This is a brief introduction to sports analytics as presented at the iSchool Inclusion Institute at the University of Pittsburgh (summer 2016).

1. SPORTS ANALYTICS IN
THE ERA OF BIG DATA
AND DATA SCIENCE
KONSTANTINOS PELECHRINIS
@kpelechrinis
https://412sportsanalytics.wordpress.com

3. DATA-DRIVEN COACHES?

4. DATA-DRIVEN FRONT OFFICES?

5. WHY NOW?
➤ Data analysis & use of statistics is not new in sports!!
➤ Now we have the technology to collect many more detailed
information about the game
➤ Detailed box score
➤ Play-by-play data
➤ Player tracking

6. TRACKING

7. RESOURCES

8. SPORT MARKETS
➤ A typical business or firm operates with the objective of profit
maximization
➤ This might not be the case for the owner of a professional
sports team!!
➤ For profit year by year
➤ Maximize wins
➤ Capital appreciation
➤ Capital can be the brand or individual players

9. SPORT MARKETS
➤ Becoming the dominant player is not the goal in sports
industry
➤ If a team were assured of victory in almost any competition
the whole league would be of little - if at all - interest
➤ Competitive balance
➤ Salary cap!
➤ Draft!

10. SPORT MARKETS
●
● ●
●
● ●
●
●●●
●
●
●
●
●
● ●
●●●
●
●● ●
●
● ●
●
●
●
LAA
BAL WSN
LAD
STL DET
SFGPIT & OAK
CLE
NYY
TOR
MIL
ATL
MIA
CHC PHI
BOS
MIN
TEX
COL
ARI
KCR
SEA
NYM
SDP & TBR
CIN
CHW
HOU
40
45
50
55
60
50 100 150 200 250
Team Payroll (Millions of Dollars)
PercentageofGamesWon
Correlation coef=0.26
p-value = 0.16!
Only 6% of the win/loss percentage is
explained by the payroll differences!

11. RANKING TEAMS
➤ Team performance is central to sports data science
➤ Ratings and rankings
➤ Challenges
➤ Imbalance in team schedules
➤ Win/Loss percentages does not consider strength schedule

12. RANKING TEAMS
➤ Network-based solution
➤ Win/loss directed network
➤ PageRank

13. RANKING TEAMS

14. RANKING TEAMS

15. RATING TEAMS
➤ Using the scores of the games played by the teams we can
obtain power ratings
➤ The main idea is that a team with a power rating of x>0 is
expected to be +x points better than an average team

16. RATING TEAMS
➤ Greek Super League
➤ 16 teams
➤ 2 games between each pair
➤ 240 total games
Code available at: https://github.com/kpelechrinis/soccer_team_ratings

17. RATING TEAMS
➤ You can use the ratings to obtain matchup probabilities
➤ Expected goals: Poisson
➤ Mean depends on
➤ Offensive rating
➤ Defensive rating
➤ Home edge

18. RATING TEAMS
➤ UEFA Champions League

19. COACHING DECISIONS
➤ Evidence-based coaching
➤ Go for it at 4th down or not?
➤ Go for the 2-point conversion or take the cheap shot?
➤ Shoot for three to win or shoot for two to tie the game?
➤ …
➤ We can now quantify the rationality of coaches!

20. COACHING DECISIONS

21. COACHING DECISIONS
OR

22. COACHING DECISIONS
E[p]= 2* - 1*
15
14
14
15
9
24
12
13
13
16
24
21
10
17
21
11
12
14
11
12
10
14
9
16
22
6
14
5
14
22
12
18
-0.50
-0.25
0.00
0.25
0.50
ARI ATL BAL BUF CAR CHI CIN CLE DAL DEN DET GB HOU IND JAC KC MIA MIN NE NO NYG NYJ OAK PHI PIT SD SEA SF STL TB TEN WAS
ExpectedPointGain

23. COACHING DECISIONS

24. COACHING DECISIONS
Touchback
-2
-1
0
1
2
3
0 25 50 75 100
Distance to the goal line when 4th down
Expectedpointsgained

25. COMPUTATIONAL GAME MODELS

26. COMPUTATIONAL GAME MODELS
-1.0
-0.5
0.0
0.5
1.0
Q1 Q2 Q3 Q4
Quarter
Ratior
Quarter
Q1
Q2
Q3
Q4
0.00
0.01
0.02
0.03
0.04
0 20 40 60
Time (minute)
TurnoverDensity

27. COMPUTATIONAL GAME MODELS
Bootstrap
BB
Historical
game data
Correlation
matrix
Logistic
Regression
Model
x1
1
1
1,· · ·· · ·,xB
1
B
1
x1
2
1
2,. . .. . .,xB
2
B
2
P1P1
P2P2
H0 : P1 = P2H0 : P1 = P2
H1 : P1 6= P2H1 : P1 6= P2
P1 P2P1 P2
pp-value
Mean accuracy=0.627 Mean accuracy=0.787
Mean accuracy=0.517 Mean accuracy=0.6
0.00
0.25
0.50
0.75
1.00
8 9 10 11 12 13 14 15 16 17
Week
Accuracy
Legend text
2014
2015

28. COMPUTATIONAL GAME MODELS
< 3mins
left
No
Yes
0.00
0.25
0.50
0.75
1.00
0.00 0.25 0.50 0.75 1.00
In-Game Win Probability
FractionofWinningInstances

29. LEAGUE CHANGES
➤ Can we predict and/or evaluate the impact of a rule change?
➤ What if we move the three point line further away?
➤ What was the impact of the new PAT rule?
➤ Will the new touchback rule give an advantage to the
offense?

30. LEAGUE CHANGES
Should the 3-point line be moved further away?

31. LEAGUE CHANGES

32. LEAGUE CHANGES

33. SPORTS MARKETING
➤ Sports are part of the entertainment market
➤ Marketing decisions can always benefit from good data!
➤ What price should the ticket have?
➤ What team-branded merchandise should you sell?
➤ Does a swag promotion justify a higher ticket price?
➤ What is the best strategy for national branding?
➤ …

34. SPORTS MARKETING
➤ Case study: Consumer preferences for Dodger’s stadium
seating
➤ Conjoint analysis
➤ Product profiles
➤ Consumers rank the products
➤ Ranking reveals their preference

35. SPORTS MARKETING
Part worths (i.e., regression coefficients),
reflect the strength of consumer preferences
for each level of each product attribute.

36. SPORTS MARKETING
➤ Can we use these results to assess willingness for a consumer
to pay for tickets?
➤ $20 tickets have part-worth of 3.25, while $95 tickets have
part-worth of -3.50.
➤ Difference in part-worth is 6.25, which in terms of $ this
corresponds to $75
➤ 1 part-worth is worth $11.11 to the consumer
➤ For this consumer we see that the part-worth differential
between a loge seat and a field seat is 2.75
➤ This consumer is willing to spend 2.75*11.11=$30.55
for a field seat compared to a loge seat

37. PROMOTING BRANDS & PRODUCTS

38. PROMOTING BRANDS & PRODUCTS
= a* + b* + c* + d

39. PROMOTING BRANDS & PRODUCTS

40. DATA SOURCES
➤ There are various websites where you can get data
➤ Mainly aggregate statistics, boxscores etc

41. DATA SOURCES
➤ Flexibility —> play-by-play data
➤ Major leagues provide an API
➤ Sport enthusiast have created libraries to access them
Case study: NFLgame in Python
https://github.com/BurntSushi/nflgame

42. DATA SOURCES
games = nflgame.games(2015,week=1,kind=‘REG’)
>>> games
[<nflgame.game.Game object at 0x107652210>, <nflgame.game.Game object at 0x107652310>,
<nflgame.game.Game object at 0x107652410>, <nflgame.game.Game object at 0x107652510>,
<nflgame.game.Game object at 0x107652610>, <nflgame.game.Game object at 0x107652710>,
<nflgame.game.Game object at 0x107652810>, <nflgame.game.Game object at 0x107652910>,
<nflgame.game.Game object at 0x107652a10>, <nflgame.game.Game object at 0x107652b10>,
<nflgame.game.Game object at 0x107652c10>, <nflgame.game.Game object at 0x107652d10>,
<nflgame.game.Game object at 0x107652e10>, <nflgame.game.Game object at 0x107652f10>,
<nflgame.game.Game object at 0x107d02050>, <nflgame.game.Game object at 0x107d02150>]
>>> games[0].home
u'NE'
>>> games[0].away
u'PIT'
>>>
>>> games[0].score_home
28
>>> games[0].score_away
21

43. DATA SOURCES
>>> for i in games[0].drives:
... print i
...
PIT (Start: Q1 15:00, End: Q1 09:40) Missed FG
NE (Start: Q1 09:40, End: Q1 07:41) Punt
PIT (Start: Q1 07:41, End: Q1 03:14) Punt
NE (Start: Q1 03:14, End: Q2 11:11) Touchdown
PIT (Start: Q2 11:11, End: Q2 08:38) Missed FG
NE (Start: Q2 08:38, End: Q2 04:01) Touchdown
PIT (Start: Q2 04:01, End: Q2 00:03) Field Goal
NE (Start: Q2 00:03, End: Q2 00:00) End of Half
NE (Start: Q3 15:00, End: Q3 10:37) Touchdown
PIT (Start: Q3 10:37, End: Q3 06:43) Touchdown
NE (Start: Q3 06:43, End: Q3 04:15) Punt
PIT (Start: Q3 04:15, End: Q4 11:39) Field Goal
NE (Start: Q4 11:39, End: Q4 09:20) Touchdown
PIT (Start: Q4 09:20, End: Q4 08:29) Punt
NE (Start: Q4 08:29, End: Q4 07:29) Punt
PIT (Start: Q4 07:29, End: Q4 07:00) Interception
NE (Start: Q4 07:00, End: Q4 02:59) Punt
PIT (Start: Q4 02:59, End: Q4 00:02) Touchdown
NE (Start: Q4 00:02, End: Q4 00:00) End of Game

44. DATA SOURCES
plays = nflgame.combine_plays(games)
>>> for p in plays:
... print p
...
(NE, NE 35, Q1) S.Gostkowski kicks 65 yards from NE 35 to end zone, Touchback.
(PIT, PIT 20, Q1, 1 and 10) (15:00) De.Williams right tackle to PIT 38 for 18 yards (D.Hightower).
(PIT, PIT 38, Q1, 1 and 10) (14:21) B.Roethlisberger pass short right to A.Brown pushed ob at PIT 47 for 9 yards (D.Hightower).
(PIT, PIT 47, Q1, 2 and 1) (14:04) De.Williams right guard to NE 49 for 4 yards (J.Collins; M.Brown).
(PIT, NE 49, Q1, 1 and 10) (13:26) B.Roethlisberger pass short right to H.Miller to NE 35 for 14 yards (J.Mayo).
(PIT, NE 35, Q1, 1 and 10) (12:42) (Shotgun) De.Williams right guard to NE 24 for 11 yards (J.Collins).
(PIT, NE 24, Q1, 1 and 10) (12:05) A.Brown sacked at NE 32 for -8 yards (M.Brown).
(PIT, NE 32, Q1, 2 and 18) (11:20) (Shotgun) De.Williams right end pushed ob at NE 28 for 4 yards (D.Hightower). PENALTY on PIT-M.Gilbert,
Offensive Holding, 10 yards, enforced at NE 32 - No Play.
(PIT, NE 42, Q1, 2 and 28) (10:53) W.Johnson right guard to NE 36 for 6 yards (R.Ninkovich). NE-D.Easley was injured during the play. He is Out.
(PIT, NE 36, Q1, 3 and 22) (10:28) (Shotgun) B.Roethlisberger pass short right to H.Miller to NE 26 for 10 yards (P.Chung; M.Butler).
(PIT, NE 26, Q1, 4 and 12) (9:44) J.Scobee 44 yard field goal is No Good, Wide Right, Center-G.Warren, Holder-J.Berry.
(NE, NE 34, Q1, 1 and 10) (9:40) (Shotgun) T.Brady pass short left to J.Edelman pushed ob at NE 47 for 13 yards (W.Gay). PENALTY on NE-N.Solder,
Unnecessary Roughness, 15 yards, enforced between downs.
(NE, NE 32, Q1, 1 and 10) (9:14) (Shotgun) T.Brady pass short left to D.Lewis to NE 44 for 12 yards (J.Harrison).
(NE, NE 44, Q1, 1 and 10) (9:00) (No Huddle, Shotgun) T.Brady pass short left to D.Lewis ran ob at PIT 43 for 13 yards.
(NE, PIT 43, Q1, 1 and 10) (8:31) (No Huddle, Shotgun) T.Brady pass incomplete short right to R.Gronkowski.
(NE, PIT 43, Q1, 2 and 10) (8:27) T.Brady pass incomplete deep right to D.Amendola.
(NE, PIT 43, Q1, 3 and 10) (8:22) (Shotgun) T.Brady sacked at PIT 43 for 0 yards (B.Dupree).
(NE, PIT 43, Q1, 4 and 10) (7:48) R.Allen punts 36 yards to PIT 7, Center-J.Cardona, fair catch by A.Brown.
(PIT, PIT 7, Q1, 1 and 10) (7:41) De.Williams left guard to PIT 13 for 6 yards (A.Branch; G.Grissom).
(PIT, PIT 13, Q1, 2 and 4) (7:07) De.Williams left tackle to PIT 12 for -1 yards (C.Jones).
(PIT, PIT 12, Q1, 3 and 5) (6:26) (Shotgun) B.Roethlisberger pass short left to A.Brown pushed ob at PIT 22 for 10 yards (D.McCourty).
(PIT, PIT 22, Q1, 1 and 10) (5:54) De.Williams right guard to PIT 26 for 4 yards (R.Ninkovich). PENALTY on PIT-K.Beachum, Illegal Formation, 5
yards, enforced at PIT 22 - No Play.
(PIT, PIT 17, Q1, 1 and 15) (5:29) (Shotgun) B.Roethlisberger pass short right to A.Brown to PIT 20 for 3 yards (J.Collins).
(PIT, PIT 20, Q1, 2 and 12) (4:48) B.Roethlisberger sacked at PIT 14 for -6 yards (D.Hightower).
(PIT, PIT 14, Q1, 3 and 18) (4:03) (Shotgun) B.Roethlisberger pass deep left to H.Miller to PIT 31 for 17 yards (D.McCourty; T.Brown).
(PIT, PIT 31, Q1, 4 and 1) (3:25) J.Berry punts 50 yards to NE 19, Center-G.Warren. D.Amendola to NE 34 for 15 yards (V.Williams). PENALTY on NE-
M.Slater, Illegal Block Above the Waist, 10 yards, enforced at NE 20.
(NE, NE 10, Q1, 1 and 10) (3:14) D.Lewis left tackle to NE 18 for 8 yards (W.Allen).
(NE, NE 18, Q1, 2 and 2) (2:40) D.Lewis up the middle to NE 19 for 1 yard (M.Mitchell).
(NE, NE 19, Q1, 3 and 1) (2:05) T.Brady up the middle to NE 20 for 1 yard (L.Timmons; S.McLendon).
(NE, NE 20, Q1, 1 and 10) (1:14) D.Lewis left end pushed ob at NE 25 for 5 yards (L.Timmons). PENALTY on NE-N.Solder, Offensive Holding, 10
yards, enforced at NE 20 - No Play.
(NE, NE 10, Q1, 1 and 20) (:45) (Shotgun) T.Brady pass short left to A.Dobson to NE 19 for 9 yards (W.Gay).
(NE, NE 19, Q1, 2 and 11) (:12) (Shotgun) T.Brady pass short left to J.Edelman to NE 28 for 9 yards (C.Allen).
….

45. What does all this mean for me?

46. Work = Fun

47. BUT…
➤ Good understanding of fundamentals of statistics and
probabilities
➤ Ability to work with APIs and data
➤ Python, R, MySQL
➤ Ability to design effective visualization for communicating
with the front office and coaching staff
➤ Of course domain knowledge

48. MODERN COMPUTATIONAL SPORTS ANALYST