Football for kms nfl 01
Download
1 / 36

Football for KMS: NFL ‘01 - PowerPoint PPT Presentation


  • 74 Views
  • Uploaded on

Football for KMS: NFL ‘01. APRIL 30 TH 2008. Abhijit Kumar Kaijia Bao Vishal Rupani. Course Instructor: Prof. Hsinchun Chen. Agenda. VISHAL. KAI. ABHI. Data Cleaning Statistical Analysis Final Paper. Data Collection Client Relations Final Presentation. Data Import

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Football for KMS: NFL ‘01' - justine-roy


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Football for kms nfl 01

Football for KMS: NFL ‘01

APRIL 30TH 2008

Abhijit Kumar

Kaijia Bao

Vishal Rupani

Course Instructor: Prof. Hsinchun Chen


Agenda
Agenda

VISHAL

KAI

ABHI

Data Cleaning

Statistical Analysis

Final Paper

Data Collection

Client Relations

Final Presentation

Data Import

Data Transformation

Data Mining

  • Data Mining Techniques

  • Key Findings

  • KMS Demonstration

Objectives

Literature Overview

Conclusion

  • Knowledge DiscoveryStatistical Analysis


Research objectives
Research Objectives

  • Pattern identification

    • Descriptive Statistics

    • Data Mining Techniques

  • Prediction

    • Developing a strategy

    • Fantasy League


Literature overview
Literature Overview

  • Moneyball: The Art of Winning an Unfair Game

    Michael Lewis

  • Las Vegas Odds

    www.VegasInsider.com

  • NFL Fantasy League

    www.Nfl.com/fantasy


Knowledge discovery process
Knowledge Discovery Process

TRANSFORMATION

DATA

Dependent Variables

Calculated Variables

Independent Variables

Play Decision, Intended Player, Play Direction, Yards

Pro-Football

-3 Tables

-40 Columns

-82,346 Rows

Lisa Ordonez

-1 Table

-90 Columns

-50,417 Rows

GameNum, IsPlayChal, PlayZone, TotalOffTO, PlayDecision, QtrTimeLeft, HalfTimeLeft, GameTimeLeft

Defense, Down, GAP, Halftime Left, Off Ydl, Offense, Play Zone, QTR, ToGo, Total Off TO

SQL 2005 AS

SQL 2005 IS


Knowledge discovery process1
Knowledge Discovery Process

MINING

PROCESSING

Models

- ID3

- Neural Networks

Accuracy

-Lift Charts

-Classification Matrix

TRANSFORMATION

  • Simple Statistics

  • -Play Decision

  • Intended Player

  • Play Direction

  • Yards

DATA

Dependent Variables

Calculated Variables

Independent Variables

Pro-Football

-3 Tables

-40 Columns

-82,346 Rows

Lisa Ordonez

-1 Table

-90 Columns

-53,000 Rows

SQL 2005 AS

MS Excel 2007

SQL 2005 AS

SQL 2005 IS




Intended player statistics
Intended Player: Statistics

Top 3 Intended Players for Passes for the 4 teams that played in the semi-finals

H.Ward (142), P.Burress (121), B.Shaw (44)

T.Brown (143), D.Patten (93), M.Edwards (39)

T.Holt (133), M.Faulk (104), I.Bruce (103)

J.Thrash (107), D.Staley (89), T.Pinkston (83)


Play direction statistics
Play Direction: Statistics

  • Direction of Rushes for all plays in 2001 season

Right Tackle

Right Guard

Left Tackle

Left Guard

Right End

Left End

Middle

Middle


Play direction statistics1
Play Direction: Statistics

  • Direction of Rushes for all plays in 2001 season

Number of Rushes

Direction


Yardage statistics
Yardage: Statistics

  • Yardage during each down for Pass and Rush

Passes

Rushes

Average Yards Covered

Yards To Go


Play decision statistics
Play Decision: Statistics

  • Play Decisions for the 4 teams that played in the semi-finals

Play Decision Type

Number of Decisions


Play decision analysis overview
Play Decision: Analysis Overview

  • Discovery of what environmental and/or game factors affect play decision

  • Discovery of football expert knowledge through data mining

  • Prediction of play decisions based on game factors








Play decision key findings
Play Decision: Key Findings

  • Football strategy can be discovered through data, instead of knowledge experts

  • Top 3 factors affecting decision:

    • Down, Off Ydl, Time

  • Accuracy of the models are different depending on the decision we are trying to predict

  • Team specific strategies may be discovered with more data.


Play direction analysis overview
Play Direction: Analysis Overview

  • Discover team’s strengths and weakness in their defense and/or offense

  • Prediction of play directions based on game factors

Right Tackle

Right Guard

Left Tackle

Left Guard

Right End

Left End

Middle

Middle




Intended player analysis overview
Intended Player: Analysis Overview

  • Discover each team’s favored recipient of a pass

  • Prediction of intended player based on game factors



Intended player key findings
Intended Player: Key Findings

  • There are 400+ intended players

  • Not enough data to accurately predict intended players

  • Not enough data to gain knowledge over statistical models



Future direction
Future Direction

  • Increase sample set

    • More instances of different scenarios

  • Incorporate additional information

    • Pro-football-Reference.com

    • VegasInsider.com (Odds for favorites)

  • Extend Analysis

    • Nested case (Historical performance)


References
References

  • Prof. Lisa Ordóñez

    • Professor in Statistics

  • Steve Aldrich

    • Author of Moneyball in Football

  • About Football

    • Glossary of terms


Knowledge discovery process2
Knowledge Discovery Process

MINING

PROCESSING

Models

- ID3

- Neural Networks

Accuracy

-Lift Charts

-Classification Matrix

TRANSFORMATION

  • Simple Statistics

  • -Play Decision

  • Intended Player

  • Play Direction

  • Yards

DATA

Dependent Variables

Calculated Variables

Independent Variables

Pro-Football

-3 Tables

-40 Columns

-82,346 Rows

Lisa Ordonez

-1 Table

-90 Columns

-53,000 Rows

SQL 2005 AS

MS Excel 2007

SQL 2005 AS

SQL 2005 IS


Research Objectives

Literature Overview

Knowledge Discovery

Statistics: Intended Player

Statistics: Play Direction

Statistics: Yardage

Statistics: Play Decision

Accuracy: Lift Chart Charts

Analysis: Play Decision

Analysis: Play Direction

Analysis: Intended Player

Conclusions

Future Directions

System Design



Data collection
Data Collection

55,000 rows

90 columns

47,033 rows

30 columns

Dependent – 4

Independent – 10

Calculated - 9


System design
System Design

NFL KMS

FOOTBALL DATA

NFL Season 2001

Model Building

DB

Testing/ Accuracy

Pattern Analysis

FIELD STRATEGY

DEFENSE STRATEGY

METRICS

Formations

Accuracy

Substitutions

Performance

Play Decisions


Yards analysis
Yards Analysis

  • Yards gained on the play is used as a metric to measure effort

  • Discover how environmental and/or game factors affect player’s efforts

  • Key Findings: Top 4 environmental factors

    • Off Ydl

    • Time

    • Down

    • Gap


ad