Loras College 2014 Business Analytics Symposium | Dan Conway: Sports Analytics

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Sports Analytics…

… game on

Daniel Conway

Director of the Center for Business Analytics

Loras College

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Who cares about sports?

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Where does analytics fit in sports?

• Improve/predict player performance• HR / Personnel• Entertainment

2006 300 attend MIT’s Sports Analytics Conference2011 & beyond capped at 2000 in person & thousands more online

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2014 SABR – Society for American Baseball Research

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Agenda

• Moneyball• Baseball, Football, and Basketball

• HR• Entertainment

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America’s Pastime

Peanuts, Crackerjack, and Numbers

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The Pythagorean Theorem

RunsAllowed

RunsScored

Estimated % of games won

Runs scored2

----------------------------------Runs scored2 + Runs allowed2

1.96 is the optimal exponent, 2.37 for football

15.4 for basketball

Magic Quadrant Boston Detroit St. Louis…

Tough Year Houston Minnesota Philadelphia

Offensive LA Angels Toronto Colorado

Duals Atlanta Pittsburgh LA Dodgers

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Some Definitions

Bill James “Runs Created” formula:

RC = (hits + BB + HBP) x (TB) / (AB + BB + HBP)Blue: # baserunnersGreen: Rate at which runners are advanced

Familiar? (Newton: Mass * Velocity)

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Decision Making in Sports

Blitz or drop backSwing, take pitch

Pass, drive, shoot

8 iron or 9 iron

Pass, run, punt

Foul or play

Pitch out

Attempt base steal

Call Timeout

Change Pitchers

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Trade? Why would you trade Albert Pujols?

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State Transitions of Baseball

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 000 .54 46,180

1 000 .29 32,921

2 000 .11 26,009

0 001 1.46 512

1 001 .98 2,069

2 001 .38 3,129

0 010 1.17 3,590

1 010 .71 6,168

2 010 .34 7,709

0 011 2.14 688

1 011 1.47 1,770

2 011 .63 1,902

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 100 .93 11,644

1 100 .55 13,483

2 100 .25 13,588

0 101 1.86 1,053

1 101 1.24 2,283

2 101 .54 3,117

0 110 1.49 2,786

1 110 .97 4,978

2 110 .46 6,545

0 111 2.27 805

1 111 1.6 1,926

2 111 .82 2,280

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State Transitions of Baseball

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 000 .54 46,180

1 000 .29 32,921

2 000 .11 26,009

0 001 1.46 512

1 001 .98 2,069

2 001 .38 3,129

0 010 1.17 3,590

1 010 .71 6,168

2 010 .34 7,709

0 011 2.14 688

1 011 1.47 1,770

2 011 .63 1,902

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 100 .93 11,644

1 100 .55 13,483

2 100 .25 13,588

0 101 1.86 1,053

1 101 1.24 2,283

2 101 .54 3,117

0 110 1.49 2,786

1 110 .97 4,978

2 110 .46 6,545

0 111 2.27 805

1 111 1.6 1,926

2 111 .82 2,280

No outs. Should I try to advance a runner from 1st to 3rd on a single? Let P be the probability of success.Yes, if P * 1.86 + (1-P) .55 >= 1.49 , or P > .72. Last year, 0.03 were thrown out…

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State Transitions of Baseball

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 000 .54 46,180

1 000 .29 32,921

2 000 .11 26,009

0 001 1.46 512

1 001 .98 2,069

2 001 .38 3,129

0 010 1.17 3,590

1 010 .71 6,168

2 010 .34 7,709

0 011 2.14 688

1 011 1.47 1,770

2 011 .63 1,902

State (outs 1st 2nd 3rd )

Avg Runs

# Plate Appearances

0 100 .93 11,644

1 100 .55 13,483

2 100 .25 13,588

0 101 1.86 1,053

1 101 1.24 2,283

2 101 .54 3,117

0 110 1.49 2,786

1 110 .97 4,978

2 110 .46 6,545

0 111 2.27 805

1 111 1.6 1,926

2 111 .82 2,280

They say to never make the 1st or 3rd out at 3rd base…

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Fielding…

• A fatally flawed metric:

Fielding percentage = (PO + A) / (PO + A + E)

PO = PutoutsA = AssistsE = Errors

The Range Factor vs.Baseball Info Solutions

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Fielding in NY

Yankees fielders cost the team 139 hits over the course of a season, or 11.2 wins worse than an average team.

Derek Jeter defense costs the Yankees 3.8 games per season

Ozzie Smith (Cardinals 1978-1996) was worth over 3.5 wins on defense – highest in history.

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Pitch Count

2003 AL championship, Red Sox vs. Yankees, 5-2 in the 8th (90% chance of winning)

Pedro Martinez on the mound.

100 pitches

OBP = 0.256 OBP = 0.364

Grady Little goes to the mound but leaves in Martinez.Jeter doubles, Yankees tie it, win in 11.Grady Little fired the following week.

90 99 108

117

126

135

144

0

20000

40000

60000

80000

100000

120000

140000

Pitcher Injuries

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How much is a 20 yard gain worth?

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State & Expected Points 1st and 10

Yard Line Cabot, Sagarin, Winston Football Outsiders.com

5 -1.33 -1.2

15 -0.58 -0.6

25 0.13 0.1

35 0.84 0.9

45 1.53 1.2

55 (45) 2.24 1.9

65 (35) 3.02 2.2

75 (25) 3.88 3.0

85 (15) 4.84 3.8

95 (5) 5.84 4.6

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How has football changed from 2003-2007 compared to 2013?

Category

2007 2013

PY/A 61.67 91.7

DPY/A -67.5 -42.2

RY/A 26.44 -37.4

DRY/A -67.5 -31.5

Pen -0.06 -1.05

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 320

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Rushing Yards / Attempt 2013 of 32 top ranked point differential teams

RY/A

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Who is the “best” quarterback? Simple – use the quarterback

rating:

1. First one takes a quarterback’s completion percentage, then subtracts 0.3 from this number and divides by 0.2.

2. You then take yards per attempt subtract 3 and divide by 4.

3. After that, you divide touchdowns per attempt by 0.05.

4. For interceptions per attempt, you start with 0.095, subtract from this number interceptions per attempt, and then divide this result by 0.04.

To get the quarterback rating, you add the values created from your first four steps, multiply this sum by 100, and divide the result by 6.

The sum from each of your first four steps cannot exceed 2.2375 or be less than zero.

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Basketball – a collaborative pastime

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Da Four Factors

• EFG – OEFG (effective field goals)

• TPP – DTPP (turnovers)

• ORP – DRP (rebounds)

• FTR – OFTR (free throws)

Then games won =

41.06

+ 351.88 (EFG-OEFG) (explains 71%) .01↑=> 3.5 more wins

+333.06 (TPP-DTPP) (explains 16%) .01↑=> 3.3 more wins

+130.61(ORP-DRP) (explains 6%) .01↑=> 1.3 more wins

+44.43(FTR-OFTR) (explains 0%) .01↑=> 0.44 more wins

(R2 = 0.91)

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A Simulation of Basketball…

What if the 1992 Dream Team could play the 2012 Dream Team?

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NBA Shooting

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One second of NBA basketball

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One Game’s Ball movement

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Sir Tim Duncan

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Expected Value of a Possession

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CIO Magazine: 8 ways Big Data and Analytics will change sports

1. Pitchf/x – balls & strikes2. Slice & Dice data for fans3. Data from wearable technologies4. Field collection systems5. Predictive insights into Fan Preferences

1. In seat concession ordering

2. Restroom congestion

6. Hiring more numbers whizzes7. Influence Coaching Decisions8. Build Arguments for Contract Negotiations

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MIT Sports Analytics Conference

• Tanking & the Drafts- incentives• Hot hand theory – players believe it & thus…• Still difficult to sell it to coaches

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Why do we love the NCAA Basketball Tourney?

• Engaged via Brackets

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Why do we love NFL Football? - Derivatives

• How big is the market? In terms of actual expenditures, – estimates that 32 million Americans spend $467 per person or about $15 billion in total playing. 

• These figures don’t count ad revenue for fantasy hosting sites. The NFL’s annual revenue falls just under $10 billion currently.

• So the “derivative” market has grown larger than the foundational market.

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Derivatives & the future of Sports Analytics

Forces driving the derivatives (future of SA)• Virtual Reality and Location Independence• Big Data & Big Money• Northwestern ruling and who owns what• Watson

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Predictive Analytics

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• Addin – bullfighter