R/bankroll_plot.R

In papagorgio23/bettoR: Sports Betting Tools

#' @title Bankroll Simulation
#'
#' @description This function simulates and visualizes a bettors bankroll over a number of bets using their edge.
#'
#' @param bets The number of bets (256)
#' @param win_rate The average expected win rate of the bets (0-1)
#' @param bet_size The dollar amount of each bet. (100)
#' @param sim_length The number of simulations. (1,000)
#' @param avg_odds The average odds of the bets (-110)
#' @param odds_type Type of odds for the output. Possible values are:
#' * `us`, American Odds
#' * `dec`, Decimal Odds
#' * `frac`, Fractional Odds
#' @param current_bet Optional input - Your current total number of tracked bets. (125)
#' @param current_win Optional input - Your current total amount won/loss from your tracked bets. (950)
#'
#' @return Plot showing the bankroll results of the simulated bets. The function also outputs the percentage of positive and negative final bankroll over the course of the simulation.
#'
#' @examples bankroll_plot(
#'   bets = 256,
#'   win_rate = 0.55,
#'   bet_size = 100,
#'   sim_length = 1000,
#'   avg_odds = -110,
#'   odds_type = "us"
#' )
#' @examples bankroll_plot(
#'   sim_length = 500,
#'   avg_odds = -110,
#'   win_rate = 0.5455
#' )
#' @examples bankroll_plot(
#'   sim_length = 250,
#'   avg_odds = -115,
#'   win_rate = 0.5255,
#'   current_bet = 100,
#'   current_win = -500
#' )
#' @examples bankroll_plot(
#'   sim_length = 300,
#'   avg_odds = -109,
#'   win_rate = 0.57,
#'   current_bet = 175,
#'   current_win = 5000
#' )
#' @examples bankroll_plot()
#'
#'
#' @export
bankroll_plot <-
  function(bets = 256,
           win_rate = 0.55,
           bet_size = 100,
           sim_length = 1000,
           avg_odds = -110,
           odds_type = "us",
           current_bet = NULL,
           current_win = NULL) {
    ## Error handling
    if (!is.numeric(c(bets, win_rate, bet_size, sim_length, avg_odds))) {
      stop("Inputs must be numeric")
    }
    if (win_rate < 0 | win_rate > 1) {
      stop("Win rate must be between 0-1")
    }
    if (!odds_type %in% c("us", "frac", "dec")) {
      stop("Odds Type must be either: ('us', 'dec', or 'frac')")
    }

    ## implied probability
    break_even <- implied_prob(avg_odds, odds_type)
    edge <- round(win_rate - break_even, 4)
    edge <-
      round(edge_calc(
        win_prob = win_rate,
        odds = avg_odds,
        type = odds_type
      ),
      4)

    # get risk and payout amount
    us_odds <- convert_odds(odds = avg_odds,
                            input = odds_type,
                            output = "us")
    risk <-
      ifelse(us_odds >= 100, bet_size, bet_size * us_odds / -100)
    payout <-
      ifelse(us_odds >= 100, bet_size * us_odds / 100, bet_size)

    # initialize result dataframe
    results_bank_sim <- data.frame()

    ## need to loop this many times
    for (i in 1:sim_length) {
      # simulate season
      simulation <- data.frame(
        bet = 1:bets,
        Bankroll = cumsum(
          random_bet(
            risk = risk,
            payout = payout,
            num_bets = bets,
            win_rate = win_rate
          )
        ),
        color = "green",
        Sim = i
      )
      simulation$color <-
        ifelse(simulation[which(simulation$bet == bets), 2] < 0, "red", "green")

      # append each season sim into dataframe
      results_bank_sim <- rbind(results_bank_sim, simulation)
    }

    # Plot
    p <-
      ggplot2::ggplot(results_bank_sim, ggplot2::aes(bet, Bankroll, group = Sim)) +
      ggplot2::geom_line(ggplot2::aes(colour = color), alpha = 0.3) +
      ggplot2::scale_colour_manual(values = c("green", "red"), guide = FALSE) +
      ggplot2::geom_hline(yintercept = 0) +
      ggplot2::scale_y_continuous(labels = scales::dollar) +
      ggplot2::labs(
        title = glue::glue("{sim_length} Simulations of {bets} Wagers"),
        subtitle = glue::glue(
          "with a {win_rate * 100}% Win Rate, Risking ${risk} to win ${payout}  ({edge * 100}% Edge)"
        ),
        caption = "A.I. Sports",
        x = "Bets",
        y = "Profit"
      ) +
      ggplot2::theme_bw()

    if (!is.null(current_bet) & !is.null(current_win)) {
      p <- p +
        ggplot2::geom_hline(yintercept = current_win, linetype = "dashed") +
        ggplot2::annotate("label",
                          x = current_bet,
                          y = current_win,
                          label = "Current")
    }

    # Get Positive and Negative simulation bankroll results
    results_bank_sim$win <-
      ifelse(results_bank_sim$color == "green", "Positive", "Negative")
    results_bank_sim$win <-
      factor(results_bank_sim$win, levels = c("Positive", "Negative"))

    # Return the ratio of positive and negative results
    print(table(results_bank_sim$win[results_bank_sim$bet == bets]) / sim_length)
    cat("\n", paste0("Median Profit/Loss = ", scales::dollar(
      stats::median(results_bank_sim$Bankroll[results_bank_sim$bet == bets], na.rm = TRUE)
    )))
    return(p)
  }