Fantasy Football Data

library(fflr)
packageVersion("fflr")
#> [1] '2.2.2'
ffl_id(leagueId = "42654852")
#> Temporarily set `fflr.leagueId` option to 42654852
#> [1] "42654852"

This vignette will demonstrate the fflr functions used to reach equivalency with the ESPN fantasy football website. The website has eight section headers with various subsections:

  1. My Team
    • Overview
    • Stats (TBD)
    • Schedule (TBD)
    • News
    • Projections (TBD)
    • Ranks (TBD)
  2. League
    • League Home
    • Settings
    • Members
    • Rosters
    • Schedule
    • Message Board
    • Transaction Counter
    • History
    • Draft Recap
    • Email League
    • Recent Activity
  3. Players
    • Add Players
    • Watch List
    • Scoring Leaders
    • Live Draft Trends
    • Points Against
    • Added / Dropped
    • Player News
    • Projections
    • Budget Summary
    • Offers Report
    • Stat Corrections
  4. FantasyCast
  5. Scoreboard
  6. Standings
  7. Opposing Teams
  8. LM Tools
    • League Membership Tools
    • Draft Tools
    • League and Scoring Settings Tools
    • Roster Tools
    • Schedule and Standings Tools
    • Miscellaneous Tools

My Team

The My Team page presents an overview of, well, your fantasy team. From this page, a team manager can set their lineup and see statistics and news on the players on their roster.

There are six subsections on the My Team page.

Overview

The team_roster() function returns all rosters in a league. The output of this function is organized to replicate the layout of the table found on the website. Players are listed in order of their “slot” with name and team information followed by projected and actual scores and ownership statistics.

my_team <- team_roster(scoringPeriodId = 1)[[1]] # select first roster
my_team[, -(1:3)]
#> # A tibble: 16 × 13
#>    abbrev lineupSlot playerId firstName lastName   proTeam position injuryStatus
#>    <fct>  <fct>         <int> <chr>     <chr>      <fct>   <fct>    <chr>       
#>  1 AUS    QB          3139477 Patrick   Mahomes    KC      QB       A           
#>  2 AUS    RB          3117251 Christian McCaffrey  SF      RB       A           
#>  3 AUS    RB          4430807 Bijan     Robinson   Atl     RB       A           
#>  4 AUS    WR          2977187 Cooper    Kupp       LAR     WR       A           
#>  5 AUS    WR          4372016 Jaylen    Waddle     Mia     WR       A           
#>  6 AUS    TE          2576925 Darren    Waller     NYG     TE       A           
#>  7 AUS    FLEX        3916148 Tony      Pollard    Dal     RB       A           
#>  8 AUS    D/ST         -16018 Saints    D/ST       NO      D/ST     A           
#>  9 AUS    K             15683 Justin    Tucker     Bal     K        A           
#> 10 AUS    BE          4361370 Chris     Olave      NO      WR       A           
#> 11 AUS    BE          4241478 DeVonta   Smith      Phi     WR       A           
#> 12 AUS    BE          4360238 Dameon    Pierce     Hou     RB       A           
#> 13 AUS    BE          3126486 Deebo     Samuel     SF      WR       A           
#> 14 AUS    BE            15795 DeAndre   Hopkins    Ten     WR       A           
#> 15 AUS    BE          3116165 Chris     Godwin     TB      WR       A           
#> 16 AUS    BE          4567048 Kenneth   Walker III Sea     RB       A           
#> # ℹ 5 more variables: projectedScore <dbl>, actualScore <dbl>, percentStarted <dbl>,
#> #   percentOwned <dbl>, percentChange <dbl>

News

The player_outlook() and player_news() functions return news on your roster. The first returns all outlooks by player and week and cannot be refined beyond setting a limit of players to return (in order of rank).

player_outlook(limit = 1)
#> # A tibble: 18 × 6
#>    seasonId scoringPeriodId      id firstName lastName outlook                            
#>       <int>           <int>   <int> <chr>     <chr>    <chr>                              
#>  1     2023               0 3116406 Tyreek    Hill     "Hill's first season with the Dolp…
#>  2     2023               1 3116406 Tyreek    Hill     "Hill is coming off a career seaso…
#>  3     2023               2 3116406 Tyreek    Hill     "Hill might just be the top fantas…
#>  4     2023               3 3116406 Tyreek    Hill     "Hill was shadowed by Patriots roo…
#>  5     2023               4 3116406 Tyreek    Hill     "Hill registered nine catches for …
#>  6     2023               5 3116406 Tyreek    Hill     "It's unique when 72 scrimmage yar…
#>  7     2023               6 3116406 Tyreek    Hill     "Hill had his way with the Giants …
#>  8     2023               7 3116406 Tyreek    Hill     "Rare speed and awesome usage equa…
#>  9     2023               8 3116406 Tyreek    Hill     "Now just the fourth player to ecl…
#> 10     2023               9 3116406 Tyreek    Hill     "Hill vowed he’d eclipse 2000 rece…
#> 11     2023              10 3116406 Tyreek    Hill     "The Chiefs managed to keep Hill o…
#> 12     2023              11 3116406 Tyreek    Hill     "A rare speedster worthy of top bi…
#> 13     2023              12 3116406 Tyreek    Hill     "Against the Jets last week, Hill …
#> 14     2023              13 3116406 Tyreek    Hill     "If Hill averages \"just\" 104 yar…
#> 15     2023              14 3116406 Tyreek    Hill     "Hill caught four out of five targ…
#> 16     2023              15 3116406 Tyreek    Hill     "Hill’s Week 15 availability was a…
#> 17     2023              16 3116406 Tyreek    Hill     "Hill returned from a one-game abs…
#> 18     2023              17 3116406 Tyreek    Hill     "Hill’s chance for a 2000-yard rec…

The second fiction takes a single playerId value and returns all the recent news on that player, including premium stories in HTML format.

player_news(playerId = "3139477", parseHTML = FALSE)
#> # A tibble: 1 × 6
#>        id published           type  premium headline                                 body 
#>     <int> <dttm>              <chr> <lgl>   <chr>                                    <chr>
#> 1 3139477 2024-02-28 04:35:17 Story FALSE   Max Strus' buzzer-beater shocks Travis … "<p>…

League

ESPN fantasy leagues have their own unique settings and structure. This package has been tested for a very narrow subset of those possible settings.

league_info(leagueId = "42654852")
#> # A tibble: 1 × 6
#>         id seasonId name             isPublic  size finalScoringPeriod
#>      <int>    <int> <chr>            <lgl>    <int>              <int>
#> 1 42654852     2023 FFLR Test League TRUE         4                 17
league_name()
#> [1] "FFLR Test League"
league_size()
#> # A tibble: 1 × 2
#>   seasonId  size
#>      <int> <int>
#> 1     2023     4
str(league_status())
#> tibble [1 × 12] (S3: tbl_df/tbl/data.frame)
#>  $ year                   : int 2023
#>  $ isActive               : logi TRUE
#>  $ activatedDate          : POSIXct[1:1], format: "2023-09-05 02:42:21"
#>  $ scoringPeriodId        : int 19
#>  $ firstScoringPeriod     : int 1
#>  $ finalScoringPeriod     : int 17
#>  $ previousSeasons        :List of 1
#>   ..$ : int [1:2] 2021 2022
#>  $ standingsUpdateDate    : POSIXct[1:1], format: "2024-01-08 08:59:37"
#>  $ teamsJoined            : int 4
#>  $ waiverLastExecutionDate: POSIXct[1:1], format: "2024-01-10 08:31:13"
#>  $ waiverNextExecutionDate: POSIXct[1:1], format: NA
#>  $ waiverProcessStatus    :List of 1
#>   ..$ :'data.frame': 0 obs. of  1 variable:
#>   .. ..$ date: 'POSIXct' num(0) 
#>  - attr(*, "tzone")= chr ""

Settings

Draft

draft_settings()
#> # A tibble: 1 × 13
#>   seasonId auctionBudget availableDate       date                isTradingEnabled
#>      <int> <chr>         <dttm>              <dttm>              <lgl>           
#> 1     2023 <NA>          2023-09-05 01:45:00 2023-09-05 02:45:00 FALSE           
#> # ℹ 8 more variables: keeperCount <int>, keeperCountFuture <int>, keeperOrderType <chr>,
#> #   leagueSubType <chr>, orderType <chr>, pickOrder <list>, timePerSelection <int>,
#> #   type <chr>

Rosters

roster_settings()
#> # A tibble: 1 × 8
#>   seasonId isBenchUnlimited isUsingUndroppableList lineupLocktimeType lineupSlotCounts
#>      <int> <lgl>            <lgl>                  <chr>              <list>          
#> 1     2023 TRUE             TRUE                   INDIVIDUAL_GAME    <df [25 × 2]>   
#> # ℹ 3 more variables: moveLimit <int>, positionLimits <list>, rosterLocktimeType <chr>

Scoring

scoring_settings()
#> # A tibble: 1 × 7
#>   seasonId scoringType playerRankType homeTeamBonus playoffHomeTeamBonus
#>      <int> <chr>       <chr>                  <int>                <int>
#> 1     2023 H2H_POINTS  PPR                        1                    0
#> # ℹ 2 more variables: playoffMatchupTieRule <chr>, scoringItems <list>

Transactions and Keepers

acquisition_settings()
#> # A tibble: 1 × 12
#>    year acquisitionBudget acquisitionLimit acquisitionType     finalPlaceTransactionElig…¹
#>   <int>             <int>            <int> <chr>                                     <int>
#> 1  2023               100               -1 WAIVERS_TRADITIONAL                           0
#> # ℹ abbreviated name: ¹​finalPlaceTransactionEligible
#> # ℹ 7 more variables: matchupLimitPerScoringPeriod <lgl>, minimumBid <int>,
#> #   transactionLockingEnabled <lgl>, waiverHours <int>, waiverOrderReset <lgl>,
#> #   waiverProcessDays <list>, waiverProcessHour <int>

Schedule

schedule_settings()
#> # A tibble: 1 × 12
#>   seasonId divisions    matchupPeriodCount matchupPeriodLength matchupPeriods periodTypeId
#>      <int> <list>                    <int>               <int> <list>                <int>
#> 1     2023 <df [2 × 3]>                 17                   1 <df [17 × 2]>             1
#> # ℹ 6 more variables: playoffMatchupPeriodLength <int>, playoffReseed <lgl>,
#> #   playoffSeedingRule <chr>, playoffSeedingRuleBy <int>, playoffTeamCount <int>,
#> #   variablePlayoffMatchupPeriodLength <lgl>

Members

league_members()
#> # A tibble: 1 × 6
#>   memberId                  displayName firstName lastName isLeagueCreator isLeagueManager
#>   <chr>                     <chr>       <chr>     <chr>    <lgl>           <lgl>          
#> 1 {22DFE7FF-9DF2-4F3B-9FE7… K5cents     Kiernan   Nicholls TRUE            FALSE

Rosters

team_roster(scoringPeriodId = 1)
#> $AUS
#> # A tibble: 16 × 16
#>    seasonId scoringPeriodId teamId abbrev lineupSlot playerId firstName lastName   proTeam
#>       <int>           <int>  <int> <fct>  <fct>         <int> <chr>     <chr>      <fct>  
#>  1     2023               1      1 AUS    QB          3139477 Patrick   Mahomes    KC     
#>  2     2023               1      1 AUS    RB          3117251 Christian McCaffrey  SF     
#>  3     2023               1      1 AUS    RB          4430807 Bijan     Robinson   Atl    
#>  4     2023               1      1 AUS    WR          2977187 Cooper    Kupp       LAR    
#>  5     2023               1      1 AUS    WR          4372016 Jaylen    Waddle     Mia    
#>  6     2023               1      1 AUS    TE          2576925 Darren    Waller     NYG    
#>  7     2023               1      1 AUS    FLEX        3916148 Tony      Pollard    Dal    
#>  8     2023               1      1 AUS    D/ST         -16018 Saints    D/ST       NO     
#>  9     2023               1      1 AUS    K             15683 Justin    Tucker     Bal    
#> 10     2023               1      1 AUS    BE          4361370 Chris     Olave      NO     
#> 11     2023               1      1 AUS    BE          4241478 DeVonta   Smith      Phi    
#> 12     2023               1      1 AUS    BE          4360238 Dameon    Pierce     Hou    
#> 13     2023               1      1 AUS    BE          3126486 Deebo     Samuel     SF     
#> 14     2023               1      1 AUS    BE            15795 DeAndre   Hopkins    Ten    
#> 15     2023               1      1 AUS    BE          3116165 Chris     Godwin     TB     
#> 16     2023               1      1 AUS    BE          4567048 Kenneth   Walker III Sea    
#> # ℹ 7 more variables: position <fct>, injuryStatus <chr>, projectedScore <dbl>,
#> #   actualScore <dbl>, percentStarted <dbl>, percentOwned <dbl>, percentChange <dbl>
#> 
#> $BOS
#> # A tibble: 16 × 16
#>    seasonId scoringPeriodId teamId abbrev lineupSlot playerId firstName lastName  proTeam
#>       <int>           <int>  <int> <fct>  <fct>         <int> <chr>     <chr>     <fct>  
#>  1     2023               1      2 BOS    QB          3916387 Lamar     Jackson   Bal    
#>  2     2023               1      2 BOS    RB          3043078 Derrick   Henry     Ten    
#>  3     2023               1      2 BOS    RB          4047365 Josh      Jacobs    LV     
#>  4     2023               1      2 BOS    WR          4362628 Ja'Marr   Chase     Cin    
#>  5     2023               1      2 BOS    WR          3116406 Tyreek    Hill      Mia    
#>  6     2023               1      2 BOS    TE          3116365 Mark      Andrews   Bal    
#>  7     2023               1      2 BOS    FLEX        4241389 CeeDee    Lamb      Dal    
#>  8     2023               1      2 BOS    D/ST         -16023 Steelers  D/ST      Pit    
#>  9     2023               1      2 BOS    K           4360234 Evan      McPherson Cin    
#> 10     2023               1      2 BOS    BE          3116385 Joe       Mixon     Cin    
#> 11     2023               1      2 BOS    BE          4047650 DK        Metcalf   Sea    
#> 12     2023               1      2 BOS    BE            15818 Keenan    Allen     LAC    
#> 13     2023               1      2 BOS    BE          4248528 Christian Watson    GB     
#> 14     2023               1      2 BOS    BE          4427366 Breece    Hall      NYJ    
#> 15     2023               1      2 BOS    BE          4045163 Miles     Sanders   Car    
#> 16     2023               1      2 BOS    BE          4379399 James     Cook      Buf    
#> # ℹ 7 more variables: position <fct>, injuryStatus <chr>, projectedScore <dbl>,
#> #   actualScore <dbl>, percentStarted <dbl>, percentOwned <dbl>, percentChange <dbl>
#> 
#> $CHI
#> # A tibble: 16 × 16
#>    seasonId scoringPeriodId teamId abbrev lineupSlot playerId firstName lastName   proTeam
#>       <int>           <int>  <int> <fct>  <fct>         <int> <chr>     <chr>      <fct>  
#>  1     2023               1      3 CHI    QB          4040715 Jalen     Hurts      Phi    
#>  2     2023               1      3 CHI    RB          3929630 Saquon    Barkley    NYG    
#>  3     2023               1      3 CHI    RB          4239996 Travis    Etienne J… Jax    
#>  4     2023               1      3 CHI    WR          4262921 Justin    Jefferson  Min    
#>  5     2023               1      3 CHI    WR          4569618 Garrett   Wilson     NYJ    
#>  6     2023               1      3 CHI    TE            15847 Travis    Kelce      KC     
#>  7     2023               1      3 CHI    FLEX        4374302 Amon-Ra   St. Brown  Det    
#>  8     2023               1      3 CHI    D/ST         -16025 49ers     D/ST       SF     
#>  9     2023               1      3 CHI    K           3055899 Harrison  Butker     KC     
#> 10     2023               1      3 CHI    BE          4429795 Jahmyr    Gibbs      Det    
#> 11     2023               1      3 CHI    BE          3042519 Aaron     Jones      GB     
#> 12     2023               1      3 CHI    BE          3915511 Joe       Burrow     Cin    
#> 13     2023               1      3 CHI    BE          2976499 Amari     Cooper     Cle    
#> 14     2023               1      3 CHI    BE          4697815 Rachaad   White      TB     
#> 15     2023               1      3 CHI    BE          3054850 Alvin     Kamara     NO     
#> 16     2023               1      3 CHI    BE          4038941 Justin    Herbert    LAC    
#> # ℹ 7 more variables: position <fct>, injuryStatus <chr>, projectedScore <dbl>,
#> #   actualScore <dbl>, percentStarted <dbl>, percentOwned <dbl>, percentChange <dbl>
#> 
#> $DEN
#> # A tibble: 16 × 16
#>    seasonId scoringPeriodId teamId abbrev lineupSlot playerId firstName lastName  proTeam
#>       <int>           <int>  <int> <fct>  <fct>         <int> <chr>     <chr>     <fct>  
#>  1     2023               1      4 DEN    QB          3918298 Josh      Allen     Buf    
#>  2     2023               1      4 DEN    RB          3068267 Austin    Ekeler    LAC    
#>  3     2023               1      4 DEN    RB          3128720 Nick      Chubb     Cle    
#>  4     2023               1      4 DEN    WR            16800 Davante   Adams     LV     
#>  5     2023               1      4 DEN    WR          2976212 Stefon    Diggs     Buf    
#>  6     2023               1      4 DEN    TE          4036133 T.J.      Hockenson Min    
#>  7     2023               1      4 DEN    FLEX        4047646 A.J.      Brown     Phi    
#>  8     2023               1      4 DEN    D/ST         -16002 Bills     D/ST      Buf    
#>  9     2023               1      4 DEN    K           3051909 Daniel    Carlson   LV     
#> 10     2023               1      4 DEN    BE          4239993 Tee       Higgins   Cin    
#> 11     2023               1      4 DEN    BE          4241457 Najee     Harris    Pit    
#> 12     2023               1      4 DEN    BE          4569173 Rhamondre Stevenson NE     
#> 13     2023               1      4 DEN    BE          3925357 Calvin    Ridley    Jax    
#> 14     2023               1      4 DEN    BE          3932905 Diontae   Johnson   Pit    
#> 15     2023               1      4 DEN    BE          3045147 James     Conner    Ari    
#> 16     2023               1      4 DEN    BE          4048244 Alexander Mattison  Min    
#> # ℹ 7 more variables: position <fct>, injuryStatus <chr>, projectedScore <dbl>,
#> #   actualScore <dbl>, percentStarted <dbl>, percentOwned <dbl>, percentChange <dbl>

Schedule

tidy_schedule(scoringPeriodId = 1)
#> # A tibble: 68 × 7
#>    seasonId matchupPeriodId matchupId teamId abbrev opponent isHome
#>       <int>           <int>     <int>  <int> <fct>  <fct>    <lgl> 
#>  1     2023               1         1      1 AUS    CHI      TRUE  
#>  2     2023               1         1      3 CHI    AUS      FALSE 
#>  3     2023               1         2      2 BOS    DEN      TRUE  
#>  4     2023               1         2      4 DEN    BOS      FALSE 
#>  5     2023               2         3      3 CHI    DEN      TRUE  
#>  6     2023               2         3      4 DEN    CHI      FALSE 
#>  7     2023               2         4      1 AUS    BOS      TRUE  
#>  8     2023               2         4      2 BOS    AUS      FALSE 
#>  9     2023               3         5      4 DEN    AUS      TRUE  
#> 10     2023               3         5      1 AUS    DEN      FALSE 
#> # ℹ 58 more rows

Message Board

league_messages(scoringPeriodId = 1)
#> # A tibble: 69 × 7
#>    id       type             author date                content        messages viewableBy
#>    <chr>    <chr>            <chr>  <dttm>              <chr>          <list>   <list>    
#>  1 91e87108 CHAT_ALL_MEMBERS LM     2024-01-02 16:22:28 <NA>           <NULL>   <NULL>    
#>  2 5e79e24b CHAT_ALL_MEMBERS LM     2024-01-02 16:22:28 <NA>           <NULL>   <NULL>    
#>  3 20f209d5 CHAT_ALL_MEMBERS LM     2024-01-02 16:22:28 <NA>           <NULL>   <NULL>    
#>  4 0d84172e CHAT_ALL_MEMBERS LM     2024-01-02 16:22:28 Here's your w… <NULL>   <NULL>    
#>  5 f8cd46d7 CHAT_ALL_MEMBERS LM     2023-12-26 16:18:09 <NA>           <NULL>   <NULL>    
#>  6 23d9ca74 CHAT_ALL_MEMBERS LM     2023-12-26 16:18:09 <NA>           <NULL>   <NULL>    
#>  7 c5d3adf5 CHAT_ALL_MEMBERS LM     2023-12-26 16:18:09 <NA>           <NULL>   <NULL>    
#>  8 e7033339 CHAT_ALL_MEMBERS LM     2023-12-26 16:18:09 Here's your w… <NULL>   <NULL>    
#>  9 21dd2654 CHAT_ALL_MEMBERS LM     2023-12-19 16:22:14 <NA>           <NULL>   <NULL>    
#> 10 fa05dbf2 CHAT_ALL_MEMBERS LM     2023-12-19 16:22:14 <NA>           <NULL>   <NULL>    
#> # ℹ 59 more rows

Transaction Counter

transaction_counter()
#> # A tibble: 4 × 14
#>   seasonId scoringPeriodId teamId abbrev waiverRank acquisitionBudgetSpent acquisitions
#>      <int>           <int>  <int> <fct>       <int>                  <int>        <int>
#> 1     2023              19      1 AUS             2                      0            0
#> 2     2023              19      2 BOS             4                      0            0
#> 3     2023              19      3 CHI             3                      0            0
#> 4     2023              19      4 DEN             1                      0            0
#> # ℹ 7 more variables: drops <int>, misc <int>, moveToActive <int>, moveToIR <int>,
#> #   paid <dbl>, teamCharges <dbl>, trades <int>

Draft Recap

draft_recap()
#> # A tibble: 64 × 15
#>    seasonId autoDraftTypeId bidAmount pickId keeper lineupSlot nominatingTeamId
#>       <int>           <int>     <int>  <int> <lgl>  <fct>      <fct>           
#>  1     2023               3        NA      1 FALSE  WR         <NA>            
#>  2     2023               3        NA      2 FALSE  WR         <NA>            
#>  3     2023               3        NA      3 FALSE  RB         <NA>            
#>  4     2023               3        NA      4 FALSE  RB         <NA>            
#>  5     2023               3        NA      5 FALSE  TE         <NA>            
#>  6     2023               3        NA      6 FALSE  WR         <NA>            
#>  7     2023               3        NA      7 FALSE  WR         <NA>            
#>  8     2023               3        NA      8 FALSE  RB         <NA>            
#>  9     2023               3        NA      9 FALSE  RB         <NA>            
#> 10     2023               3        NA     10 FALSE  RB         <NA>            
#> # ℹ 54 more rows
#> # ℹ 8 more variables: overallPickNumber <int>, playerId <int>, reservedForKeeper <lgl>,
#> #   roundId <int>, roundPickNumber <int>, teamId <int>, abbrev <fct>, tradeLocked <lgl>

Recent Activity

recent_activity(scoringPeriodId = 1)
#> # A tibble: 64 × 14
#>    bidAmount executionType id          isActingAsTeamOwner isLeagueManager isPending items
#>        <int> <chr>         <chr>       <lgl>               <lgl>           <lgl>     <lis>
#>  1         0 EXECUTE       e37ca451-8… FALSE               FALSE           FALSE     <df> 
#>  2         0 EXECUTE       5c34b91b-f… FALSE               FALSE           FALSE     <df> 
#>  3         0 EXECUTE       6a76532c-7… FALSE               FALSE           FALSE     <df> 
#>  4         0 EXECUTE       0752bae0-b… FALSE               FALSE           FALSE     <df> 
#>  5         0 EXECUTE       5a6bb4cd-e… FALSE               FALSE           FALSE     <df> 
#>  6         0 EXECUTE       62782a84-3… FALSE               FALSE           FALSE     <df> 
#>  7         0 EXECUTE       d4687048-4… FALSE               FALSE           FALSE     <df> 
#>  8         0 EXECUTE       08785f73-0… FALSE               FALSE           FALSE     <df> 
#>  9         0 EXECUTE       bb07ac92-7… FALSE               FALSE           FALSE     <df> 
#> 10         0 EXECUTE       f4a9d451-7… FALSE               FALSE           FALSE     <df> 
#> # ℹ 54 more rows
#> # ℹ 7 more variables: proposedDate <dttm>, scoringPeriodId <int>,
#> #   skipTransactionCounters <lgl>, status <chr>, teamId <int>, type <chr>,
#> #   processDate <dttm>

Players

list_players(limit = 10, proTeam = "Mia", status = "ALL")
#> # A tibble: 10 × 19
#>    seasonId scoringPeriodId      id firstName lastName   proTeam defaultPosition
#>       <int>           <dbl>   <int> <chr>     <chr>      <fct>   <fct>          
#>  1     2023              19 3116406 Tyreek    Hill       Mia     WR             
#>  2     2023              19 2576414 Raheem    Mostert    Mia     RB             
#>  3     2023              19 4372016 Jaylen    Waddle     Mia     WR             
#>  4     2023              19 4429160 De'Von    Achane     Mia     RB             
#>  5     2023              19 4241479 Tua       Tagovailoa Mia     QB             
#>  6     2023              19  -16015 Dolphins  D/ST       Mia     D/ST           
#>  7     2023              19 3124679 Jason     Sanders    Mia     K              
#>  8     2023              19 3122976 Jeff      Wilson Jr. Mia     RB             
#>  9     2023              19 4046692 Chase     Claypool   Mia     WR             
#> 10     2023              19 4036335 Cedrick   Wilson Jr. Mia     WR             
#> # ℹ 12 more variables: injuryStatus <chr>, percentStarted <dbl>, percentOwned <dbl>,
#> #   percentChange <dbl>, positionalRanking <int>, totalRating <dbl>,
#> #   auctionValueAverage <dbl>, averageDraftPosition <dbl>, projectedScore <dbl>,
#> #   lastScore <dbl>, lastSeason <dbl>, currentSeason <dbl>

Scoreboard

live_scoring()
#> # A tibble: 4 × 6
#>   currentMatchupPeriod matchupId teamId abbrev totalPointsLive totalProjectedPointsLive
#>                  <int>     <int>  <int> <fct>            <dbl>                    <dbl>
#> 1                   17        33      1 AUS               87.4                     86.4
#> 2                   17        33      2 BOS              108.                     108. 
#> 3                   17        34      3 CHI              136.                     135. 
#> 4                   17        34      4 DEN              107.                     107.

Standings

league_standings()
#> # A tibble: 4 × 17
#>   seasonId scoringPeriodId teamId abbrev draftDayProjectedRank currentProjectedRank
#>      <int>           <int>  <int> <fct>                  <int>                <int>
#> 1     2023              19      1 AUS                        3                    3
#> 2     2023              19      2 BOS                        1                    1
#> 3     2023              19      3 CHI                        2                    2
#> 4     2023              19      4 DEN                        4                    4
#> # ℹ 11 more variables: playoffSeed <int>, rankCalculatedFinal <int>, gamesBack <dbl>,
#> #   losses <int>, percentage <dbl>, pointsAgainst <dbl>, pointsFor <dbl>,
#> #   streakLength <int>, streakType <chr>, ties <int>, wins <int>