In this vignette, I’ll walk through how to get started with a basic dynasty value analysis on Fleaflicker.
We’ll start by loading the packages:
In Fleaflicker, you can find the league ID by looking in the URL - it’s the number immediately after /league/ in this example URL: https://www.fleaflicker.com/nfl/leagues/312861.
Let’s set up a connection to this league:
aaa <- fleaflicker_connect(season = 2020, league_id = 312861)
aaa
#> <Fleaflicker connection 2020_312861>
#> List of 4
#> $ platform : chr "Fleaflicker"
#> $ season : chr "2020"
#> $ user_email: NULL
#> $ league_id : chr "312861"
#> - attr(*, "class")= chr "flea_conn"I’ve done this with the fleaflicker_connect() function, although you can also do this from the ff_connect() call - they are equivalent. Most if not all of the remaining functions after this point are prefixed with “ff_”.
Cool! Let’s have a quick look at what this league is like.
aaa_summary <- ff_league(aaa)
#> Using request.R from "ffscrapr"
str(aaa_summary)
#> tibble [1 × 14] (S3: tbl_df/tbl/data.frame)
#> $ league_id : chr "312861"
#> $ league_name : chr "Avid Auctioneers Alliance"
#> $ league_type : chr "dynasty"
#> $ franchise_count: num 12
#> $ qb_type : chr "2QB/SF"
#> $ idp : logi FALSE
#> $ scoring_flags : chr "0.5_ppr, PP1D"
#> $ best_ball : logi FALSE
#> $ salary_cap : logi FALSE
#> $ player_copies : num 1
#> $ qb_count : chr "1-2"
#> $ roster_size : int 28
#> $ league_depth : num 336
#> $ keeper_count : int 28Okay, so it’s the Avid Auctioneers Alliance, it’s a 2QB league with 12 teams, half ppr scoring, and rosters about 340 players.
Let’s grab the rosters now.
aaa_rosters <- ff_rosters(aaa)
head(aaa_rosters)
#> # A tibble: 6 x 7
#> franchise_id franchise_name player_id player_name pos team sportradar_id
#> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 1578553 Running Bear 12032 Carson Wentz QB PHI e9a5c16b-4472-…
#> 2 1578553 Running Bear 7378 Cam Newton QB NE 214e55e4-a089-…
#> 3 1578553 Running Bear 15622 Joshua Kell… RB LAC 62542e04-3c44-…
#> 4 1578553 Running Bear 13358 Matt Breida RB MIA 6249d2c0-75dc-…
#> 5 1578553 Running Bear 7369 A.J. Green WR CIN c9701373-23f6-…
#> 6 1578553 Running Bear 13782 Anthony Mil… WR CHI bfaedf99-7618-…Cool! Let’s pull in some additional context by adding DynastyProcess player values.
player_values <- dp_values("values-players.csv")
#> No encoding supplied: defaulting to UTF-8.
# The values are stored by fantasypros ID since that's where the data comes from.
# To join it to our rosters, we'll need playerID mappings.
player_ids <- dp_playerids() %>%
select(sportradar_id,fantasypros_id) %>%
filter(!is.na(sportradar_id),!is.na(fantasypros_id))
#> No encoding supplied: defaulting to UTF-8.
# We'll be joining it onto rosters, so we can trim down the values dataframe
# to just IDs, age, and values
player_values <- player_values %>%
left_join(player_ids, by = c("fp_id" = "fantasypros_id")) %>%
select(sportradar_id,age,ecr_1qb,ecr_pos,value_1qb)
# ff_rosters() will return the sportradar_id, which we can then match to our player values!
aaa_values <- aaa_rosters %>%
left_join(player_values, by = c("sportradar_id"="sportradar_id")) %>%
arrange(franchise_id,desc(value_1qb))
head(aaa_values)
#> # A tibble: 6 x 11
#> franchise_id franchise_name player_id player_name pos team sportradar_id
#> <int> <chr> <int> <chr> <chr> <chr> <chr>
#> 1 1578553 Running Bear 13325 Austin Eke… RB LAC e5b8c439-a48…
#> 2 1578553 Running Bear 12926 Chris Godw… WR TB baa61bb5-f8d…
#> 3 1578553 Running Bear 15531 Brandon Ai… WR SF c90471cc-fa6…
#> 4 1578553 Running Bear 9338 Robert Woo… WR LAR 618bedee-925…
#> 5 1578553 Running Bear 12159 Dak Presco… QB DAL 86197778-8d4…
#> 6 1578553 Running Bear 13788 Michael Ga… WR DAL 9e174ff2-ca0…
#> # … with 4 more variables: age <lgl>, ecr_1qb <dbl>, ecr_pos <dbl>,
#> # value_1qb <int>Let’s do some team summaries now!
value_summary <- aaa_values %>%
group_by(franchise_id,franchise_name,pos) %>%
summarise(total_value = sum(value_1qb,na.rm = TRUE)) %>%
ungroup() %>%
group_by(franchise_id,franchise_name) %>%
mutate(team_value = sum(total_value)) %>%
ungroup() %>%
pivot_wider(names_from = pos, values_from = total_value) %>%
arrange(desc(team_value)) %>%
select(franchise_id,franchise_name,team_value,QB,RB,WR,TE)
value_summary
#> # A tibble: 12 x 7
#> franchise_id franchise_name team_value QB RB WR TE
#> <int> <chr> <int> <int> <int> <int> <int>
#> 1 1581803 ZachFarni's Team 40718 2026 20237 18428 27
#> 2 1582416 Ray Jay Team 39961 1245 15838 12686 10192
#> 3 1581719 Jmuthers's Team 38982 3180 12345 13653 9804
#> 4 1581753 fede_mndz's Team 37853 627 19882 16248 1096
#> 5 1581722 syd12nyjets's Team 36579 3327 7987 22732 2533
#> 6 1581726 SCJaguars's Team 34270 443 21417 12325 85
#> 7 1581988 The DK Crew 33879 2442 8881 18056 4406
#> 8 1581718 AlexG5386's Team 33534 1582 20534 6896 4522
#> 9 1581720 brosene's Team 32887 3826 15919 9468 3674
#> 10 1581721 Mjenkyns2004's Team 32645 8830 7047 16325 443
#> 11 1582423 The Verblanders 32357 3912 12489 15409 547
#> 12 1578553 Running Bear 23608 2994 6348 13364 902So with that, we’ve got a team summary of values! I like applying some context, so let’s turn these into percentages - this helps normalise it to your league environment.
value_summary_pct <- value_summary %>%
mutate_at(c("team_value","QB","RB","WR","TE"),~.x/sum(.x)) %>%
mutate_at(c("team_value","QB","RB","WR","TE"),round, 3)
value_summary_pct
#> # A tibble: 12 x 7
#> franchise_id franchise_name team_value QB RB WR TE
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1581803 ZachFarni's Team 0.098 0.059 0.12 0.105 0.001
#> 2 1582416 Ray Jay Team 0.096 0.036 0.094 0.072 0.267
#> 3 1581719 Jmuthers's Team 0.093 0.092 0.073 0.078 0.256
#> 4 1581753 fede_mndz's Team 0.091 0.018 0.118 0.093 0.029
#> 5 1581722 syd12nyjets's Team 0.088 0.097 0.047 0.129 0.066
#> 6 1581726 SCJaguars's Team 0.082 0.013 0.127 0.07 0.002
#> 7 1581988 The DK Crew 0.081 0.071 0.053 0.103 0.115
#> 8 1581718 AlexG5386's Team 0.08 0.046 0.122 0.039 0.118
#> 9 1581720 brosene's Team 0.079 0.111 0.094 0.054 0.096
#> 10 1581721 Mjenkyns2004's Team 0.078 0.256 0.042 0.093 0.012
#> 11 1582423 The Verblanders 0.078 0.114 0.074 0.088 0.014
#> 12 1578553 Running Bear 0.057 0.087 0.038 0.076 0.024Armed with a value summary like this, we can see team strengths and weaknesses pretty quickly, and figure out who might be interested in your positional surpluses and who might have a surplus at a position you want to look at.
Another question you might ask: what is the average age of any given team?
I like looking at average age by position, but weighted by dynasty value. This helps give a better idea of age for each team - including who might be looking to offload an older veteran!
age_summary <- aaa_values %>%
filter(pos %in% c("QB","RB","WR","TE")) %>%
group_by(franchise_id,pos) %>%
mutate(position_value = sum(value_1qb,na.rm=TRUE)) %>%
ungroup() %>%
mutate(weighted_age = age*value_1qb/position_value,
weighted_age = round(weighted_age, 1)) %>%
group_by(franchise_id,franchise_name,pos) %>%
summarise(count = n(),
age = sum(weighted_age,na.rm = TRUE)) %>%
pivot_wider(names_from = pos,
values_from = c(age,count))
age_summary
#> # A tibble: 12 x 10
#> # Groups: franchise_id, franchise_name [12]
#> franchise_id franchise_name age_QB age_RB age_TE age_WR count_QB count_RB
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <int> <int>
#> 1 1578553 Running Bear 0 0 0 0 6 6
#> 2 1581718 AlexG5386's T… 0 0 0 0 3 12
#> 3 1581719 Jmuthers's Te… 0 0 0 0 5 8
#> 4 1581720 brosene's Team 0 0 0 0 6 10
#> 5 1581721 Mjenkyns2004'… 0 0 0 0 5 9
#> 6 1581722 syd12nyjets's… 0 0 0 0 5 7
#> 7 1581726 SCJaguars's T… 0 0 0 0 5 7
#> 8 1581753 fede_mndz's T… 0 0 0 0 6 12
#> 9 1581803 ZachFarni's T… 0 0 0 0 5 9
#> 10 1581988 The DK Crew 0 0 0 0 4 6
#> 11 1582416 Ray Jay Team 0 0 0 0 4 8
#> 12 1582423 The Verblande… 0 0 0 0 4 8
#> # … with 2 more variables: count_TE <int>, count_WR <int>