Learn how you can use sports analytics to improve and predict player performance in baseball, basketball and football. For more information on the Loras College 2014 Business Analytics Symposium, the Loras College MBA in Business Analytics or the Loras College Business Analytics Certificate visit www.loras.edu/mba or www.loras.edu/bigdata.

1. 1
Sports Analytics…
… game on
Daniel Conway
Director of the Center for Business Analytics
Loras College

2. 2
Who cares about sports?

3. 3
Where does analytics fit in sports?
• Improve/predict player performance
• HR / Personnel
• Entertainment
2006 300 attend MIT’s Sports Analytics Conference
2011 & beyond capped at 2000 in person & thousands more online

4. 4
2014 SABR – Society for American Baseball Research

5. 5
Agenda
• Moneyball
• Baseball, Football, and Basketball
• HR
• Entertainment

6. 6
America’s Pastime
Peanuts, Crackerjack, and Numbers

7. 7
The Pythagorean Theorem
Runs
Allowed
Runs
Scored
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

8. 8
Some Definitions
Bill James “Runs Created” formula:
RC = (hits + BB + HBP) x (TB) / (AB + BB + HBP)
Blue: # baserunners
Green: Rate at which runners are advanced
Familiar? (Newton: Mass * Velocity)

9. 9
Decision Making in Sports
Blitz or drop back
Swing, 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

10. 10
Trade? Why would you trade Albert Pujols?

11. 11
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

12. 12
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…

13. 13
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…

14. 14
Fielding…
• A fatally flawed metric:
Fielding percentage = (PO + A) / (PO + A + E)
PO = Putouts
A = Assists
E = Errors
The Range Factor vs.
Baseball Info Solutions

15. 15
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.

16. 16
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.
0
20000
40000
60000
80000
100000
120000
140000
90
97
104
111
118
125
132
139
146
Pitcher Injuries

17. 17
How much is a 20 yard gain worth?

18. 18
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

19. 19
How has football changed from
2003-2007 compared to 2013?
Categor
y
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
0
1
2
3
4
5
6
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 32
Rushing Yards / Attempt 2013
of 32 top ranked point differential teams
RY/A

20. 20
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.

21. 21

22. 22
Basketball – a collaborative pastime

23. 23
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)

24. 24
A Simulation of Basketball…
What if the 1992 Dream Team could play the 2012 Dream Team?

25. 25
NBA Shooting

26. 26
One second of NBA basketball

27. 27
One Game’s Ball movement

28. 28
Sir Tim Duncan

29. 29
Expected Value of a Possession
(this slide intentionally blank)

30. 30
CIO Magazine: 8 ways
Big Data and Analytics will change sports
1. Pitchf/x – balls & strikes
2. Slice & Dice data for fans
3. Data from wearable technologies
4. Field collection systems
5. Predictive insights into Fan Preferences
1. In seat concession ordering
2. Restroom congestion
6. Hiring more numbers whizzes
7. Influence Coaching Decisions
8. Build Arguments for Contract Negotiations

31. 31
MIT Sports Analytics Conference
• Tanking & the Drafts- incentives
• Hot hand theory – players believe it & thus…
• Still difficult to sell it to coaches

32. 32
Why do we love the NCAA Basketball Tourney?
• Engaged via Brackets

33. 33
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.

34. 34
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

35. 35
Predictive Analytics

36. 36

37. 37
• Addin – bullfighter