2022-02-17-russ-effect-on-run-rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "842037c4",
   "metadata": {},
   "source": [
    "## Adding Libraries, Importing Datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0e06cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "library(tidyverse)\n",
    "library(nflfastR)\n",
    "library(ggthemes) # Themes for ggplot2.\n",
    "library(ggimage) # Supports images in ggplot2.\n",
    "library(gt) # Create tables.\n",
    "library(readr) # Read in datasets.\n",
    "options(scipen = 9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43589028",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom plot settings.\n",
    "theme_reach <- function() {\n",
    "  theme_fivethirtyeight() +\n",
    "    theme(\n",
    "      legend.position = \"none\",\n",
    "      plot.title = element_text(size = 22, hjust = 0.5, face = \"bold\"),\n",
    "      plot.subtitle = element_text(size = 18, hjust = 0.5),\n",
    "      plot.caption = element_text(size = 16),\n",
    "      axis.title.x = element_text(size=18, margin = margin(t = 20)),\n",
    "      axis.title.y = element_text(size=18, margin = margin(r = 20)),\n",
    "      axis.text = element_text(size = 14),\n",
    "      strip.text = element_text(size = 16, face = \"bold\"),\n",
    "      legend.text = element_text(size = 14)\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a8f80c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "ignore <- c(\"enforced between downs.\")\n",
    "pbp_raw <- load_pbp(2021) %>%\n",
    "  filter(season == 2021) %>%\n",
    "  filter(season_type == \"REG\") %>%\n",
    "  filter(play_type != \"no_play\" | str_detect(desc, paste(ignore, collapse = \"|\"))) %>% \n",
    "  filter(!(str_detect(desc, \"enforced between downs.\") & str_detect(desc, \"Kick formation\"))) %>%\n",
    "  filter(!(str_detect(desc, \"enforced between downs.\") & str_detect(desc, \"No Play.\")))\n",
    "pbp_raw$new_play_num <- ave(pbp_raw$play_id, pbp_raw$game_id, FUN = seq_along)\n",
    "pbp_raw <- pbp_raw %>%\n",
    "  mutate(UNIQUE_PLAY_ID = paste(game_id, new_play_num, sep = \"_\"))\n",
    "\n",
    "pbp_sportradar <- read_csv(file = \"C:/Users/chris/Documents/Datasets/Sportradar NFL/CSV/2021-Sportradar-NFL.csv\",\n",
    "                          show_col_types = FALSE)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea92a1a6",
   "metadata": {},
   "source": [
    "## Joining Datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2654376d",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_pbp <- inner_join(pbp_raw, pbp_sportradar, by = \"UNIQUE_PLAY_ID\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a29de557",
   "metadata": {},
   "source": [
    "## Adding Columns for Cumulative Rushes/Passes, Run/Pass Share."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7f592bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "joined_pbp <- joined_pbp %>%\n",
    "  filter(rush == 1 | pass == 1) %>%\n",
    "  filter(!is.na(epa)) %>%\n",
    "  mutate(gid_team = paste(game_id, posteam, sep = \"-\")) %>%\n",
    "  mutate(yr_team = paste(season, posteam, sep = \"-\"))\n",
    "\n",
    "joined_pbp$rushes_game <- ave(joined_pbp$rush, joined_pbp$gid_team, FUN = cumsum)\n",
    "joined_pbp$passes_game <- ave(joined_pbp$pass, joined_pbp$gid_team, FUN = cumsum)\n",
    "joined_pbp$rushes_season <- ave(joined_pbp$rush, joined_pbp$yr_team, FUN = cumsum)\n",
    "joined_pbp$passes_season <- ave(joined_pbp$pass, joined_pbp$yr_team, FUN = cumsum)\n",
    "joined_pbp$season_play_num <- ave(joined_pbp$play_id, joined_pbp$yr_team, FUN = seq_along)\n",
    "\n",
    "joined_pbp <- joined_pbp %>%\n",
    "  mutate(run_share_gm = round(rushes_game/(rushes_game + passes_game), 4)) %>%\n",
    "  mutate(pass_share_gm = round(passes_game/(rushes_game + passes_game), 4)) %>%\n",
    "  mutate(run_share_szn = round(rushes_season/(rushes_season + passes_season), 4)) %>%\n",
    "  mutate(pass_share_szn = round(passes_season/(rushes_season + passes_season), 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4330e3",
   "metadata": {},
   "source": [
    "## Creating Subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7e6ad396",
   "metadata": {},
   "outputs": [],
   "source": [
    "sea_pbp <- joined_pbp %>%\n",
    "  filter(posteam == \"SEA\") %>%\n",
    "  filter(week > 4)\n",
    "\n",
    "nfl_pbp <- joined_pbp %>%\n",
    "  filter(posteam != \"SEA\")\n",
    "\n",
    "russ_pbp_pre <- sea_pbp %>%\n",
    "  filter(season_play_num < 246)\n",
    "geno_pbp <- sea_pbp %>%\n",
    "  filter(season_play_num >= 246) %>%\n",
    "  filter(season_play_num < 438)\n",
    "russ_pbp_post <- sea_pbp %>%\n",
    "  filter(season_play_num >= 438)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6000edb5",
   "metadata": {},
   "source": [
    "## Create and Save 'Russ Effect on Run Rate' Scatterplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22436207",
   "metadata": {},
   "outputs": [
    {
     "data": {
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ZXC0iItmXJrTry/4fDbrhc0sHflLcctSslmbyL6Y7Tt/Iq0gswHw1iDSKyEK68r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\nAxMiaTUdF+z1AAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot() +\n",
    "  geom_line(data = russ_pbp_pre,\n",
    "            aes(x = season_play_num, y = run_share_szn),\n",
    "            size = 2) +\n",
    "  geom_line(data = geno_pbp,\n",
    "            aes(x = season_play_num, y = run_share_szn),\n",
    "            size = 0.25) +\n",
    "  geom_line(data = russ_pbp_post,\n",
    "            aes(x = season_play_num, y = run_share_szn),\n",
    "            size = 2) +\n",
    "  geom_hline(yintercept = sum(nfl_pbp$rush)/nrow(nfl_pbp), linetype = \"dashed\") +\n",
    "  geom_segment(aes(x = 246, xend = 246, y = 0.36, yend = 0.3984), linetype = \"dashed\") +\n",
    "  geom_segment(aes(x = 438, xend = 438, y = 0.36, yend = 0.4087), linetype = \"dashed\") +\n",
    "  theme_reach() +\n",
    "  theme(panel.grid.major = element_blank(),\n",
    "        panel.grid.minor = element_blank()) +\n",
    "  labs(x = \"Offensive Plays\",\n",
    "       y = \"Run Rate\",\n",
    "       title = \"Seahawks Run Rate Over Time, 2021\",\n",
    "       subtitle = \"Weeks 5-18\") +\n",
    "  annotate(\"text\", x = 650, y = 0.363, label = \"Week 13 vs. SF\", size = 4) +\n",
    "  annotate(\"text\", x = 276, y = 0.402, label = \"R.Wilson injured.\\nWeek 5 vs. LA\", size = 4, fontface = 2) +\n",
    "  annotate(\"text\", x = 500, y = 0.41, label = \"R.Wilson returns.\\nWeek 10 vs. GB\", size = 4, fontface = 2) +\n",
    "  annotate(\"point\", x = 651, y = 0.366, size = 7, color = \"red\") +\n",
    "  annotate(\"point\", x = 438, y= 0.4087, size = 7, color = \"darkgreen\") +\n",
    "  annotate(\"point\", x = 246, y = 0.3984, size = 7, color = \"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39a194ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"russ-effect-on-run-rate.png\", width=15, height=10, dpi=\"retina\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceff1094",
   "metadata": {},
   "source": [
    "## Getting Lowest Run Share Point for Annotation Above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0559f183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1 × 6</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>game_id</th><th scope=col>UNIQUE_PLAY_ID</th><th scope=col>rushes_game</th><th scope=col>passes_game</th><th scope=col>run_share_szn</th><th scope=col>season_play_num</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2021_13_SF_SEA</td><td>2021_13_SF_SEA_131</td><td>19</td><td>41</td><td>0.3656</td><td>651</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1 × 6\n",
       "\\begin{tabular}{llllll}\n",
       " game\\_id & UNIQUE\\_PLAY\\_ID & rushes\\_game & passes\\_game & run\\_share\\_szn & season\\_play\\_num\\\\\n",
       " <chr> & <chr> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2021\\_13\\_SF\\_SEA & 2021\\_13\\_SF\\_SEA\\_131 & 19 & 41 & 0.3656 & 651\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1 × 6\n",
       "\n",
       "| game_id &lt;chr&gt; | UNIQUE_PLAY_ID &lt;chr&gt; | rushes_game &lt;dbl&gt; | passes_game &lt;dbl&gt; | run_share_szn &lt;dbl&gt; | season_play_num &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| 2021_13_SF_SEA | 2021_13_SF_SEA_131 | 19 | 41 | 0.3656 | 651 |\n",
       "\n"
      ],
      "text/plain": [
       "  game_id        UNIQUE_PLAY_ID     rushes_game passes_game run_share_szn\n",
       "1 2021_13_SF_SEA 2021_13_SF_SEA_131 19          41          0.3656       \n",
       "  season_play_num\n",
       "1 651            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "russ_pbp_post %>%\n",
    "  slice_min(run_share_szn) %>%\n",
    "  summarize(game_id, UNIQUE_PLAY_ID, rushes_game, passes_game, run_share_szn, season_play_num)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f246f927",
   "metadata": {},
   "source": [
    "## Getting Headshots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9f897425",
   "metadata": {},
   "outputs": [],
   "source": [
    "headshots <- data.frame(passer = c(\"R.Wilson\", \"G.Smith\", \"League Avg.\"), \n",
    "                        headshot = c(\"https://a.espncdn.com/combiner/i?img=/i/headshots/nfl/players/full/14881.png\",\n",
    "                                     \"https://a.espncdn.com/combiner/i?img=/i/headshots/nfl/players/full/15864.png\",\n",
    "                                     \"https://a.espncdn.com/combiner/i?img=/i/espn/misc_logos/500/nfl.png\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "411d2034",
   "metadata": {},
   "source": [
    "## Filtering Subsets and Formatting Columns for EPA Table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5a61db2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "russ_epa_pre <- joined_pbp %>%\n",
    "  filter(posteam == \"SEA\") %>%\n",
    "  filter(season_play_num < 246) %>%\n",
    "  filter(!is.na(passer)) %>%\n",
    "  group_by(passer) %>%\n",
    "  summarize(weeks = \"1-5\", epa_dropback = round(mean(epa),3), dropbacks = n())\n",
    "\n",
    "geno_epa <- geno_pbp %>%\n",
    "  filter(!is.na(passer)) %>%\n",
    "  group_by(passer) %>%\n",
    "  summarize(weeks = \"5-9\", epa_dropback = round(mean(epa),3), dropbacks = n())\n",
    "\n",
    "russ_epa_post <- russ_pbp_post %>%\n",
    "  filter(season_play_num <= 651) %>%\n",
    "  filter(!is.na(passer)) %>%\n",
    "  group_by(passer) %>%\n",
    "  summarize(weeks = \"10-13\", epa_dropback = round(mean(epa),3), dropbacks = n())\n",
    "\n",
    "russ_epa_post_sf <- russ_pbp_post %>%\n",
    "  filter(season_play_num > 651) %>%\n",
    "  filter(!is.na(passer)) %>%\n",
    "  group_by(passer) %>%\n",
    "  summarize(weeks = \"13+\", epa_dropback = round(mean(epa),3), dropbacks = n())\n",
    "\n",
    "nfl_epa <- nfl_pbp %>%\n",
    "  filter(!is.na(passer)) %>%\n",
    "  summarize(weeks = \"1-18\", epa_dropback = round(mean(epa),3), dropbacks = n())\n",
    "nfl_epa <- cbind(passer = \"League Avg.\", nfl_epa)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05df5a35",
   "metadata": {},
   "source": [
    "## Bind Rows. Add 'Headshots' Column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c7de750",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_epa <- rbind(russ_epa_pre, geno_epa, russ_epa_post, russ_epa_post_sf, nfl_epa)\n",
    "final_epa <- inner_join(final_epa, headshots, by = \"passer\")\n",
    "final_epa <- final_epa[, c(1, 5, 2, 3, 4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5297558",
   "metadata": {},
   "source": [
    "## Create and Save 'Russ Effect' Table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b94edd97",
   "metadata": {},
   "outputs": [],
   "source": [
    "russ_table <- final_epa %>%\n",
    "  gt() %>%\n",
    "  gtExtras::gt_img_rows(headshot) %>%\n",
    "  cols_label(passer = \"Passer\",\n",
    "             headshot = \"\",\n",
    "             weeks = \"Weeks\",\n",
    "             epa_dropback = \"EPA/Dropback\",\n",
    "             dropbacks = \"Total Dropbacks\") %>%\n",
    "  cols_align(align = \"center\",\n",
    "             columns = c(headshot, weeks, epa_dropback, dropbacks)) %>%\n",
    "  gtExtras::gt_theme_espn() %>%\n",
    "  tab_header(title = \"Seahawks EPA/Dropback, 2021\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "57ac6562",
   "metadata": {},
   "outputs": [],
   "source": [
    "gtsave(russ_table, \"russ-effect-table.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "950da927",
   "metadata": {},
   "source": [
    "## Preparing Labels and Colors for Bar Graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a48e6c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_epa$bar_labels <- paste(final_epa$passer, \"\\nWeeks\", final_epa$weeks)\n",
    "final_epa$bar_labels <- factor(final_epa$bar_labels, levels = final_epa$bar_labels)\n",
    "final_epa$team_color <- c(\"#002244\",\"#002244\",\"#002244\",\"#002244\",\"#a5acaf\")\n",
    "final_epa$team_color2 <- c(\"#a5acaf\",\"#a5acaf\",\"#a5acaf\",\"#a5acaf\",\"#000000\")\n",
    "final_epa$label_color <- c(\"white\", \"black\", \"white\", \"white\", \"black\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5603039",
   "metadata": {},
   "source": [
    "## Calculating Difference from League Average EPA/Play."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81c7a5d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "league_avg_epa_val <- final_epa$epa_dropback[5]\n",
    "  \n",
    "final_epa <- final_epa %>%\n",
    "  filter(passer != \"League Avg.\")\n",
    "final_epa$epa_dropback <- final_epa$epa_dropback - league_avg_epa_val"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f877831c",
   "metadata": {},
   "source": [
    "## Create and Save 'Russ Effect' Bar Graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f3b71d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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y0tGT5jL5D6P07YX0eItCQ+YyNSx/46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\nIAGIBJAARAJIACIBJACRABLw/3Ld+MW4LohyAAAAAElFTkSuQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(final_epa, aes(x = bar_labels, y = epa_dropback)) +\n",
    "  geom_bar(aes(fill = team_color, color = team_color2), \n",
    "           stat = \"identity\", \n",
    "           alpha = 1,\n",
    "           size = 1.25) +\n",
    "  scale_color_identity(aesthetics = c(\"fill\", \"color\")) + \n",
    "  geom_text(aes(label = ifelse(epa_dropback < 0, epa_dropback, paste(\"+\", epa_dropback))), \n",
    "            vjust = ifelse(final_epa$epa_dropback > 0, 1.5, -0.7), \n",
    "            color = \"white\",\n",
    "            fontface = \"bold\") +\n",
    "  labs(x = \"\",\n",
    "       y = \"\",\n",
    "       title = \"Seahawks EPA/Dropback, 2021\",\n",
    "       subtitle = \"Compared to League Average\") +\n",
    "  theme(axis.text.x = element_text(size = 10, face = \"bold\"),\n",
    "        plot.title = element_text(size = 16, hjust = 0.5),\n",
    "        plot.subtitle = element_text(size = 12, hjust = 0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5371846d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ggsave(\"russ-effect-bar-graph2.png\", width=10, height=6, dpi=\"retina\")"
   ]
  }
 ],
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