NFL Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('nfldata.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#cleaning data for evaluation\n",
    "\n",
    "nfl_data = df.drop(columns = ['Rnk','Lg','Result','Pass_Comp','Comp_Perc','Pass_Rt','Sacks_Given','Sack_Yds','Yds_Gained_Att','Adj_Yds_Gained_Att','Rush_Yds_Att','Rec_Yds_Rec','Rec_Catch_Perc','Rec_Yds_Tgt','K_Ret_Att','K_Yds','K_Yds_Ret_Att','P_Ret','P_Ret_Yds','P_Ret_Yds_Ret','Fmb_Forc','Fmb','Fmb_Rec','Fmb_Yds'])\n",
    "nfl_data['Player'] = nfl_data['Player'].str.rstrip('*')\n",
    "nfl_data['Player'] = nfl_data['Player'].str.rstrip(' ')\n",
    "nfl_data['Player'] = nfl_data['Player'].str.lower()\n",
    "nfl_data = nfl_data.dropna(subset=['Player'])\n",
    "nfl_data ['Away'] = nfl_data['Away'].fillna('False')\n",
    "nfl_data ['Away'] = nfl_data['Away'].replace(['@'],'True')\n",
    "nfl_data ['Pos'] = nfl_data['Pos'].replace(['LG','T','RT','RG','G','OL','LT','S','DT','C','CB','LS','NT','MIKE','G/T','DE','OT','C/G','DB','LB','G-C','LDT','G/OT','T/G','G/C','OS','DL','LOT','T/C','0'],'Delete')\n",
    "nfl_data ['Pos'] = nfl_data['Pos'].replace(['FB','TB','HB','FB-T','FB/RB','FB/R','H-B','FB/T','HB/F','FB/DL','RB/F','RB/K','OW','Rb'],'RB')\n",
    "nfl_data ['Pos'] = nfl_data['Pos'].replace(['WR ','WR/P','WR/PR','KR','WR/R','WR-K','WR/RS','WR-R','PR/W','WR W','WR/K','WC','K','P'],'WR')\n",
    "nfl_data ['Pos'] = nfl_data['Pos'].replace(['FB/TE','F','TE/F','TE-F','TE/D','TE/L','TE/W'],'TE')\n",
    "nfl_data ['Pos'] = nfl_data['Pos'].replace(['QB/W','QB-W'],'QB')\n",
    "nfl_data = nfl_data.drop(nfl_data[nfl_data['Pos']=='Delete'].index)\n",
    "nfl_data = nfl_data.fillna(0)\n",
    "#list(nfl_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 point equals how many pass yards? 25\n",
      "1 pass TD equals how many points? 4\n",
      "1 INT equals how many points?-1\n",
      "1 point equals how many rushing yards? 10\n",
      "1 rushing TD equals how many points? 6\n",
      "1 point is equal to how many receptions? 2\n",
      "1 point equals how many receiving yards? 10\n",
      "1 receiving TD equals how many points? 6\n",
      "1 special teams TD equals how many points? 6\n",
      "1 fumble equals how many points? -2\n"
     ]
    }
   ],
   "source": [
    "#accept league scoring settings from user\n",
    "\n",
    "lg_pass_yds = int(input('1 point equals how many pass yards? '))\n",
    "lg_pass_tds = int(input('1 pass TD equals how many points? '))\n",
    "lg_int = int(input('1 INT equals how many points?'))\n",
    "lg_rush_yds = int(input('1 point equals how many rushing yards? '))\n",
    "lg_rush_tds = int(input('1 rushing TD equals how many points? '))\n",
    "lg_rec_rec = int(input('1 point is equal to how many receptions? '))\n",
    "lg_rec_yds = int(input('1 point equals how many receiving yards? '))\n",
    "lg_rec_tds = int(input('1 receiving TD equals how many points? '))\n",
    "lg_spec_tds = int(input('1 special teams TD equals how many points? '))\n",
    "lg_fmb = int(input('1 fumble equals how many points? '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#add fantasy score totals based off league info\n",
    "\n",
    "nfl_data['Pass_Yds_Pts'] = nfl_data['Pass_Yds'].apply(lambda x: x/lg_pass_yds)\n",
    "nfl_data['Pass_TD_Pts'] = nfl_data['Pass_TD'].apply(lambda x: x*lg_pass_tds)\n",
    "nfl_data['Pass_INT_Pts'] = nfl_data['Pass_INT'].apply(lambda x: x*lg_int)\n",
    "nfl_data['Rush_Yds_Pts'] = nfl_data['Rush_Yds'].apply(lambda x: x/lg_rush_yds)\n",
    "nfl_data['Rush_TD_Pts'] = nfl_data['Rush_TD'].apply(lambda x: x*lg_rush_tds)\n",
    "nfl_data['Rec_Rec_Pts'] = nfl_data['Rec_Rec'].apply(lambda x: x/lg_rec_rec)\n",
    "nfl_data['Rec_Yds_Pts'] = nfl_data['Rec_Yds'].apply(lambda x: x/lg_rec_yds)\n",
    "nfl_data['Rec_TD_Pts'] = nfl_data['Rec_TD'].apply(lambda x: x*lg_rec_tds)\n",
    "nfl_data['K_Ret_TD_Pts'] = nfl_data['K_Ret_TD'].apply(lambda x: x*lg_spec_tds)\n",
    "nfl_data['P_Ret_TD_Pts'] = nfl_data['P_Ret_TD'].apply(lambda x: x*lg_spec_tds)\n",
    "nfl_data['Fmb_L_Pts'] = nfl_data['Fmb_L'].apply(lambda x: x*lg_fmb)\n",
    "nfl_data['Fmb_TD_Pts'] = nfl_data['Fmb_TD'].apply(lambda x: x*lg_spec_tds)\n",
    "\n",
    "nfl_data['Fantasy_Pts_Total'] = nfl_data['Pass_Yds_Pts']+nfl_data['Pass_TD_Pts']+nfl_data['Pass_INT_Pts']+nfl_data['Rush_Yds_Pts']+nfl_data['Rush_TD_Pts']+nfl_data['Rec_Rec_Pts']+nfl_data['Rec_Yds_Pts']+nfl_data['Rec_TD_Pts']+nfl_data['K_Ret_TD_Pts']+nfl_data['P_Ret_TD_Pts']+nfl_data['Fmb_L_Pts']+nfl_data['Fmb_TD_Pts']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#making some values integeres as they cannot be floats\n",
    "nfl_data = nfl_data.astype({'Game_Num':'int64','Week':'int64','Pass_Att':'int64','Pass_TD':'int64','Pass_Yds':'int64','Pass_INT':'int64','Rush_Att':'int64','Rush_Yds':'int64','Rush_TD':'int64','Rec_Tgt':'int64','Rec_Rec':'int64','Rec_Yds':'int64','Rec_TD':'int64','K_Ret_TD':'int64','P_Ret_TD':'int64','Fmb_L':'int64','Fmb_TD':'int64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Filter by how many pass attempts minimum? 6\n",
      "Filter by how many rush attempts minimum? 4\n",
      "Filter by how many receiving targets minimum? 2\n"
     ]
    }
   ],
   "source": [
    "#Choose how to filter the NFL data\n",
    "filter_pass_att = int(input('Filter by how many pass attempts minimum? '))\n",
    "filter_rush_att = int(input('Filter by how many rush attempts minimum? '))\n",
    "filter_rec_tgt = int(input('Filter by how many receiving targets minimum? '))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Filter out pass attempts or rush attempts or receiving targets lower than X\n",
    "nfl_data = nfl_data.query(f'Pass_Att >= {filter_pass_att} or Rush_Att >= {filter_rush_att} or Rec_Tgt>= {filter_rec_tgt}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define x axis\n",
    "nfl_week = nfl_data.Week.unique()\n",
    "nfl_day = nfl_data.Day.unique()\n",
    "nfl_pos = nfl_data.Pos.unique()\n",
    "nfl_opp = nfl_data.Opp.unique()\n",
    "nfl_week.sort()\n",
    "nfl_day.sort()\n",
    "nfl_pos.sort()\n",
    "nfl_opp.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#define y axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouping data for plotting\n",
    "nfl_week_avg = nfl_data.groupby('Week')['Fantasy_Pts_Total'].mean()\n",
    "nfl_pos_avg = nfl_data.groupby('Pos')['Fantasy_Pts_Total'].mean()\n",
    "nfl_player_avg = nfl_data.groupby('Player')['Fantasy_Pts_Total'].mean()\n",
    "nfl_day_avg = nfl_data.groupby('Day')['Fantasy_Pts_Total'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouping data for plotting\n",
    "nfl_week_std = nfl_data.groupby('Week')['Fantasy_Pts_Total'].std()\n",
    "nfl_pos_std = nfl_data.groupby('Pos')['Fantasy_Pts_Total'].std()\n",
    "nfl_player_std = nfl_data.groupby('Player')['Fantasy_Pts_Total'].std()\n",
    "nfl_day_std = nfl_data.groupby('Day')['Fantasy_Pts_Total'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#grouping data for plotting\n",
    "nfl_pos_week_avg = nfl_data.groupby(['Pos','Week'])['Fantasy_Pts_Total'].mean()\n",
    "nfl_player_week_avg = nfl_data.groupby(['Player','Week'])['Fantasy_Pts_Total'].mean()\n",
    "nfl_player_day_avg = nfl_data.groupby(['Player','Day'])['Fantasy_Pts_Total'].mean()\n",
    "nfl_player_opp_avg = nfl_data.groupby(['Player','Opp'])['Fantasy_Pts_Total'].mean()\n",
    "nfl_pos_week_std = nfl_data.groupby(['Pos','Week'])['Fantasy_Pts_Total'].std()\n",
    "nfl_player_week_std = nfl_data.groupby(['Player','Week'])['Fantasy_Pts_Total'].std()\n",
    "nfl_player_day_std = nfl_data.groupby(['Player','Day'])['Fantasy_Pts_Total'].std()\n",
    "nfl_player_opp_std = nfl_data.groupby(['Player','Opp'])['Fantasy_Pts_Total'].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.bar(nfl_pos,nfl_pos_avg,yerr=nfl_pos_std,align='center',color =['red','blue','green','orange'],ecolor='black',capsize=10)\n",
    "ax1.set_ylabel('Average Fantasy Pts')\n",
    "ax1.set_xticks(nfl_pos)\n",
    "ax1.set_xticklabels(nfl_pos)\n",
    "ax1.set_xlabel('NFL Position')\n",
    "ax1.set_title('Average Fantasy Pts per Week by Position')\n",
    "ax1.yaxis.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nfl_week_avg,'r')\n",
    "plt.ylabel('Average Fantasy Pts')\n",
    "plt.xlabel('NFL Week')\n",
    "plt.xticks(nfl_week)\n",
    "plt.ylim(0,15)\n",
    "plt.title('Average Fantasy Pts per Week by Week')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(nfl_day_avg,'r')\n",
    "plt.ylabel('Average Fantasy Pts')\n",
    "plt.xlabel('NFL Week')\n",
    "plt.xticks(nfl_day)\n",
    "plt.ylim(0,15)\n",
    "plt.title('Average Fantasy Pts per Week by Day of Week')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a NFL Player from 2010 to 2019: Tom Brady\n",
      "Start week: 1\n",
      "End week: 17\n"
     ]
    }
   ],
   "source": [
    "player1 = str(input(\"Enter a NFL Player from 2010 to 2019: \"))\n",
    "low = int(input(\"Start week: \"))-1\n",
    "high = int(input(\"End week: \"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = nfl_week[low:high]\n",
    "y = nfl_player_week_avg[player1.lower()][low:high]\n",
    "std = nfl_player_week_std[player1.lower()][low:high]\n",
    "\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.bar(x,y,yerr=std,align='center',color='g',ecolor='black',capsize=10)\n",
    "ax2.set_ylabel('Average Fantasy Pts')\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels(x)\n",
    "ax2.set_xlabel('NFL Week')\n",
    "ax2.set_title(f'Average Fantasy Pts each Week by {player1}')\n",
    "ax2.yaxis.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = nfl_opp[low:high]\n",
    "y = nfl_player_opp_avg[player1.lower()][low:high]\n",
    "std = nfl_player_opp_std[player1.lower()][low:high]\n",
    "\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.bar(x,y,yerr=std,align='center',color='g',ecolor='black',capsize=10)\n",
    "ax2.set_ylabel('Average Fantasy Pts')\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels(x)\n",
    "ax2.set_xlabel('NFL Week')\n",
    "ax2.set_title(f'Average Fantasy Pts each Week by {player1}')\n",
    "ax2.yaxis.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}