute Powers Î data mining will finally

find and explain everything

á Gold Rush Î with the right tool one can rip

the stock-market or predict World-Series

winner to become obscenely rich

á Quest for the Holy Grail Î search for an

algorithm that will always produce 100%

accurate models

á Magic Wand Î getting a complete solution

from start to finish with a single button push

The End The End


Documents

Data Mining in Sports Analytics - Salford Systemsmedia.salford-systems.com/Salford_Analytics_and_Data_Mining...Data in Sports Analytics áAny game is the ultimate and unambiguous source