{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import pickle as pkl\n", "\n", "predictions_this_year = {}\n", "\n", "with open('Source/Data/predictions_this_year.pkl', 'wb') as f:\n", " pkl.dump(predictions_this_year, f)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "key = '{\"type\": \"service_account\",\"project_id\": \"bmllc-398613\",\"private_key_id\": \"dae9f17eb79aee559b936bd88406ea399f729d9e\",\"private_key\": \"-----BEGIN PRIVATE KEY-----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\\n7dS4tXLQR9w31YhZ5ONBNTQ9\\n-----END PRIVATE KEY-----\\n\",\"client_email\": \"huggingface@bmllc-398613.iam.gserviceaccount.com\",\"client_id\": \"103296969416633370732\", \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\"token_uri\": \"https://oauth2.googleapis.com/token\",\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/huggingface%40bmllc-398613.iam.gserviceaccount.com\", \"universe_domain\": \"googleapis.com\"}'.replace('\\n','\\\\n')" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'{\"type\":\"service_account\",\"project_id\":\"bmllc-398613\",\"private_key_id\":\"dae9f17eb79aee559b936bd88406ea399f729d9e\",\"private_key\":\"-----BEGIN PRIVATE KEY-----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\\\\n7dS4tXLQR9w31YhZ5ONBNTQ9\\\\n-----END PRIVATE KEY-----\\\\n\",\"client_email\":\"huggingface@bmllc-398613.iam.gserviceaccount.com\",\"client_id\":\"103296969416633370732\",\"auth_uri\":\"https://accounts.google.com/o/oauth2/auth\",\"token_uri\":\"https://oauth2.googleapis.com/token\",\"auth_provider_x509_cert_url\":\"https://www.googleapis.com/oauth2/v1/certs\",\"client_x509_cert_url\":\"https://www.googleapis.com/robot/v1/metadata/x509/huggingface%40bmllc-398613.iam.gserviceaccount.com\",\"universe_domain\":\"googleapis.com\"}'" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "\n", "your_json = {\n", " \"type\": \"service_account\",\n", " \"project_id\": \"bmllc-398613\",\n", " \"private_key_id\": \"dae9f17eb79aee559b936bd88406ea399f729d9e\",\n", " \"private_key\": \"-----BEGIN PRIVATE KEY-----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\\n7dS4tXLQR9w31YhZ5ONBNTQ9\\n-----END PRIVATE KEY-----\\n\",\n", " \"client_email\": \"huggingface@bmllc-398613.iam.gserviceaccount.com\",\n", " \"client_id\": \"103296969416633370732\",\n", " \"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n", " \"token_uri\": \"https://oauth2.googleapis.com/token\",\n", " \"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n", " \"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/huggingface%40bmllc-398613.iam.gserviceaccount.com\",\n", " \"universe_domain\": \"googleapis.com\"\n", "}\n", "\n", "one_line_json = json.dumps(your_json, separators=(',', ':')).replace(\"\\n\", \"\\\\n\")\n", "one_line_json" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'2023_1_CIN_CLE': {'Moneyline': {'Winner': 'NA',\n", " 'Probabilities': ['N/A'],\n", " 'rowIndex': 1},\n", " 'Over/Under': {'Over/Under': 'N/A', 'Probability': ['N/A'], 'rowIndex': 1}}}" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "from google.cloud import storage\n", "\n", "# authenticate gcp\n", "gcp_sa_key = json.loads(key)\n", "client = storage.Client.from_service_account_info(gcp_sa_key)\n", "bucket = client.get_bucket('bmllc-marci-data-bucket')\n", "\n", "# download\n", "blob = bucket.blob('predictions_this_year.pkl')\n", "buffer = blob.download_as_bytes()\n", "predictions_this_year = pickle.loads(buffer)\n", "\n", "predictions_this_year" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
MoneylineOver/Under
2023_1_CIN_CLE{'Winner': 'NA', 'Probabilities': ['N/A'], 'ro...{'Over/Under': 'N/A', 'Probability': ['N/A'], ...
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" ], "text/plain": [ " Moneyline \\\n", "2023_1_CIN_CLE {'Winner': 'NA', 'Probabilities': ['N/A'], 'ro... \n", "\n", " Over/Under \n", "2023_1_CIN_CLE {'Over/Under': 'N/A', 'Probability': ['N/A'], ... " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.DataFrame(predictions_this_year).T" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "predictions_this_year = {}\n", "buffer = pkl.dumps(predictions_this_year)\n", "blob.upload_from_string(buffer, content_type='application/octet-stream')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "service_account_id = 'service-950327177777@gs-project-accounts.iam.gserviceaccount.com'" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6666666666666666" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gpt_picks = ['Kansas City Chiefs',\n", " 'Atlanta Falcons',\n", " 'Baltimore Ravens',\n", " 'Cleveland Browns',\n", " 'Indianapolis Colts',\n", " 'Tampa Bay Buccaneers',\n", " 'New Orleans Saints',\n", " 'San Francisco 49ers',\n", " 'Washington Commanders',\n", " 'Green Bay Packers',\n", " 'Las Vegas Raiders',\n", " 'Los Angeles Chargers',\n", " 'New England Patriots',\n", " 'Seattle Seahawks',\n", " 'Dallas Cowboys']\n", "\n", "correct = [0,\n", " 1,\n", " 1,\n", " 1,\n", " 0,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 1,\n", " 0,\n", " 0,\n", " 0,\n", " 1]\n", "\n", "sum(correct)/len(correct)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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32023_01_GB_CHI1.00.01.00.017119.02.02.01.0000001.0...1.09.015.09.07.03.010.038.018.02023-09-10
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52023_01_DAL_NYG1.00.01.00.023666.02.00.00.0000000.0...0.05.018.06.07.05.011.040.040.02023-09-10
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72023_01_ARI_WAS1.01.00.01.014896.02.02.01.0000001.0...3.09.013.04.010.04.08.016.0-4.02023-09-10
82023_01_TB_MIN1.00.01.00.019010.01.01.01.0000002.0...1.03.016.06.011.06.08.020.03.02023-09-10
92023_01_HOU_BAL1.01.00.01.015653.01.01.01.0000000.0...5.09.017.07.011.08.07.09.0-16.02023-09-10
102023_01_LA_SEA1.00.01.00.013979.03.02.00.6666671.0...0.07.027.011.06.02.07.030.017.02023-09-10
112023_01_TEN_NO1.01.00.01.014954.03.03.01.0000001.0...3.06.016.02.010.07.09.015.0-1.02023-09-10
122023_01_JAX_IND1.00.01.00.013879.00.00.0NaN1.0...2.04.020.03.09.02.010.031.010.02023-09-10
132023_01_CAR_ATL1.01.00.01.010398.01.01.01.0000001.0...2.09.020.05.09.02.08.010.0-14.02023-09-10
142023_01_LV_DEN1.00.01.00.029267.02.01.00.5000002.0...0.010.020.05.06.05.06.017.01.02023-09-10
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15 rows × 65 columns

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" ], "text/plain": [ " game_id GP W L W_PCT TOP FGA FGM FG_PCT \\\n", "0 2023_01_SF_PIT 1.0 0.0 1.0 0.0 11183.0 0.0 0.0 NaN \n", "1 2023_01_MIA_LAC 1.0 0.0 1.0 0.0 24718.0 2.0 2.0 1.000000 \n", "2 2023_01_CIN_CLE 1.0 1.0 0.0 1.0 15262.0 3.0 3.0 1.000000 \n", "3 2023_01_GB_CHI 1.0 0.0 1.0 0.0 17119.0 2.0 2.0 1.000000 \n", "4 2023_01_PHI_NE 1.0 0.0 1.0 0.0 17085.0 0.0 0.0 NaN \n", "5 2023_01_DAL_NYG 1.0 0.0 1.0 0.0 23666.0 2.0 0.0 0.000000 \n", "6 2023_01_DET_KC 1.0 0.0 1.0 0.0 15329.0 2.0 2.0 1.000000 \n", "7 2023_01_ARI_WAS 1.0 1.0 0.0 1.0 14896.0 2.0 2.0 1.000000 \n", "8 2023_01_TB_MIN 1.0 0.0 1.0 0.0 19010.0 1.0 1.0 1.000000 \n", "9 2023_01_HOU_BAL 1.0 1.0 0.0 1.0 15653.0 1.0 1.0 1.000000 \n", "10 2023_01_LA_SEA 1.0 0.0 1.0 0.0 13979.0 3.0 2.0 0.666667 \n", "11 2023_01_TEN_NO 1.0 1.0 0.0 1.0 14954.0 3.0 3.0 1.000000 \n", "12 2023_01_JAX_IND 1.0 0.0 1.0 0.0 13879.0 0.0 0.0 NaN \n", "13 2023_01_CAR_ATL 1.0 1.0 0.0 1.0 10398.0 1.0 1.0 1.000000 \n", "14 2023_01_LV_DEN 1.0 0.0 1.0 0.0 29267.0 2.0 1.0 0.500000 \n", "\n", " PassTD ... Sacks_Allowed.Away Penalties.Away FirstDowns.Away \\\n", "0 1.0 ... 3.0 11.0 21.0 \n", "1 1.0 ... 0.0 6.0 29.0 \n", "2 1.0 ... 2.0 4.0 6.0 \n", "3 1.0 ... 1.0 9.0 15.0 \n", "4 3.0 ... 3.0 5.0 17.0 \n", "5 0.0 ... 0.0 5.0 18.0 \n", "6 2.0 ... 1.0 4.0 19.0 \n", "7 1.0 ... 3.0 9.0 13.0 \n", "8 2.0 ... 1.0 3.0 16.0 \n", "9 0.0 ... 5.0 9.0 17.0 \n", "10 1.0 ... 0.0 7.0 27.0 \n", "11 1.0 ... 3.0 6.0 16.0 \n", "12 1.0 ... 2.0 4.0 20.0 \n", "13 1.0 ... 2.0 9.0 20.0 \n", "14 2.0 ... 0.0 10.0 20.0 \n", "\n", " 3rdDownConverted.Away 3rdDownFailed.Away 3rdDownAllowed.Away \\\n", "0 6.0 7.0 5.0 \n", "1 4.0 5.0 9.0 \n", "2 2.0 13.0 4.0 \n", "3 9.0 7.0 3.0 \n", "4 4.0 9.0 5.0 \n", "5 6.0 7.0 5.0 \n", "6 5.0 10.0 5.0 \n", "7 4.0 10.0 4.0 \n", "8 6.0 11.0 6.0 \n", "9 7.0 11.0 8.0 \n", "10 11.0 6.0 2.0 \n", "11 2.0 10.0 7.0 \n", "12 3.0 9.0 2.0 \n", "13 5.0 9.0 2.0 \n", "14 5.0 6.0 5.0 \n", "\n", " 3rdDownDefended.Away PTS.Away PointDiff.Away game_date \n", "0 10.0 30.0 23.0 2023-09-10 \n", "1 6.0 36.0 2.0 2023-09-10 \n", "2 10.0 3.0 -21.0 2023-09-10 \n", "3 10.0 38.0 18.0 2023-09-10 \n", "4 10.0 25.0 5.0 2023-09-10 \n", "5 11.0 40.0 40.0 2023-09-10 \n", "6 9.0 21.0 1.0 2023-09-07 \n", "7 8.0 16.0 -4.0 2023-09-10 \n", "8 8.0 20.0 3.0 2023-09-10 \n", "9 7.0 9.0 -16.0 2023-09-10 \n", "10 7.0 30.0 17.0 2023-09-10 \n", "11 9.0 15.0 -1.0 2023-09-10 \n", "12 10.0 31.0 10.0 2023-09-10 \n", "13 8.0 10.0 -14.0 2023-09-10 \n", "14 6.0 17.0 1.0 2023-09-10 \n", "\n", "[15 rows x 65 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "scores = pd.read_csv('Source/Data/gbg_this_year.csv')\n", "scores" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "import requests\n", "\n", "headers = {\n", "'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',\n", "'Accept-Encoding': 'gzip, deflate',\n", "'Accept-Language': 'en-US,en;q=0.9',\n", "'Cache-Control': 'max-age=0',\n", "'Connection': 'keep-alive',\n", "'Dnt': '1',\n", "'Upgrade-Insecure-Requests': '1',\n", "'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36'\n", "}\n", "\n", "url = 'https://www.bettingpros.com/nfl/matchups/'\n", "resp = requests.get(url, headers=headers)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "BettingPros\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
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\n", "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from bs4 import BeautifulSoup\n", "soup = BeautifulSoup(resp.text, 'html.parser')\n", "soup" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'nfl_data_py'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Users\\Brayden\\OneDrive - stern.nyu.edu\\Brayden Moore LLC\\Python\\Projects\\MARCI 3.0\\MARCI-NFL-Betting\\Notebook.ipynb Cell 12\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mSource\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mBuild\u001b[39;00m \u001b[39mimport\u001b[39;00m build\n\u001b[0;32m 3\u001b[0m pbp \u001b[39m=\u001b[39m build\u001b[39m.\u001b[39mget_pbp_data([\u001b[39m2023\u001b[39m])\n\u001b[0;32m 4\u001b[0m pbp \u001b[39m=\u001b[39m pbp\u001b[39m.\u001b[39mdrop_duplicates(subset\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mgame_id\u001b[39m\u001b[39m'\u001b[39m)\n", "File \u001b[1;32mc:\\Users\\Brayden\\OneDrive - stern.nyu.edu\\Brayden Moore LLC\\Python\\Projects\\MARCI 3.0\\MARCI-NFL-Betting\\Source\\Build\\build.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mnfl_data_py\u001b[39;00m \u001b[39mimport\u001b[39;00m nfl_data_py \u001b[39mas\u001b[39;00m nfl\n\u001b[0;32m 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mtqdm\u001b[39;00m \u001b[39mimport\u001b[39;00m tqdm\n\u001b[0;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n", "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'nfl_data_py'" ] } ], "source": [ "from Source.Build import build\n", "\n", "pbp = build.get_pbp_data([2023])\n", "pbp = pbp.drop_duplicates(subset='game_id')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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game_idtotalwinner
02023_01_ARI_WAS36Washington Commanders
1752023_01_CAR_ATL34Atlanta Falcons
3442023_01_CIN_CLE27Cleveland Browns
5172023_01_DAL_NYG40Dallas Cowboys
6832023_01_DET_KC41Detroit Lions
8622023_01_GB_CHI58Green Bay Packers
10422023_01_HOU_BAL34Baltimore Ravens
12272023_01_JAX_IND52Jacksonville Jaguars
14092023_01_LA_SEA43Los Angeles Rams
15862023_01_LV_DEN33Las Vegas Raiders
17442023_01_MIA_LAC70Miami Dolphins
19352023_01_PHI_NE45Philadelphia Eagles
21262023_01_SF_PIT37San Francisco 49ers
23022023_01_TB_MIN37Tampa Bay Buccaneers
24772023_01_TEN_NO31New Orleans Saints
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" ], "text/plain": [ " game_id total winner\n", "0 2023_01_ARI_WAS 36 Washington Commanders\n", "175 2023_01_CAR_ATL 34 Atlanta Falcons\n", "344 2023_01_CIN_CLE 27 Cleveland Browns\n", "517 2023_01_DAL_NYG 40 Dallas Cowboys\n", "683 2023_01_DET_KC 41 Detroit Lions\n", "862 2023_01_GB_CHI 58 Green Bay Packers\n", "1042 2023_01_HOU_BAL 34 Baltimore Ravens\n", "1227 2023_01_JAX_IND 52 Jacksonville Jaguars\n", "1409 2023_01_LA_SEA 43 Los Angeles Rams\n", "1586 2023_01_LV_DEN 33 Las Vegas Raiders\n", "1744 2023_01_MIA_LAC 70 Miami Dolphins\n", "1935 2023_01_PHI_NE 45 Philadelphia Eagles\n", "2126 2023_01_SF_PIT 37 San Francisco 49ers\n", "2302 2023_01_TB_MIN 37 Tampa Bay Buccaneers\n", "2477 2023_01_TEN_NO 31 New Orleans Saints" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "import pickle as pkl\n", "\n", "# get team abbreviations\n", "file_path = 'Source/Pickles/team_name_to_abbreviation.pkl'\n", "with open(file_path, 'rb') as f:\n", " team_name_to_abbreviation = pkl.load(f)\n", "\n", "file_path = 'Source/Pickles/team_abbreviation_to_name.pkl'\n", "with open(file_path, 'rb') as f:\n", " team_abbreviation_to_name = pkl.load(f)\n", "\n", "pbp[['season','week','away','home']] = pbp['game_id'].str.split('_', expand=True)\n", "games = pbp[['game_id','away_score','home_score','season','week','away','home']]\n", "games[['away_score','home_score','season','week']] = games[['away_score','home_score','season','week']].astype(int)\n", "\n", "games['away_team'] = games['away'].map(team_abbreviation_to_name)\n", "games['home_team'] = games['home'].map(team_abbreviation_to_name)\n", "\n", "games['total'] = games['away_score'] + games['home_score']\n", "games['winner'] = [a if a_s>h_s else h if h_s>a_s else 'Tie' for a,h,a_s,h_s in games[['away_team','home_team','away_score','home_score']].values]\n", "results = games[['game_id','total','winner']]" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'games' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[1;32mc:\\Users\\Brayden\\OneDrive - stern.nyu.edu\\Brayden Moore LLC\\Python\\Projects\\MARCI 3.0\\MARCI-NFL-Betting\\Notebook.ipynb Cell 14\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> 1\u001b[0m results \u001b[39m=\u001b[39m games[[\u001b[39m'\u001b[39m\u001b[39mgame_id\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mtotal\u001b[39m\u001b[39m'\u001b[39m,\u001b[39m'\u001b[39m\u001b[39mwinner\u001b[39m\u001b[39m'\u001b[39m]]\n", "\u001b[1;31mNameError\u001b[0m: name 'games' is not defined" ] } ], "source": [ "results = games[['game_id','total','winner']]" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Brayden\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\dateutil\\parser\\_parser.py:1207: UnknownTimezoneWarning: tzname EST identified but not understood. Pass `tzinfos` argument in order to correctly return a timezone-aware datetime. In a future version, this will raise an exception.\n", " warnings.warn(\"tzname {tzname} identified but not understood. \"\n" ] }, { "data": { "text/html": [ "
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game_idAway TeamHome TeamDatewinnertotal
02023_01_DET_KCDetroit LionsKansas City ChiefsThursday 9/7 08:20Detroit Lions41
12023_01_CIN_CLECincinnati BengalsCleveland BrownsSunday 9/10 01:00Cleveland Browns27
22023_01_JAX_INDJacksonville JaguarsIndianapolis ColtsSunday 9/10 01:00Jacksonville Jaguars52
32023_01_TB_MINTampa Bay BuccaneersMinnesota VikingsSunday 9/10 01:00Tampa Bay Buccaneers37
42023_01_TEN_NOTennessee TitansNew Orleans SaintsSunday 9/10 01:00New Orleans Saints31
52023_01_CAR_ATLCarolina PanthersAtlanta FalconsSunday 9/10 01:00Atlanta Falcons34
62023_01_HOU_BALHouston TexansBaltimore RavensSunday 9/10 01:00Baltimore Ravens34
72023_01_SF_PITSan Francisco 49ersPittsburgh SteelersSunday 9/10 01:00San Francisco 49ers37
82023_01_ARI_WASArizona CardinalsWashington CommandersSunday 9/10 01:00Washington Commanders36
92023_01_GB_CHIGreen Bay PackersChicago BearsSunday 9/10 04:25Green Bay Packers58
102023_01_MIA_LACMiami DolphinsLos Angeles ChargersSunday 9/10 04:25Miami Dolphins70
112023_01_LV_DENLas Vegas RaidersDenver BroncosSunday 9/10 04:25Las Vegas Raiders33
122023_01_PHI_NEPhiladelphia EaglesNew England PatriotsSunday 9/10 04:25Philadelphia Eagles45
132023_01_DAL_NYGDallas CowboysNew York GiantsSunday 9/10 08:20Dallas Cowboys40
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" ], "text/plain": [ " game_id Away Team Home Team \\\n", "0 2023_01_DET_KC Detroit Lions Kansas City Chiefs \n", "1 2023_01_CIN_CLE Cincinnati Bengals Cleveland Browns \n", "2 2023_01_JAX_IND Jacksonville Jaguars Indianapolis Colts \n", "3 2023_01_TB_MIN Tampa Bay Buccaneers Minnesota Vikings \n", "4 2023_01_TEN_NO Tennessee Titans New Orleans Saints \n", "5 2023_01_CAR_ATL Carolina Panthers Atlanta Falcons \n", "6 2023_01_HOU_BAL Houston Texans Baltimore Ravens \n", "7 2023_01_SF_PIT San Francisco 49ers Pittsburgh Steelers \n", "8 2023_01_ARI_WAS Arizona Cardinals Washington Commanders \n", "9 2023_01_GB_CHI Green Bay Packers Chicago Bears \n", "10 2023_01_MIA_LAC Miami Dolphins Los Angeles Chargers \n", "11 2023_01_LV_DEN Las Vegas Raiders Denver Broncos \n", "12 2023_01_PHI_NE Philadelphia Eagles New England Patriots \n", "13 2023_01_DAL_NYG Dallas Cowboys New York Giants \n", "\n", " Date winner total \n", "0 Thursday 9/7 08:20 Detroit Lions 41 \n", "1 Sunday 9/10 01:00 Cleveland Browns 27 \n", "2 Sunday 9/10 01:00 Jacksonville Jaguars 52 \n", "3 Sunday 9/10 01:00 Tampa Bay Buccaneers 37 \n", "4 Sunday 9/10 01:00 New Orleans Saints 31 \n", "5 Sunday 9/10 01:00 Atlanta Falcons 34 \n", "6 Sunday 9/10 01:00 Baltimore Ravens 34 \n", "7 Sunday 9/10 01:00 San Francisco 49ers 37 \n", "8 Sunday 9/10 01:00 Washington Commanders 36 \n", "9 Sunday 9/10 04:25 Green Bay Packers 58 \n", "10 Sunday 9/10 04:25 Miami Dolphins 70 \n", "11 Sunday 9/10 04:25 Las Vegas Raiders 33 \n", "12 Sunday 9/10 04:25 Philadelphia Eagles 45 \n", "13 Sunday 9/10 08:20 Dallas Cowboys 40 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from Source.Predict import predict\n", "\n", "predict.get_games(week=1,season=2023)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from Source.Predict import predict\n", "predictions = []\n", "for _,row in games.iterrows():\n", " prediction = predict.predict(row['home'],row['away'],row['season'],row['week']+1, 45)\n", " predictions.append(prediction)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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012
02023_2_ARI_WAS{'Winner': ['WAS'], 'Probabilities': [0.719565...{'Over/Under': ['Over'], 'Probability': [0.633...
12023_2_CAR_ATL{'Winner': ['ATL'], 'Probabilities': [0.596400...{'Over/Under': ['Over'], 'Probability': [0.876...
22023_2_CIN_CLE{'Winner': ['CLE'], 'Probabilities': [0.769957...{'Over/Under': ['Over'], 'Probability': [0.654...
32023_2_DAL_NYG{'Winner': ['DAL'], 'Probabilities': [0.548317...{'Over/Under': ['Over'], 'Probability': [0.653...
42023_2_DET_KC{'Winner': ['DET'], 'Probabilities': [0.518149...{'Over/Under': ['Over'], 'Probability': [0.566...
52023_2_GB_CHI{'Winner': ['GB'], 'Probabilities': [0.5585460...{'Over/Under': ['Under'], 'Probability': [0.88...
62023_2_HOU_BAL{'Winner': ['BAL'], 'Probabilities': [0.782784...{'Over/Under': ['Over'], 'Probability': [0.603...
72023_2_JAX_IND{'Winner': ['JAX'], 'Probabilities': [0.595993...{'Over/Under': ['Under'], 'Probability': [0.50...
82023_2_LA_SEA{'Winner': ['LA'], 'Probabilities': [0.5388965...{'Over/Under': ['Over'], 'Probability': [0.638...
92023_2_LV_DEN{'Winner': ['DEN'], 'Probabilities': [0.605623...{'Over/Under': ['Under'], 'Probability': [0.70...
102023_2_MIA_LAC{'Winner': ['LAC'], 'Probabilities': [0.599673...{'Over/Under': ['Over'], 'Probability': [0.522...
112023_2_PHI_NE{'Winner': ['NE'], 'Probabilities': [0.5209159...{'Over/Under': ['Over'], 'Probability': [0.537...
122023_2_SF_PIT{'Winner': ['SF'], 'Probabilities': [0.7199300...{'Over/Under': ['Under'], 'Probability': [0.60...
132023_2_TB_MIN{'Winner': ['TB'], 'Probabilities': [0.5235458...{'Over/Under': ['Over'], 'Probability': [0.543...
142023_2_TEN_NO{'Winner': ['NO'], 'Probabilities': [0.6887440...{'Over/Under': ['Over'], 'Probability': [0.742...
\n", "
" ], "text/plain": [ " 0 1 \\\n", "0 2023_2_ARI_WAS {'Winner': ['WAS'], 'Probabilities': [0.719565... \n", "1 2023_2_CAR_ATL {'Winner': ['ATL'], 'Probabilities': [0.596400... \n", "2 2023_2_CIN_CLE {'Winner': ['CLE'], 'Probabilities': [0.769957... \n", "3 2023_2_DAL_NYG {'Winner': ['DAL'], 'Probabilities': [0.548317... \n", "4 2023_2_DET_KC {'Winner': ['DET'], 'Probabilities': [0.518149... \n", "5 2023_2_GB_CHI {'Winner': ['GB'], 'Probabilities': [0.5585460... \n", "6 2023_2_HOU_BAL {'Winner': ['BAL'], 'Probabilities': [0.782784... \n", "7 2023_2_JAX_IND {'Winner': ['JAX'], 'Probabilities': [0.595993... \n", "8 2023_2_LA_SEA {'Winner': ['LA'], 'Probabilities': [0.5388965... \n", "9 2023_2_LV_DEN {'Winner': ['DEN'], 'Probabilities': [0.605623... \n", "10 2023_2_MIA_LAC {'Winner': ['LAC'], 'Probabilities': [0.599673... \n", "11 2023_2_PHI_NE {'Winner': ['NE'], 'Probabilities': [0.5209159... \n", "12 2023_2_SF_PIT {'Winner': ['SF'], 'Probabilities': [0.7199300... \n", "13 2023_2_TB_MIN {'Winner': ['TB'], 'Probabilities': [0.5235458... \n", "14 2023_2_TEN_NO {'Winner': ['NO'], 'Probabilities': [0.6887440... \n", "\n", " 2 \n", "0 {'Over/Under': ['Over'], 'Probability': [0.633... \n", "1 {'Over/Under': ['Over'], 'Probability': [0.876... \n", "2 {'Over/Under': ['Over'], 'Probability': [0.654... \n", "3 {'Over/Under': ['Over'], 'Probability': [0.653... \n", "4 {'Over/Under': ['Over'], 'Probability': [0.566... \n", "5 {'Over/Under': ['Under'], 'Probability': [0.88... \n", "6 {'Over/Under': ['Over'], 'Probability': [0.603... \n", "7 {'Over/Under': ['Under'], 'Probability': [0.50... \n", "8 {'Over/Under': ['Over'], 'Probability': [0.638... \n", "9 {'Over/Under': ['Under'], 'Probability': [0.70... \n", "10 {'Over/Under': ['Over'], 'Probability': [0.522... \n", "11 {'Over/Under': ['Over'], 'Probability': [0.537... \n", "12 {'Over/Under': ['Under'], 'Probability': [0.60... \n", "13 {'Over/Under': ['Over'], 'Probability': [0.543... \n", "14 {'Over/Under': ['Over'], 'Probability': [0.742... " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "pd.DataFrame(predictions)" ] } ], "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": "3.11.5" }, "orig_nbformat": 4 }, "nbformat": 4, "nbformat_minor": 2 }