英文:
Trying to calculate the Expected Value of an observation
问题
这是计算的部分：
-4.482633 = (.55718 * 71.43) - (.44282 * 100)
上述计算中的数值对应以下变量：
-4.482633 = 我试图得出的预期值
.55718 = "Pinnacle" 的 "no_vig_bep"
71.43 = 观察1的 "win"
.44282 = 1 - "Pinnacle" 的 "no_vig_bep" 或最后一行
100 = 一固定金额
然后，我想计算赔率的另一侧，如下所示：
-2.5796 = (.44282 * 120.00) - (.55718 * 100)
最终目标是使用 "Pinnacle" 书中的值执行上述计算，并将EV写入新列。
英文:
I have a tibble and am trying to use values from two specific rows (Pinnacle book) to perform a calculation. The values of the calculation will be written to a new column. Here is the output of dput
structure(list(id = c("5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51","5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51","5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51"), start = structure(c(1676691000, 1676691000, 1676691000, 1676691000,1676691000, 1676691000), tzone = "UTC", class = c("POSIXct","POSIXt")), book = c("BetUS", "BetUS", "Bovada", "Bovada", "Pinnacle","Pinnacle"), home = c("San José St Spartans", "San José St Spartans","San José St Spartans", "San José St Spartans", "San José St Spartans","San José St Spartans"), away = c("New Mexico Lobos", "New Mexico Lobos","New Mexico Lobos", "New Mexico Lobos", "New Mexico Lobos", "New Mexico Lobos"), team = c("San José St Spartans", "New Mexico Lobos", "San José St Spartans","New Mexico Lobos", "San José St Spartans", "New Mexico Lobos"), price = c(-140, 120, -140, 120, -138, 117), update = c("2023-02-18T00:24:43Z","2023-02-18T00:24:43Z", "2023-02-18T00:25:10Z", "2023-02-18T00:25:10Z","2023-02-18T00:25:04Z", "2023-02-18T00:25:04Z"), bep = c(0.58333,0.45455, 0.58333, 0.45455, 0.57983, 0.46083), no_vig = c(-128.33333,128.33333, -128.33333, 128.33333, -125.82353, 125.82353), no_vig_bep = c(0.56204,0.43796, 0.56204, 0.43796, 0.55718, 0.44282), win = c(71.43,120, 71.43, 120, 72.46, 117)), class = c("grouped_df", "tbl_df","tbl", "data.frame"), row.names = c(NA, -6L), groups = structure(list(id = c("5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51","5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51","5d8f6b2536fbdc4ab6a3e9759ebc6c51", "5d8f6b2536fbdc4ab6a3e9759ebc6c51"), book = c("BetUS", "BetUS", "Bovada", "Bovada", "Pinnacle","Pinnacle"), team = c("New Mexico Lobos", "San José St Spartans","New Mexico Lobos", "San José St Spartans", "New Mexico Lobos","San José St Spartans"), .rows = structure(list(2L, 1L,4L, 3L, 6L, 5L), ptype = integer(0), class = c("vctrs_list_of","vctrs_vctr", "list"))), row.names = c(NA, -6L), class = c("tbl_df","tbl", "data.frame"), .drop = TRUE))
The following is the calculation
-4.482633 = (.55718 * 71.43) - (.44282 * 100)
The values in the calculation above correspond with the following variables
-4.482633 = Expected Value I am trying to derive.55718 = "no_vig_bep" of Pinnacle71.43 = "win" of observation 1.44282 = 1 - "no_vig_bep" of Pinnacle or the last row100 = a set amount
I Would then like to calculate the other side of the odds as follow
-2.5796 = (.44282 * 120.00) - (.55718 * 100)
The ultimate goal is to use the values of the Pinnacle book to perform the above calculation against all other books. The EV will be written to a new column.
Included additional id for further clarification
structure(list(id = c("073c154f3c8586868a3ba21522161a70","073c154f3c8586868a3ba21522161a70","073c154f3c8586868a3ba21522161a70", "073c154f3c8586868a3ba21522161a70","073c154f3c8586868a3ba21522161a70", "073c154f3c8586868a3ba21522161a70"), book = c("Bovada", "Pinnacle", "MyBookie.ag", "MyBookie.ag","Pinnacle", "Bovada"), home = c("Western Michigan Broncos", "WesternMichigan Broncos","Western Michigan Broncos", "Western Michigan Broncos", "WesternMichigan Broncos","Western Michigan Broncos"), away = c("Ball State Cardinals","Ball State Cardinals", "Ball State Cardinals", "Ball State Cardinals","Ball State Cardinals", "Ball State Cardinals"), team = c("WesternMichigan Broncos","Ball State Cardinals", "Western Michigan Broncos", "Ball StateCardinals","Western Michigan Broncos", "Ball State Cardinals"), price = c(-185,-143, -142, 100, 108, 140), bep = c(0.64912, 0.58848, 0.58678,0.5, 0.48077, 0.41667), no_vig = c(-155.78947, -122.40329, -117.35537,117.35537, 122.40329, 155.78947), no_vig_bep = c(0.60905, 0.55037,0.53992, 0.46008, 0.44963, 0.39095), win = c(54.05, 69.93, 70.42,100, 108, 140), EV_1 = c(-15.2155015, -6.47562589999999,-6.20594459999999,-10.074, -6.47696000000001, 7.91119999999999)), class = c("grouped_df","tbl_df", "tbl", "data.frame"), row.names = c(NA, -6L), groups =structure(list(book = c("Bovada", "MyBookie.ag", "Pinnacle"), .rows = structure(list(c(1L, 6L), 3:4, c(2L, 5L)), ptype = integer(0), class =c("vctrs_list_of","vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -3L), .drop = TRUE))
答案1
得分: 1
编辑1：也许 purrr 可以在这里帮助你：
library(purrr)no_vig_pin_list <- df |ungroup() |split(df$id) |map(~.x | filter(book == "Pinnacle") | pull(no_vig_bep))df |ungroup() |group_split(id) |purrr::map2_dfr(no_vig_pin_list, ~ .x |group_by(book) |mutate(EV_1 = ifelse(row_number() == 1,(.y[1] * win)- ((1-.y[1])*100),(.y[2] * win)- ((1-.y[2])*100)))) |select(EV_1)
或许这可以帮到你，我不太确定你想要实现什么。
library(dplyr)no_vig_pin <- df |filter(book == "Pinnacle") |pull(no_vig_bep)df |group_by(book) |mutate(EV_1 = ifelse(row_number() == 1,(no_vig_pin[1] * win)- ((1-no_vig_pin[1])*100),(no_vig_pin[2] * win)- ((1-no_vig_pin[2])*100))) |select(EV_1)
输出：
# A tibble: 6 × 2# Groups: book [3]book EV_1<chr> <dbl>1 BetUS -4.482 BetUS -2.583 Bovada -4.484 Bovada -2.585 Pinnacle -3.916 Pinnacle -3.91
英文:
Edit1: Maybe purrr can help you here:
library(purrr)no_vig_pin_list <- df |>ungroup() |>split(df$id) |>map(~.x |> filter(book == "Pinnacle") |> pull(no_vig_bep))df |>ungroup() |>group_split(id) |>purrr::map2_dfr(no_vig_pin_list, ~ .x |>group_by(book) |>mutate(EV_1 = ifelse(row_number() == 1,(.y[1] * win)- ((1-.y[1])*100),(.y[2] * win)- ((1-.y[2])*100)))) |>select(EV_1)A tibble: 8 × 2# Groups: book [2]book EV_1<chr> <dbl>1 BetUS -5.032 BetUS -1.103 Pinnacle -4.074 Pinnacle -4.075 BetUS -4.486 BetUS -2.587 Pinnacle -3.918 Pinnacle -3.91
Maybe this helps, I am not quite sure that I understood what you are trying to achieve.
library(dplyr)no_vig_pin <- df |>filter(book == "Pinnacle") |>pull(no_vig_bep)df |>group_by(book) |>mutate(EV_1 = ifelse(row_number() == 1,(no_vig_pin[1] * win)- ((1-no_vig_pin[1])*100),(no_vig_pin[2] * win)- ((1-no_vig_pin[2])*100))) |>select(EV_1)
Output:
# A tibble: 6 × 2# Groups: book [3]book EV_1<chr> <dbl>1 BetUS -4.482 BetUS -2.583 Bovada -4.484 Bovada -2.585 Pinnacle -3.916 Pinnacle -3.91
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