These datasets are currently included:
All datasets include the market lines from www.pinnacle.com for the events and timestamps are in UTC. Bettings odds are in the USODDS format that can be transformed into other formats (e.g. probability) with the use of library(odds.converter).
# Install from Cran (NOT ON CRAN YET)
#install.packages("pinnacle.data")
# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("marcoblume/pinnacle.data")This dataset contains all wagering lines for the entire MLB 2016 season including playoffs. The data is in tibble format with metadata such as time, team names, starting pitchers, etc. In-Game data is stored in a nested column named “Lines”.
Example question: What was the range of expected total runs according to the prediction market at Pinnacle?
library(tidyverse)
MLB2016 %>%
unnest() %>%
group_by(GameID) %>%
arrange(desc(EnteredDateTimeUTC)) %>%
slice(1) %>%
ungroup() %>%
group_by(TotalPoints) %>%
summarize(Count = n())
# A tibble: 16 x 2
TotalPoints Count
<dbl> <int>
1 5.5 2
2 6.0 16
3 6.5 54
4 7.0 196
5 7.5 464
6 8.0 421
7 8.5 513
8 9.0 425
9 9.5 180
10 10.0 74
11 10.5 46
12 11.0 24
13 11.5 18
14 12.0 11
15 12.5 15
16 13.0 3Example question: How many games went Over/Under/Landed on the total?
library(tidyverse)
MLB2016 %>%
unnest() %>%
group_by(GameID) %>%
arrange(desc(EnteredDateTimeUTC)) %>%
slice(1) %>%
ungroup() %>%
select(GameID,TotalPoints,FinalScoreAway,FinalScoreHome) %>%
mutate(TotalOutcome = case_when(
FinalScoreAway + FinalScoreHome > TotalPoints ~ "Over",
FinalScoreAway + FinalScoreHome < TotalPoints ~ "Under",
FinalScoreAway + FinalScoreHome == TotalPoints ~ "Landed"
)
) %>%
group_by(TotalPoints,TotalOutcome) %>%
summarize(Count = n()) %>%
print(n=100)
# A tibble: 37 x 3
# Groups: TotalPoints [?]
TotalPoints TotalOutcome Count
<dbl> <chr> <int>
1 5.5 Under 2
2 6.0 Over 7
3 6.0 Under 9
4 6.5 Over 30
5 6.5 Under 24
6 7.0 Landed 19
7 7.0 Over 91
8 7.0 Under 86
9 7.5 Over 234
10 7.5 Under 230
11 8.0 Landed 31
12 8.0 Over 212
13 8.0 Under 178
14 8.5 Over 249
15 8.5 Under 264
16 9.0 Landed 52
17 9.0 Over 186
18 9.0 Under 187
19 9.5 Over 84
20 9.5 Under 96
21 10.0 Landed 5
22 10.0 Over 33
23 10.0 Under 36
24 10.5 Over 16
25 10.5 Under 30
26 11.0 Landed 2
27 11.0 Over 12
28 11.0 Under 10
29 11.5 Over 10
30 11.5 Under 8
31 12.0 Landed 2
32 12.0 Over 4
33 12.0 Under 5
34 12.5 Over 7
35 12.5 Under 8
36 13.0 Landed 1
37 13.0 Over 2US 2016 Presidential Election data is also available now:
library(pinnacle.data)
USA_Election_2016
# A tibble: 1,443 × 5
EnteredDateTime TeamName1 TeamName2 MoneyUS1 MoneyUS2
<dttm> <chr> <chr> <dbl> <dbl>
1 2016-07-18 09:34:38 Hilary Clinton Donald Trump -243 212
2 2016-07-18 19:15:42 Hilary Clinton Donald Trump -251 218
3 2016-07-19 04:49:41 Hilary Clinton Donald Trump -260 226
4 2016-07-19 21:48:05 Hilary Clinton Donald Trump -281 243
5 2016-07-21 17:47:46 Hilary Clinton Donald Trump -275 238
6 2016-07-21 20:09:50 Hilary Clinton Donald Trump -271 235
7 2016-07-21 20:36:35 Hilary Clinton Donald Trump -253 220
8 2016-07-21 20:37:24 Hilary Clinton Donald Trump -237 207
9 2016-07-21 20:49:05 Hilary Clinton Donald Trump -233 204
10 2016-07-21 20:55:19 Hilary Clinton Donald Trump -252 219
# ... with 1,433 more rowsNote that we only have monney line prices for this event because outcome of the election is binary.
We can easily plot and track how the prices of Hilary Clinton and Donald Trump changed:
library(ggplot2)
ggplot(data = USA_Election_2016 %>% filter(EnteredDateTime >='2016-11-08')) +
geom_line(alpha = 1,
size = 2,
aes(x= EnteredDateTime,
y = MoneyUS1,
color = "Hilary Clinton")) +
geom_line(alpha = 1,
size = 2,
aes(x= EnteredDateTime,
y = MoneyUS2,
color = "Donald Trump")) +
theme_bw() +
ylab("Price")