Extracting JSON data from websites and public APIs with R

Finding JSON Sources

I’ve covered some strategies for parsing JSON with a few methods in base R and/or tidyverse in a previous blog post. I’d like to go one step up in the chain, and talk about pulling raw data/JSON from sites. While having a direct link to JSON is common, in some situations where you’re scraping JavaScript fed by APIs the raw data source is not always as easy to find.

I have three examples for today:
- FiveThirtyEight 2020 NFL Predictions
- ESPN Win Percentage/play-by-play (embedded JSON)
- ESPN Public API

Web vs Analysis

Most of these JSON data sources are intended to be used with JavaScript methods, and have not been oriented to a “flat” data style. This means the JSON has lots of separations of the data for a specific use/purpose inside the site, and efficient singular representations of each data in JSON storage as opposed to normalized data with repeats in a dataframe. While extreme detail is out of scope for this blogpost, JSON is structured as a “collection of name/value pairs” or a “an ordered list of values”. This means it is typically represented in R as repeated lists of list elements, where the list elements can be named lists, vectors, dataframes, or character strings.

Alternatively typically data for analysis is usually most useful as a normalized rectangle eg a dataframe/tibble. “Under the hood a data frame is a list of equal length vectors” per Advanced R.

One step further is tidy data which is essentially “3rd normal form”. Hadley goes into more detail in his “Tidy Data” publication. The takeaway here is that web designers are optimizing for their extremely focused interactive JavaScript apps and websites, as opposed to novel analyses that we often want to work with. This is often why there are quite a few steps to “rectangle” a JSON.

An aside on Subsetting

Subsetting in R is done many different ways, and Hadley Wickham has an entire chapter dedicated to this in Advanced R. It’s worth reading through that chapter to better understand the nuance, but I’ll provide a very brief summary of the options.

# a VERY basic list of named elements
car_list <- list(manufacturer = "Honda", vehicle = "Civic", year = 2020)

“Subsetting a list works in the same way as subsetting an atomic vector. Using [ always returns a list; [[ and $, … let you pull out elements of a list.”

• Advanced R

When working with lists, you can typically use $ and [[ interchangeably to extract single list elements by name. [[ requires exact matching whereas $ allows for partial matching, so I typically prefer to use [[. To extract by location from base R you need to use [[.

purrr functions pluck() and chuck() implement a generalised form of [[ that allow you to index deeply and flexibly into data structures. pluck() consistently returns NULL when an element does not exist, chuck() always throws an error in that case.”

• purrr docs.

So in short, you can use $, [[ and pluck/chuck in many of the same ways. I’ll compare all the base R and purrr versions below (all should return “Honda”).

# $ subsets by name
car_list$manufacturer

[1] "Honda"

# notice partial match
car_list$man

[1] "Honda"

# [[ requires exact match or position
car_list[["manufacturer"]]

[1] "Honda"

car_list[[1]]

[1] "Honda"

# pluck and chuck provide a more strict version of [[
# and can subset by exact name or position
purrr::pluck(car_list, "manufacturer")

[1] "Honda"

purrr::pluck(car_list, 1)

[1] "Honda"

purrr::chuck(car_list, "manufacturer")

[1] "Honda"

purrr::chuck(car_list, 1)

[1] "Honda"

For one more warning of partial name matching with $, where we now have a case of two elements with similar names see below:

car_list2 <- list(manufacturer = "Honda", vehicle = "Civic", manufactured_year = 2020)

# partial match throws a null
car_list2$man

NULL

# exact name returns actual elements
car_list2$manufacturer

[1] "Honda"

An aside on JavaScript

If we dramatically oversimplify JavaScript or their R-based counterparts htmlwidgets, they are a combination of some type of JSON data and then functions to display or interact with that data.

We can quickly show a htmlwidget example via the fantastic reactable R package.

library(reactable)

table_ex <- mtcars %>% 
  select(cyl, mpg, disp) %>% 
  reactable()

table_ex

That gives us the power of JavaScript in R! However, what’s going on with this function behind the scenes? We can extract the dataframe that has now been represented as a JSON file from the htmlwidget!

table_data <- table_ex[["x"]][["tag"]][["attribs"]][["data"]]

table_data %>% class()

[1] "json"

This basic idea, that the data is embedded as JSON to fill the JavaScript app can be further applied to web-based apps! We can use a similar idea to scrape raw JSON or query a web API that returns JSON from a site.

FiveThirtyEight

FiveThirtyEight publishes their ELO ratings and playoff predictions for the NFL via a table at projects.fivethirtyeight.com/2020-nfl-predictions/. They are also kind enough to post this data as a download publicly! However, let’s see if we can “find” the data source feeding the JavaScript table.

rvest

We can try our classical rvest based approach to scrape the HTML content and get back a table. However, the side effect of this is we’re returning the literal data with units, some combined columns, and other formatting. You’ll notice that all the columns show up as character and this introduces a lot of other work we’d have to do to “clean” the data.

library(xml2)
library(rvest)

url_538 <- "https://projects.fivethirtyeight.com/2020-nfl-predictions/"

raw_538_html <- read_html(url_538)

raw_538_table <- raw_538_html %>% 
  html_node("#standings-table") %>% 
  html_table(fill = TRUE) %>% 
  janitor::clean_names() %>% 
  tibble()

raw_538_table %>% glimpse()

Rows: 36
Columns: 12
$ x                 <chr> "", "", "", "elo with top qbelo rating", "1734", "17…
$ x_2               <chr> "", "", "", "1-week change", "+34", "", "", "", "", …
$ x_3               <chr> "", "", "", "current qb adj.", "", "", "", "", "", "…
$ x_4               <chr> "", "playoff chances", "", "", "", "", "", "", "", "…
$ x_5               <chr> "playoff chances", "playoff chances", "", "team", "B…
$ x_6               <chr> "playoff chances", "playoff chances", "", "division"…
$ playoff_chances   <chr> "playoff chances", "playoff chances", "", "make div.…
$ playoff_chances_2 <chr> "playoff chances", NA, "", "make conf. champ", "✓", …
$ playoff_chances_3 <chr> NA, NA, "", "make super bowl", "✓", "✓", "—", "—", "…
$ playoff_chances_4 <chr> NA, NA, "", "win super bowl", "✓", "—", "—", "—", "—…
$ x_7               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
$ x_8               <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …

Inspect + Network

Alternatively, we can Right Click + inspect the site, go to the Network tab, reload the site and see what sources are loaded. Again, FiveThirtyEight is very kind and essentially just loads the JSON as data.json.

I have screenshots below of each item, and the below is a short video of the entire process.

Network Tab

We can click over to the Network Tab after inspecting the site

Network Tab Reloaded

We need to reload the web page to find sources

Network Tab Data

We can examine specific elements by clicking on them, which then shows us JSON!

---

---

In our browser inspect, we can see the structure, and that it has some info about games, QBs, and forecasts. This looks like the right dataset! You can right click on data.json and open it in a new page. The url is https://projects.fivethirtyeight.com/2020-nfl-predictions/data.json, and note that we can adjust the year to get older or current data. So https://projects.fivethirtyeight.com/2019-nfl-predictions/data.json returns the data for 2019, and you can go all the way back to 2016! 2015 also exists, but with a different JSON structure, and AFAIK they don’t have data before 2015.

Read the JSON

Now that we have a JSON source, we can read it into R with jsonlite. By using the RStudio viewer or listviewer::jsonedit() we can take a look at what the overall structure of the JSON.

library(jsonlite)

raw_538_json <- fromJSON("https://projects.fivethirtyeight.com/2020-nfl-predictions/data.json", simplifyVector = FALSE)

raw_538_json %>% str(max.level = 1)

List of 9
 $ archie                :List of 24
 $ clinches              :List of 114
 $ distances             :List of 32
 $ games                 :List of 269
 $ pageconfig            :List of 20
 $ playoff_qb_adjustments:List of 32
 $ qbs                   :List of 87
 $ urls                  :List of 2
 $ weekly_forecasts      :List of 2

Don’t forget that the RStudio Viewer also gives you the ability to export the base R code to access a specific component of the JSON!

Screenshot of RStudio Viewer

Which gives us the following code:

raw_538_json[["weekly_forecasts"]][["forecasts"]][[1]][["types"]][["elo"]][[1]]

ex_538_data <- raw_538_json[["weekly_forecasts"]][["forecasts"]][[1]][["types"]][["elo"]][[1]]

ex_538_data %>% str()

List of 29
 $ conference           : chr "NFC"
 $ current_losses       : int 9
 $ current_ties         : int 0
 $ current_wins         : int 7
 $ division             : chr "NFC North"
 $ elo                  : num 1489
 $ losses               : int 9
 $ make_conference_champ: int 0
 $ make_divisional_round: int 0
 $ make_playoffs        : int 0
 $ make_superbowl       : int 0
 $ name                 : chr "MIN"
 $ point_diff           : int -45
 $ points_allowed       : int 475
 $ points_scored        : int 430
 $ rating               : num 1481
 $ rating_current       : num 1502
 $ rating_top           : num 1502
 $ seed_1               : int 0
 $ seed_2               : int 0
 $ seed_3               : int 0
 $ seed_4               : int 0
 $ seed_5               : int 0
 $ seed_6               : int 0
 $ seed_7               : int 0
 $ ties                 : int 0
 $ win_division         : int 0
 $ win_superbowl        : int 0
 $ wins                 : int 7

We can also play around with listviewer.

raw_538_json %>% 
  listviewer::jsonedit()

Since these are unique list elements, we can turn it into a dataframe! This is the current projection for Minnesota.

data.frame(ex_538_data) %>% glimpse()

Rows: 1
Columns: 29
$ conference            <chr> "NFC"
$ current_losses        <int> 9
$ current_ties          <int> 0
$ current_wins          <int> 7
$ division              <chr> "NFC North"
$ elo                   <dbl> 1489.284
$ losses                <int> 9
$ make_conference_champ <int> 0
$ make_divisional_round <int> 0
$ make_playoffs         <int> 0
$ make_superbowl        <int> 0
$ name                  <chr> "MIN"
$ point_diff            <int> -45
$ points_allowed        <int> 475
$ points_scored         <int> 430
$ rating                <dbl> 1480.954
$ rating_current        <dbl> 1501.583
$ rating_top            <dbl> 1501.583
$ seed_1                <int> 0
$ seed_2                <int> 0
$ seed_3                <int> 0
$ seed_4                <int> 0
$ seed_5                <int> 0
$ seed_6                <int> 0
$ seed_7                <int> 0
$ ties                  <int> 0
$ win_division          <int> 0
$ win_superbowl         <int> 0
$ wins                  <int> 7

Parse the JSON

Ok so we’ve found at least one set of data that is pretty dataframe ready, let’s clean it all up in bulk! I’m most interested in the weekly_forecasts data, so let’s start there.

raw_538_json$weekly_forecasts %>% str(max.level = 1)

List of 2
 $ last_updated: chr "2021-02-08T03:15:55.357Z"
 $ forecasts   :List of 22

Ok so last_updated is good to know, but not something I need right now. Let’s go one step deeper into forecasts.

raw_538_json$weekly_forecasts$forecasts %>% str(max.level = 1)

List of 22
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3
 $ :List of 3

Ok now we have a list of 14 lists. This may seem not helpful, BUT remember that as of 2020-12-12, we are in Week 14 of the NFL season! So this is likely 1 list for each of the weekly forecasts, which makes sense as we are in weekly_forecasts$forecasts!

At this point, I think I’m at the right data, so I’m going to take the list and put it in a tibble via tibble::enframe().

raw_538_json$weekly_forecasts$forecasts %>% 
  enframe()

# A tibble: 22 × 2
    name value           
   <int> <list>          
 1     1 <named list [3]>
 2     2 <named list [3]>
 3     3 <named list [3]>
 4     4 <named list [3]>
 5     5 <named list [3]>
 6     6 <named list [3]>
 7     7 <named list [3]>
 8     8 <named list [3]>
 9     9 <named list [3]>
10    10 <named list [3]>
# … with 12 more rows

We need to separate the list items out, so we can try unnest_auto() to see if tidyr can parse the correct structure. Note that unnest_auto works and tells us we could have used unnest_wider().

raw_538_json$weekly_forecasts$forecasts %>% 
  enframe() %>% 
  unnest_auto(value)

Using `unnest_wider(value)`; elements have 3 names in common

# A tibble: 22 × 4
    name last_updated              week types           
   <int> <chr>                    <int> <list>          
 1     1 2021-02-08T03:15:55.357Z    21 <named list [2]>
 2     2 2021-01-25T03:10:04.809Z    20 <named list [2]>
 3     3 2021-01-22T19:28:01.278Z    19 <named list [2]>
 4     4 2021-01-15T01:25:55.192Z    18 <named list [2]>
 5     5 2021-01-09T16:38:13.126Z    17 <named list [2]>
 6     6 2021-01-03T16:03:42.517Z    16 <named list [2]>
 7     7 2020-12-24T01:32:05.045Z    15 <named list [2]>
 8     8 2020-12-16T14:13:49.344Z    14 <named list [2]>
 9     9 2020-12-10T16:21:56.731Z    13 <named list [2]>
10    10 2020-12-06T15:29:51.523Z    12 <named list [2]>
# … with 12 more rows

We can keep going on the types list column! Note that as unnest_auto() tells us “what” to do, I’m going to replace it with the appropriate function.

raw_538_json$weekly_forecasts$forecasts %>% 
  enframe() %>% 
  unnest_wider(value) %>% # changed per recommendation
  unnest_auto(types)

Using `unnest_wider(types)`; elements have 2 names in common

# A tibble: 22 × 5
    name last_updated              week elo         rating     
   <int> <chr>                    <int> <list>      <list>     
 1     1 2021-02-08T03:15:55.357Z    21 <list [32]> <list [32]>
 2     2 2021-01-25T03:10:04.809Z    20 <list [32]> <list [32]>
 3     3 2021-01-22T19:28:01.278Z    19 <list [32]> <list [32]>
 4     4 2021-01-15T01:25:55.192Z    18 <list [32]> <list [32]>
 5     5 2021-01-09T16:38:13.126Z    17 <list [32]> <list [32]>
 6     6 2021-01-03T16:03:42.517Z    16 <list [32]> <list [32]>
 7     7 2020-12-24T01:32:05.045Z    15 <list [32]> <list [32]>
 8     8 2020-12-16T14:13:49.344Z    14 <list [32]> <list [32]>
 9     9 2020-12-10T16:21:56.731Z    13 <list [32]> <list [32]>
10    10 2020-12-06T15:29:51.523Z    12 <list [32]> <list [32]>
# … with 12 more rows

We now have a list of 32 x 14 weeks. There are 32 teams so we’re most likely at the appropriate depth and can go longer vs wider now. We can also see that name/week don’t align so let’s drop name, and we can use unchop() to increase the length of the data for elo and rating at the same time.

raw_538_json$weekly_forecasts$forecasts %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest_wider(types) %>% # Changed per recommendation
  unchop(cols = c(elo, rating)) %>% 
  select(-name)

# A tibble: 704 × 4
   last_updated              week elo               rating           
   <chr>                    <int> <list>            <list>           
 1 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 2 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 3 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 4 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 5 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 6 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 7 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 8 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
 9 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
10 2021-02-08T03:15:55.357Z    21 <named list [29]> <named list [29]>
# … with 694 more rows

We now have 14 weeks x 32 teams (448 rows), along with last_updated, week, elo and rating data. We can use unnest_auto() on the elo column to see what’s the next step. Rating is duplicated so there’s been name repair to avoid duplicated names. We get the following warning that tells us this has occurred.

* rating -> rating...18
* rating -> rating...32

You’ll see that I’ve done unnest_auto() on both elo and rating...32 (the renamed rating list column). If you look closely at the names, we can also see that there is duplication of the names for MANY of the columns. A tricky part is that elo/rating each have a LOT of overlap, and are most appropriate as separate data frames that could be stacked if desired.

elo_raw <- raw_538_json$weekly_forecasts$forecasts %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest_wider(types) %>% # Changed per recommendation
  unchop(cols = c(elo, rating)) %>% 
  select(-name) %>% 
  unnest_auto(elo) %>% 
  unnest_auto(rating...32)

Using `unnest_wider(elo)`; elements have 29 names in common

New names:
Using `unnest_wider(rating...32)`; elements have 29 names in common
New names:
• `rating` -> `rating...18`
• `rating` -> `rating...32`

elo_raw %>% 
  names()

 [1] "last_updated"               "week"                      
 [3] "conference...3"             "current_losses...4"        
 [5] "current_ties...5"           "current_wins...6"          
 [7] "division...7"               "elo...8"                   
 [9] "losses...9"                 "make_conference_champ...10"
[11] "make_divisional_round...11" "make_playoffs...12"        
[13] "make_superbowl...13"        "name...14"                 
[15] "point_diff...15"            "points_allowed...16"       
[17] "points_scored...17"         "rating...18"               
[19] "rating_current...19"        "rating_top...20"           
[21] "seed_1...21"                "seed_2...22"               
[23] "seed_3...23"                "seed_4...24"               
[25] "seed_5...25"                "seed_6...26"               
[27] "seed_7...27"                "ties...28"                 
[29] "win_division...29"          "win_superbowl...30"        
[31] "wins...31"                  "conference...32"           
[33] "current_losses...33"        "current_ties...34"         
[35] "current_wins...35"          "division...36"             
[37] "elo...37"                   "losses...38"               
[39] "make_conference_champ...39" "make_divisional_round...40"
[41] "make_playoffs...41"         "make_superbowl...42"       
[43] "name...43"                  "point_diff...44"           
[45] "points_allowed...45"        "points_scored...46"        
[47] "rating...47"                "rating_current...48"       
[49] "rating_top...49"            "seed_1...50"               
[51] "seed_2...51"                "seed_3...52"               
[53] "seed_4...53"                "seed_5...54"               
[55] "seed_6...55"                "seed_7...56"               
[57] "ties...57"                  "win_division...58"         
[59] "win_superbowl...59"         "wins...60"                 

Let’s try this again, with the knowledge that elo and rating should be treated separately for now. Since they have the same names, we can also combine the data by stacking (bind_rows()). I have added a new column so that we can differentiate between the two datasets (ELO vs Rating).

weekly_raw <- raw_538_json$weekly_forecasts$forecasts %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest_wider(types) %>% 
  select(-name) %>%
  unchop(cols = c(elo, rating))

weekly_elo <- weekly_raw %>% 
  select(-rating) %>% 
  unnest_wider(elo) %>% 
  mutate(measure = "ELO", .after = last_updated)

weekly_rating <- weekly_raw %>% 
  select(-elo) %>% 
  unnest_wider(rating) %>% 
  mutate(measure = "Rating", .after = last_updated)

# confirm same names
all.equal(
  names(weekly_elo),
  names(weekly_rating)
)

[1] TRUE

weekly_forecasts <- bind_rows(weekly_elo, weekly_rating)

weekly_forecasts %>% glimpse()

Rows: 1,408
Columns: 32
$ last_updated          <chr> "2021-02-08T03:15:55.357Z", "2021-02-08T03:15:55…
$ measure               <chr> "ELO", "ELO", "ELO", "ELO", "ELO", "ELO", "ELO",…
$ week                  <int> 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, …
$ conference            <chr> "NFC", "AFC", "NFC", "NFC", "NFC", "AFC", "AFC",…
$ current_losses        <int> 9, 6, 10, 12, 11, 11, 14, 11, 6, 10, 6, 5, 10, 9…
$ current_ties          <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ current_wins          <int> 7, 10, 7, 4, 5, 4, 2, 5, 12, 6, 11, 13, 6, 7, 15…
$ division              <chr> "NFC North", "AFC East", "NFC East", "NFC South"…
$ elo                   <dbl> 1489.284, 1545.820, 1442.351, 1474.240, 1333.468…
$ losses                <dbl> 9, 6, 9, 12, 11, 11, 14, 11, 5, 10, 5, 4, 10, 9,…
$ make_conference_champ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
$ make_divisional_round <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, …
$ make_playoffs         <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, …
$ make_superbowl        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ name                  <chr> "MIN", "MIA", "WSH", "ATL", "DET", "CIN", "NYJ",…
$ point_diff            <dbl> -45, 66, 6, -18, -142, -113, -214, -123, 165, -7…
$ points_allowed        <dbl> 475, 338, 329, 414, 519, 424, 457, 446, 303, 357…
$ points_scored         <dbl> 430, 404, 335, 396, 377, 311, 243, 323, 468, 280…
$ rating                <dbl> 1480.954, 1565.307, 1446.254, 1449.242, 1340.178…
$ rating_current        <dbl> 1501.583, 1529.565, 1462.552, 1448.424, 1338.309…
$ rating_top            <dbl> 1501.583, 1529.565, 1462.552, 1448.424, 1338.309…
$ seed_1                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ seed_2                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, …
$ seed_3                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
$ seed_4                <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ seed_5                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
$ seed_6                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ seed_7                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ties                  <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ win_division          <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, …
$ win_superbowl         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ wins                  <dbl> 7, 10, 7, 4, 5, 4, 2, 5, 11, 6, 11, 12, 6, 7, 13…

Create a Function

Finally, we can combine the techniques we showed above as a function (and I’ve added it to espnscrapeR). Now we can use this to get data throughout the current season OR get info from past seasons (2015 and beyond). Again, note that 2015 has a different JSON structure but the core forecasts portion is still the same.

get_weekly_forecast <- function(season) {
  
  # Fill URL and read in JSON
  raw_url <- glue::glue("https://projects.fivethirtyeight.com/{season}-nfl-predictions/data.json")
  raw_json <- fromJSON(raw_url, simplifyVector = FALSE)
  
  # get the two datasets
  weekly_raw <- raw_538_json$weekly_forecasts$forecasts %>%
    enframe() %>%
    unnest_wider(value) %>%
    unnest_wider(types) %>%
    select(-name) %>%
    unchop(cols = c(elo, rating))
  
  # get ELO
  weekly_elo <- weekly_raw %>%
    select(-rating) %>%
    unnest_wider(elo) %>%
    mutate(measure = "ELO", .after = last_updated)
  # get Rating
  weekly_rating <- weekly_raw %>%
    select(-elo) %>%
    unnest_wider(rating) %>%
    mutate(measure = "Rating", .after = last_updated)
  # combine
  bind_rows(weekly_elo, weekly_rating)
}

get_weekly_forecast(2015) %>% 
  glimpse()

Rows: 1,408
Columns: 32
$ last_updated          <chr> "2021-02-08T03:15:55.357Z", "2021-02-08T03:15:55…
$ measure               <chr> "ELO", "ELO", "ELO", "ELO", "ELO", "ELO", "ELO",…
$ week                  <int> 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, …
$ conference            <chr> "NFC", "AFC", "NFC", "NFC", "NFC", "AFC", "AFC",…
$ current_losses        <int> 9, 6, 10, 12, 11, 11, 14, 11, 6, 10, 6, 5, 10, 9…
$ current_ties          <int> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ current_wins          <int> 7, 10, 7, 4, 5, 4, 2, 5, 12, 6, 11, 13, 6, 7, 15…
$ division              <chr> "NFC North", "AFC East", "NFC East", "NFC South"…
$ elo                   <dbl> 1489.284, 1545.820, 1442.351, 1474.240, 1333.468…
$ losses                <dbl> 9, 6, 9, 12, 11, 11, 14, 11, 5, 10, 5, 4, 10, 9,…
$ make_conference_champ <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, …
$ make_divisional_round <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, …
$ make_playoffs         <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, …
$ make_superbowl        <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ name                  <chr> "MIN", "MIA", "WSH", "ATL", "DET", "CIN", "NYJ",…
$ point_diff            <dbl> -45, 66, 6, -18, -142, -113, -214, -123, 165, -7…
$ points_allowed        <dbl> 475, 338, 329, 414, 519, 424, 457, 446, 303, 357…
$ points_scored         <dbl> 430, 404, 335, 396, 377, 311, 243, 323, 468, 280…
$ rating                <dbl> 1480.954, 1565.307, 1446.254, 1449.242, 1340.178…
$ rating_current        <dbl> 1501.583, 1529.565, 1462.552, 1448.424, 1338.309…
$ rating_top            <dbl> 1501.583, 1529.565, 1462.552, 1448.424, 1338.309…
$ seed_1                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ seed_2                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, …
$ seed_3                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, …
$ seed_4                <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, …
$ seed_5                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, …
$ seed_6                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ seed_7                <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ ties                  <dbl> 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ win_division          <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, …
$ win_superbowl         <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ wins                  <dbl> 7, 10, 7, 4, 5, 4, 2, 5, 11, 6, 11, 12, 6, 7, 13…

Other FiveThirtyEight Data

There’s several other interesting data points in this JSON, but they’re also much easier to extract.

QB playoff adjustment values

qb_playoff_adj <- raw_538_json$playoff_qb_adjustments %>% 
  enframe() %>% 
  unnest_wider(value)

Output

qb_playoff_adj

# A tibble: 32 × 4
    name team   week  qb_adj
   <int> <chr> <int>   <dbl>
 1     1 ARI      21  17.3  
 2     2 ATL      21  -0.819
 3     3 BAL      21  -2.72 
 4     4 BUF      21  33.1  
 5     5 CAR      21   4.26 
 6     6 CHI      21  19.2  
 7     7 CIN      21 -84.5  
 8     8 CLE      21  16.9  
 9     9 DAL      21 -60.9  
10    10 DEN      21  15.3  
# … with 22 more rows

Games Data

This one is interesting, it’s got ELO change as a result of win/loss along with the spread and ratings.

games_df <- raw_538_json$games %>% 
  enframe() %>% 
  unnest_auto(value) %>% 
  select(-name)

Using `unnest_wider(value)`; elements have 40 names in common

Output

games_df

# A tibble: 269 × 40
        id date  datetime  week status team1 team2 neutral playoff score1 score2
     <int> <chr> <chr>    <int> <chr>  <chr> <chr> <lgl>   <chr>    <int>  <int>
 1  4.01e8 2020… 2020-09…     1 post   KC    HOU   FALSE   <NA>        34     20
 2  4.01e8 2020… 2020-09…     1 post   DET   CHI   FALSE   <NA>        23     27
 3  4.01e8 2020… 2020-09…     1 post   BAL   CLE   FALSE   <NA>        38      6
 4  4.01e8 2020… 2020-09…     1 post   MIN   GB    FALSE   <NA>        34     43
 5  4.01e8 2020… 2020-09…     1 post   JAX   IND   FALSE   <NA>        27     20
 6  4.01e8 2020… 2020-09…     1 post   NE    MIA   FALSE   <NA>        21     11
 7  4.01e8 2020… 2020-09…     1 post   BUF   NYJ   FALSE   <NA>        27     17
 8  4.01e8 2020… 2020-09…     1 post   CAR   OAK   FALSE   <NA>        30     34
 9  4.01e8 2020… 2020-09…     1 post   WSH   PHI   FALSE   <NA>        27     17
10  4.01e8 2020… 2020-09…     1 post   ATL   SEA   FALSE   <NA>        25     38
# … with 259 more rows, and 29 more variables: overtime <lgl>, elo1_pre <dbl>,
#   elo2_pre <dbl>, elo_spread <dbl>, elo_prob1 <dbl>, elo_prob2 <dbl>,
#   elo1_post <dbl>, elo2_post <dbl>, rating1_pre <dbl>, rating2_pre <dbl>,
#   rating_spread <dbl>, rating_prob1 <dbl>, rating_prob2 <dbl>,
#   rating1_post <dbl>, rating2_post <dbl>, bettable <lgl>, outcome <dbl>,
#   qb_adj1 <dbl>, qb_adj2 <dbl>, rest_adj1 <int>, rest_adj2 <int>,
#   dist_adj <dbl>, rating1_top_qb <dbl>, rating2_top_qb <dbl>, …

Distances

This data has the distances for each team to other locations/stadiums.

distance_df <- raw_538_json$distances %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  unnest_longer(distances)

Output

distance_df

# A tibble: 1,024 × 6
    name team    lat   lon distances distances_id
   <int> <chr> <dbl> <dbl>     <dbl> <chr>       
 1     1 TEN    36.2 -86.8        0  TEN         
 2     1 TEN    36.2 -86.8      758. NYG         
 3     1 TEN    36.2 -86.8      471. PIT         
 4     1 TEN    36.2 -86.8      339. CAR         
 5     1 TEN    36.2 -86.8      595. BAL         
 6     1 TEN    36.2 -86.8      619. TB          
 7     1 TEN    36.2 -86.8      251. IND         
 8     1 TEN    36.2 -86.8      698. MIN         
 9     1 TEN    36.2 -86.8     1454. ARI         
10     1 TEN    36.2 -86.8      634. DAL         
# … with 1,014 more rows

QB Adjustment

I believe this is the in-season QB adjustment for each team.

qb_adj <- raw_538_json$qbs %>% 
  enframe() %>% 
  select(-name) %>% 
  unnest_wider(value)

Output

qb_adj

# A tibble: 87 × 6
    api_id name            team  priority elo_value starts
     <int> <chr>           <chr>    <int>     <int>  <int>
 1 3040206 Chris Streveler ARI          2         0      1
 2 4035003 Jacob Eason     IND          3        58      1
 3 3124900 Jake Luton      JAX          3        20      1
 4 3915436 Steven Montez   WSH          3         0      1
 5 4241479 Tua Tagovailoa  MIA          1       128      1
 6 4038941 Justin Herbert  LAC          1       200      1
 7 4040715 Jalen Hurts     PHI          1       120      1
 8 3895785 Ben DiNucci     DAL          3         6      1
 9 4036378 Jordan Love     GB           3       102      1
10   12471 Chase Daniel    DET          2        33      1
# … with 77 more rows

ESPN

ESPN has interactive win probability charts for their games. They also go a step farther than FiveThirtyEight and the JSON is embedded into the HTML “bundle”. They also have a hidden API, but I’m going to first show an example of how to get the JSON from within the page itself.

Example End Function

get_espn_win_prob <- function(game_id){

  raw_url <-glue::glue("https://www.espn.com/nfl/game?gameId={game_id}")

  raw_html <- raw_url %>%
    read_html()

  raw_text <- raw_html %>%
    html_nodes("script") %>%
    .[23] %>%
    html_text()

  raw_json <- raw_text %>%
    gsub(".*(\\[\\{)", "\\1", .) %>%
    gsub("(\\}\\]).*", "\\1", .)

  parsed_json <- jsonlite::parse_json(raw_json)

  raw_df <- parsed_json %>%
    enframe() %>%
    rename(row_id = name) %>%
    unnest_wider(value) %>%
    unnest_wider(play) %>%
    hoist(period, quarter = "number") %>%
    unnest_wider(start) %>%
    hoist(team, pos_team_id = "id") %>%
    hoist(clock, clock = "displayValue") %>%
    hoist(type, play_type = "text") %>%
    select(-type) %>%
    janitor::clean_names() %>%
    mutate(game_id = game_id)

  raw_df

}

Get the data

Let’s use an example from a pretty wild swing in Win Percentage from Week 13 of the 2020 NFL season. The Vikings and Jaguars went to overtime, with a lot of back and forth. Since there is an interactive data visualization, I’m assuming the JSON data is present there as well.

---

If we try our previous trick from FiveThirtyEight and the Inspect -> Network we get a total of… about 300 different requests! None of them appear big enough to be the “right” data. We’re expecting 4-5 Mb of data.

---

Another trick is to look for embedded JSON in the site itself. The basic representation of JSON is [{name: item}], so let’s try looking for [{ as the start of a JSON structure.

Inside the Google Chrome Dev tools we can search and find a few JSON files, including one inside some JavaScript called espn.gamepackage.probability! There’s a good amount of data there, but we need to extract it from the raw HTML. This JSON is inside a <script> object, so let’s parse the HTML and get script nodes.

---

espn_url <-glue::glue("https://www.espn.com/nfl/game?gameId=401220303")

raw_espn_html <- espn_url %>%
    read_html()

raw_espn_html %>%
    html_nodes("script") 

{xml_nodeset (27)}
 [1] <script type="application/ld+json">\n\t{\n\t\t"@context": "https://schem ...
 [2] <script type="text/javascript" src="https://dcf.espn.com/TWDC-DTCI/prod/ ...
 [3] <script type="text/javascript">\n;(function(){\n\nfunction rc(a){for(var ...
 [4] <script src="https://secure.espn.com/core/format/modules/head/i18n?editi ...
 [5] <script src="https://a.espncdn.com/redesign/0.591.3/js/espn-head.js"></s ...
 [6] <script>\n\t\t\tif (espn && espn.geoRedirect){\n\t\t\t\tespn.geoRedirect ...
 [7] <script>\n\tvar espn = espn || {};\n\tespn.isOneSite = false;\n\tespn.bu ...
 [8] <script src="https://a.espncdn.com/redesign/0.591.3/node_modules/espn-la ...
 [9] <script type="text/javascript">\n\t(function () {\n\t\tvar featureGating ...
[10] <script>\n\t\twindow.googletag = window.googletag || {};\n\n\t\t(functio ...
[11] <script type="text/javascript">\n\tif( typeof s_omni === "undefined" ) w ...
[12] <script type="text/javascript" src="https://a.espncdn.com/prod/scripts/a ...
[13] <script>\n\t// Picture element HTML shim|v it for old IE (pairs with Pic ...
[14] <script type="text/javascript">\n\t\t\tvar abtestData = {};\n\t\t\t\n\t\ ...
[15] <script type="text/javascript">\n\t\tvar espn = espn || {};\n\t\tespn.na ...
[16] <script type="text/javascript">\n\n    var __dataLayer = window.__dataLa ...
[17] <script>\n\tvar espn_ui = window.espn_ui || {};\n\tespn_ui.staticRef = " ...
[18] <script src="https://a.espncdn.com/redesign/0.591.3/js/espn-critical.js" ...
[19] <script type="text/javascript">\n\t\t\tvar espn = espn || {};\n\n\t\t\t/ ...
[20] <script type="text/javascript">jQuery.subscribe('espn.defer.end', functi ...
...

Big oof. There’s 27 scripts here and just parsing through the start of the script as they are in XML is not helpful…So let’s get the raw text from each of these nodes and see if we can find the espn.gamepackage.probability element which we’re looking for!

raw_espn_text <- raw_espn_html %>%
  html_nodes("script") %>% 
  html_text()

raw_espn_text %>% 
  str_which("espn.gamepackage.probability")

[1] 23

Ok! So we’re looking for the 23rd node, I’m going to hide the output inside an expandable tag, as it’s a long output!

example_embed_json <- raw_espn_html %>%
    html_nodes("script") %>% 
    .[23] %>%
    html_text()

Example Embed JSON

example_embed_json

[1] "\n\t\t\t\t(function($) {\n\t\t\t\t\tvar espn = window.espn || {},\n\t\t\t\t\t\tDTCpackages = window.DTCpackages || [];\n\n\t\t\t\t\tespn.gamepackage = espn.gamepackage || {};\n\t\t\t\t\tespn.gamepackage.gameId = \"401220303\";\n\t\t\t\t\tespn.gamepackage.type = \"game\";\n\t\t\t\t\tespn.gamepackage.timestamp = \"2020-12-06T18:00Z\";\n\t\t\t\t\tespn.gamepackage.status = \"post\";\n\t\t\t\t\tespn.gamepackage.league = \"nfl\";\n\t\t\t\t\tespn.gamepackage.leagueId = 28;\n\t\t\t\t\tespn.gamepackage.sport = \"football\";\n\t\t\t\t\tespn.gamepackage.network = \"CBS\";\n\t\t\t\t\tespn.gamepackage.awayTeamName = \"jacksonville-jaguars\";\n\t\t\t\t\tespn.gamepackage.homeTeamName = \"minnesota-vikings\";\n\t\t\t\t\tespn.gamepackage.awayTeamId = \"30\";\n\t\t\t\t\tespn.gamepackage.homeTeamId = \"16\";\n\t\t\t\t\tespn.gamepackage.awayTeamColor = \"00839C\";\n\t\t\t\t\tespn.gamepackage.homeTeamColor = \"240A67\";\n\t\t\t\t\tespn.gamepackage.showGamebreak = true;\n\t\t\t\t\tespn.gamepackage.supportsHeadshots = true\n\t\t\t\t\tespn.gamepackage.playByPlaySource = \"full\";\n\t\t\t\t\tespn.gamepackage.numPeriods = null;\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\n\n\t\t\t\t\t\n\n\t\t\t\t\t\n\t\t\t\t\t\tespn.gamepackage.probability = espn.gamepackage.probability || {};\n\t\t\t\t\t\tespn.gamepackage.probability.data = [{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":0,\"start\":{\"distance\":0,\"yardLine\":35,\"team\":{\"id\":\"16\"},\"down\":0,\"yardsToEndzone\":65},\"text\":\"D.Bailey kicks 65 yards from MIN 35 to end zone, Touchback.\",\"clock\":{\"displayValue\":\"15:00\"},\"type\":{\"id\":\"53\",\"text\":\"Kickoff\",\"abbreviation\":\"K\"}},\"homeWinPercentage\":0.828,\"playId\":\"40122030340\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":0,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 25\",\"downDistanceText\":\"1st & 10 at JAX 25\",\"distance\":10,\"yardLine\":75,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":75},\"text\":\"(15:00) M.Glennon pass deep middle to J.O'Shaughnessy to JAX 49 for 24 yards (A.Harris; E.Wilson).\",\"clock\":{\"displayValue\":\"15:00\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.807,\"playId\":\"40122030355\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":0,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 49\",\"downDistanceText\":\"1st & 10 at JAX 49\",\"distance\":10,\"yardLine\":51,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":51},\"text\":\"(14:25) (Shotgun) M.Glennon pass short right to J.Robinson pushed ob at MIN 43 for 8 yards (T.Dye).\",\"clock\":{\"displayValue\":\"14:25\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.796,\"playId\":\"40122030379\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":0,\"start\":{\"shortDownDistanceText\":\"2nd & 2\",\"possessionText\":\"MIN 43\",\"downDistanceText\":\"2nd & 2 at MIN 43\",\"distance\":2,\"yardLine\":43,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":43},\"text\":\"(13:54) J.Robinson left guard to MIN 34 for 9 yards (T.Davis; E.Wilson).\",\"clock\":{\"displayValue\":\"13:54\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.785,\"playId\":\"401220303108\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":0,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"MIN 34\",\"downDistanceText\":\"1st & 10 at MIN 34\",\"distance\":10,\"yardLine\":34,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":34},\"text\":\"(13:21) J.Robinson left end to MIN 28 for 6 yards (A.Harris; S.Stephen).\",\"clock\":{\"displayValue\":\"13:21\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.777,\"playId\":\"401220303129\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"2nd & 4\",\"possessionText\":\"MIN 28\",\"downDistanceText\":\"2nd & 4 at MIN 28\",\"distance\":4,\"yardLine\":28,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":28},\"text\":\"Laviska Shenault Jr. 28 Yd pass from Mike Glennon (Chase McLaughlin PAT failed)\",\"clock\":{\"displayValue\":\"12:33\"},\"type\":{\"id\":\"67\",\"text\":\"Passing Touchdown\",\"abbreviation\":\"TD\"}},\"homeWinPercentage\":0.742,\"playId\":\"401220303150\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"distance\":0,\"yardLine\":-4,\"team\":{\"id\":\"30\"},\"down\":0,\"yardsToEndzone\":65},\"text\":\"L.Cooke kicks 69 yards from JAX 35 to MIN -4. A.Abdullah to MIN 21 for 25 yards (S.Quarterman).\",\"clock\":{\"displayValue\":\"12:33\"},\"type\":{\"id\":\"12\",\"text\":\"Kickoff Return (Offense)\"}},\"homeWinPercentage\":0.741,\"playId\":\"401220303191\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"MIN 21\",\"downDistanceText\":\"1st & 10 at MIN 21\",\"distance\":10,\"yardLine\":21,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":79},\"text\":\"(12:28) K.Cousins pass short left to A.Thielen pushed ob at MIN 29 for 8 yards (T.Herndon). PENALTY on JAX-M.Jack, Defensive Holding, 5 yards, enforced at MIN 21 - No Play.\",\"clock\":{\"displayValue\":\"12:28\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.747,\"playId\":\"401220303213\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"MIN 26\",\"downDistanceText\":\"1st & 10 at MIN 26\",\"distance\":10,\"yardLine\":26,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":74},\"text\":\"(12:11) (Shotgun) K.Cousins pass incomplete deep right to A.Thielen.\",\"clock\":{\"displayValue\":\"12:11\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.719,\"playId\":\"401220303257\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"2nd & 10\",\"possessionText\":\"MIN 26\",\"downDistanceText\":\"2nd & 10 at MIN 26\",\"distance\":10,\"yardLine\":26,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":74},\"text\":\"(12:06) D.Cook right tackle to MIN 33 for 7 yards (M.Jack).\",\"clock\":{\"displayValue\":\"12:06\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.731,\"playId\":\"401220303279\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"3rd & 3\",\"possessionText\":\"MIN 33\",\"downDistanceText\":\"3rd & 3 at MIN 33\",\"distance\":3,\"yardLine\":33,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":67},\"text\":\"(11:27) (Shotgun) K.Cousins pass incomplete short right to D.Cook.\",\"clock\":{\"displayValue\":\"11:27\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.683,\"playId\":\"401220303300\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":1},\"homeScore\":0,\"awayScore\":6,\"start\":{\"shortDownDistanceText\":\"4th & 3\",\"possessionText\":\"MIN 33\",\"downDistanceText\":\"4th & 3 at MIN 33\",\"distance\":3,\"yardLine\":33,\"team\":{\"id\":\"16\"},\"down\":4,\"yardsToEndzone\":67},\"text\":\"(11:23) B.Colquitt punts 50 yards to JAX 17, Center-A.DePaola. 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Ham Pass From Kirk Cousins for 12 Yrds D.Bailey extra point is GOOD, Center-A.DePaola, Holder-B.Colquitt.\",\"clock\":{\"displayValue\":\"11:08\"},\"type\":{\"id\":\"67\",\"text\":\"Passing Touchdown\",\"abbreviation\":\"TD\"}},\"homeWinPercentage\":0.538,\"playId\":\"4012203032410\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"distance\":0,\"yardLine\":35,\"team\":{\"id\":\"16\"},\"down\":0,\"yardsToEndzone\":65},\"text\":\"D.Bailey kicks 65 yards from MIN 35 to end zone, Touchback.\",\"clock\":{\"displayValue\":\"11:08\"},\"type\":{\"id\":\"53\",\"text\":\"Kickoff\",\"abbreviation\":\"K\"}},\"homeWinPercentage\":0.531,\"playId\":\"4012203032451\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 25\",\"downDistanceText\":\"1st & 10 at JAX 25\",\"distance\":10,\"yardLine\":75,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":75},\"text\":\"(11:08) J.Robinson right end pushed ob at JAX 30 for 5 yards (H.Smith).\",\"clock\":{\"displayValue\":\"11:08\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.521,\"playId\":\"4012203032466\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"distance\":5,\"yardLine\":70,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":70},\"text\":\"Timeout #1 by JAX at 10:28.\",\"clock\":{\"displayValue\":\"10:28\"},\"type\":{\"id\":\"21\",\"text\":\"Timeout\",\"abbreviation\":\"TO\"}},\"homeWinPercentage\":0.536,\"playId\":\"4012203032493\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 5\",\"possessionText\":\"JAX 30\",\"downDistanceText\":\"2nd & 5 at JAX 30\",\"distance\":5,\"yardLine\":70,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":70},\"text\":\"(10:28) M.Glennon pass short right to J.Robinson to JAX 36 for 6 yards (K.Boyd).\",\"clock\":{\"displayValue\":\"10:28\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.513,\"playId\":\"4012203032510\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 36\",\"downDistanceText\":\"1st & 10 at JAX 36\",\"distance\":10,\"yardLine\":64,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":64},\"text\":\"(9:47) J.Robinson left end pushed ob at JAX 42 for 6 yards (C.Dantzler).\",\"clock\":{\"displayValue\":\"9:47\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.498,\"playId\":\"4012203032534\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 4\",\"possessionText\":\"JAX 42\",\"downDistanceText\":\"2nd & 4 at JAX 42\",\"distance\":4,\"yardLine\":58,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":58},\"text\":\"(9:23) (Shotgun) M.Glennon pass short right to J.O'Shaughnessy to JAX 48 for 6 yards (E.Wilson; J.Gladney).\",\"clock\":{\"displayValue\":\"9:23\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.475,\"playId\":\"4012203032566\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 48\",\"downDistanceText\":\"1st & 10 at JAX 48\",\"distance\":10,\"yardLine\":52,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":52},\"text\":\"(8:47) J.Robinson up the middle to 50 for 2 yards (E.Yarbrough; S.Stephen).\",\"clock\":{\"displayValue\":\"8:47\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.552,\"playId\":\"4012203032590\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 8\",\"possessionText\":\"50\",\"downDistanceText\":\"2nd & 8 at 50\",\"distance\":8,\"yardLine\":50,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":90},\"text\":\"(8:09) (Shotgun) M.Glennon pass incomplete deep right to C.Johnson.\",\"clock\":{\"displayValue\":\"8:09\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.615,\"playId\":\"4012203032611\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"3rd & 8\",\"possessionText\":\"50\",\"downDistanceText\":\"3rd & 8 at 50\",\"distance\":8,\"yardLine\":50,\"team\":{\"id\":\"30\"},\"down\":3,\"yardsToEndzone\":50},\"text\":\"(8:04) (Shotgun) M.Glennon sacked at JAX 40 for -10 yards (sack split by H.Smith and H.Mata'afa).\",\"clock\":{\"displayValue\":\"8:04\"},\"type\":{\"id\":\"7\",\"text\":\"Sack\"}},\"homeWinPercentage\":0.556,\"playId\":\"4012203032633\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":13,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"4th & 18\",\"possessionText\":\"JAX 40\",\"downDistanceText\":\"4th & 18 at JAX 40\",\"distance\":18,\"yardLine\":60,\"team\":{\"id\":\"30\"},\"down\":4,\"yardsToEndzone\":60},\"text\":\"(7:26) L.Cooke punts 60 yards to end zone, Center-R.Matiscik, Touchback. 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PENALTY on JAX-L.Barcoo, Defensive Pass Interference, 18 yards, enforced at JAX 38 - No Play.\",\"clock\":{\"displayValue\":\"3:40\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.67,\"playId\":\"4012203032872\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 20\",\"downDistanceText\":\"1st & 10 at JAX 20\",\"distance\":10,\"yardLine\":80,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":20},\"text\":\"Justin Jefferson 20 Yd pass from Kirk Cousins (Dan Bailey PAT failed)\",\"clock\":{\"displayValue\":\"3:27\"},\"type\":{\"id\":\"67\",\"text\":\"Passing Touchdown\",\"abbreviation\":\"TD\"}},\"homeWinPercentage\":0.729,\"playId\":\"4012203032905\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"distance\":0,\"yardLine\":35,\"team\":{\"id\":\"16\"},\"down\":0,\"yardsToEndzone\":65},\"text\":\"D.Bailey kicks 65 yards from MIN 35 to end zone, Touchback.\",\"clock\":{\"displayValue\":\"3:27\"},\"type\":{\"id\":\"53\",\"text\":\"Kickoff\",\"abbreviation\":\"K\"}},\"homeWinPercentage\":0.722,\"playId\":\"4012203032946\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 25\",\"downDistanceText\":\"1st & 10 at JAX 25\",\"distance\":10,\"yardLine\":75,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":75},\"text\":\"(3:27) M.Glennon pass incomplete deep left to C.Johnson.\",\"clock\":{\"displayValue\":\"3:27\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.749,\"playId\":\"4012203032961\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 10\",\"possessionText\":\"JAX 25\",\"downDistanceText\":\"2nd & 10 at JAX 25\",\"distance\":10,\"yardLine\":75,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":75},\"text\":\"(3:22) (Shotgun) M.Glennon pass short right to C.Johnson to JAX 30 for 5 yards (K.Boyd).\",\"clock\":{\"displayValue\":\"3:22\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.739,\"playId\":\"4012203032983\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"3rd & 5\",\"possessionText\":\"JAX 30\",\"downDistanceText\":\"3rd & 5 at JAX 30\",\"distance\":5,\"yardLine\":70,\"team\":{\"id\":\"30\"},\"down\":3,\"yardsToEndzone\":70},\"text\":\"(2:43) (Shotgun) M.Glennon scrambles right end to JAX 32 for 2 yards (J.Brailford). FUMBLES (J.Brailford), RECOVERED by MIN-J.Brailford at JAX 34.\",\"clock\":{\"displayValue\":\"2:43\"},\"type\":{\"id\":\"29\",\"text\":\"Fumble Recovery (Opponent)\"}},\"homeWinPercentage\":0.834,\"playId\":\"4012203033007\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 34\",\"downDistanceText\":\"1st & 10 at JAX 34\",\"distance\":10,\"yardLine\":66,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":34},\"text\":\"(2:24) K.Cousins pass incomplete short right [A.Gotsis].\",\"clock\":{\"displayValue\":\"2:24\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.813,\"playId\":\"4012203033044\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 10\",\"possessionText\":\"JAX 34\",\"downDistanceText\":\"2nd & 10 at JAX 34\",\"distance\":10,\"yardLine\":66,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":34},\"text\":\"(2:19) D.Cook right end pushed ob at JAX 22 for 12 yards (J.Wilson).\",\"clock\":{\"displayValue\":\"2:19\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.726,\"playId\":\"4012203033066\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 22\",\"downDistanceText\":\"1st & 10 at JAX 22\",\"distance\":10,\"yardLine\":78,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":22},\"text\":\"(1:50) K.Cousins pass short right to A.Thielen to JAX 17 for 5 yards (L.Barcoo).\",\"clock\":{\"displayValue\":\"1:50\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.735,\"playId\":\"4012203033097\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 5\",\"possessionText\":\"JAX 17\",\"downDistanceText\":\"2nd & 5 at JAX 17\",\"distance\":5,\"yardLine\":83,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":17},\"text\":\"(1:16) D.Cook left guard to JAX 11 for 6 yards (J.Giles-Harris).\",\"clock\":{\"displayValue\":\"1:16\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.882,\"playId\":\"4012203033121\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 11\",\"downDistanceText\":\"1st & 10 at JAX 11\",\"distance\":10,\"yardLine\":89,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":11},\"text\":\"(:38) D.Cook up the middle to JAX 1 for 10 yards (J.Wilson; J.Schobert).\",\"clock\":{\"displayValue\":\"0:38\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.9,\"playId\":\"4012203033142\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":3},\"homeScore\":19,\"awayScore\":16,\"start\":{\"distance\":0,\"yardLine\":0,\"down\":1,\"yardsToEndzone\":1},\"text\":\"END QUARTER 3\",\"clock\":{\"displayValue\":\"0:00\"},\"type\":{\"id\":\"2\",\"text\":\"End Period\",\"abbreviation\":\"EP\"}},\"homeWinPercentage\":0.902,\"playId\":\"4012203033163\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & Goal\",\"possessionText\":\"JAX 1\",\"downDistanceText\":\"1st & Goal at JAX 1\",\"distance\":1,\"yardLine\":99,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":1},\"text\":\"(15:00) K.Cousins FUMBLES (Aborted) at JAX 4, RECOVERED by JAX-M.Jack at JAX 2.\",\"clock\":{\"displayValue\":\"15:00\"},\"type\":{\"id\":\"29\",\"text\":\"Fumble Recovery (Opponent)\"}},\"homeWinPercentage\":0.76,\"playId\":\"4012203033182\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 2\",\"downDistanceText\":\"1st & 10 at JAX 2\",\"distance\":10,\"yardLine\":98,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":98},\"text\":\"(14:55) J.Robinson up the middle to JAX 5 for 3 yards (J.Gladney). MIN-J.Gladney was injured during the play. JAX-B.Linder was injured during the play.\",\"clock\":{\"displayValue\":\"14:55\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.776,\"playId\":\"4012203033223\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":19,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 7\",\"possessionText\":\"JAX 5\",\"downDistanceText\":\"2nd & 7 at JAX 5\",\"distance\":7,\"yardLine\":95,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":95},\"text\":\"(14:33) J.Robinson left end to JAX 4 for -1 yards (A.Harris). FUMBLES (A.Harris), RECOVERED by MIN-A.Harris at JAX 4. A.Harris to JAX 2 for 2 yards (E.Saubert). The Replay Official reviewed the fumble ruling, and the play was REVERSED. 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M.Jack).\",\"clock\":{\"displayValue\":\"12:14\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.853,\"playId\":\"4012203033445\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 9\",\"possessionText\":\"JAX 44\",\"downDistanceText\":\"2nd & 9 at JAX 44\",\"distance\":9,\"yardLine\":56,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":44},\"text\":\"(11:34) K.Cousins pass short left to J.Jefferson to JAX 40 for 4 yards (T.Herndon).\",\"clock\":{\"displayValue\":\"11:34\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.858,\"playId\":\"4012203033466\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"3rd & 5\",\"possessionText\":\"JAX 40\",\"downDistanceText\":\"3rd & 5 at JAX 40\",\"distance\":5,\"yardLine\":60,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":40},\"text\":\"(10:50) (Shotgun) K.Cousins pass incomplete deep right to J.Jefferson.\",\"clock\":{\"displayValue\":\"10:50\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.81,\"playId\":\"4012203033490\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"4th & 5\",\"possessionText\":\"JAX 40\",\"downDistanceText\":\"4th & 5 at JAX 40\",\"distance\":5,\"yardLine\":60,\"team\":{\"id\":\"16\"},\"down\":4,\"yardsToEndzone\":40},\"text\":\"(10:45) B.Colquitt punts 40 yards to end zone, Center-A.DePaola, Touchback.\",\"clock\":{\"displayValue\":\"10:45\"},\"type\":{\"id\":\"52\",\"text\":\"Punt\",\"abbreviation\":\"PUNT\"}},\"homeWinPercentage\":0.796,\"playId\":\"4012203033512\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 20\",\"downDistanceText\":\"1st & 10 at JAX 20\",\"distance\":10,\"yardLine\":80,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":80},\"text\":\"(10:32) J.Robinson left end to JAX 22 for 2 yards (H.Hand).\",\"clock\":{\"displayValue\":\"10:32\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.813,\"playId\":\"4012203033529\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 8\",\"possessionText\":\"JAX 22\",\"downDistanceText\":\"2nd & 8 at JAX 22\",\"distance\":8,\"yardLine\":78,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":78},\"text\":\"(9:58) (Shotgun) M.Glennon pass incomplete short middle to J.O'Shaughnessy (E.Wilson).\",\"clock\":{\"displayValue\":\"9:58\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.828,\"playId\":\"4012203033550\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"3rd & 8\",\"possessionText\":\"JAX 22\",\"downDistanceText\":\"3rd & 8 at JAX 22\",\"distance\":8,\"yardLine\":78,\"team\":{\"id\":\"30\"},\"down\":3,\"yardsToEndzone\":78},\"text\":\"(9:51) (Shotgun) M.Glennon pass short left to T.Eifert to JAX 28 for 6 yards (K.Boyd; T.Davis).\",\"clock\":{\"displayValue\":\"9:51\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.848,\"playId\":\"4012203033572\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"4th & 2\",\"possessionText\":\"JAX 28\",\"downDistanceText\":\"4th & 2 at JAX 28\",\"distance\":2,\"yardLine\":72,\"team\":{\"id\":\"30\"},\"down\":4,\"yardsToEndzone\":72},\"text\":\"(9:13) L.Cooke punts 44 yards to MIN 28, Center-R.Matiscik. K.Osborn to MIN 34 for 6 yards (Q.Williams; R.Matiscik). FUMBLES (Q.Williams), and recovers at MIN 34.\",\"clock\":{\"displayValue\":\"9:13\"},\"type\":{\"id\":\"52\",\"text\":\"Punt\",\"abbreviation\":\"PUNT\"}},\"homeWinPercentage\":0.66,\"playId\":\"4012203033596\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"MIN 34\",\"downDistanceText\":\"1st & 10 at MIN 34\",\"distance\":10,\"yardLine\":34,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":66},\"text\":\"(9:02) D.Cook up the middle to MIN 35 for 1 yard (D.Ekuale).\",\"clock\":{\"displayValue\":\"9:02\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.664,\"playId\":\"4012203033637\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 9\",\"possessionText\":\"MIN 35\",\"downDistanceText\":\"2nd & 9 at MIN 35\",\"distance\":9,\"yardLine\":35,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":65},\"text\":\"(8:23) K.Cousins sacked at MIN 26 for -9 yards (D.Ekuale).\",\"clock\":{\"displayValue\":\"8:23\"},\"type\":{\"id\":\"7\",\"text\":\"Sack\"}},\"homeWinPercentage\":0.771,\"playId\":\"4012203033658\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"3rd & 18\",\"possessionText\":\"MIN 26\",\"downDistanceText\":\"3rd & 18 at MIN 26\",\"distance\":18,\"yardLine\":26,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":74},\"text\":\"(7:41) (Shotgun) D.Cook up the middle to MIN 33 for 7 yards (A.Lynch).\",\"clock\":{\"displayValue\":\"7:41\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.774,\"playId\":\"4012203033677\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"4th & 11\",\"possessionText\":\"MIN 33\",\"downDistanceText\":\"4th & 11 at MIN 33\",\"distance\":11,\"yardLine\":33,\"team\":{\"id\":\"16\"},\"down\":4,\"yardsToEndzone\":67},\"text\":\"(7:08) B.Colquitt punts 49 yards to JAX 18, Center-A.DePaola, fair catch by K.Cole Sr..\",\"clock\":{\"displayValue\":\"7:08\"},\"type\":{\"id\":\"52\",\"text\":\"Punt\",\"abbreviation\":\"PUNT\"}},\"homeWinPercentage\":0.802,\"playId\":\"4012203033698\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 18\",\"downDistanceText\":\"1st & 10 at JAX 18\",\"distance\":10,\"yardLine\":82,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":82},\"text\":\"(7:01) M.Glennon pass deep left to D.Chark Jr. to JAX 37 for 19 yards (K.Boyd; E.Wilson).\",\"clock\":{\"displayValue\":\"7:01\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.757,\"playId\":\"4012203033726\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 37\",\"downDistanceText\":\"1st & 10 at JAX 37\",\"distance\":10,\"yardLine\":63,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":63},\"text\":\"(6:26) M.Glennon scrambles up the middle to JAX 41 for 4 yards (E.Wilson).\",\"clock\":{\"displayValue\":\"6:26\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.769,\"playId\":\"4012203033750\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"period\":{\"number\":4},\"homeScore\":21,\"awayScore\":16,\"start\":{\"shortDownDistanceText\":\"2nd & 6\",\"possessionText\":\"JAX 41\",\"downDistanceText\":\"2nd & 6 at JAX 41\",\"distance\":6,\"yardLine\":59,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":59},\"text\":\"(5:39) M.Glennon pass short right to C.Conley to JAX 45 for 4 yards (C.Dantzler). FUMBLES (C.Dantzler), RECOVERED by MIN-C.Dantzler at JAX 44. 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PENALTY on MIN-J.Jefferson, Offensive Pass Interference, 7 yards, enforced at MIN 15 - No Play.\",\"clock\":{\"displayValue\":\"9:13\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.255,\"playId\":\"4012203034687\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":6,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & 26\",\"possessionText\":\"MIN 8\",\"downDistanceText\":\"3rd & 26 at MIN 8\",\"distance\":26,\"yardLine\":8,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":92},\"text\":\"(8:52) (Shotgun) K.Cousins pass short right to D.Cook to MIN 20 for 12 yards (J.Scott).\",\"clock\":{\"displayValue\":\"8:52\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.083,\"playId\":\"4012203034722\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":7,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"4th & 14\",\"possessionText\":\"MIN 20\",\"downDistanceText\":\"4th & 14 at MIN 20\",\"distance\":14,\"yardLine\":20,\"team\":{\"id\":\"16\"},\"down\":4,\"yardsToEndzone\":80},\"text\":\"(8:16) B.Colquitt punts 52 yards to JAX 28, Center-A.DePaola. K.Cole Sr. to JAX 29 for 1 yard (D.Chisena). PENALTY on JAX-B.Watson, Offensive Holding, 10 yards, enforced at JAX 28.\",\"clock\":{\"displayValue\":\"8:16\"},\"type\":{\"id\":\"52\",\"text\":\"Punt\",\"abbreviation\":\"PUNT\"}},\"homeWinPercentage\":0.52,\"playId\":\"4012203034746\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":8,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 18\",\"downDistanceText\":\"1st & 10 at JAX 18\",\"distance\":10,\"yardLine\":82,\"team\":{\"id\":\"30\"},\"down\":1,\"yardsToEndzone\":82},\"text\":\"(8:06) M.Glennon pass incomplete short right to J.O'Shaughnessy (A.Harris).\",\"clock\":{\"displayValue\":\"8:06\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.593,\"playId\":\"4012203034782\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":9,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & 10\",\"possessionText\":\"JAX 18\",\"downDistanceText\":\"2nd & 10 at JAX 18\",\"distance\":10,\"yardLine\":82,\"team\":{\"id\":\"30\"},\"down\":2,\"yardsToEndzone\":82},\"text\":\"(8:01) J.Robinson left end to JAX 20 for 2 yards (K.Boyd; H.Hand).\",\"clock\":{\"displayValue\":\"8:01\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.597,\"playId\":\"4012203034804\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":10,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & 8\",\"possessionText\":\"JAX 20\",\"downDistanceText\":\"3rd & 8 at JAX 20\",\"distance\":8,\"yardLine\":80,\"team\":{\"id\":\"30\"},\"down\":3,\"yardsToEndzone\":80},\"text\":\"(7:19) (Shotgun) M.Glennon pass deep middle intended for D.Chark Jr. INTERCEPTED by H.Smith [I.Odenigbo] at JAX 46. H.Smith to JAX 46 for no gain (D.Chark Jr.).\",\"clock\":{\"displayValue\":\"7:19\"},\"type\":{\"id\":\"26\",\"text\":\"Pass Interception Return\",\"abbreviation\":\"INTR\"}},\"homeWinPercentage\":0.764,\"playId\":\"4012203034825\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":11,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 46\",\"downDistanceText\":\"1st & 10 at JAX 46\",\"distance\":10,\"yardLine\":54,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":46},\"text\":\"(7:13) K.Cousins pass short left to A.Thielen to JAX 37 for 9 yards (J.Wilson).\",\"clock\":{\"displayValue\":\"7:13\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.793,\"playId\":\"4012203034851\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":12,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & 1\",\"possessionText\":\"JAX 37\",\"downDistanceText\":\"2nd & 1 at JAX 37\",\"distance\":1,\"yardLine\":63,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":37},\"text\":\"(6:41) K.Cousins pass incomplete short right to T.Conklin (J.Jones). PENALTY on MIN-J.Jefferson, Offensive Offside, 5 yards, enforced at JAX 37 - No Play.\",\"clock\":{\"displayValue\":\"6:41\"},\"type\":{\"id\":\"3\",\"text\":\"Pass Incompletion\"}},\"homeWinPercentage\":0.719,\"playId\":\"4012203034875\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":13,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & 6\",\"possessionText\":\"JAX 42\",\"downDistanceText\":\"2nd & 6 at JAX 42\",\"distance\":6,\"yardLine\":58,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":42},\"text\":\"(6:35) K.Cousins pass short middle to T.Conklin to JAX 32 for 10 yards (J.Jones).\",\"clock\":{\"displayValue\":\"6:35\"},\"type\":{\"id\":\"24\",\"text\":\"Pass Reception\",\"abbreviation\":\"REC\"}},\"homeWinPercentage\":0.844,\"playId\":\"4012203034908\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":14,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 32\",\"downDistanceText\":\"1st & 10 at JAX 32\",\"distance\":10,\"yardLine\":68,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":32},\"text\":\"(5:57) D.Cook right guard to JAX 27 for 5 yards (J.Giles-Harris; J.Jones).\",\"clock\":{\"displayValue\":\"5:57\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.883,\"playId\":\"4012203034932\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":15,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & 5\",\"possessionText\":\"JAX 27\",\"downDistanceText\":\"2nd & 5 at JAX 27\",\"distance\":5,\"yardLine\":73,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":27},\"text\":\"(5:21) D.Cook left end to JAX 28 for -1 yards (J.Schobert; M.Jack).\",\"clock\":{\"displayValue\":\"5:21\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.871,\"playId\":\"4012203034953\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":16,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & 6\",\"possessionText\":\"JAX 28\",\"downDistanceText\":\"3rd & 6 at JAX 28\",\"distance\":6,\"yardLine\":72,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":28},\"text\":\"(4:38) D.Cook up the middle to JAX 18 for 10 yards (J.Jones).\",\"clock\":{\"displayValue\":\"4:38\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.925,\"playId\":\"4012203034974\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":17,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"1st & 10\",\"possessionText\":\"JAX 18\",\"downDistanceText\":\"1st & 10 at JAX 18\",\"distance\":10,\"yardLine\":82,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":18},\"text\":\"(4:03) D.Cook up the middle to JAX 14 for 4 yards (J.Schobert; T.Herndon).\",\"clock\":{\"displayValue\":\"4:03\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.949,\"playId\":\"4012203034995\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":18,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & 6\",\"possessionText\":\"JAX 14\",\"downDistanceText\":\"2nd & 6 at JAX 14\",\"distance\":6,\"yardLine\":86,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":14},\"text\":\"(3:28) D.Cook right end to JAX 11 for 3 yards (D.Costin).\",\"clock\":{\"displayValue\":\"3:28\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.966,\"playId\":\"4012203035016\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":20,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & 3\",\"possessionText\":\"JAX 11\",\"downDistanceText\":\"3rd & 3 at JAX 11\",\"distance\":3,\"yardLine\":89,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":11},\"text\":\"(3:03) D.Cook left guard to JAX 5 for 6 yards (J.Wilson; J.Jones).\",\"clock\":{\"displayValue\":\"3:03\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.981,\"playId\":\"4012203035054\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":19,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"distance\":5,\"yardLine\":95,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":5},\"text\":\"Timeout #2 by MIN at 03:03.\",\"clock\":{\"displayValue\":\"3:01\"},\"type\":{\"id\":\"21\",\"text\":\"Timeout\",\"abbreviation\":\"TO\"}},\"homeWinPercentage\":0.966,\"playId\":\"4012203035037\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":21,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"1st & Goal\",\"possessionText\":\"JAX 5\",\"downDistanceText\":\"1st & Goal at JAX 5\",\"distance\":5,\"yardLine\":95,\"team\":{\"id\":\"16\"},\"down\":1,\"yardsToEndzone\":5},\"text\":\"(2:27) D.Cook left guard to JAX 2 for 3 yards (J.Schobert).\",\"clock\":{\"displayValue\":\"2:27\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.992,\"playId\":\"4012203035075\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":22,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"distance\":2,\"yardLine\":98,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":2},\"text\":\"Two-Minute Warning\",\"clock\":{\"displayValue\":\"2:00\"},\"type\":{\"id\":\"75\",\"text\":\"Two-minute warning\",\"abbreviation\":\"2Min Warn\"}},\"homeWinPercentage\":0.992,\"playId\":\"4012203035096\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":23,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"2nd & Goal\",\"possessionText\":\"JAX 2\",\"downDistanceText\":\"2nd & Goal at JAX 2\",\"distance\":2,\"yardLine\":98,\"team\":{\"id\":\"16\"},\"down\":2,\"yardsToEndzone\":2},\"text\":\"(2:00) D.Cook up the middle to JAX 1 for 1 yard (D.Smoot; A.Gotsis).\",\"clock\":{\"displayValue\":\"2:00\"},\"type\":{\"id\":\"5\",\"text\":\"Rush\",\"abbreviation\":\"RUSH\"}},\"homeWinPercentage\":0.996,\"playId\":\"4012203035113\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":24,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"distance\":1,\"yardLine\":99,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":1},\"text\":\"Timeout #1 by JAX at 01:53.\",\"clock\":{\"displayValue\":\"1:53\"},\"type\":{\"id\":\"21\",\"text\":\"Timeout\",\"abbreviation\":\"TO\"}},\"homeWinPercentage\":0.996,\"playId\":\"4012203035134\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":25,\"period\":{\"number\":5},\"homeScore\":24,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & Goal\",\"possessionText\":\"JAX 1\",\"downDistanceText\":\"3rd & Goal at JAX 1\",\"distance\":1,\"yardLine\":99,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":1},\"text\":\"(1:53) PENALTY on MIN-D.Dozier, False Start, 4 yards, enforced at JAX 1 - No Play.\",\"clock\":{\"displayValue\":\"1:53\"},\"type\":{\"id\":\"8\",\"text\":\"Penalty\",\"abbreviation\":\"PEN\"}},\"homeWinPercentage\":0.987,\"playId\":\"4012203035151\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":26,\"period\":{\"number\":5},\"homeScore\":27,\"awayScore\":24,\"start\":{\"shortDownDistanceText\":\"3rd & Goal\",\"possessionText\":\"JAX 5\",\"downDistanceText\":\"3rd & Goal at JAX 5\",\"distance\":5,\"yardLine\":95,\"team\":{\"id\":\"16\"},\"down\":3,\"yardsToEndzone\":5},\"text\":\"Dan Bailey 23 Yd Field Goal\",\"clock\":{\"displayValue\":\"1:49\"},\"type\":{\"id\":\"59\",\"text\":\"Field Goal Good\",\"abbreviation\":\"FG\"}},\"homeWinPercentage\":1,\"playId\":\"4012203035174\",\"tiePercentage\":0,\"secondsLeft\":0},{\"play\":{\"overtimePlayCount\":27,\"period\":{\"number\":5},\"homeScore\":27,\"awayScore\":24,\"start\":{\"distance\":0,\"yardLine\":0,\"down\":1,\"yardsToEndzone\":65},\"text\":\"END GAME\",\"clock\":{\"displayValue\":\"0:00\"},\"type\":{\"id\":\"66\",\"text\":\"End of Game\",\"abbreviation\":\"EG\"}},\"homeWinPercentage\":1,\"playId\":\"4012203035193\",\"tiePercentage\":0,\"secondsLeft\":0}];\n\t\t\t\t\t\n\n\t\t\t\t\t\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\n\t\t\t\t\t\t\n\n\t\t\t\t\t\tfunction loadScripts() {\n\t\t\t\t\t\t\tvar src = \"https://a.espncdn.com/redesign/0.591.3/js/dist/gamepackage-static.min.js\",\n\t\t\t\t\t\t\t\tscript;\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tif (espn.gamepackage.status === \"in\") {\n\t\t\t\t\t\t\t\tif (!espn.gamepackage.core) {\n\t\t\t\t\t\t\t\t\tsrc = src.replace(\"gamepackage.\", \"gamepackage.core.\");\n\t\t\t\t\t\t\t\t} else if (espn.gamepackage.bundles && typeof espn.gamepackage.bundles[espn.gamepackage.sport] === \"function\") {\n\t\t\t\t\t\t\t\t\tespn.gamepackage.init();\n\t\t\t\t\t\t\t\t\tespn.gamepackage.bundles[espn.gamepackage.sport]();\n\t\t\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t} else if (espn.gamepackage.init) {\n\t\t\t\t\t\t\t\tespn.gamepackage.init();\n\t\t\t\t\t\t\t\treturn;\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tscript = document.createElement(\"script\");\n\t\t\t\t\t\t\tscript.type = \"text/javascript\";\n\t\t\t\t\t\t\tscript.src = src;\n\t\t\t\t\t\t\tdocument.getElementsByTagName(\"head\")[0].appendChild(script);\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tif (window.espn_ui.deferReady === true) {\n\t\t\t\t\t\t\t\t$(document).one(\"page.render\", loadScripts);\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t$.subscribe(\"espn.defer.end\", loadScripts);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\n\n\t\t\t\t\t\tif( typeof(newrelic) !== \"undefined\" ){\n\t\t\t\t\t\t\tnewrelic.setCustomAttribute( \"game-state\", espn.gamepackage.status );\n\t\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t})(jQuery);\n\t\t\t"

Now the short of this is that we have the full script text, so there’s a lot of random JavaScript and logic along with HTML around the actual JSON we want. We want to find the “starting edge” and the end of the JSON file, which should be [{ and }] respectively.

I’ll start by removing all the new lines (\n) and tabs (\t), then finding anything that matches [{ and replace anything before it with [{, followed by the same idea for }].

This should return a raw string of the JSON content, which we can then parse with jsonlite! Regex is #magic, but in short the pattern of ".*(\\[\\{)" finds any text .* ahead of [{ which needs to be escaped as (\\[\\{) and replaces it with the matched component (\\1) which should be [{. We do that for both the start and end of the JSON file, to remove all the extra text and just get the JSON file itself.

raw_json_embed <- example_embed_json %>%
  str_remove_all("\\n|\\t") %>% 
  str_replace(".*(\\[\\{)", "\\1") %>% 
  str_replace("(\\}\\]).*", "\\1")

ex_parsed_json <- jsonlite::parse_json(raw_json_embed)

ex_parsed_json %>% enframe()

# A tibble: 216 × 2
    name value           
   <int> <list>          
 1     1 <named list [5]>
 2     2 <named list [5]>
 3     3 <named list [5]>
 4     4 <named list [5]>
 5     5 <named list [5]>
 6     6 <named list [5]>
 7     7 <named list [5]>
 8     8 <named list [5]>
 9     9 <named list [5]>
10    10 <named list [5]>
# … with 206 more rows

ESPN’s native API

Now that was a lot of work to just get at the JSON, but I wanted to show an example of where JSON data is embedded into the site. Alternatively, ESPN has a native pseudo-public API that returns the same data!

Let’s use that to get the data instead! (Note the summary below shows the full script if you want to skip ahead).

Full Script

espn_url <- "http://site.api.espn.com/apis/site/v2/sports/football/nfl/summary?event=401220303"

raw_espn_json <- httr::GET(espn_url) %>% 
  httr::content()

win_pct_df <- raw_espn_json[["winprobability"]] %>%
  enframe() %>%
  rename(row_id = name) %>%
  unnest_wider(value) %>%
  janitor::clean_names()

pbp_raw_espn <- raw_espn_json[["drives"]][[1]] %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  rename(
    drive_id = id,
    drive_start = start,
    drive_end = end
  ) 

pbp_raw_espn_plays <- pbp_raw_espn %>% 
  unnest_longer(plays) %>% 
  unnest_wider(plays) %>% 
  rename(play_id = id)

espn_pbp_clean <- pbp_raw_espn_plays %>% 
  hoist(
    .col = team,
    pos_team = "name",
    pos_abb = "abbreviation",
    pos_full = "displayName",
    pos_short = 4 # could also use "shortDisplayName"
  ) %>% 
  hoist(
    type,
    play_type_id = "id",
    play_type = "text",
    play_type_abb = "abbreviation"
  ) %>% 
  hoist(
    period,
    quarter = "number"
  ) %>% 
  hoist(
    clock,
    clock = "displayValue"
  ) %>% 
    janitor::clean_names() %>% 
  # drop remaining list columns
  select(-where(is.list))

espn_joined <- left_join(espn_pbp_clean, win_pct_df, by = "play_id") 

final_espn <- espn_joined %>% 
  mutate(
    home_team = raw_espn_json[["boxscore"]][["teams"]][[1]][["team"]][["abbreviation"]],
    away_team = raw_espn_json[["boxscore"]][["teams"]][[2]][["team"]][["abbreviation"]]
    )

final_espn %>% 
  ggplot(aes(x = row_id, y = 1 - home_win_percentage)) +
  geom_line(size = 1) +
  scale_y_continuous(breaks = c(0, 0.25, .5, .75, 1), labels = c(100, "", 50, "", 100), limits = c(0, 1)) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
  ) + 
  labs(x = "", y = "")

Warning: Removed 1 row(s) containing missing values (geom_path).

tidyr::hoist()

We’re going to introduce a new tidyr function in this section called hoist(). You can think of tidyr::hoist() as a way of extracting or hoisting specific list column elements into a new named column. This is especially helpful where the JSON has duplicate names, like id which really means game_id and id which really means play_id. With repeated names, unnest_ functions will error or append names as you’re essentially recreating existing columns.

espn_url <- "http://site.api.espn.com/apis/site/v2/sports/football/nfl/summary?event=401220303"

raw_espn_json <- httr::GET(espn_url) %>% 
  httr::content()

raw_espn_json %>% str(max.level = 1)

List of 17
 $ boxscore        :List of 2
 $ format          :List of 2
 $ gameInfo        :List of 3
 $ drives          :List of 1
 $ leaders         :List of 2
 $ broadcasts      : list()
 $ predictor       :List of 3
 $ pickcenter      :List of 4
 $ againstTheSpread:List of 2
 $ odds            : list()
 $ winprobability  :List of 217
 $ header          :List of 8
 $ scoringPlays    :List of 10
 $ news            :List of 3
 $ article         :List of 22
 $ videos          : list()
 $ standings       :List of 2

Now I’m most interested in the winprobability data, although you could get all sorts of metadata about simple play-by-play, news, etc. This is a VERY long output, so I’m just going to grab the first few.

raw_espn_json[["winprobability"]] %>% length()

[1] 217

raw_espn_json[["winprobability"]] %>% 
  .[1:3] %>% 
  str(max.level = 2)

List of 3
 $ :List of 4
  ..$ tiePercentage    : num 0
  ..$ homeWinPercentage: num 0.832
  ..$ secondsLeft      : int 0
  ..$ playId           : chr "4012203031"
 $ :List of 4
  ..$ tiePercentage    : num 0
  ..$ homeWinPercentage: num 0.828
  ..$ secondsLeft      : int 0
  ..$ playId           : chr "40122030340"
 $ :List of 4
  ..$ tiePercentage    : num 0
  ..$ homeWinPercentage: num 0.807
  ..$ secondsLeft      : int 0
  ..$ playId           : chr "40122030355"

Sinec we have a long list of repeated lists of length 4, we can assume that unnest_wider() is the right choice here (4 distinct columns, that are already “long”). We could always rely on unnest_auto() to guess for us and then replace it with the correct function afterwards.

I’m going to stick with my preferred workflow of tibble::enframe() and then unnest_wider.

win_pct_df <- raw_espn_json[["winprobability"]] %>%
  enframe() %>%
  rename(row_id = name) %>%
  unnest_wider(value) %>%
  janitor::clean_names()

win_pct_df

# A tibble: 217 × 5
   row_id tie_percentage home_win_percentage seconds_left play_id     
    <int>          <dbl>               <dbl>        <int> <chr>       
 1      1              0               0.832            0 4012203031  
 2      2              0               0.828            0 40122030340 
 3      3              0               0.807            0 40122030355 
 4      4              0               0.796            0 40122030379 
 5      5              0               0.785            0 401220303108
 6      6              0               0.777            0 401220303129
 7      7              0               0.742            0 401220303150
 8      8              0               0.741            0 401220303191
 9      9              0               0.747            0 401220303213
10     10              0               0.719            0 401220303257
# … with 207 more rows

Play-by-play

Now the other datasets we’re interested in are the drive info (for play-by-play), along with the game info like teams playing, play type, period, and time remaining.

Let’s start with the core JSON data, and then extract the drives data.

raw_espn_json %>% str(max.level = 1)

List of 17
 $ boxscore        :List of 2
 $ format          :List of 2
 $ gameInfo        :List of 3
 $ drives          :List of 1
 $ leaders         :List of 2
 $ broadcasts      : list()
 $ predictor       :List of 3
 $ pickcenter      :List of 4
 $ againstTheSpread:List of 2
 $ odds            : list()
 $ winprobability  :List of 217
 $ header          :List of 8
 $ scoringPlays    :List of 10
 $ news            :List of 3
 $ article         :List of 22
 $ videos          : list()
 $ standings       :List of 2

raw_espn_json[["drives"]][[1]] %>% enframe()

# A tibble: 30 × 2
    name value            
   <int> <list>           
 1     1 <named list [13]>
 2     2 <named list [13]>
 3     3 <named list [13]>
 4     4 <named list [13]>
 5     5 <named list [13]>
 6     6 <named list [13]>
 7     7 <named list [13]>
 8     8 <named list [13]>
 9     9 <named list [13]>
10    10 <named list [13]>
# … with 20 more rows

Again we notice that this is already “long” so we can assume we need to unnest_wider(). I’ll also rename a few columns since they’re not very descriptive (and duplicates of other columns).

pbp_raw_espn <- raw_espn_json[["drives"]][[1]] %>% 
  enframe() %>% 
  unnest_wider(value) %>% 
  rename(
    drive_id = id,
    drive_start = start,
    drive_end = end
  ) 

pbp_raw_espn %>% str(max.level = 2)

tibble [30 × 14] (S3: tbl_df/tbl/data.frame)
 $ name              : int [1:30] 1 2 3 4 5 6 7 8 9 10 ...
 $ drive_id          : chr [1:30] "4012203031" "4012203032" "4012203033" "4012203034" ...
 $ description       : chr [1:30] "5 plays, 75 yards, 2:27" "3 plays, 12 yards, 1:22" "10 plays, 66 yards, 5:07" "5 plays, 5 yards, 3:08" ...
 $ team              :List of 30
 $ drive_start       :List of 30
 $ drive_end         :List of 30
 $ timeElapsed       :List of 30
 $ yards             : int [1:30] 75 12 66 5 16 8 49 78 9 6 ...
 $ isScore           : logi [1:30] TRUE FALSE TRUE FALSE FALSE FALSE ...
 $ offensivePlays    : int [1:30] 5 3 10 5 4 3 11 10 3 5 ...
 $ result            : chr [1:30] "TD" "PUNT" "FG" "PUNT" ...
 $ shortDisplayResult: chr [1:30] "TD" "PUNT" "FG" "PUNT" ...
 $ displayResult     : chr [1:30] "Touchdown" "Punt" "Field Goal" "Punt" ...
 $ plays             :List of 30

Let’s go longer

At this point we can see the data is at the drive level, and the description is the metadata about how long the drive lasted. We want actual plays, and there is a list column named plays. We’ll need to increase the length of the data, so let’s try unnest_longer().

pbp_raw_espn %>%  
  unnest_longer(plays) %>% 
  str(max.level = 2)

tibble [217 × 14] (S3: tbl_df/tbl/data.frame)
 $ name              : int [1:217] 1 1 1 1 1 1 2 2 2 2 ...
 $ drive_id          : chr [1:217] "4012203031" "4012203031" "4012203031" "4012203031" ...
 $ description       : chr [1:217] "5 plays, 75 yards, 2:27" "5 plays, 75 yards, 2:27" "5 plays, 75 yards, 2:27" "5 plays, 75 yards, 2:27" ...
 $ team              :List of 217
 $ drive_start       :List of 217
 $ drive_end         :List of 217
 $ timeElapsed       :List of 217
 $ yards             : int [1:217] 75 75 75 75 75 75 12 12 12 12 ...
 $ isScore           : logi [1:217] TRUE TRUE TRUE TRUE TRUE TRUE ...
 $ offensivePlays    : int [1:217] 5 5 5 5 5 5 3 3 3 3 ...
 $ result            : chr [1:217] "TD" "TD" "TD" "TD" ...
 $ shortDisplayResult: chr [1:217] "TD" "TD" "TD" "TD" ...
 $ displayResult     : chr [1:217] "Touchdown" "Touchdown" "Touchdown" "Touchdown" ...
 $ plays             :List of 217

This gives us 188 plays, but plays is still a list column, so we need to “widen” it with unnest_wider(). Let’s go ahead and do that and then look at what the data looks like.

pbp_raw_espn_plays <- pbp_raw_espn %>% 
  unnest_longer(plays) %>% 
  unnest_wider(plays) %>% 
  rename(play_id = id)

pbp_raw_espn_plays %>% 
  glimpse()

Rows: 217
Columns: 29
$ name               <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, …
$ drive_id           <chr> "4012203031", "4012203031", "4012203031", "40122030…
$ description        <chr> "5 plays, 75 yards, 2:27", "5 plays, 75 yards, 2:27…
$ team               <list> ["Jaguars", "JAX", "Jacksonville Jaguars", "Jaguar…
$ drive_start        <list> [["quarter", 1], ["15:00"], 75, "JAX 25"], [["quar…
$ drive_end          <list> [["quarter", 1], ["12:33"], 0, "MIN 0"], [["quarte…
$ timeElapsed        <list> ["2:27"], ["2:27"], ["2:27"], ["2:27"], ["2:27"], …
$ yards              <int> 75, 75, 75, 75, 75, 75, 12, 12, 12, 12, 12, 12, 66,…
$ isScore            <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, F…
$ offensivePlays     <int> 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 10, 10, 10, 10,…
$ result             <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT",…
$ shortDisplayResult <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT",…
$ displayResult      <chr> "Touchdown", "Touchdown", "Touchdown", "Touchdown",…
$ play_id            <chr> "40122030340", "40122030355", "40122030379", "40122…
$ sequenceNumber     <chr> "4000", "5500", "7900", "10800", "12900", "15000", …
$ type               <list> ["53", "Kickoff", "K"], ["24", "Pass Reception", "…
$ text               <chr> "D.Bailey kicks 65 yards from MIN 35 to end zone, T…
$ awayScore          <int> 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, …
$ homeScore          <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, …
$ period             <list> [1], [1], [1], [1], [1], [1], [1], [1], [1], [1], …
$ clock              <list> ["15:00"], ["15:00"], ["14:25"], ["13:54"], ["13:2…
$ scoringPlay        <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FAL…
$ priority           <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FA…
$ modified           <chr> "2020-12-06T22:12Z", "2020-12-06T22:12Z", "2020-12-…
$ wallclock          <chr> "2020-12-06T18:02:32Z", "2020-12-06T18:03:13Z", "20…
$ start              <list> [0, 0, 35, 65, ["16"]], [1, 10, 75, 75, "1st & 10 …
$ end                <list> [1, 10, 75, 75, "1st & 10 at JAX 25", "1st & 10", …
$ statYardage        <int> 0, 24, 8, 9, 6, 28, 25, 5, 0, 7, 0, 13, 4, 4, 6, 9,…
$ scoringType        <list> <NULL>, <NULL>, <NULL>, <NULL>, <NULL>, ["touchdow…

Hoist away!

Looking good! We still have some list-columns to work on though (team, period, clock, start). Let’s pull some selected items from those columns via hoist(). I’m going to start with a small example, and limit the columns shown so that we don’t get overwhelmed.

pbp_raw_espn_plays %>%
  select(drive_id, play_id, yards, team) %>% 
  slice(1) %>% 
  str(max.level = 4)

tibble [1 × 4] (S3: tbl_df/tbl/data.frame)
 $ drive_id: chr "4012203031"
 $ play_id : chr "40122030340"
 $ yards   : int 75
 $ team    :List of 1
  ..$ :List of 5
  .. ..$ name            : chr "Jaguars"
  .. ..$ abbreviation    : chr "JAX"
  .. ..$ displayName     : chr "Jacksonville Jaguars"
  .. ..$ shortDisplayName: chr "Jaguars"
  .. ..$ logos           :List of 4

This shows us that the team list column has 5 named list items within it (name, abbreviation, displayName, shortDisplayName, and another list of lists for logos).

hoist() takes several arguments, first a column to work on (.col), and then some new columns to create. Note that we’re using raw strings ("name") to pull named list elements from the list column or we could use a number to grab by position.

pbp_raw_espn_plays %>%
  select(drive_id, play_id, yards, team) %>% 
  hoist(
    .col = team,
    pos_team = "name",
    pos_abb = "abbreviation",
    pos_full = "displayName",
    pos_short = 4 # could also use "shortDisplayName"
  )

# A tibble: 217 × 8
   drive_id   play_id     yards pos_team pos_abb pos_full pos_short team        
   <chr>      <chr>       <int> <chr>    <chr>   <chr>    <chr>     <list>      
 1 4012203031 40122030340    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 2 4012203031 40122030355    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 3 4012203031 40122030379    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 4 4012203031 4012203031…    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 5 4012203031 4012203031…    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 6 4012203031 4012203031…    75 Jaguars  JAX     Jackson… Jaguars   <named list>
 7 4012203032 4012203031…    12 Vikings  MIN     Minneso… Vikings   <named list>
 8 4012203032 4012203032…    12 Vikings  MIN     Minneso… Vikings   <named list>
 9 4012203032 4012203032…    12 Vikings  MIN     Minneso… Vikings   <named list>
10 4012203032 4012203032…    12 Vikings  MIN     Minneso… Vikings   <named list>
# … with 207 more rows

Ok, so hopefully that shows you how we can first find the list column elements, and then hoist() a select few of them by name/position. For the next section I’m just going to hoist() the ones I want and then save the dataframe. While there are some other list columns, I’m just going to drop them as I’m not interested in them right now.

espn_pbp_clean <- pbp_raw_espn_plays %>% 
  hoist(
    .col = team,
    pos_team = "name",
    pos_abb = "abbreviation",
    pos_full = "displayName",
    pos_short = 4 # could also use "shortDisplayName"
  ) %>% 
  hoist(
    type,
    play_type_id = "id",
    play_type = "text",
    play_type_abb = "abbreviation"
  ) %>% 
  hoist(
    period,
    quarter = "number"
  ) %>% 
  hoist(
    clock,
    clock = "displayValue"
  ) %>% 
    janitor::clean_names() %>% 
  # drop remaining list columns
  select(-where(is.list))

Join the data

The next thing we have to do is join the Win Probability data with the play-by-play info! This is easy as we have a common key column with play_id.

espn_joined <- left_join(espn_pbp_clean, win_pct_df, by = "play_id") 

espn_joined %>% 
  glimpse()

Rows: 217
Columns: 31
$ name                 <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3…
$ drive_id             <chr> "4012203031", "4012203031", "4012203031", "401220…
$ description          <chr> "5 plays, 75 yards, 2:27", "5 plays, 75 yards, 2:…
$ pos_team             <chr> "Jaguars", "Jaguars", "Jaguars", "Jaguars", "Jagu…
$ pos_abb              <chr> "JAX", "JAX", "JAX", "JAX", "JAX", "JAX", "MIN", …
$ pos_full             <chr> "Jacksonville Jaguars", "Jacksonville Jaguars", "…
$ pos_short            <chr> "Jaguars", "Jaguars", "Jaguars", "Jaguars", "Jagu…
$ yards                <int> 75, 75, 75, 75, 75, 75, 12, 12, 12, 12, 12, 12, 6…
$ is_score             <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE,…
$ offensive_plays      <int> 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 10, 10, 10, 1…
$ result               <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT…
$ short_display_result <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT…
$ display_result       <chr> "Touchdown", "Touchdown", "Touchdown", "Touchdown…
$ play_id              <chr> "40122030340", "40122030355", "40122030379", "401…
$ sequence_number      <chr> "4000", "5500", "7900", "10800", "12900", "15000"…
$ play_type_id         <chr> "53", "24", "24", "5", "5", "67", "12", "24", "3"…
$ play_type            <chr> "Kickoff", "Pass Reception", "Pass Reception", "R…
$ play_type_abb        <chr> "K", "REC", "REC", "RUSH", "RUSH", "TD", NA, "REC…
$ text                 <chr> "D.Bailey kicks 65 yards from MIN 35 to end zone,…
$ away_score           <int> 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
$ home_score           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ quarter              <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ scoring_play         <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, F…
$ priority             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ modified             <chr> "2020-12-06T22:12Z", "2020-12-06T22:12Z", "2020-1…
$ wallclock            <chr> "2020-12-06T18:02:32Z", "2020-12-06T18:03:13Z", "…
$ stat_yardage         <int> 0, 24, 8, 9, 6, 28, 25, 5, 0, 7, 0, 13, 4, 4, 6, …
$ row_id               <int> 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ tie_percentage       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ home_win_percentage  <dbl> 0.828, 0.807, 0.796, 0.785, 0.777, 0.742, 0.741, …
$ seconds_left         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…

Now there’s one more tricky point, which is that there isn’t a column that says “home” vs “away”.

ESPN’s JSON reports this simply by position, we can access this data, and will need to create a logical statement in order to assign home vs away.

raw_espn_json[["boxscore"]][["teams"]] %>% str(max.level = 2)

List of 2
 $ :List of 2
  ..$ team      :List of 11
  ..$ statistics:List of 25
 $ :List of 2
  ..$ team      :List of 11
  ..$ statistics:List of 25

From comparing the box scores and the JSON, the team listed first is always the away team. So for this game that means the Jaguars.

# 1st team == AWAY
raw_espn_json[["boxscore"]][["teams"]][[1]][["team"]][["abbreviation"]]

[1] "JAX"

# 2nd team == HOME
raw_espn_json[["boxscore"]][["teams"]][[2]][["team"]][["abbreviation"]]

[1] "MIN"

final_espn <- espn_joined %>% 
  mutate(
    home_team = raw_espn_json[["boxscore"]][["teams"]][[1]][["team"]][["abbreviation"]],
    away_team = raw_espn_json[["boxscore"]][["teams"]][[2]][["team"]][["abbreviation"]]
    )

Just for fun, let’s create a very quick graph to compare against the ESPN Win Percentage plot! Note that we still need to calculate the game seconds (from the clock data), so I’ll just use the row number as the x-axis for now. We can see that this aligns well with the “actual” plot from ESPN’s box score!

final_espn %>% 
  ggplot(aes(x = row_id, y = 1 - home_win_percentage)) +
  geom_line(size = 1) +
  scale_y_continuous(breaks = c(0, 0.25, .5, .75, 1), labels = c(100, "", 50, "", 100), limits = c(0, 1)) +
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank(),
    axis.text.y.right = element_text(size = 24),
    axis.text.x = element_blank()
  ) + 
  labs(x = "", y = "")

Warning: Removed 1 row(s) containing missing values (geom_path).

---

Roll it into a Function

We can quickly convert that into a function. Eventually this will be rolled into espnscrapeR proper. For now, I just have espnscrapeR::get_espn_win_prob().

get_espn_pbp <- function(game_id) {
  espn_url <- glue::glue("http://site.api.espn.com/apis/site/v2/sports/football/nfl/summary?event={game_id}")

  raw_espn_json <- fromJSON(espn_url, simplifyVector = FALSE)

  win_pct_df <- raw_espn_json[["winprobability"]] %>%
    enframe() %>%
    rename(row_id = name) %>%
    unnest_wider(value) %>%
    janitor::clean_names()

  espn_df <- raw_espn_json[["drives"]][[1]] %>%
    enframe() %>%
    unnest_wider(value) %>%
    rename(
      drive_id = id,
      drive_start = start,
      drive_end = end
    ) %>%
    unnest_longer(plays) %>%
    unnest_wider(plays)

  pbp_raw_espn_plays <- pbp_raw_espn %>%
    unnest_longer(plays) %>%
    unnest_wider(plays) %>%
    rename(play_id = id)

  espn_pbp_clean <- pbp_raw_espn_plays %>%
    hoist(
      .col = team,
      pos_team = "name",
      pos_abb = "abbreviation",
      pos_full = "displayName",
      pos_short = 4 # could also use "shortDisplayName"
    ) %>%
    hoist(
      type,
      play_type_id = "id",
      play_type = "text",
      play_type_abb = "abbreviation"
    ) %>%
    hoist(
      period,
      quarter = "number"
    ) %>%
    hoist(
      clock,
      clock = "displayValue"
    ) %>%
    janitor::clean_names() %>%
    # drop remaining list columns
    select(-where(is.list))

  espn_joined <- left_join(espn_pbp_clean, win_pct_df, by = "play_id")

  final_espn <- espn_joined %>%
    mutate(
      home_team = raw_espn_json[["boxscore"]][["teams"]][[1]][["team"]][["abbreviation"]],
      away_team = raw_espn_json[["boxscore"]][["teams"]][[2]][["team"]][["abbreviation"]]
    )

  final_espn
}

get_espn_pbp("401220303") %>% glimpse()

Rows: 217
Columns: 33
$ name                 <int> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3…
$ drive_id             <chr> "4012203031", "4012203031", "4012203031", "401220…
$ description          <chr> "5 plays, 75 yards, 2:27", "5 plays, 75 yards, 2:…
$ pos_team             <chr> "Jaguars", "Jaguars", "Jaguars", "Jaguars", "Jagu…
$ pos_abb              <chr> "JAX", "JAX", "JAX", "JAX", "JAX", "JAX", "MIN", …
$ pos_full             <chr> "Jacksonville Jaguars", "Jacksonville Jaguars", "…
$ pos_short            <chr> "Jaguars", "Jaguars", "Jaguars", "Jaguars", "Jagu…
$ yards                <int> 75, 75, 75, 75, 75, 75, 12, 12, 12, 12, 12, 12, 6…
$ is_score             <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE,…
$ offensive_plays      <int> 5, 5, 5, 5, 5, 5, 3, 3, 3, 3, 3, 3, 10, 10, 10, 1…
$ result               <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT…
$ short_display_result <chr> "TD", "TD", "TD", "TD", "TD", "TD", "PUNT", "PUNT…
$ display_result       <chr> "Touchdown", "Touchdown", "Touchdown", "Touchdown…
$ play_id              <chr> "40122030340", "40122030355", "40122030379", "401…
$ sequence_number      <chr> "4000", "5500", "7900", "10800", "12900", "15000"…
$ play_type_id         <chr> "53", "24", "24", "5", "5", "67", "12", "24", "3"…
$ play_type            <chr> "Kickoff", "Pass Reception", "Pass Reception", "R…
$ play_type_abb        <chr> "K", "REC", "REC", "RUSH", "RUSH", "TD", NA, "REC…
$ text                 <chr> "D.Bailey kicks 65 yards from MIN 35 to end zone,…
$ away_score           <int> 0, 0, 0, 0, 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6…
$ home_score           <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ quarter              <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ scoring_play         <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, F…
$ priority             <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
$ modified             <chr> "2020-12-06T22:12Z", "2020-12-06T22:12Z", "2020-1…
$ wallclock            <chr> "2020-12-06T18:02:32Z", "2020-12-06T18:03:13Z", "…
$ stat_yardage         <int> 0, 24, 8, 9, 6, 28, 25, 5, 0, 7, 0, 13, 4, 4, 6, …
$ row_id               <int> 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 1…
$ tie_percentage       <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ home_win_percentage  <dbl> 0.828, 0.807, 0.796, 0.785, 0.777, 0.742, 0.741, …
$ seconds_left         <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
$ home_team            <chr> "JAX", "JAX", "JAX", "JAX", "JAX", "JAX", "JAX", …
$ away_team            <chr> "MIN", "MIN", "MIN", "MIN", "MIN", "MIN", "MIN", …

More ESPN

Now you may be asking yourself, how the heck do I get the game_id to use with our fancy new function?!

ESPN provides the “scoreboard” for many sports, including the NFL, you can query it like so, which returns 333 games from 2018 for example. Note that this includes playoffs from the 2017 season (it is just returning any games that were played in the 2018 calendar year).

espn_scores_url <- "http://site.api.espn.com/apis/site/v2/sports/football/nfl/scoreboard?dates=2018&limit=500"

raw_json_scores <- httr::GET(espn_scores_url) %>% 
  httr::content()

raw_json_scores %>% str(max.level = 1)

List of 2
 $ leagues:List of 1
 $ events :List of 333

Looks like we’re interested in the events list element, so let’s take a look at the first few observations in it, and see what we’re working with.

raw_json_scores[["events"]] %>% 
  .[1:3] %>% # get the first 3 elements
  str(max.level = 2)

List of 3
 $ :List of 9
  ..$ id          : chr "400999175"
  ..$ uid         : chr "s:20~l:28~e:400999175"
  ..$ date        : chr "2018-01-06T21:20Z"
  ..$ name        : chr "Tennessee Titans at Kansas City Chiefs"
  ..$ shortName   : chr "TEN @ KC"
  ..$ season      :List of 3
  ..$ competitions:List of 1
  ..$ links       :List of 5
  ..$ status      :List of 4
 $ :List of 9
  ..$ id          : chr "400999171"
  ..$ uid         : chr "s:20~l:28~e:400999171"
  ..$ date        : chr "2018-01-07T01:15Z"
  ..$ name        : chr "Atlanta Falcons at Los Angeles Rams"
  ..$ shortName   : chr "ATL @ LAR"
  ..$ season      :List of 3
  ..$ competitions:List of 1
  ..$ links       :List of 3
  ..$ status      :List of 4
 $ :List of 9
  ..$ id          : chr "400999176"
  ..$ uid         : chr "s:20~l:28~e:400999176"
  ..$ date        : chr "2018-01-07T18:05Z"
  ..$ name        : chr "Buffalo Bills at Jacksonville Jaguars"
  ..$ shortName   : chr "BUF @ JAX"
  ..$ season      :List of 3
  ..$ competitions:List of 1
  ..$ links       :List of 5
  ..$ status      :List of 4

Yep! That’s the one of interest, and we’ve got essentially a data.frame inside each of the list elements. So we can apply our typical workflow of enframe() + unnest_wider().

espn_games_2018 <- raw_json_scores[["events"]] %>% 
  enframe() %>% 
  rename(row_id = name) %>% 
  unnest_wider(value) %>% 
  rename(game_id = id)

espn_games_2018 %>% 
  glimpse()

Rows: 333
Columns: 10
$ row_id       <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17…
$ game_id      <chr> "400999175", "400999171", "400999176", "400999177", "4009…
$ uid          <chr> "s:20~l:28~e:400999175", "s:20~l:28~e:400999171", "s:20~l…
$ date         <chr> "2018-01-06T21:20Z", "2018-01-07T01:15Z", "2018-01-07T18:…
$ name         <chr> "Tennessee Titans at Kansas City Chiefs", "Atlanta Falcon…
$ shortName    <chr> "TEN @ KC", "ATL @ LAR", "BUF @ JAX", "CAR @ NO", "ATL @ …
$ season       <list> [2017, 3, "post-season"], [2017, 3, "post-season"], [201…
$ competitions <list> [["400999175", "s:20~l:28~e:400999175~c:400999175", "201…
$ links        <list> [["en-US", ["summary", "desktop", "event"], "https://www…
$ status       <list> [0, "0:00", 4, ["3", "STATUS_FINAL", "post", TRUE, "Fina…

We still need to grab some information from the season column, and there are few other list-columns if we wanted to further clean the data.

espn_season_2018 <- espn_games_2018 %>% 
  unnest_wider(season) %>% 
  unchop(competitions)

espn_season_2018 %>% 
  select(competitions) %>% 
  slice(1) %>% 
  str(max.level = 5)

tibble [1 × 1] (S3: tbl_df/tbl/data.frame)
 $ competitions:List of 1
  ..$ :List of 19
  .. ..$ id                   : chr "400999175"
  .. ..$ uid                  : chr "s:20~l:28~e:400999175~c:400999175"
  .. ..$ date                 : chr "2018-01-06T21:20Z"
  .. ..$ attendance           : int 73319
  .. ..$ type                 :List of 2
  .. .. ..$ id          : chr "14"
  .. .. ..$ abbreviation: chr "RD16"
  .. ..$ timeValid            : logi TRUE
  .. ..$ neutralSite          : logi FALSE
  .. ..$ conferenceCompetition: logi FALSE
  .. ..$ recent               : logi FALSE
  .. ..$ venue                :List of 5
  .. .. ..$ id      : chr "3622"
  .. .. ..$ fullName: chr "GEHA Field at Arrowhead Stadium"
  .. .. ..$ address :List of 2
  .. .. ..$ capacity: int 72936
  .. .. ..$ indoor  : logi FALSE
  .. ..$ competitors          :List of 2
  .. .. ..$ :List of 11
  .. .. ..$ :List of 11
  .. ..$ notes                :List of 1
  .. .. ..$ :List of 2
  .. ..$ status               :List of 4
  .. .. ..$ clock       : num 0
  .. .. ..$ displayClock: chr "0:00"
  .. .. ..$ period      : int 4
  .. .. ..$ type        :List of 7
  .. ..$ broadcasts           :List of 1
  .. .. ..$ :List of 2
  .. ..$ leaders              :List of 3
  .. .. ..$ :List of 5
  .. .. ..$ :List of 5
  .. .. ..$ :List of 5
  .. ..$ format               :List of 1
  .. .. ..$ regulation:List of 1
  .. ..$ startDate            : chr "2018-01-06T21:20Z"
  .. ..$ geoBroadcasts        :List of 2
  .. .. ..$ :List of 5
  .. .. ..$ :List of 5
  .. ..$ headlines            :List of 1
  .. .. ..$ :List of 3

hoist again

So we’re going to use hoist() to again grab specific items from our list columns. Here I’m passing increasing “depth” to hoist() via a list(). While it can be a bit hard to initially wrap your head around, hoist() is similar to native subsetting with R.

So the following code is roughly equivalent in base R and hoist(). Note that the subsetting base R version with [[ is getting the first element (of 333), so I’m going to grab the first element of our new home_team_id column from our vectorized hoist() call.

ex_id_subset <- espn_season_2018[["competitions"]][[1]][["competitors"]][[1]][["id"]]

ex_id_hoist <- espn_season_2018 %>%
  hoist(competitions, home_team_id = list("competitors", 1, "id"))

all.equal(ex_id_subset, ex_id_hoist[["home_team_id"]][1])

[1] TRUE

So we can now use that same idea to grab a few desired columns. We can sanity check our work by seeing how many games we have of each “type” with a quick count(). There should be 256 regular season games in each season.

espn_season_2018_final <- espn_season_2018 %>%
  hoist(
    competitions,
    home_team_id = list("competitors", 1, "id"),
    home_team_abb = list("competitors", 1, "team", "abbreviation"),
    away_team_id = list("competitors", 2, "id"),
    away_team_abb = list("competitors", 2, "team", "abbreviation"),
    home_score = list("competitors", 1, "score"),
    away_score = list("competitors", 2, "score")
  ) %>%
  select(-where(is.list), -row_id) %>%
  janitor::clean_names() %>%
  rename(season_type = type) %>%
  mutate(
    season_type = case_when(
      season_type == 1L ~ "Preseason",
      season_type == 2L ~ "Regular Season",
      season_type == 3L ~ "Playoffs",
      TRUE ~ as.character(season_type),
    )
  )

espn_season_2018_final %>% 
  count(season_type)

# A tibble: 3 × 2
  season_type        n
  <chr>          <int>
1 Playoffs          12
2 Preseason         65
3 Regular Season   256

We could then find a specific game or purrr::map() across all the games as needed.

Inside the below detail tag, we have the full method of getting the data as JSON, cleaning/rectangling, and returning as a relatively tidy dataframe.

ESPN Schedule Function

get_espn_schedule <- function(season) {
  espn_scores_url <- glue::glue("http://site.api.espn.com/apis/site/v2/sports/football/nfl/scoreboard?dates={season}&limit=500")

  raw_json_scores <- fromJSON(espn_scores_url, simplifyVector = FALSE)

  espn_games <- raw_json_scores[["events"]] %>%
    enframe() %>%
    rename(row_id = name) %>%
    unnest_wider(value) %>%
    rename(game_id = id)
  
  espn_season <- espn_games %>% 
    unnest_wider(season) %>% 
    unchop(competitions)

  espn_season_final <- espn_season %>%
    hoist(
      competitions,
      home_team_id = list("competitors", 1, "id"),
      home_team_abb = list("competitors", 1, "team", "abbreviation"),
      away_team_id = list("competitors", 2, "id"),
      away_team_abb = list("competitors", 2, "team", "abbreviation"),
      home_score = list("competitors", 1, "score"),
      away_score = list("competitors", 2, "score")
    ) %>%
    select(-where(is.list), -row_id) %>%
    janitor::clean_names() %>%
    rename(season_type = type) %>%
    mutate(
      season_type = case_when(
        season_type == 1L ~ "Preseason",
        season_type == 2L ~ "Regular Season",
        season_type == 3L ~ "Playoffs",
        TRUE ~ as.character(season_type),
      )
    )
  
  espn_season_final
}

Expand for Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.2.0 (2022-04-22)
 os       macOS Monterey 12.2.1
 system   aarch64, darwin20
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/Chicago
 date     2022-04-28
 pandoc   2.18 @ /Applications/RStudio.app/Contents/MacOS/quarto/bin/tools/ (via rmarkdown)
 quarto   0.9.294 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package     * version date (UTC) lib source
 dplyr       * 1.0.8   2022-02-08 [1] CRAN (R 4.2.0)
 forcats     * 0.5.1   2021-01-27 [1] CRAN (R 4.2.0)
 ggplot2     * 3.3.5   2021-06-25 [1] CRAN (R 4.2.0)
 jsonlite    * 1.8.0   2022-02-22 [1] CRAN (R 4.2.0)
 purrr       * 0.3.4   2020-04-17 [1] CRAN (R 4.2.0)
 reactable   * 0.2.3   2020-10-04 [1] CRAN (R 4.2.0)
 readr       * 2.1.2   2022-01-30 [1] CRAN (R 4.2.0)
 rvest       * 1.0.2   2021-10-16 [1] CRAN (R 4.2.0)
 sessioninfo * 1.2.2   2021-12-06 [1] CRAN (R 4.2.0)
 stringr     * 1.4.0   2019-02-10 [1] CRAN (R 4.2.0)
 tibble      * 3.1.6   2021-11-07 [1] CRAN (R 4.2.0)
 tidyr       * 1.2.0   2022-02-01 [1] CRAN (R 4.2.0)
 tidyverse   * 1.3.1   2021-04-15 [1] CRAN (R 4.2.0)
 xml2        * 1.3.3   2021-11-30 [1] CRAN (R 4.2.0)

 [1] /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/library

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