{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"<center>\n<h1>NFL Big data bowl 2022</h1>\n</center>\n<center>\nBy Massimo Hong and Lohan Meunier\n</center>","metadata":{}},{"cell_type":"markdown","source":"\n# Abstract\n<center>\n<p>In this notebook we will explain the process we went through going into the Kaggle competition: NFL Big Data Bowl 2022. \n\nThe task we chose to focus on was the ranking of special teams players. \n\nWe are trying to incorporate Machine Learning into the project and our goal is to predict how well players would perform in certain plays and compare their results by position. We will go through the methods and models we used to achieve this goal, as well as the results we gathered.</p></center>\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:26:48.096417Z","iopub.execute_input":"2022-01-06T16:26:48.097291Z","iopub.status.idle":"2022-01-06T16:26:48.104563Z","shell.execute_reply.started":"2022-01-06T16:26:48.097243Z","shell.execute_reply":"2022-01-06T16:26:48.103008Z"}}},{"cell_type":"markdown","source":"# Introduction\n<p>Founded in 1920 by the American Professional Football Association, the NFL is right now THE most popular sports league in the United States.</p>\n<center>\n<img src=\"https://static.nfl.com/static/content/public/static/wildcat/assets/img/application-shell/shield/default.svg\", width=\"300\">    \n</center>\n\n<p>Our aim is to help improving every aspect of the play, in particular we will concentrate on analysing special teams.</p>\nThe competition states 3 potential  topics:\n <ul>\n  <li>Create a new special teams metric</li>\n  <li>Compare different strategies and their respective results</li>\n  <li>Rank special team players based on a specific metric</li>\n</ul> \nWe chose to research the third option: rank special team players based on their probability of success when pulling off a <b>Field Goal</b>.\n    \n<p>The « Placekicker » or simply « Kicker » in American Football is the player who is responsible for the kicking of field goals and extra points, sometimes also kickoffs and punts.\n    \nA field goal is a mean of scoring in American football. To score a field goal, the team in possession of the ball must kick the ball in between the poles (or \"Uprights\") of the opposing team, awarding three points. American football requires that a field goal must only come during a play from scrimmage.\n</p>","metadata":{}},{"cell_type":"markdown","source":"# Data analysis and cleaning\nIn this notebook we have used the following files:\n    \n* players.csv\n* plays.csv\n* all three tracking files\n    \nFirstly, we need to do some cleaning of the raw data we have available before passing them to the machine learning models. Operation such as:\n    \n* replacing non valid values with 0s\n* convert height from feet into cm to have a usable numeric format\n* convert pounds into kg\n* calculate the age based on the birth date\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport plotly.express as px\nimport numpy as np","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:50.250926Z","iopub.execute_input":"2022-01-06T16:30:50.251507Z","iopub.status.idle":"2022-01-06T16:30:51.610219Z","shell.execute_reply.started":"2022-01-06T16:30:50.251393Z","shell.execute_reply":"2022-01-06T16:30:51.609318Z"},"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"players_file = '../input/nfl-big-data-bowl-2022/players.csv'\nplayers_df = pd.read_csv(players_file)\nplayers_df.fillna(0)","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:51.612264Z","iopub.execute_input":"2022-01-06T16:30:51.612774Z","iopub.status.idle":"2022-01-06T16:30:51.662195Z","shell.execute_reply.started":"2022-01-06T16:30:51.612728Z","shell.execute_reply":"2022-01-06T16:30:51.660907Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Filter players based on playing position\n<p>This is  how we perform the filtering based on player position, the weight height conversion.\nFilter the dataframe by position, \"K\" for kicker in this case. If we want to consider other positions in the future, we just need to change a string.\nReplace birthDate with age.\n    \nConvert height and weight into cm and kg. \n    \nA quick graphic visualization to check the results of the filtering and conversion operations.</p>","metadata":{}},{"cell_type":"code","source":"#convert birth date to age\nplayers_df['birthDate'] = pd.to_datetime(players_df['birthDate'], infer_datetime_format = True) \nplayers_df['birthDate'] = np.round((pd.Timestamp.now() - players_df['birthDate']).dt.days/365)\nplayers_df = players_df.rename(columns={'birthDate': 'age'})\n\nposition= \"K\"\nplot_title_height = \"Height of \"+ position\nplot_title_weight = \"Weight of \"+ position\nplayer_by_position = players_df.loc[players_df['Position']== position]\n\n#convert height into cm\nplayers_heights = player_by_position[\"height\"]\nplayers_heights = players_heights.apply(lambda x: x.split(\"-\")) \nplayer_by_position[\"height\"] = players_heights.apply(lambda x: int(x[0]) * 12 + int(x[1]) if len(x) == 2 else int(x[0])) * 2.54\n\n#convert weight into kg\nplayer_by_position[\"weight\"] = round(players_df.weight * 0.453592, 2)\n\nfig = px.bar(player_by_position, x = player_by_position['displayName'], y = player_by_position['height'], title = plot_title_height)\nfig.show()\nfig = px.bar(player_by_position, x = player_by_position['displayName'], y = player_by_position['weight'], title = plot_title_weight)\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:51.663727Z","iopub.execute_input":"2022-01-06T16:30:51.663978Z","iopub.status.idle":"2022-01-06T16:30:52.956548Z","shell.execute_reply.started":"2022-01-06T16:30:51.66395Z","shell.execute_reply":"2022-01-06T16:30:52.955721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plays_file ='../input/nfl-big-data-bowl-2022/plays.csv'\nplays_df = pd.read_csv(plays_file)\nplays_df.fillna(0)\nplays_df.head(5)","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:52.958548Z","iopub.execute_input":"2022-01-06T16:30:52.958885Z","iopub.status.idle":"2022-01-06T16:30:53.137033Z","shell.execute_reply.started":"2022-01-06T16:30:52.958847Z","shell.execute_reply":"2022-01-06T16:30:53.13639Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<p>We will use xgboost and logistic regression to predict the probability of a kicker to score a field goal later on. \n    \nIn order to do that we need to filter the dataframes by \"specialTeamsPlayType\" = \"Field Goal\", then add a new column to the dataframe called \"play_result\", with value = 1 if \"specialTeamsResult\" = \"Kick Attempt Good\", 0 if not. \n    \nWe are only gonna take into consideration data of players who have attempted at least 10 fiel goals (to avoid cases where a player only has one attempt in which he scored, because this might \"ruin\" our prediction).\n</p>","metadata":{}},{"cell_type":"code","source":"play_type = 'Field Goal'\nkickAttempt = 'Kick Attempt Good'\nplays_df = plays_df.loc[plays_df['specialTeamsPlayType'] == play_type]\n\n#only take player who have at least 10 attempts at field goals\nplays_df = plays_df.groupby(\"kickerId\").filter(lambda x: len(x) > 10)\n\n#add a  new column play result and initialize it to 0\nplays_df['play_result'] = 0\n# if kick attempt = good, then it means he scored\nplays_df.loc[plays_df['specialTeamsResult'] == kickAttempt,'play_result' ] = 1\n\n#some features we will use to make predictions\nfiltered_plays = plays_df[['yardsToGo','yardlineNumber','absoluteYardlineNumber','kickerId','play_result','gameId','playId']]\nfiltered_plays[['weight', 'height', 'age']] = 0\nfiltered_plays\n#print(filtered_plays.shape)","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:53.13807Z","iopub.execute_input":"2022-01-06T16:30:53.138382Z","iopub.status.idle":"2022-01-06T16:30:53.168958Z","shell.execute_reply.started":"2022-01-06T16:30:53.138341Z","shell.execute_reply":"2022-01-06T16:30:53.167953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We have previously initialized weight, height, age columns to 0. \n\nWe need to perform a \"join\" with the filtered players dataframe we obtained before (based on the player id) and insert the correct values of weight, height, age for each player.","metadata":{}},{"cell_type":"code","source":"player_id = player_by_position['nflId'].values.tolist()\nweight = np.array(player_by_position['weight'].values.tolist())\nheight = np.array(player_by_position['height'].values.tolist())\nage = np.array(player_by_position['age'].values.tolist())\nfor i in range(len(player_id)):\n    filtered_plays.loc[filtered_plays.kickerId == player_id[i], ['weight','height','age']] = weight[i], height[i], age[i]\nprint(filtered_plays.head(5))\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:53.170724Z","iopub.execute_input":"2022-01-06T16:30:53.171037Z","iopub.status.idle":"2022-01-06T16:30:53.286051Z","shell.execute_reply.started":"2022-01-06T16:30:53.170998Z","shell.execute_reply":"2022-01-06T16:30:53.285126Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<p>In order to be able to use the tracking data of the past three seasons we had to use all three tracking files.</p>\n    \n<p>Because the size of each file is around 1.7 GBs, we first need to reduce the number of samples and then merge them together to get a single frame with all the information we need.</p>\n\n<p>After we have a single dataframe \"tracking\" for all three past seasons, we can perform a merge with what we already have to finally get all the data we need in a single data structure.</p>\n\n<p>A look into the final dataframe:</p>\n","metadata":{}},{"cell_type":"code","source":"track_2018_file = '../input/nfl-big-data-bowl-2022/tracking2018.csv'\ntrack_2018_df = pd.read_csv(track_2018_file)\n\ntemp = track_2018_df[['gameId','playId','nflId']]\ntrack_2018_df = track_2018_df[(temp.ne(temp.shift())).any(axis=1)]\ndel temp\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:30:53.287271Z","iopub.execute_input":"2022-01-06T16:30:53.287512Z","iopub.status.idle":"2022-01-06T16:31:32.109678Z","shell.execute_reply.started":"2022-01-06T16:30:53.28748Z","shell.execute_reply":"2022-01-06T16:31:32.108758Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"track_2019_file = '../input/nfl-big-data-bowl-2022/tracking2019.csv'\ntrack_2019_df = pd.read_csv(track_2019_file)\ntemp = track_2019_df[['gameId','playId','nflId']]\ntrack_2019_df = track_2019_df[(temp.ne(temp.shift())).any(axis=1)]\ndel temp\ntracking = pd.concat([track_2018_df,track_2019_df],ignore_index = False)\ndel track_2018_df\ndel track_2019_df\ntracking\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:31:32.111254Z","iopub.execute_input":"2022-01-06T16:31:32.111547Z","iopub.status.idle":"2022-01-06T16:32:08.387921Z","shell.execute_reply.started":"2022-01-06T16:31:32.11151Z","shell.execute_reply":"2022-01-06T16:32:08.387006Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"track_2020_file = '../input/nfl-big-data-bowl-2022/tracking2020.csv'\ntrack_2020_df = pd.read_csv(track_2020_file)\ntemp = track_2020_df[['gameId','playId','nflId']]\ntrack_2020_df = track_2020_df[(temp.ne(temp.shift())).any(axis=1)]\ndel temp\ntracking = pd.concat([tracking,track_2020_df],ignore_index = False)\ndel track_2020_df\ntracking\n\n\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:08.389424Z","iopub.execute_input":"2022-01-06T16:32:08.389726Z","iopub.status.idle":"2022-01-06T16:32:42.213289Z","shell.execute_reply.started":"2022-01-06T16:32:08.389684Z","shell.execute_reply":"2022-01-06T16:32:42.212305Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"merged_df = pd.merge(filtered_plays, tracking, how='left', left_on=['gameId','playId','kickerId'],right_on=['gameId','playId','nflId'])\nmerged_df","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:42.215504Z","iopub.execute_input":"2022-01-06T16:32:42.215804Z","iopub.status.idle":"2022-01-06T16:32:43.170188Z","shell.execute_reply.started":"2022-01-06T16:32:42.215769Z","shell.execute_reply":"2022-01-06T16:32:43.169508Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Finally, we will define an XGBoost model to perform prediction on the data we have.\n\nSome features we will use for predictions are:\n\nweight, height, age and data related to the position on the pitch (x, y, dir, speed etc..).","metadata":{}},{"cell_type":"code","source":"x = np.array(merged_df[['kickerId','yardsToGo','yardlineNumber','absoluteYardlineNumber','weight','height','age','x','y','s','a','dir','dis']].values.tolist())\nx = np.nan_to_num(x)\ny = np.array(merged_df['play_result'].values.tolist())\ny = np.nan_to_num(y)\n#print(x.shape)","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:43.171497Z","iopub.execute_input":"2022-01-06T16:32:43.171927Z","iopub.status.idle":"2022-01-06T16:32:43.187909Z","shell.execute_reply.started":"2022-01-06T16:32:43.171879Z","shell.execute_reply":"2022-01-06T16:32:43.187157Z"},"_kg_hide-output":true,"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import xgboost as xgb\n","metadata":{"_kg_hide-input":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Approaches to rank players for each position\nWe have two approaches to rank players that we will describe in the following sections. In this example we will show the top 5 players ordered by probability of success.\n## One model for each player\nWe list all the players of a specific position and for each one of them, we extract only the plays he has performed and then:\n\n* We pick the relevant features in the data\n* We train one model for each player’s plays\n* We test each model on the same sample of testing data\n* We compare scores and rank the players\n<p>This approach allows us to really focus on each player's performances to compare them. It takes players one by one, train a model on the player's plays and then test it on a sample of plays to see how well the player would perform in some specific conditions.</p>\n\n","metadata":{}},{"cell_type":"code","source":"##One model per player\nid_list = merged_df['nflId'].unique().tolist()\nscore_list = []\nfor i in range(len(id_list)):\n    filter_merge_df = merged_df[merged_df['kickerId'] == id_list[i]]\n    x_by_player = np.array(filter_merge_df[['kickerId','yardsToGo','yardlineNumber','absoluteYardlineNumber','weight','height','age','x','y','s','a','dir','dis']].values.tolist())\n    y_by_player = np.array(filter_merge_df['play_result'].values.tolist())\n    model = xgb.XGBClassifier(max_depth=12,\n                        n_estimators=500,\n                        learning_rate = 0.001,\n                        eval_metric='rmse') \n    model.fit(x_by_player, y_by_player)\n    x[:,0] = id_list[i]\n    score_list.append(np.mean(model.predict(x)))\n\nout_df = pd.DataFrame(\n    {'nflId': id_list,\n     'probabilty': score_list\n    })\nsorted_output = out_df.sort_values(by = 'probabilty', ascending = False)\nsorted_output.head(10)\nmerge_output = pd.merge(sorted_output, player_by_position, how ='left', left_on='nflId', right_on='nflId')\nno_1_output = merge_output[merge_output['probabilty'] != 1]\nprint(no_1_output[['nflId','probabilty','displayName']].head(5))\n\ndel out_df\ndel sorted_output\ndel merge_output\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:51.09573Z","iopub.execute_input":"2022-01-06T16:32:51.096144Z","iopub.status.idle":"2022-01-06T16:34:20.983692Z","shell.execute_reply.started":"2022-01-06T16:32:51.096101Z","shell.execute_reply":"2022-01-06T16:34:20.982507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## A single model for all players\n<p>We considered a simpler approach: we train a single model with all the data related to a specific play performed by players in the same position (In our experiments, kickers), and then we perform the prediction with that single model.</p>\n<p>Because each player appears in multiple rows of our data set, we took the average success rate of the player.</p>","metadata":{}},{"cell_type":"code","source":"model = xgb.XGBClassifier(max_depth=12,\n                        n_estimators=500,\n                        learning_rate = 0.001,\n                        eval_metric='rmse')                       \nmodel.fit(x,y)","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:46.587796Z","iopub.execute_input":"2022-01-06T16:32:46.588564Z","iopub.status.idle":"2022-01-06T16:32:51.050103Z","shell.execute_reply.started":"2022-01-06T16:32:46.588509Z","shell.execute_reply":"2022-01-06T16:32:51.049453Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"##one model for all players\noutput = model.predict_proba(x)\n\nprob_list = output[:,1]\n\nplayer_id_list = merged_df['kickerId'].values.tolist()\n\nout_df = pd.DataFrame(\n    {'nflId': player_id_list,\n     'probabilty': prob_list\n    })\n    \nout = out_df.groupby(['nflId']).mean()\nout = out.reset_index()\nsorted_out = out.sort_values(by = 'probabilty', ascending = False)\nmerge_output = pd.merge(sorted_out, player_by_position, how ='left', left_on='nflId', right_on='nflId')\nprint(merge_output.head(5))\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:51.051107Z","iopub.execute_input":"2022-01-06T16:32:51.051442Z","iopub.status.idle":"2022-01-06T16:32:51.094486Z","shell.execute_reply.started":"2022-01-06T16:32:51.051415Z","shell.execute_reply":"2022-01-06T16:32:51.093677Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###Second approach, one model per all players\nfrom sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression().fit(x,y)\noutput = model.predict_proba(x)\n\nprob_list = output[:,1]\n\nplayer_id_list = merged_df['kickerId'].values.tolist()\n\nout_df = pd.DataFrame(\n    {'nflId': player_id_list,\n     'probabilty': prob_list\n    })\n    \nout = out_df.groupby(['nflId']).mean()\nout = out.reset_index()\nsorted_out = out.sort_values(by = 'probabilty', ascending = False)\nmerge_output = pd.merge(sorted_out, player_by_position, how ='left', left_on='nflId', right_on='nflId')\nprint(sorted_out.head(5))\nprint(merge_output.head(5))\n","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:43.188927Z","iopub.execute_input":"2022-01-06T16:32:43.189571Z","iopub.status.idle":"2022-01-06T16:32:44.05768Z","shell.execute_reply.started":"2022-01-06T16:32:43.189528Z","shell.execute_reply":"2022-01-06T16:32:44.056639Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###First approach, one model per player\nid_list = merged_df['nflId'].unique().tolist()\nscore_list = []\nfor i in range(len(id_list)):\n    filter_merge_df = merged_df[merged_df['kickerId'] == id_list[i]]\n    x_by_player = np.array(filter_merge_df[['kickerId','yardsToGo','yardlineNumber','absoluteYardlineNumber','weight','height','age','x','y','s','a','dir','dis']].values.tolist())\n    y_by_player = np.array(filter_merge_df['play_result'].values.tolist())\n    model = LogisticRegression(max_iter=1000)\n    model.fit(x_by_player, y_by_player)\n    x[:,0] = id_list[i]\n    score_list.append(np.mean(model.predict(x)))\n\nout_df = pd.DataFrame(\n    {'nflId': id_list,\n     'probabilty': score_list\n    })\n\nsorted_output = out_df.sort_values(by = 'probabilty', ascending = False)\nsorted_output.head(10)\nmerge_output = pd.merge(sorted_output, player_by_position, how ='left', left_on='nflId', right_on='nflId')\nno_1_output = merge_output[merge_output['probabilty'] != 1]\nprint(no_1_output[['nflId','probabilty','displayName']].head(5))\n\ndel out_df\ndel sorted_output\ndel merge_output","metadata":{"execution":{"iopub.status.busy":"2022-01-06T16:32:44.059746Z","iopub.execute_input":"2022-01-06T16:32:44.060638Z","iopub.status.idle":"2022-01-06T16:32:46.585741Z","shell.execute_reply.started":"2022-01-06T16:32:44.060574Z","shell.execute_reply":"2022-01-06T16:32:46.584778Z"},"_kg_hide-input":true,"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Results interpretation\n<p>These results are encouraging, since the kickers listed are actually very famous ones and considered some of the best in the NFL.</p>\n<p>Justin Tucker for example, is one of the highest paid kicker of the whole league, with an accuracy of 90% throughout his career, and he can be found in both rankings.</p>\n\n![Justin_Tucker.jpeg](attachment:b04a0676-bd65-4984-a14d-93e49726896d.jpeg)\n\n","metadata":{},"attachments":{"b04a0676-bd65-4984-a14d-93e49726896d.jpeg":{"image/jpeg":"/9j/4AAQSkZJRgABAQEASABIAAD/7QKEUGhvdG9zaG9wIDMuMAA4QklNBAQAAAAAAmccAQAAAgAEHAEFAARVU1BXHAEFAAZVU1NQVFMcAUYACDIwMjEwOTI2HAFQAAsyMTA4MTArMDAwMBwCAAACAAMcAgUAFUZPT1RCQUxMLU5GTC1ERVQtQkFMLxwCCgABNBwCDwABUxwCFAADRkJOHAIUAANTUE8cAhYACDE2ODM0OTM5HAIZAAhOUFNUcmFucxwCGQAGdG9wcGljHAI3AAgyMDIxMDkyNhwCPAALMjEwODEwKzAwMDAcAlAACVJhaiBNZWh0YRwCVQAVQ29udHJhY3QgUGhvdG9ncmFwaGVyHAJaAAdEZXRyb2l0HAJfAAhNaWNoaWdhbhwCZQANVW5pdGVkIFN0YXRlcxwCZwAMSU1BR04tNDUxNTQ5HAJpACZORkw6IEJhbHRpbW9yZSBSYXZlbnMgYXQgRGV0cm9pdCBMaW9ucxwCbgAQVVNBIFRPREFZIFNwb3J0cxwCcwAGWDAyODM1HAJ0ABBVU0EgVE9EQVkgU3BvcnRzHAJ4ANBTZXAgMjYsIDIwMjE7IERldHJvaXQsIE1pY2hpZ2FuLCBVU0E7IEJhbHRpbW9yZSBSYXZlbnMga2lja2VyIEp1c3RpbiBUdWNrZXIgKDkpIGNlbGVicmF0ZXMgd2hpbGUgbGVhdmluZyB0aGUgZmllbGQgYWZ0ZXIgZGVmZWF0aW5nIHRoZSBEZXRyb2l0IExpb25zIGF0IEZvcmQgRmllbGQuIE1hbmRhdG9yeSBDcmVkaXQ6IFJhaiBNZWh0YS1VU0EgVE9EQVkgU3BvcnRzHAJ6AANhaTUcBwoAAQEcB1oABAAmAGwA/9sAQwAKBwcIBwYKCAgICwoKCw4YEA4NDQ4dFRYRGCMfJSQiHyIhJis3LyYpNCkhIjBBMTQ5Oz4+PiUuRElDPEg3PT47/9sAQwEKCwsODQ4cEBAcOygiKDs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7Ozs7/8IAEQgFNQfQAwEiAAIRAQMRAf/EABsAAAMBAQEBAQAAAAAAAAAAAAABAgMEBQYH/8QAGQEBAQEBAQEAAAAAAAAAAAAAAAECAwQF/9oADAMBAAIQAxAAAAH5ka6ZSalQ0AAAADEwAAQwQwQAA4AFYOBjlQyEMEqVJNWCaBCsABAQgBDQNA2gYhWAjExuWOpa3UUlNMGFCaCaRKaAQAgE0AgAAEAmgTQIAAoAGAMTG5ZQmMCmANAAAAAAAwEwQwQwQANAwAABMRDBDAAATAAExUMEMEwABAAABDQAKDQAxMEAATBAAAAANMTAEwSYIYAAAAMEAAAAhiYAKAAmIgAAUAGIGJoAKhoAEAAAUAAAQ0IahDQhqAAQwQwQBsmaiTUqTQAADAAAABiGCABAMTgY5RjlGOEMlQwU3NiTLJVKpTLEAIYIYSNABAAAIYgbRVNUFJjpMppg0QAqE0JNCTQJoE0CAAATBACAENAAAAwAadDTG0DaYxAxFMQNADQMAYgYgaAAAABoGJg0AAgAoIGgRiBgKDEQ0AAAAAAAAxDQAAAJpgAAAhgmAJioYgAAAJghipgiYACBgAAhggYhggAAAAEwQMTAAAAAAQxUNAAAAAxACGEjIkYIYIZCGElI1TVJNQk0AFAAxAwBgAAIaEMACGxylJysHKA5UNIlSslUrFNKpVKxJgACGhDQhghoEwQwGA6TG0ymmUJjQAIBNCBAgAQCaEAAAAAmCGhDQhgmAAwBg06GAMAAoAAAAAGAAAAAgAAAAYAIAKwQAAIGIGIGADQjAAQAAAKAA0DARoABDQAAAANAwABDBDEA0DEDE1AEAAAAEMQMQMAAAABMEAAAAAAAAAAAAAAKACYIAAACYqABMhJghghghgho0TJZVIlVKCChoBpgxgxiGCKCCgh04mqcqdEqKJZKIkYSqVSqViTViTVIYiGCVISpEjBDBJgmAMB1LKaY2gbTGgAAQIEAgQAUJqAATAAATQJggABiGCYAMoBg0waBgUhkIHSYAAAA0AJghghgAA1Qn6DzfNXazhW2ViGqAYAADABBMEAoJgAAAwEAABKAhgImhWAAAMQAAJoAgaFYCAAwQ0CgAAkYmoAACNAAAAKCBiAAGJgCRgAAoIGIQAViBiaAmoCAAAQAQAUAQAAAWBmpNEpqkCRidDVBScMbpDAGyXTJduWXZElOWCyWHYZrSTNXJKtWQrVkK1UjBDZJQQUJCtVIwlUCAAGDGNpjExg4AAAEmhKkJNABSGCAAAAATQAQAxDKQwAYAAADTBp0AAADQAAAACGANDEwEwAAr1F18tHndFY1gtcSlq68vP1fO64zKWohoABiBgAmCGCGAAAAAAAIAAAAQaBiYAAmhDBMFTAGmACAAkwQMQwTBQYIYJUhAAAAAAxDBDQAhgAAAmAAAAAAgYmAAAIgFAAAgAAAACwMaSaRTU0k1YNOmxjYxsYhsG6FTol1USWSyWSw6ozWilznSTOdEZllma0VZlpIXR6C+OfR8B5ZpNkq0SrmyFciTBDBDAaY2mNpytjhNgiiInRGa0VZlFQUJIwQAACGUhghoBgmMQ2SMEMEDAChpgAAwE0AAIAAGJg0DAAAPU4PcxTJTy2LLUeM5lZZKvWrw+4y5vd8fpjJVO4AAADQMABghoAABDEDE0AFBoABDBAIADQwTBDFQwQ0jE1AEEwQwQwQ0AAxAAKJghgAAAAAACGCTAAAAAAAAEAAADAAAAATUCYIAABgFNHPQhWEuaQFjaoKVUUmNlQqboooLKhU2IslkoiXQsK1Gc6Jc50VkK1ZKsXP0/R7c64+HJY9FmZnr6U8Nb5cPJ9hG/N8iujDWYm5slUgGUiiJbYmUKis0bcqbrNh01zWqMlokyWi1MywhaBmrVkq0SUrJKVIYJjpMABogFABDBAwABgACAAhoQxUMAAGAAxBoehvfJx25wItccGlxpU8muaRcFnsex8j7U0cHtvU+fXr46nnLoxsQOgBBgCaBMEAAmAAwFYgALBNQAhiYADAAAAQ00AIYANAwAAAAEAAAADQrABAgAAAIBiFYAJggAAAAQCAAMFAAAAAQ0AAJkJgDABrGkqSSqKkbsVDBjBsHc2Oh07m4dqwZQhkqGQhiyqCFaInSSFasXt8n0eemnzvufNZ3lWeme6CpqhJX18ks/VfL19J18vwM/WfLb45GiqW3SGQDZNNyp08aTdZqdOWXTlhaoynVWZLVWZGiIWirJaSkK1qQUrJGWJMpAUAA00AAAAAQwAFAAAQGgAAAQwTABoYCndx+zm7cXXw8t8ufUR5p1XXLG2dmU6Z2MbSC2vd2eTpL6hjgexxeP32c+fq8m5ylGsoAECgCCYIAAdAOUAoTSAAhqEwoAGBANAACYJMEwACmBAAAAhoQ0AANMGgAAAENAAAANAxCgCACoAAAEDaBiaACiaAAEANEMTAA0KeNZmhZktZIdOodAmNBjHa0B0xXNFXNFNVCGKhkJMEAqKRM3JNLrX0fR86s9X43ZwTee3N256TVapzrolcCyamdUe7Hge/083x8fafHdOGZSsQAUqldFZqp1jSdPOk25U6cStFGa0VZrRVktFZCtJnGs2Zq1qZrSNSBrUEyyQLAAGANFAAAQAUAAAAAwIAAAEAAAAUVNS9foeJ6vHfVw+ppnXiv0dZrzOn0ojyeb1eGzzcO7LWeQ7CzgrqzIozQzuLJGq9Pr8P0JfS5+L1jzsOrl6ZANRDATIQwQ1QwAAEwQMQ0AAmANMEwQ0AOEmCGUhgNOENAAAAhoBoAABiGCGCGgTBDQAAAoAAAJghoQ0MAAAAGmCAENABAAMQdFJ5oNCTCaGJUiRg6mqu4tLEx1FF1FF1NDESgKABQYCYSqRPfy9We3TznNNzzmedT3+Z2L1dPF23FGqTlz685vndjeeXQHrZ+L7u/Pz+b62Uvyx73idOEWrHRWNFFZoMzRqpXSqVDCVQTNyQrVkq0ZxrFkTaszjSN5hUt5ka1JAsAYMKAAAEAAAACYADAAAATQAABABQ04v6ryftOXThz6ozrle851MVMYc/fa+Hl7snhntRHi8/02dnyke/52s+THdlrHNOqrMoQ7eHY7ubp5+kQGoADQAAAACBiBgAAAAAAAAgYgYAAAAAAAAAAEAFCYIYICAAAKAIAATBDBDQhgACGCAUABMEMEAgAowAAQwQAhqACgCOlp5rTQJgNMJpEjQ6miqh2aOGW4o0rOjSs6iyGtJOBoG0DEKJovfl1x6q5a55SCJc+zj6U29DzOyz01ndwlEzWjjYwnpya58urFr0OTm72Kw871ZPnn7HkdOFVNZOprOqAzRqiqTzQARSJm1UKglUkiLiyU1ZEaZ7kpreJVTuICgYgBQCGCGgGmCABgAAAAACAAAAAAbXrS/SelJy2s7xzVOuWdYZ7GdZaUxUUiLws0wWBHndnPZ53F35WcU9M6nPOudkFQdHf5fpWchtl0ymnQmCABNAAAAMABgmCAAEAAADEwAAYIAABAA0DAAAAATQAAAAAAxAxDQAAmgAACAAEwQ0ACiaBpgAAAAAmCARDSgEdQGaAACCpY0IEwKljaLKchdZs1rOjRwzQhy2S4oQUJFJJaSmW5WfP1mZmBAVvzdKX0cui+jpzdO+MyTnpdYi9r5d2Xz9PO1j9T5Xs65fO8f03x2b9B5mvdHzT6ObXO6is2hPNdTRbTlYgYMlUiVaIm4SIuLJTnUmKjWUg3lS1uAFAFgAJi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5eVuKKqLG5Zr9P53u8umiK56m5dnjcn1fjds+a27JKKlUElETTol1VQ6EhWqznVJmrSzGk1lOkksJW1UoMsANQA3GI3Bp6gmy8uyvD18nKkaelHoZ3XF0cUTwb8m8ndwdUehtwPGuzbHbOrQxUmu156DmkkaJyzjv17x8tP2HzvflwKplSpLM3MTNykqlUDQhqxDEAKGnqNoAClNKpYABA05XSYDIBiobJKEgpElKpVKyRhKpWSNCGhDQk1CAUAhtPNbHK6TWmqhjaoYSqQhiAwmbVZq5SE5sEimKrBqqBhF51jViIAQIWjQrBCpiZTVqUVkiiJVyRNyZxpFkNFW4opy4YlDSY26smnQUMGmomRHP0zHGurLOsS5UBiZUr1lWMR6Od3nZbmoYEAByc/p8WLlUOLc0P0s/a5726ON8unfrx7G5LK2wVnP5n0GXXHhLr5tSC0SU1VDBsRDVkqlUqkSqRM3NZzpJmNSlTQwAA1AFuMRoxPUeuPuR5jjj8fSq52vZXLMvo8V8qRGelzRLl06OfXOuzfk1xvrrG11rPRb1x0S3NAExUVgej6nwX1Pq80+H9kz8/X3HNL8hP1eK/ML3uU8lenxJgtCslrJCtElKxMdDTsQFJNAmgByjTG04bTlGMBuJGVIwlWrIVzUlKpKSQUiVSJVSJNCAgAh1NS0086dK5opVK2MQyxKkS2WIZZKqbJmpsiaklUhUnTpUDGc1xUW5cA1SmpoQqQim50K0Wko6eUqpiU0KKVmcaRWaqadSyiXDHUK61IrSkyexWRqozLCClLM3BE3BEXMsKgVIGI3KEyriq0c1DcsYiHLDiz7+fLL0Mennr0NeHo59ekm5Vvkl9DTg602EIMRtzvbefJx+h5958N9fLoABSaCpVKpJKpVKqSU1UzaM5uYTTVgAg1ARoCWo3Jqdf0nn93G/Lcv0/y/K5RmHUc2i6ZRQ9oUW50zo7DrxvN75S6XnotaQ12Ci3IjUdJme1r144bEdMaLLA618r0S/RL430cvWr5Lj6vvdvkfY5l4n2j1Pzqf0XBfgJ+74T5Jb5WSMpDVgmhAUhghko04GOVtOVsqAYJUVIypKEhWqhWJmrVQrklUiVSSFckDkGnDuazp0qzt2qztscrYxMaJUakFFzIzWYm5sibmyJtEjZJQKk1bVRyVNazTlqCQ5c0IVAVRpOkXpN5rYZSqkmbgU1NkRcVMXNJpo7WsLU1g0epNXZkbBgtkYrZGMbRLlGmZEXBEXEJCpiBiY3NFVNFVDqyWUSxiB3mo2lvlq9eXoxvq349ZvrMtpTaKTprn0TZ5ssKFtkk6eZ79M+Pz/Rce55NXNiGElKoVSkzU0k5EmqmLiEIKJY0GggpJqw2w989TPO+Tp1jU8HD6GD4zH6rtPgr/Qfkc68g0WK+vm7Jro059sbuGlLiltzRrrjsWzuucfVhd+Gk5qwbZj8z9bxYvwM/SvpfL6PW15vN8v0vI6vd4434vswOmWIGAZfO/Th+cx938dXIqmkBYJlJgAyVDJRpytqsqqaVgxDZJSsSpCVBCtWQrRnOk2SqRKoIm0ZTpFkg5auNMbdDx0pqs6dKhtMbGIbuZVrUhWrnOdJ1nOdJshWrJbJZKCHSlGBysOmGCAECapJgUrV6Rct1NRYLITRM1JM1NkRcVM0rFRoPWdIrWdYes6DtWJ0ELRRnGsmWeua4Z7YmeekRnNwTNSAANMdTS000bQNyqtwGhAUkD253G5NctdW3m9md9PTzaTfVpz6yaNUVeVGmmLNiWCoK25izo83u36Z8A9Pz95iakmaiplzRLlBJUZ1mgSLThlidAFKamw+y8P6fDxevz+zM69cNlrDp4Tn9fh9IXlen5cvy2PseVz1O/Pc107822d6rJTXRWNGu3Pqa7R62sZ9SO3HSY3sjUYhghgmmHm9HzXPXBvn6VeX9zpW8gAAAADTDDcPhvN/S/la+dTWoxFNpgwlAJQCV1NRTTlpyyhAwBDBDBKlZCuSFaszVolUkmaRGesVmUSvSL59apVjbpOVtMpplVNQweohlkzc6zE2rmJubJVGsyMiRiyMlAZygdeYmUk0IaAGOppXcXLVTWVicCaFNSTFRZE1NSMHpOhek6Rek6FXNxdTaNgCqSYuFzz0zMctsYzz0zWJuSI0hEADTHSqHSYDVJNACpiCiApwGnRyXHSVny119fl7536OnNtNdGvJtG6mhtKNNMKrd5M1IYFBpMGpx8vtZ9MeNHRy7kw4HKVjkmiHNkgDapW20RaqFSPq+7Kubxds2no64aL1eZ6PAdvRKMfI9bzZeLj9Kcb+epvLTTOs9AkXbXHsJ9Xr268m7nfOYeppQwaAABMEwFx9ocnaAAAgAGAMQwAAAPjfE/S/hdPNY9QaYMcqTJQGNqpXScowhtMGAAAACaJTViVImalJVTSmpSZpLKp50qT59bqKmqqXLTTinNSulVMDWRp6ylSuYVKyZ0VmZauYVIkalSqZSpo5AfbkihJVFsjBDAY1KTzaqKi6hxSCFFQkxUVKapNUVrnoaa5ammmekVc0XcWlCaiELOoIz0zjLLbJco0iIm5qJpJCqQpMdKobVAMEqVSmkSapAgAV1Ls16OXSXSnPHeu/Neb6G/k9E16enHvHQ8mtoIq8WaPMNqws2eVjqStfJ9HTpn5ePb8PriSVZSRYlRUFAUUFlCVKs98fTPo8NeXm5curlTvFB6HPsl6EqMOHu5SfI9X4/Ou3Ho4uetmibVV6JP0c79uWemdXJIFbRqDQMTAAAATQ2AyUWkilHlV63J4Hv13NLKyQoljADx/YR+aPv4ukTAAFAJRgNpw6klppw2mrE4ABAACQTQk0TNSKXNhLRKYIZnaaMdLqKmqqXLTmoqpuV1LptG8sDWGBYkyxK0kTcazKaEms1JqVNM5mPryAYhlSmqABg5RhK2nk6kikkEuEUtUk1Q0y9c9I00z0NNM9Iu5otpjBAhCipliNJMo2gwz3zMo1izKdJIVoltoUqBtiGCm5JVIlVNJNADoYx0nWuvPrm1pmcN7aYuXr25tprs24OmOioqVqglWiWyCold656TdQVvlNanled9Zv2x8Xt9hdnyvR9FJ4h7Sjwo+gK+bz+nk+Wj6bLU+b+k5++q5+jHMjCrTbLXI74QvXMgY9EnifD/c/Cnt8+O3Ppso+olj6DWenPA0LM89ESxlaZ2NoGIGJjEAIDyPV+P0+l6PEK9nzPnq0v0/V74VJczaYDBDAcBoY0eT8j9x8RuSBogajTgaajTBp5NzUracraYA4lVKAgaaUlomalJmpsUuQAUEZ0AZ26Tzq3LlqpqWri1bRQ0bxQnvDE7Bp2NNJMXFkTUwgUCDNGqXlarpzGMQ0SqnRDKGnDAhgZo0oamSpQgJ0kwVJl6ZaRrpjquumWhpedxo5oE0KWEjCZ0RlOsGOe8VzxtCYzrJmaIzdtJqqJdBKsM1aM1pFRNySqVJjG1SjHTuLrQm8VaZHDXT0cW811VnpNb9PBcd1Y3FkBrWOoR0Scq6cyKlBOIetr52/XPS+ZJ0zzuXac0aTEreSxzWY7HTy7eN0x9Vj5Ps9OfndGSOvO0dE6UOYs32x2Plvj/e8Inoz+/jn+hWVWsQukCGEzUEdHJqbkMojI3rn3HFSUiRfE/beJ0fOX6vudb5P0WnNwnSeXlHtPxuw7RVCBiGCm5IjTM5PjPu/htIVLZDVAOBjEwUaeQ05aE8qc0rAFNTCBDExJyiioFNRYkIBCsRnTaJqnNSupqWqm1qlSsZZI1rLA1lidDRZSQimoSZqYSZKkyFSo5Kirm2mJMqVSska0GgYiKJMqkmBCAAYmIChjh3FLpeVxvphobVjRvWNGpDGgATGmhRc1nGkJlGsGS1msy0QUIWqhFBJSWVaM41gznSazVzYgY6mlbTHSZTl6XJWLnanhe3p83pm+4y3zqenniO8x6DOiDsri6QmGZ41gpaLK15iu05HZ0rEjauYOpYsuSVvTns18D2/n945ftvz30u3P67k7OSO1zR1KdDLaOgdxR+f8Hv/AEYekBM6BDqSU2JVkEqqxx34zo5+rlk2zpm/ThstSkMKG58GvZ8HzrOfP6DzzbJrI24Nzz/vfz36jT6ACAAE0TFyT8p9dlXwa0joQypY4GqEUSy2RIErqXFVIUIlJahIQyRKkVKXKKWqlVIk1A05oYStqpXc0tVN1VJ2MFQBqDTBgAFiGkSqSZuYlUskmKUnJxUmU0wApArEmaiABOYAQxEICAAAAaYMYUqldKpaqGaVlZpedGrirKEDcuyhMU1JMXJE3NTNohWiFaBlQmwkoJVIznSTObmojSahsQpNW04blq2npTl2VLeLnZHG9fR52udejXHvnW+/IL3LGoq8YOzPPKlOTNTJWarNGxk11rCjUyE2eLXdYpNXi16PK7+OzxHD9HH6P0/i/s5L6OPvXbSNC6AbVEayxBJZnkdFSxVMixkNKWZPFvhZ343UvFvzdCdZlqtqdCoYeR839V8VX2Hjel1875Wk+hHJstOF5vAfobmOv1fxy/eAejIACQE1JVZh8Ty+v5HQJlIbExqwcJUpZVKBpwwQxIaJhpECFQJIIVEgCASahNOVjFbVK6VFVNWU09GJ3KBU6kWyWMQNAiTUKamEmpUMhtM4mnm05oYKkmrlINQApDUIahDmAABghkDGDdKqKlKKll04VXVTVvUVNoiiySpG0xTcEpqpmkSqQlSWVSgpUDoiVSJm5Im5rOdIsibmpGqGnDaIbTlYnTclVUVWgtJOddXPx1p0cN4vonJpNabckS+kuTcuJzBTFmxi7NFmS6vB2bPFHQYhuYtdpzRsYkbxmHirfm9HJe14ofceh8/9EmtRS6ysjSPIivpL5OuGiTn0y1NHDDn3wJqap82zjiw7eNN/Q8j1jgevKd+uGq3pANoMPh/vfh6+t8vfy/HuvQw29Oej0vN9Xw6/OP0j4z6bpPA8n675jq++05ujvlogLASaIaZ43zX2nxewM0RQqYQMcqGRKtEsATQk0iTUCaEmhJoSaEhQABLQqmimnK2OqqaHSqm09QYWJVNDRTEDcsYiACVTSylMhMIGmvE0YrqGuiRYk5sBPWQCgCBNQkwQyUYxDFVOgp1E03Km6lWhoK6vUiruzN6VZkaiYrZViayZxpBmqklNCTFQ1KlQFqoABTcCmkkzU1E3FRNzZIIGgYENoloQMCnU0aXndmhNRzx3587y0LGtE7lxeiVBJVZNKaBiAEJSQMQUS0oktoRDQGfnephrPnprrnf9A/OPs190AEoPn8sO66+j6+LqmbweghoObp5TeeTqFNzU3Fxnz9GSef7Pkd5rwejxL0azmnUpa6VlYeJ7cV8V9N8p9pi1z9/hzpw+h2cHP0OfF+q6+PxPO+j+Zs++2ChKwCRpySKh/Cfd/IacDHsA1QyEDzQGCYSNAmrJVTYhgk0SNQlSzZmpiU0CYSqCaehNa2mNbO3J6hm7KTHSYCmp1ARQ0WMRDES0IzRNQk0CDNbTjiB5qAGSU0lY3JZRLsbQCFDacoMlBijLCiwp1EuiVWWGq0R071FTuyaqqhaCZTqqyjaDHPbKsY1yhISoCUAUYRTTAEEtClyilqpmpJmpsU1NgIG04bVyqiiS0TQ6dS0u89DTTPQvLao4sfUzxrz3OGL0xko0ebNHDKBDEDaYwAYUwYlREFIkYnP5/sc255/veJ73SfXAqfF25HyPpc3pTXrXObHZpzbLaYRy9PMc11xp6b4uwVIV4dPMc0dXnp7HNvmtc+uCdtRVFQo1WWpw/LfcfO6v1HnfG/RY693Hv0538v8AR8vZz4/Ier5X2Xoz6acxVShNMJcg5sfy/wBV8lXmCOinIMBWDgGAmoSc0wLJVBJRUFEQrUQrmM5tZuZRLLurM71sy001M71sxe4YLokwnomsDSSFckJzQBqAKwBSsRFEvNaFDQlaCGJy8bTyFSJVTZKc2UIqiWlCAE1dJysCBqpassVuxOnLJQFqytJ0sq1djoqwodgqCJ0ms40zM8tsjLLbJcpqZQHKgFYnFCBiLCWhRUkppFNTUpzYpqUkAdToOipRlCGySixMY9JsvTOzSouKqQfB3qXxZ9nHF8x9eMZsIbkiyGraChA3IXWbNHmykBI0JMM/Y8v3tz2Zpdcgg8/0FpJk1VVYS61z7mWOuROe2dnnds+dHvLLU05+nNcPP7sToXNsVzb8yeg8bKpAtsdhw2vy3i/oXhaufr/C9ude753idXKej9tz79shNwSUKXRMWh6GYvifrfitmJ7DQNqoKTGBRNSSmgY6TqkzNAzWijNaLNznRRlOqlyelSzpWiRemlZ61pJN3S5PVmM7o556ZOaOnOubPoyMJ0zECpoEE0AErE4BksjUoAAhOWhwlSJmpsmakTRTEJTljactNOViau51i7Losco2ySiC50HpN2XpN2OlVjpOwGExcVEXmRnpnWWWucuUXE0CJWIlYgokRklUkkE0KbSRNTUy5RJqyUwrSLLpXBRQm2QUCbqlQwuGa3nRbkKJFYKUTUTGkrjG8xzrdxzHQjnXS05TrZxvqUc5tMQNQAANk/R+B9PudiF0k6ZVGnOxLpu26VxlnvJG3Bino5VsvFxetgnner4nYetJa+dqQcHVjaVBRlKxPYOHvLcNdr8/A9leV1E+D9Qq+E+060ac/wA981H6bzfnCPteD5or6Hf5aj9A9D4D7aOiFseT8p7/AM/0lCew1Q6mlbTQaKU1IkUFFBQxFESrUQtFm5rWYyNHLnV0LR6QtHoLR2k1TJLZmtUuS1kxz6Mzmx6sa5MunCMpuVTGiKCCglslBqBNKIlGkGbYiVIibkzm4skaE0DaZTmpactW5qW98ugq1UOlQUUqLCaKR3NmlTdlVNWU07AciioqIuDOLzXPPTOXKNIWGnKJkqGhACAsBlDBFLlJlykpolNWSDK0jQ0udIKdCKZKsJbYhzQINKyurctacsYnKgmHDlSWhIVAANOxsaMZUq5IKVibJUDV/UfL/R5m6lsvHztjbu8XePWfL0LrUXUxecZ56Fec+/nk6r8PoOnx/d5jP1vmOw9Xk6+VXydPJJW3JuRz31nH3+c67o8Xy1+k4/nEfZ+l+dyfpi/OdD9B8v4/E6oyuqakcXmU83H0X1fj/RAkHnfH/oXwXSZuK6ynLLcuLJdUIpS1RSoqlWRQ4BuJVmbBZLCsjN2RNupTVaDs0CykTYJtqlaSJ0lco1gwy3yOXDqxl543gydAm2Q7EhaKXNVIk0KWqECJikE1UzUkRcEpoBMGgqpctOQuouXffDoNKm1KVDtUMYJsC5pNLzurqaSql2OXJMuSYvNYzuDPPTJZzuJUBKMFE0SqVS2IMLEqmxTSIWiTJayZRrCZsZeueppc6RVJgAAmU0yZuKSc07zZq4ZblrbQpDkSBSaRMtIwdDKRtgAxKghWqkpRI0p9J8572Zreds+Jh9CR4WHtVXF7Xl+kaCZRLhNVbJUD4uuo8Po9PhS/NOk5vX4uOO2dqOG+a4fzU5aTUarLl05chUEaJWS67przz6DszflK+16pfhtftozfznX3+Dpz+068+nUy00kPkfr/AJjc8Mpd4UnY2mNp00KwEyqm4dzebTHKMeUtmaijNlWRDoUouDSdErSbHSoGMAYDRM3K5xpmZ5bZRhj0ZLhO0RlOsktuwYyVoGGfTkYzcEpoEKy5uYSaFFwRNwTNSIEUIKJZTll646y9G/Puu15aF1NFUqClQlSCpou87S6h1o86KkQoqCYcExWZOdQszUyoBQFDQqAAAQAoVFkliSWGUbwYZ75piUi9M9DbTPWKEwEDEynLDO4pSKhyVpWV1pedy2hAmlTGKakhUqKVBSqRjaIbJGEjCVSWVSD1vJ9fLo3wqZ1w4cuW/Wx0rrmjihPUvh6DYRQiSngjfNVBUIOHuZ4nR2+ZGnT5NmnH04x4/N7fiUm5tmp1iDp7ZrzL+l6ca+U7fpdc64fRTzacVFuHVSESr3642ub680Ug+e+i4K+Pm59WE07G0U2lTSLHU0XU3i3cXm1SrIBypjzUx5qKIkolKKHpNpVTZVTQwYMAGiZqSIvNYy0yM89IlidJMo1zEykKKpFBGW+acufRiZzUkpo3m5zYm5qJqSZpGc3KSqkBAxMpzRWuWh0bc+y76ZaS6aZ6F1NhSYJhNJ1TlpbgNHmzRQioUBBmEOFUNClqUAlSqRAqGiykIGmlMqhtySUERrKY5dGZzxtnTuKNtufaNEAAAAMkHDmpSWjJK0vPQ0qKWhEMBRoBASmWNjClUNoRiYk0CAQJRNB63kelmejw93zOL5+nEdH0H0Pxfv8AC+lh0rrjk01C98diaSpZ6KOeOyTle8GTeZtXFlG3nbyY4d3PHP5XteYcOi9BrHs7+jl0y61edVcVFuWtkI0edFuLqgBXJZ26+dr34dqwutnFnyXlfV/KejEtHbNOSmIARZVxct6Z3hdxedXUVlTTzRhmlJ5oMhDSu4pLuLLc0W5oppjaBy0KHBMOCM7zWZpEzcxnGkkUUFKrAAUaSY475nNG2RCqTdIzpQ5SZqRS5JmpEmqkaQAHUsvTOjfbm2OnXn2XbTPQ0uLWgYDBDBAICBkhSmSoUjhSEkqSIBOUBDQhTUg5KtAhU1VXNxTGiKRKtJjnvmYZdGJDFWmvPpHQ86KJClKKSVVCkJJ0ZLra8rrS8qjRxSsQMkKJYAi3NRVTSAACASAEDQCQlfbw9Uno/IfRfLZNaPoz9jxu3nr7OsexnnpljuaiWkPO0ZLWVgISiEaGaW8bkz8n2/Kjmx6Zjm9zh9Ln00ZWNQWSyxLTkKJC6ii7iqpggnNaeh5PsdeY2t4ZEnlfLfS/NejMoXfFCdgAAFVcVGl53i6VF41dRWLdRWa2nk2OUQoBMLii7zo0rOjRwzR5utDNlypHBAs3EKHKggJqYibVJjgYxDLFGkGWeuZlltmYxpBSU400lTklFLkJaEmhJqwACpZblmm3PqdW/NsvRrhqbaZWuhLKaYwBJoScgiUJIHKgcyhyQNSl0cg0lFJAIVAhaEWVcUaaZ6RbVIJgk0kRpJlj0ZGE3FKsw6L59I1IZSSGlNOVI0pp3lda3ndXUUt3FgAIAGimS5LqGaVFQ0IaEIEAIckjJRWmMmni+j5uXXtyzz1G0R1z731n5/8ASc77kZ6bzCMY1b1MJ7ReBeijzjvhOI6xeVdocM+jBw+b9BxngZdnDJXreJ6WN+heGnLpq5qVKgzLFgqR3nRreWlW5djlojr5NtZ7lVduWbpHifO/QfP+nMJrthiLG5dMTHUVGlxWLped4t1F41bms2qis2hECCATG0ynDNHmzV5M0eRWrxZqs0XEwVCkckQyEuhDgGUmMBgJpFNQTncEZ6QY5bZGahY1cpFKShCCWkSaEBYAhuWW4orTKzo35djq25d16LxtdnnZbllOWCaRTUkzUExUEw4ElI0pGSyyUUhUxIYlKOQskq7ys30x1k0qaGCBCRTSIz2zOfHo5zNKa0vGjd4s2WaLUIqVJUpVd52a6ZaW1cWXU1YwAQCBUCC3DNazcWkookKSQ5JHBNNSFKULzvS48JU6ZVz+hwGvV5+59zHzX0dkZbwRV7nJfXJjZEbPkR2PgR6L80PTflh6vLy869Xlb5RGkEd2vLvx7dNRS0AAypVIgblrTLUtosJqSNIk9omvRwlVB4HhdnF6sSnPXLAsAKYgdRUaaZ1m6VFYt3nWNaVDxdHFRTl5rQAIimgYkU4DQzK0WaNTFGyxVbTio1MgtQpWpmNaxs1cXTExoSNJBJIocihwZ56ZnKksaYgBFNACASasQJAABBVRRdRRrrhqu+/NqdOuGi7XlZq4oppjBIpqSYuCM9MyM9MqiahBJKyQpyFElVKBiUMkWlKL159Dp25tpN6zsoQCBEmhRcLjh0YJzZb4BeLrZ5M0MwtSilIUJlXFGt5aW6XndaVnaUIAChAJNU3LKcOLUhZItJJGlIQRQSinmzSRxwabcvN3c19GXmtLo17fPrD6fpy6Uh40Ur6Djfo6V5l+m182vQZ556DPNXpo4V388cvje/4ZiCjp35Ojl17NOffOrqLGgolqEhLeuOpbHZKoJjSF9Do4uvtwPG9L5zpnzYcerKQt5YixiKYgdRRred4t1DxdKisW6is26h5uhLzaQABDaATQkIaU1SmSpmS5zVaGTjUzDRQStJRV5UbXjoaOHZSSHJIQ5FLkUPOVQ5ONUoljEMVDCRlkFIlUJKpCGCpFXUVF6ZaLtrhqdOmGy66ZaGtZ2XUUMBFLkUVBEXFkZ3mZ56Z0pEAkUIpiRSSWkkUpClKiryo6duXaOrTn2k0JYxJWkhRURGOuVY4b5JiqkHJZThjELTllNMdxRemV26Xmzasqs1JZSRTEAmhCRRJVEkW4C1KKgmiCRJTVuKLcOL4+rDCe/wAvrw5p6MNpoKr2vHw5P0Lo+I9nU93Ph6wnp0XgXpUeSeqjy36UpwX1Zqsd+cnk7FHl5dfNBtz6Y109HH08+u146Fkg0IEEta4bVq5djBxMaQt7c/Z248fzOvL6cKKjtlCN5BFjEU0iqqKjWs9MW3NYt1FYt1NZtVLzbc1KwcIZAACEKXNKXIpJFLgJUlOGaOHFJKKElpyy7ypNqxqtCUOSQRMEkizcSzLkyViQWLDoJVlQtBM1oiFojNaqszREFiQ7FLVxWsarprnqaXGi1cWW1SAAouamLkzjWEyz1isc9oMY2msi0SWEFqoLDNaKsyyM1ooh0D2z0jbbHaLacA0qVSRGkmWe2Zhl05pzR0QYmqrN2EGgQ6ZLqiKpip1aqGFKrHUspyxiYTUqppVIFMAAATQk5smakhUyWwYnDReXA7zy3znQiktPQ5dsOGvT+o8H6zpnzI9YTyK9UXzK9EPPfejirpDB7BGPZBxreTzeD1vKiakjXq5N+Pbe8dJdHFA0SuaQVFm1TVlNARaIx08ztz8yHHs5qSd5aRvLcvUAQ0A6ijS87xdKisW7zrF0qLzaqazaqXFNErE4ABS5FLilLkmXIoqKlEjqKinLhuXDEDaCnLKqA1M2VKQ5UqSSKXEKXI1RZJSAAAYhhIypKRJQSqCSgkoC1Y9c9Y01z1W7myrmiqmgAFLLJmpIjSDONM6zjSCJuKhUhMKAATBKipVKJGyCiFpFRrthpG7iooTBCVTUpEaQZxpBnOirI0DMsILCHQJt0MqFQ1GOxMKbTpuagAAASpLKsqSglUEqlZKpVCsILIgtEFhBajTzPpvE5uRpbVVRyreJqez9r+Y+0fb18run0j+e0X3TxdD1l51HcuRnUczOqcszoyzyNfK7OCAxUa5yufTqvGsb2rK40edLbmiW0a6Y6WW0xzSMvM9TzuufGz2y9nHNWt5gs3IdmpCssgtSy2x3N5O1WK6VY1VxeVVNZtNPJtC0BDQhTUEzUVMuRTU1MXBE1IUqhscJsE2QhgAwAAEABM0pYm5Ji5shUilSpDIQwQykNkjBDCRhIwQ2S6YrdhoWO1cVc0t1NFNAxAAWTNwRNSRF51EaZkTU0pqQAAChpgmCGElISoiWEXedRteVxoSxiQJyKKkmaklWqlWiVYSrSSUWy2IMatpjAAHQ0wYA0wEDBiGKhglSqVSslNUhkIbJKIkoJKDt46y53zVa6SR6ZYsdTrG2HL7Pj/WpG/o0c28ZG+R0nBl7Wi/Ox9NEeBn9FmeAezkeVPrcEc5mjV5Xm7ac2vLtteGku1Z6F3NgqBXAbXjpZQmZ8Xbzbng5bY+3hIzUQzUAeohhIxEMHc1LbVYrqazaqaxaqaimnK3LihA0IU1JE3FQnIk1SjSDOdIhMZTThgQAAADQMRTEICIECzNImaRM2i51VZlkQaFZmgQWGZoGa0DM0RmaBnVsirojR2K3QqdBTYUUIoJGAmExpJlOkGcaSZRtFYzrJnOqrM0RBYQWyCyoNEQWRmaBmaEZumFzUW04BhM2iJ0kzLDM0DNaFZlhmaCZmitzNEklMkotTGIYAwbTAYSMAYAEoDpJoSasSapMYDcSURJQSUha5/R878UvT49M+js5uWuAZ6sa3ph5tc33/wAV+gbzgul2c2mzlwnqDlreSKzg61gjXOYNOWpjy3RDvOzDLu8/n03vm2xvr1jSWri4bTpK0LXK60E0nLWNPnMOvk9nBDNZAeohlCZSGIhgUqlpzWbVQ826isrvOsrctaJcUSRSSpyIUVFKakQ0JUiI0RBQKk4AAAgAsAAGgAhJoEAk0qTQk0dE6LUgoJKCWwkoEqCVYQWiSwToE3QUVKWmVSqnU3DYAAAAIQS0RFwTFzUzcmc6SkK1UlCyWElBJQSU6lWRKsILJILCWxSk4YAhhM2hFMgtEKxIKLZLCCwzWiSCnUOgkpkOmQ6aw7cZmgZlhDoJKCShZVKyVSqSgl1RDsiFYQWRBYR9d8p6Ryrt+R4a9GJWnMB6s+j5fq+P5Nep9l8V9f0zcIRS2ZzvZyL0rPKXqqXzb72eWd+RyPcPMjowhNsWPQpeHp5enl179Z0zopVAx0MZNK6G1RncHkeb7XkerhBRvMlmpLpkFlmZoogp1LblVDhUGa6TiqmpaExicDQNCBCpS0SmCTQJoSYSUJJTiSmZlhJYQWElEQrRCtEK0SqRKoIKR1Tc9CGIhghghlIYJUEjcqKaS24VDUpMdKh1NFVDLcsYkrciORBIghyKWhKkRNzUlBJRUlBLYIYiG1QwQxEMhFCyMEUCGAmQhgMZJSJKCSgkoJKKlWiCglsRFNZdMl04l21g0IyNUZGgZGiMy0QrVZlqySgVFCGSpUElBLYS2H0Hgdnu5fHef9R8rjWZNd8+r4nteL5ddP2/xX3XXNa8zTqMtFabM1sjKwKrPQUVJnNI4OP0/OidJ3JNEcR26c+kXRz2qTldKrKEBcVTi4Vw0YeH9B4nfliUdeaKKTYiKRJQSUhDAY5UxwNUOpqVtAxEMQNCGgpTUiTQlUiGCGIlSAHAwEMAGIYIYSqQlSiFaIVogoJVBtJPfNEsYgYimJgADVQqq8WKt5ua2DE0WpLFpVRRTlxTkNCAolS082XKQ5FYS0JBQCRTUiYUAAAAADBMABiGCGAAACiYAEgAoANjEmCGCGCGCYxTckjKBsTKhNuBtyqmxNvKS2uS1UZTtNZTrNZzorIVokpWKh0hkqGCGCYAAdHueJ7OGHx32nhc785UnV6nk+h53C+79d8n9F0z1Rzi6GKTojOhTtS871kN+e41rLeppyPi6eYWvnaHpGOsY1UcuswLGm04ty6pUEpyWhWpqljyPa87rz85s9HEG0QykMJKIkYIYIZAAraIppjaBoUrEIyWNBSmpBDEmhDABiVITHAMEAAMAATCRoSpEqkJUiSkJUEjXp5pgAAwAABlwtHpz0rdctJ1WLmaKs51nUxnWOuZcmpbgWyGWZuW1KinCNFAUSrKUlMkspJDUlUSFEhRIUIG5CiWNyFEsYkUIGIGIGIGIGIKcuKQAAAAxAwAloBMGmOpqV1NQ6VStjyG3KDcSULmtJM41mzKdZMylUq1ZLZSGUkxE0DEDAo+j+c9vF35vQy5a+J872fIl6uDqjLt+p+N+r3n1L8nrOx5WtBRlO0mJYQUyG+UvDKY3WbrjuIj09+Hvzt895cuk3NYtMdiqWOs3VodJCVzUW6cPXy6x5o37POhgDBDBKkIZIhioYJUgAG0FOXDQlAQxNGASqkAATQAxMATQMAAEwAAAATISYJMEqQlSEAIYQM9PNA6QwAAZUGheNPQ04aVVXPUuniyrDONo1MY2jrnGNY7ZkFqMklZJLZDzWIGgQQaAFgCsE1YgKQwQwQwTABgmAAAAAMQ0AADBAAAAA2gbTlAAAAAaENIsGgoTlqoqKqalqprNqprNpp5racDCEqSwrlM50ms5uakasQGgCsAVgCoBWUSFel5ftYvd89r4Hi7Lnp+rOmfteH5ta/W/LfY9MYT1ZmO2MHb0eVZ678zZexZWU6DCNMB87kJjIiTXN69Jnn1UUueik4dS7G1JSlqkpq3mFLPPV35Vz9ecsr1cExypsJKSSNWIaBoGmgBAADQNyxgCAFUuGCBNABQAAmDlghDaEYgGgaBRoGhDEDQQCKEAAACiSX6eYJjEIxNaubzb0jTlq9Y04bqiuVRRmyNQppbmeeuXSZZ65ejEST0zSRKxPNAM6AIGimg1kA1ARrIBQmAAIYJgAAAUDIAKAIAAGCGCGCGCGCABgAAwAAgAoQAmAADGNp5tVN5tVNZtUnmuk8VscoDhDFlWjOdFZlG01itFZmrKgpaklKyVa0lUWSqVi9zxO7N1+Q+8+S8nbh9bPoxrk8xa3PsfR4YayloyK1s5zqRzLqDm6AOs5N1c7Bjz92ZyPoDzOmq57Ic8uk0PNKl2MEEVMsigaErmc9R81HXENv2cUxqDJUwgTViTViTWsgi5YgBFMQMQNyyhENCBoGIhiKYgaQMQAIaEDRTAgEwAAAAQxAAhoAEAAABkI9HNiBuQqoqNLz0xrTXLXhvXXLbju6V8dIozYVTEzU7kZaZdcxleXoxEOO2KIa25OeqEc9USKxKyiTeWSbzRBvNkOyiQokKJCiWUSFEhRIUIG5ChC0SRRLViBiBiSUSDEFEsYgYgYgaChoGIihBVRUXeV41pUVi3UXjVVLzacvNoTlYECoqFaInRWZTrNZLSdSFaslUtRKlUqlqSqVk74+nHqfLfX/I+DuvDzIPoPE+x6Z79M91U9DOR9bOWugOddIck9fOcyqQYDm2ZTtzylC5dFCOeqYQ05qkkENSxFQrlFmb1y1FLj28Bj6BhnQMEBAmWSqVkqp3hJrWQFcgFAMTTgAGIVoAAACRArWgGJDEIxIYimANAAOATAAAQ0AACGgABMENGIHbIADVwtHrmzpevHca1py2tHfKll89SW8XGdpOeN89TDLfHrnHLXL0Yzz0z7YTGrGctoZz0hioasEzplJrphAdMAFgAA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"}}},{"cell_type":"markdown","source":"# Conclusion\n<p>For this competition, we worked on ranking special teams players from the NFL.</p>\nOne of the most challenging part of this project was to handle the data, we had a lot of files to use and had to go through a lot of data cleaning.\n<p>We focused on some of the most important positions in special teams and compared players by position.\\\\\nTo achieve this, we tried two different approaches to compare players: one comparing players' results with only one machine learning model and one comparing them with one model per player.</p>\n<p>For the models predicting outcomes of different plays, we especially used XGBoost and we gathered with it interesting results when comparing players' performances.</p>\n<p>Using our different approaches and with different features, we were able to rank \"Kickers\" from the NFL. Based on their past performances, we tested how each player would perform in some specific conditions in a sample of plays (the sample being the same for every player). In this way we were somewhat able to judge them fairly and not with the bias of the conditions of each player's plays.</p>\n<p>For example, a kicker with an 85% accuracy might be ranked lower than someone with an 80% accuracy because the average distance of the first kicker's shots was of 20 yards when it was of 50 yards for the second one. And what really matters is how well the players would perform compared to the others in the same exact context.</p>\n<p>Finally, for further improvement, we would continue working on other special teams positions, and especially the \"punter\" position for which we need deep learning models to do the predictions.</p>","metadata":{}},{"cell_type":"markdown","source":"# Image references\n<div id=\"nfl_logo\">1. NFL Logo, NFL: https://static.nfl.com/static/content/public/static/wildcat/assets/img/application-shell/shield/default.svg </div>\n<div id=\"tucker\"> 2. Justin tucker, japantimes: https://cdn-japantimes.com/wp-content/uploads/2021/09/np_file_114442.jpeg</div>\n","metadata":{}}]}