We will give an overview of how data visualization and data analysis are used within the Florida Panthers organization, around the National Hockey League, and in the sports industry in general, in a variety of different contexts. We discuss how analytics can be used to assist an NHL team’s front office, coaching staff, and scouting department. We also discuss the kinds of data we encounter on the business side of the organization in departments like sales and marketing, as well as the kinds of questions the league offices try to answer with the help of data. Next DSS MIA Event - https://datascience.salon/miami/

1. Data Visualization and Analysis in the Florida
Panthers Hockey Organization
Brian Macdonald
Florida Panthers Hockey Club, Sunrise, FL
Data Science SALON, Miami, 2/29/2018
Twitter: @bmacNHL
LinkedIn: bmacNHL

2. Outline
One specific example that involves hockey and business
data

3. Outline
One specific example that involves hockey and business
data
Overview of other ways we use data
Renewal prediction
Arena maps
South Florida maps

4. Outline
One specific example that involves hockey and business
data
Overview of other ways we use data
Renewal prediction
Arena maps
South Florida maps
Future areas of focus

5. Attendance Model
Goal:
Develop a model for predicting attendance for games using
only information that is known before tickets go on sale.

6. Attendance Model
Goal:
Develop a model for predicting attendance for games using
only information that is known before tickets go on sale.
This could help answer questions like:
Which games should be in which tiers for variable pricing?

7. Attendance Model
Goal:
Develop a model for predicting attendance for games using
only information that is known before tickets go on sale.
This could help answer questions like:
Which games should be in which tiers for variable pricing?
What kinds of things could we request when the league is
developing the schedule?

8. Attendance Model
Goal:
Develop a model for predicting attendance for games using
only information that is known before tickets go on sale.
This could help answer questions like:
Which games should be in which tiers for variable pricing?
What kinds of things could we request when the league is
developing the schedule?
Specific question: Do we prefer good team on a Saturday
and bad team during the week, or a good team during the
week and a bad team on Saturday?"

9. Attendance Model
Goal:
Develop a model for predicting attendance for games using
only information that is known before tickets go on sale.
This could help answer questions like:
Which games should be in which tiers for variable pricing?
What kinds of things could we request when the league is
developing the schedule?
Specific question: Do we prefer good team on a Saturday
and bad team during the week, or a good team during the
week and a bad team on Saturday?"
What do we want Thanksgiving week?

10. First, let’s plot some raw data.
Attendance* by game, from 2007-08 to 2016-17, for all 30
teams.

11. First, let’s plot some raw data.
Attendance* by game, from 2007-08 to 2016-17, for all 30
teams.
*Announced attendance, as published on nhl.com

13. Snippet

15. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.

16. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.

17. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.
Model
1. Remove the teams that have flat attendance.
2. That leaves us with ANA, CAR, CBJ, COL, DAL, FLA, NJ,
NSH, NYI, OTT, PHX, STL, and TB.

18. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.
Model
1. Remove the teams that have flat attendance.
2. That leaves us with ANA, CAR, CBJ, COL, DAL, FLA, NJ,
NSH, NYI, OTT, PHX, STL, and TB.
3. Remove a few games unusual characteristics.
European games

19. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.
Model
1. Remove the teams that have flat attendance.
2. That leaves us with ANA, CAR, CBJ, COL, DAL, FLA, NJ,
NSH, NYI, OTT, PHX, STL, and TB.
3. Remove a few games unusual characteristics.
European games
Blizzards

20. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.
Model
1. Remove the teams that have flat attendance.
2. That leaves us with ANA, CAR, CBJ, COL, DAL, FLA, NJ,
NSH, NYI, OTT, PHX, STL, and TB.
3. Remove a few games unusual characteristics.
European games
Blizzards
4. Use several predictor variables (next slide)

21. Attendance Data and Model
Two observations
1. For many teams, attendance is relatively flat.
2. Winning matters. See BOS, CHI, LA, NSH, TB, WSH.
Model
1. Remove the teams that have flat attendance.
2. That leaves us with ANA, CAR, CBJ, COL, DAL, FLA, NJ,
NSH, NYI, OTT, PHX, STL, and TB.
3. Remove a few games unusual characteristics.
European games
Blizzards
4. Use several predictor variables (next slide)
5. Announced attendance is outcome we’re trying to predict

22. Predictors
home team, away team

23. Predictors
home team, away team
day of week, month

24. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)

25. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)

26. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)

27. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)

28. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)
points during previous year for home/away (lag)

29. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)
points during previous year for home/away (lag)
year-to-date points relative to average for home/away.

30. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)
points during previous year for home/away (lag)
year-to-date points relative to average for home/away.
day and month interaction (Sundays different in fall?)

31. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)
points during previous year for home/away (lag)
year-to-date points relative to average for home/away.
day and month interaction (Sundays different in fall?)
home team and day interaction

32. Predictors
home team, away team
day of week, month
holiday (Columbus Day, Thanksgiving week, etc., or none.)
season opener (Y or N)
same division (Y or N)
same conference (Y or N)
points during previous year for home/away (lag)
year-to-date points relative to average for home/away.
day and month interaction (Sundays different in fall?)
home team and day interaction
home team and month interaction
(snowbird months good for us?)

33. Interpretation of results
Impact that each of these variables have on attendance,

34. Interpretation of results
Impact that each of these variables have on attendance,
independent of all other variables.

35. Interpretation of results
Impact that each of these variables have on attendance,
independent of all other variables.
For example, we find the effect of day, controlling for all of
the other variables in our model

36. Interpretation of results
Impact that each of these variables have on attendance,
independent of all other variables.
For example, we find the effect of day, controlling for all of
the other variables in our model
That’s an important point. Example: If teams schedule big
opponents on the weekend, then the effect of a weekend
game could be overstated if we just look at day and ignore
opponent.

37. Example: day of week
240
1056
786
−245
−582
−565
−690
Sun
Sat
Fri
Thu
Wed
Tue
Mon
−1000 −500 0 500 1000
Announced Attendance
DayOfWeek
Effect of Day Of Week on Attendance

38. Example: day of week
240
1056
786
−245
−582
−565
−690
Sun
Sat
Fri
Thu
Wed
Tue
Mon
−1000 −500 0 500 1000
Announced Attendance
DayOfWeek
Effect of Day Of Week on Attendance
1. Attendance on Saturday is expected to be 1,056 higher than average,
“holding all other variables constant."

39. Example: day of week
240
1056
786
−245
−582
−565
−690
Sun
Sat
Fri
Thu
Wed
Tue
Mon
−1000 −500 0 500 1000
Announced Attendance
DayOfWeek
Effect of Day Of Week on Attendance
1. Attendance on Saturday is expected to be 1,056 higher than average,
“holding all other variables constant."
2. The difference between Saturday and Monday is expected to be 1,746
(1,056 + 690).

40. Example: day of week
240
1056
786
−245
−582
−565
−690
Sun
Sat
Fri
Thu
Wed
Tue
Mon
−1000 −500 0 500 1000
Announced Attendance
DayOfWeek
Effect of Day Of Week on Attendance
1. Attendance on Saturday is expected to be 1,056 higher than average,
“holding all other variables constant."
2. The difference between Saturday and Monday is expected to be 1,746
(1,056 + 690).
3. Not surprising. Stuff we knew. But now we’ve quantified.

41. Month
1057
852
350
54
−553
−803
−957
Apr
Mar
Feb
Jan
Dec
Nov
Oct
−1000 −500 0 500 1000
Announced Attendance
Month Effect of Month on Attendance

42. Month
1057
852
350
54
−553
−803
−957
Apr
Mar
Feb
Jan
Dec
Nov
Oct
−1000 −500 0 500 1000
Announced Attendance
Month Effect of Month on Attendance
Attendance increases over the course of the season

43. Away Team
54
88
273
307
360
583
618
821
1154
1162
1270
−696
−669
−660
−623
−614
−592
−564
−527
−376
−340
−171
−162
−138
−121
−112
−107
−69
−52
−52
−46
STL
OTT
NSH
PHX
CAR
CBJ
MIN
DAL
ATL
CGY
ANA
EDM
NYI
WPG
COL
FLA
T.B
BUF
L.A
S.J
VAN
N.J
WSH
PHI
TOR
MTL
CHI
BOS
PIT
NYR
DET
−1000 0 1000
Announced Attendance
Away
Effect of Away on Attendance

44. Holidays
491
1049
1214
2092
2232
2918
2110
2111
681
150
905
1748
2570
580
1237
1361
1006
630
177
564
846
−146
−800
−1602
0
Veterans Day
Valentine's Day
Thanksgiving Sun
Thanksgiving Sat
Thanksgiving Fri
Thanksgiving Wed
Thanksgiving Tue
Thanksgiving Mon
Season Opener
Presidents Day
none
MLK Day
Halloween
Good Friday
Election Day
Easter
Dec 31
Dec 30
Dec 29
Dec 28
Dec 27
Dec 26
Dec 23
Dec 22
Columbus Day
−2000 0 2000
Announced Attendance
Holiday
Effect of Holiday on Attendance

45. Day-month combinations
64
80
90
39
31
43
109
99
262
150
2
12
79
195
84
51
54
220
278
170
23
182
3
288
9
53
−30
−39
−49
−39
−1
−147
−12
−282
−219
−232
−135
−38
−120
−301
−94
−77
−69
−158
−209
−70
−196
−22
−131
Sun.Apr
Sun.Mar
Sun.Feb
Sun.Jan
Sun.Dec
Sun.Nov
Sun.Oct
Sat.Apr
Sat.Mar
Sat.Feb
Sat.Jan
Sat.Dec
Sat.Nov
Sat.Oct
Fri.Apr
Fri.Mar
Fri.Feb
Fri.Jan
Fri.Dec
Fri.Nov
Fri.Oct
Thu.Apr
Thu.Mar
Thu.Feb
Thu.Jan
Thu.Dec
Thu.Nov
Thu.Oct
Wed.Apr
Wed.Mar
Wed.Feb
Wed.Jan
Wed.Dec
Wed.Nov
Wed.Oct
Tue.Apr
Tue.Mar
Tue.Feb
Tue.Jan
Tue.Dec
Tue.Nov
Tue.Oct
Mon.Apr
Mon.Mar
Mon.Feb
Mon.Jan
Mon.Dec
Mon.Nov
Mon.Oct
−200 0 200
Announced Attendance
DayMonth
Effect of Day Month on Attendance

46. Actual vs Budget for 16-17
5.0
7.5
10.0
12.5
5.0 7.5 10.0 12.5
Budget
Actual
Actual vs Budget for 16−17

47. Total tickets, tickets 60 days out

48. Total tickets, tickets 60 days out

49. Renewal Prediction

50. Renewal Prediction
88% (n=768)
89% (n=226)
90% (n=136)
82% (n=27)
88% (n=26)
82% (n=11)
Broward
Palm Beach
Miami−Dade
Other FL counties
Northeast States
Canada
0 25 50 75 100 125
Renewal Rate
Region

51. Show rate
17% (n=12)
43% (n=14)
78% (n=18)
71% (n=24)
70% (n=46)
85% (n=68)
88% (n=112)
87% (n=135)
90% (n=224)
92% (n=345)
95% (n=210)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 25 50 75 100 125
ShowRate

52. Win% in games attended
33% (n=24)
70% (n=30)
70% (n=66)
84% (n=144)
89% (n=341)
93% (n=592)
73% (n=11)
0
0.1
0.2
0.3
0.4
0.5
0.6
0 25 50 75 100 125
Renewal Rate
WinsAttP

53. Average total goals
58% (n=12)
71% (n=14)
59% (n=17)
76% (n=45)
84% (n=31)
78% (n=72)
94% (n=53)
84% (n=147)
94% (n=125)
94% (n=302)
91% (n=242)
93% (n=117)
0.8
1.6
2
2.4
2.8
3.2
3.6
4
4.4
4.8
5.2
5.6
0 25 50 75 100 125
Renewal Rate
TotGoalsAttP

54. Proportion of 1-goal games
21% (n=14)
79% (n=14)
60% (n=20)
77% (n=30)
79% (n=43)
88% (n=67)
83% (n=72)
86% (n=116)
90% (n=179)
92% (n=258)
94% (n=372)
0
0.1
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0 25 50 75 100 125
Renewal Rate
OneGoalAttP

55. Data visualization

56. Arena maps

57. South FL maps

58. South FL maps

59. Overview of Hockey Data
Kinds of data
Play by play
Shifts
Shot locations

60. Overview of Hockey Data
Kinds of data
Play by play
Shifts
Shot locations
What can be done with this data?

61. Overview of Hockey Data
Kinds of data
Play by play
Shifts
Shot locations
What can be done with this data?
Player performance metrics
Team performance metrics
Data visualization

62. Data visualization: League data

63. Data visualization: Team data

64. Data visualization: Player data

65. Data visualization: Player data