{"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":"# Table of Contents\n\n* [Introduction](#section-one)\n* [Analysis](#section-two)\n    - [Average Accuracy over Stadium Average (AccOA)](#subsection-one)\n    - [Average Deviation over Kicker Average (DevOA)](#anything-you-like)\n* [Conclusion](#section-three)\n* [Code](#code)","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-one\"></a>\n# Introduction\n**The issue with accuracy**\n\nIn the words of Pat McAfee: \"\\[Kickers\\] are people too\". Often times we forget this truth, and tend to picure them as robots. Every kick should be automatic, regardless of the conditions. It's time we re-evaluate our evaluation metrics and get a better understanding of who are the best kickers in the game.\n\nThe analysis of the best kickers typically starts and ends with accuracy. This is intuitive; the best kickers will make their kicks. But any fan of the NFL would agree that not all kicks are made equal. For instance, Justin Tucker’s record setting 66 yard game winner against the Lions is clearly very different from the 19 yard kick by Dustin Hopkins in the first quarter of the Chargers' game against the Broncos, which ultimatley ended up being a 34 to 13 blowout in the Chargers' favor. However, both of these kicks count the same towards the kicker’s accuracy.\n\nAnother fundamental deficiency in our current evaluation of kickers is that not all stadiums are made equal. Outdoor stadiums in cold weather climates, such as Soldier Field, Highmark Stadium and Lambeu Field are known for their harsh kicking conditions later in the season. Kickers of teams who play their home games at these stadiums are at a clear disadvantage. They kick a much higher proportion of their kicks in these stadiums which are not friendly to the position. This makes it difficult to compare these kickers directly using accuracy alone.","metadata":{}},{"cell_type":"markdown","source":"<a id=\"section-two\"></a>\n# Analysis\n\n<a id=\"subsection-one\"></a>\n## Average Accuracy over Stadium Average (AccOA)\nMy first idea to remedy this is accuracy over stadium average. This metric evaluates which kickers performed better than their peers in a given stadium. The idea is that since each stadium has its own challenges or advantages in the kicking game, each stadium will have different accuracies reflecting those differences. For instance, we expect Soldier Field, located in the windy city, to have a lower average accuracy than a domed stadium in a temparate climate, such as the one in Atlanta. If we break down each kicker’s total accuracy into their accuracy per stadium, we can now compare those two figures and see which kickers outperform the stadiums they kick in. \n\nWith the data provided, we are a bit limited in our analysis. Not every kicker kicks in every stadium within a three year period, and when they do, they often only attempt a few kicks. Thus, this analysis technique would work best on a more complete set of historical kicking data. However, we can still gain some insight with the data provided. If we store the kicking data in a matrix, where the rows represent a kicker, the columns represent a stadium, and each entry is the kicker’s accuracy in that stadium, we can find a low rank approximation of the kicking matrix and obtain accuracy predictions for each kicker in each stadium. This is a technique that used to be used by netflix to generate user suggetions from a sparse ratings matrix. Note that in the low rank approximation, the actual numbers lose their meaning, but their relative size still provides the same information that we can use to rank kickers.\n\nNow that we have a full matrix, we can find the accuracy over stadium average by subtracting from each entry the sum of the column it resides in. Lastly, we average together each row’s values to obtain the average accuracy over stadium average for each kicker. Finally, we can compare these values to get a wholistic ranking of kickers performance. This technique is a good start, but we can still improve.\n\nThe biggest issue with accuracy is that every kick counts the same. A make is a one and a miss is a zero, regardless of wind or distance. A kicker who is asked to kick many long kicks, the result of a subpar offense fizzling out after entering the opponent’s territory, will likely have a lower accuracy than a kicker whose offense routinely gets stopped in the red zone.\n\nTo account for distance, we just have to change how we count makes and misses. According to the data, the average kick was about a 34 yard attempt, just longer than an extra point.  Ideally, we would like to reward kickers for making kicks longer than this and penalize kickers for missing kicks shorter than this. To accomplish this, we first divide each kicks distance by the average to yield a distance ratio. This ratio has the property that an average kick yields a value of one, an above average kick yields a value greater than one, and a below average kick yields a value less than one. Now, instead of naively adding a one for each make and zero for each miss, we add the maximum of one and the distance ratio upon a make (to avoid penalizing a short make) and add the minimum of zero and the distance ratio minus one upon a miss (to avoid rewarding long misses). Totalling up this score and dividing by the number of attempts now gives us a distance adjusted accuracy score. Note that this is no longer a percentage, as it is possible to obtain a value greater than one, but rather a score that we can use to compare kickers accounting for distance.\n\nIf we replace the niave accuracies with our distance adjusted accuracy scores in our average accuracy stadium accuracy, we now have a means to rank kickers that accounts for the distance of the kicks and the stadiums which they were kicked in. This gives us a much clearer picture of which kickers can withstand unfavorable circumstances and which kickers are benefitting from ideal kicking situations.\n\n![image.png](attachment:faf0116e-aca0-4ffa-b64b-b791ab85c744.png)\n\nThis figure displays the relative kicking difficulty of each stadium, where the most difficult stadiums are on the left and the easiest stadiums are on the right.\n\nHere is a preview of the rankings movement we observe through this analysis. The first column is the original kicker rankings using accuracy alone, the middle column is the rankings using AccOA, and the third column shows how much the kicker in the middle column moved from the accuracy ranking to their AccOA ranking.\n\n![image.png](attachment:b21b1222-e83a-48db-903d-4ea4c7ad6bb0.png)\n\nThe biggest mover was Kai Forbath, who dropped a whopping 40 spots to last place. It appears that he was harshly penalized for missing an easy kick in Jacksonville, which is the easiest stadium to kick in according to the figure above. The average movement for each kicker was 10.53 places. This highlights how using accuracy alone can miss a lot of the details of the bigger picture.\n\n<a id=\"anything-you-like\"></a>\n## Average Deviation over Kicker Average (DevOA)\nAnother factor that can greatly influence a kicker’s performance is wind. Once a ball leaves the foot, whether or not it reaches its intended target is at the mercy of wind. As the ball moves through the air, the wind will change the trajectory of the ball and ultimatley influence where it ends up. While wind is absent from the provided data, the tracking data allows us to measure how much the path of the ball changes per frame during its flight.\n\nIn an ideal world, the kicker will send the football on a straight path directly to the middle of the goalpost. This is often not how it travels in reality, though. Often times we see the ball begin on one trajectory and then change paths once or even a few times before reaching the net. Could it be the case that certain stadiums cause more deviation than others? Lets find out.\n\nThe best way to measure this is the average deviation per frame. In the tracking data, each play is broken down into frames that provide the position of the football at each interval. Taking two consecutive frames, we can calculate a vector that represents an approximate path that the ball traveled from one frame to the next. With three frames, we have two vectors that combine to represent an approximate ball path. If the ball changes its direction of travel (deviates), the two vectors will have different direction, meaning we can measure a non-zero angle between them. If we sum all of the angles between all of the consecutive vectors for the balls whole flight, we now have some measure of how much a ball “moves” on a given kick. Since longer kicks have more potential to accumulate deviations, we will divide the total deviation by the number of frames to yield a per frame deviation for each kick.\n\nComparing these devations on a per stadium basis will not fully answer our question yet. Its possible that certain kickers hit the ball in a way that causes more deviation by default, regardless of the kicking conditions. To control for this, we instead want to measure the average deviation over the given kicker’s average deviation. This gives us a sense of how much more or less the ball moves in a given stadium relative to what we would expect.\n\nThe method for finding this figure is nearly identical to finding the AccOA. Using this method gives us the average deviation over the kicker average for each stadium. In simpler terms this tells us how much each stadium influences the ball path indepndent of who kicks it.\n\nHere are the stadiums ranked by DevOA. The stadiums on the left have the smallest influence on the ball's path, while the stadiums on the right move the ball the most.\n\n![image.png](attachment:6c71db21-c242-4945-bf2e-f48faad6006b.png)\n","metadata":{},"attachments":{"faf0116e-aca0-4ffa-b64b-b791ab85c744.png":{"image/png":"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"},"b21b1222-e83a-48db-903d-4ea4c7ad6bb0.png":{"image/png":"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"},"6c71db21-c242-4945-bf2e-f48faad6006b.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"<a id=\"section-three\"></a>\n# Conclusion\n\n**Drawbacks**\n\nOne drawback of AccOA is that coaches may use the same qualitative intuitions we have about kicking difficulty to make decisions that influence the accuracy. For instance, in a fourth and seven scenario on the opponent's 38 yard line, a coach would be more likely to opt for the 55 yard field goal in Mile High stadium than he would be to take the same kick on Heinz field. This can artificially inflate the accuracy of Heinz Field, since kickers are, on average, attempting shorter field goals.\n\nOne major drawback with DevOA is that one poor kick can severely influence the averages. To combat this we are only using kickers who have a fair sample of 30 or more attempts in the data. Still, though, since we are dealing with the same sparse data discussed earlier, these outliers can have large impact on the final results we observe. More data would help provide clarity to this issue.\n\n**Combining the Metrics**\n\nCombining these two forms of analysis can give us an additional sense of the relative kicking difficulties of each stadium. If we put AccOA on the y-axis and DevOA on the x-axis, we obtain the following figure:\n\n![image.png](attachment:2b8149a5-8237-458e-b00e-a775a6dd30c2.png)\n\nEach of the diagonal black lines represents a line of equal difficulty. Stadiums in the bottom left have the most favorable kicking conditions, while those in the top right have the harshest conditions. If we rotate the data so that the black lines are vertical, the x-values will yield a final ranking according to both metrics. The following figure is the result of this rotation.\n\n![image.png](attachment:1108fc9e-1ccc-49cc-b48c-dbc2de1f7eb2.png)\n\nThe rankings appears to have been convoluted by the addition of DevOA. Qualitatively, we reasoned that outdoor stadiums in cool and windy climates would be the most difficult to kick in, and this makes intuitive sense. This was reflected in our AccOA analysis where we observed Chicago, Cleveland, Kansas City and Buffalo all among the hardest stadiums to kick in, but appears lost in the combined analysis.\n\n**Final Thoughts**\n\nWhile the stadium rankings did not provide much insight, the ultimate goal of this analysis was to find fairer ways to rank kickers. AccOA proposes a novel way to account for the varying distance and stadium conditions that kickers must overcome on a weekly basis. Using this metric provides a quantitative method for evaluating which kickers are making the qualitatively tough 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"}}},{"cell_type":"markdown","source":"<a id=\"code\"></a>\n# Code","metadata":{}},{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport torch as T\nimport matplotlib.pyplot as plt\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\n# for dirname, _, filenames in os.walk('/kaggle/input'):\n#     for filename in filenames:\n#         print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-08T17:32:01.034059Z","iopub.execute_input":"2022-02-08T17:32:01.034528Z","iopub.status.idle":"2022-02-08T17:32:01.038906Z","shell.execute_reply.started":"2022-02-08T17:32:01.034493Z","shell.execute_reply":"2022-02-08T17:32:01.038087Z"},"jupyter":{"source_hidden":true},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"player_data = pd.read_csv('../input/nfl-big-data-bowl-2022/players.csv')\nnames = {player['nflId']: player['displayName'] for _, player in player_data.iterrows()}\nnames[-1.0] = 'Unknown'","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.04031Z","iopub.execute_input":"2022-02-08T17:32:01.040804Z","iopub.status.idle":"2022-02-08T17:32:01.203976Z","shell.execute_reply.started":"2022-02-08T17:32:01.040773Z","shell.execute_reply":"2022-02-08T17:32:01.203422Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plays = pd.read_csv('../input/nfl-big-data-bowl-2022/plays.csv')\nkickers = plays[['gameId','playId', 'possessionTeam', 'specialTeamsPlayType','specialTeamsResult','kickerId', 'yardlineSide', 'yardlineNumber']].copy()\nkickers = kickers.dropna(subset=['kickerId'])\nkickers = kickers.loc[\n    ((kickers['specialTeamsPlayType'] == 'Field Goal') | (kickers['specialTeamsPlayType'] == 'Extra Point'))\n    & (kickers['specialTeamsResult'] != 'Blocked Kick Attempt')]\nkickers = kickers.sort_values(by=['kickerId'])\ndistance = []\nfor i, data in kickers.iterrows():\n    distance.append(data['yardlineNumber']+17 if data['yardlineSide'] != data['possessionTeam'] else 117-data['yardlineNumber'])\nkickers['distance'] = distance","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.204894Z","iopub.execute_input":"2022-02-08T17:32:01.205428Z","iopub.status.idle":"2022-02-08T17:32:01.6567Z","shell.execute_reply.started":"2022-02-08T17:32:01.205396Z","shell.execute_reply":"2022-02-08T17:32:01.655918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"run = False\n\n# One kick at a time calculate deviation\nif run:\n    tracking = [pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2018.csv'),\n         pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2019.csv'),\n         pd.read_csv('../input/nfl-big-data-bowl-2022/tracking2020.csv')]\n    tracking = pd.concat(tracking, axis=0)\n    tracking = tracking.loc[tracking.team == 'football']","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.658091Z","iopub.execute_input":"2022-02-08T17:32:01.658516Z","iopub.status.idle":"2022-02-08T17:32:01.663666Z","shell.execute_reply.started":"2022-02-08T17:32:01.658484Z","shell.execute_reply":"2022-02-08T17:32:01.662827Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"if run:\n    deviations = []\n    for row in kickers.iterrows():\n        game, play = row[1].gameId, row[1].playId\n        ball = tracking.loc[\n            (tracking.gameId==game) & (tracking.playId==play) & (tracking.team == 'football')]\n        type_ = 'field_goal' if row[1].specialTeamsPlayType == 'Field Goal' else 'extra_point'\n\n        if not ball.loc[(ball.event == 'field_goal_blocked') | (ball.event == 'extra_point_blocked')].empty: \n            deviations.append(np.nan)\n            continue\n\n        kick = ball.index[ball.event == type_+'_attempt'] - ball.iloc[0,:].name\n        made = ball.index[\n            (ball.event == type_) | (ball.event==type_ + '_missed')] - ball.iloc[0,:].name\n        if made.empty: made = [ball.shape[0] - 1]\n        if kick.empty: \n            kick = made\n            made = [ball.shape[0] - 1]\n\n        pos = ball[['x', 'y']]\n        pos_0, pos_f = pos.iloc[kick[0],:].to_numpy(), pos.iloc[made[0],:].to_numpy()\n\n        straight = np.subtract(pos_f, pos_0)\n\n        frames = 1\n        tot = 0\n        while kick[0] < made[0]:\n            pos_i = pos.iloc[kick[0],:].to_numpy()\n            kick += 1\n            pos_j = pos.iloc[kick[0],:].to_numpy()\n            dir_ = np.subtract(pos_j, pos_i)\n            tot += abs(np.arccos(np.dot(dir_, straight)/(np.linalg.norm(dir_)*np.linalg.norm(straight))))\n            frames += 1\n\n        deviations.append(tot/frames)\n\n    kickers['deviation'] = deviations\n    kickers = kickers.dropna(subset=['deviation'])\nelse:\n    kickers = pd.read_csv('../input/kickdata/kickers.csv')","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.665821Z","iopub.execute_input":"2022-02-08T17:32:01.666159Z","iopub.status.idle":"2022-02-08T17:32:01.696833Z","shell.execute_reply.started":"2022-02-08T17:32:01.666103Z","shell.execute_reply":"2022-02-08T17:32:01.696268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Map eatch team abbreviation string to a unique index between 0-31\ntidx = {\n    'ARI': 0,\n    'BUF': 1,\n    'CAR': 2,\n    'CLE': 3,\n    'DAL': 4,\n    'GB': 5,\n    'HOU': 6,\n    'KC': 7,\n    'LAC': 8,\n    'MIN': 9,\n    'NE': 10,\n    'NYJ': 11,\n    'LV': 12,\n    'SF': 13,\n    'SEA': 14,\n    'WAS': 15,\n    'ATL': 16,\n    'BAL': 17,\n    'CHI': 18,\n    'CIN': 19,\n    'DEN': 20,\n    'DET': 21,\n    'IND': 22,\n    'JAX': 23,\n    'LAR': 24,\n    'MIA': 25,\n    'NO': 26,\n    'NYG': 27,\n    'PHI': 28,\n    'PIT': 29,\n    'TB': 30,\n    'TEN': 31\n}\nteam_abbr = [\n    'ARI',\n    'BUF',\n    'CAR',\n    'CLE',\n    'DAL',\n    'GB',\n    'HOU',\n    'KC',\n    'LAC',\n    'MIN',\n    'NE',\n    'NYJ',\n    'LV',\n    'SF',\n    'SEA',\n    'WAS',\n    'ATL',\n    'BAL',\n    'CHI',\n    'CIN',\n    'DEN',\n    'DET',\n    'IND',\n    'JAX',\n    'LAR',\n    'MIA',\n    'NO',\n    'NYG',\n    'PHI',\n    'PIT',\n    'TB',\n    'TEN'\n]\n# Set of kicker names with attempts >= 30\nvalid_names = {'Adam Vinatieri',\n 'Aldrick Rosas',\n 'Austin Seibert',\n 'Brandon McManus',\n 'Brett Maher',\n 'Cairo Santos',\n 'Chandler Catanzaro',\n 'Chase McLaughlin',\n 'Chris Boswell',\n 'Cody Parkey',\n 'Dan Bailey',\n 'Daniel Carlson',\n 'Dustin Hopkins',\n 'Eddy Pineiro',\n 'Graham Gano',\n 'Greg Joseph',\n 'Greg Zuerlein',\n 'Harrison Butker',\n 'Jake Elliott',\n 'Jason Myers',\n 'Jason Sanders',\n 'Joey Slye',\n 'Josh Lambo',\n 'Justin Tucker',\n \"Ka'imi Fairbairn\",\n 'Kai Forbath',\n 'Mason Crosby',\n 'Matt Bryant',\n 'Matt Gay',\n 'Matt Prater',\n 'Michael Badgley',\n 'Mike Nugent',\n 'Nick Folk',\n 'Randy Bullock',\n 'Robbie Gould',\n 'Rodrigo Blankenship',\n 'Ryan Succop',\n 'Sam Ficken',\n 'Sebastian Janikowski',\n 'Stephen Gostkowski',\n 'Stephen Hauschka',\n 'Tyler Bass',\n 'Wil Lutz',\n 'Younghoe Koo',\n 'Zane Gonzalez'}\n\n# map team abbreviation to image path\nlogo = {\n    'ARI': '../input/logo-images/cardinals.png',\n    'BUF': '../input/logo-images/bills.png',\n    'CAR': '../input/logo-images/panthers.png',\n    'CLE': '../input/logo-images/browns.png',\n    'DAL': '../input/logo-images/cowboys.png',\n    'GB': '../input/logo-images/packers.png',\n    'HOU': '../input/logo-images/texans.png',\n    'KC': '../input/logo-images/chiefs.png',\n    'LAC': '../input/logo-images/chargers.png',\n    'MIN': '../input/logo-images/vikings.png',\n    'NE': '../input/logo-images/patriots.png',\n    'NYJ': '../input/logo-images/jets.png',\n    'LV': '../input/logo-images/raiders.png',\n    'SF': '../input/logo-images/49ers.png',\n    'SEA': '../input/logo-images/seahawks.png',\n    'WAS': '../input/logo-images/wtf.png',\n    'ATL': '../input/logo-images/falcons.png',\n    'BAL': '../input/logo-images/ravens.png',\n    'CHI': '../input/logo-images/bears.png',\n    'CIN': '../input/logo-images/bengals.png',\n    'DEN': '../input/logo-images/broncos.png',\n    'DET': '../input/logo-images/lions.png',\n    'IND': '../input/logo-images/colts.png',\n    'JAX': '../input/logo-images/jaguars.png',\n    'LAR': '../input/logo-images/rams.png',\n    'MIA': '../input/logo-images/dolphins.png',\n    'NO': '../input/logo-images/saints.png',\n    'NYG': '../input/logo-images/giants.png',\n    'PHI': '../input/logo-images/eagles.png',\n    'PIT': '../input/logo-images/steelers.png',\n    'TB': '../input/logo-images/buccaneers.png',\n    'TEN': '../input/logo-images/titans.png'\n}","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.698187Z","iopub.execute_input":"2022-02-08T17:32:01.698504Z","iopub.status.idle":"2022-02-08T17:32:01.714552Z","shell.execute_reply.started":"2022-02-08T17:32:01.698464Z","shell.execute_reply":"2022-02-08T17:32:01.713654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"avg_dist = sum(kickers.loc[:,'distance'])/kickers.shape[0]\nkickers['distoa'] = [row['distance']/avg_dist for i, row in kickers.iterrows()]","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.715589Z","iopub.execute_input":"2022-02-08T17:32:01.71583Z","iopub.status.idle":"2022-02-08T17:32:01.984683Z","shell.execute_reply.started":"2022-02-08T17:32:01.715802Z","shell.execute_reply":"2022-02-08T17:32:01.983809Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"g = pd.read_csv('../input/nfl-big-data-bowl-2022/games.csv')\n\n\nkicker_make = {}\nkicker_att = {}\nkicker_dev = {}\n\nmade_per_stadium = {}\natt_per_stadium = {}\ndev_per_stadium = {}\n\nfor i, kick in kickers.iterrows():\n    k = names[kick['kickerId']]\n    if k not in valid_names: continue\n    home = g.loc[g['gameId'] == kick['gameId']]['homeTeamAbbr'].values[0]\n    if home == 'OAK': home = 'LV'\n    if home == 'LA': home = 'LAR'\n    att_per_stadium[home] = att_per_stadium.get(home,0) + 1\n    dev_per_stadium[home] = dev_per_stadium.get(home,0) + kick.deviation\n    attempts = kicker_att.setdefault(k,[0 for _ in range(32)])\n    attempts[tidx[home]] += 1\n    devs = kicker_dev.setdefault(k, [0 for _ in range(32)])\n    devs[tidx[home]] += kick.deviation\n    \n    made = kicker_make.setdefault(k,[0 for _ in range(32)])\n    if 'No' not in kick['specialTeamsResult']: \n        made_per_stadium[home] = made_per_stadium.get(home,0) + max(kick['distoa'], 1)\n        made[tidx[home]] += max(kick['distoa'], 1)\n    else:\n        made_per_stadium[home] = made_per_stadium.get(home,0) + min(kick['distoa']-1, 0)\n        made[tidx[home]] += min(kick['distoa']-1, 0)\n        \n    \n    \nacc_per_stadium = {}\nfor home in made_per_stadium:\n    acc_per_stadium[home] = made_per_stadium[home]/att_per_stadium[home]\n    dev_per_stadium[home] = dev_per_stadium[home]/att_per_stadium[home]\n    \n    \nkicker_per_stadium = {}\nkicker_dev_per_stadium = {}\nfor kicker in kicker_make:\n    att = kicker_att[kicker]\n    made = kicker_make[kicker]\n    d = kicker_dev[kicker]\n    kicker_per_stadium[kicker] = [made[i]/att[i] if att[i] != 0 else 0 for i in range(32)]\n    kicker_dev_per_stadium[kicker] = [d[i]/att[i] if att[i] != 0 else 0 for i in range(32)]\n    \nacc = pd.DataFrame.from_dict({k: sum(kicker_make[k])/sum(kicker_att[k]) for k in kicker_per_stadium.keys()}, orient='index', columns=['accuracy']).sort_values(by='accuracy')\n    \nacc_per_stadium = pd.DataFrame.from_dict(acc_per_stadium, orient='index', columns=['accuracy']).sort_values(by='accuracy')\n\nkicker_per_stadium = pd.DataFrame.from_dict(kicker_per_stadium, orient='index', columns=team_abbr)\n\ndev_per_stadium = pd.DataFrame.from_dict(dev_per_stadium, orient='index', columns=['deviation']).sort_values(by='deviation')\nkicker_dev = pd.DataFrame.from_dict(kicker_dev_per_stadium, orient='index', columns=team_abbr)","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:01.985925Z","iopub.execute_input":"2022-02-08T17:32:01.986139Z","iopub.status.idle":"2022-02-08T17:32:04.790021Z","shell.execute_reply.started":"2022-02-08T17:32:01.986112Z","shell.execute_reply":"2022-02-08T17:32:04.789421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Hex colors for each team\nteam_to_color = {\n    'ARI': '#97233F',\n    'BUF': '#00338D',\n    'CAR': '#0085CA',\n    'CLE': '#311D00',\n    'DAL': '#041E42',\n    'GB': '#203731',\n    'HOU': '#03202F',\n    'KC': '#E31837',\n    'LAC': '#0080C6',\n    'MIN': '#4F2683',\n    'NE': '#002244',\n    'NYJ': '#125740',\n    'LV': '#000000',\n    'SF': '#B3995D',\n    'SEA': '#69BE28',\n    'WAS': '#773141',\n    'ATL': '#A71930',\n    'BAL': '#241773',\n    'CHI': '#C83803',\n    'CIN': '#FB4F14',\n    'DEN': '#002244',\n    'DET': '#0076B6',\n    'IND': '#002C5F',\n    'JAX': '#006778',\n    'LAR': '#003594',\n    'MIA': '#008E97',\n    'NO': '#D3BC8D',\n    'NYG': '#0B2265',\n    'PHI': '#004C54',\n    'PIT': '#FFB612',\n    'TB': '#D50A0A',\n    'TEN': '#4B92DB'\n}","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:04.791268Z","iopub.execute_input":"2022-02-08T17:32:04.792323Z","iopub.status.idle":"2022-02-08T17:32:04.79992Z","shell.execute_reply.started":"2022-02-08T17:32:04.792278Z","shell.execute_reply":"2022-02-08T17:32:04.799119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# colors = [team_to_color[name] for name in acc_per_stadium.index]\n# plt.figure(figsize=(16,4))\n# plt.scatter(acc_per_stadium.index,acc_per_stadium, c=colors)\n# plt.show()","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:04.801001Z","iopub.execute_input":"2022-02-08T17:32:04.80151Z","iopub.status.idle":"2022-02-08T17:32:04.816151Z","shell.execute_reply.started":"2022-02-08T17:32:04.801479Z","shell.execute_reply":"2022-02-08T17:32:04.815586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def low_rank_approx(data, rank):\n    u, s, vt = np.linalg.svd(data)\n    u_t, s_t, vt_t = u[:,:rank], np.diag(s[:rank]), vt[:rank,:]\n    lra = np.matmul(np.matmul(u_t, s_t), vt_t)\n    return pd.DataFrame(lra, columns=team_abbr, index=data.index)","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:04.817595Z","iopub.execute_input":"2022-02-08T17:32:04.818113Z","iopub.status.idle":"2022-02-08T17:32:04.827077Z","shell.execute_reply.started":"2022-02-08T17:32:04.818073Z","shell.execute_reply":"2022-02-08T17:32:04.826515Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kicker_approx = low_rank_approx(kicker_per_stadium, 16)\n\navg_acc = [0 for _ in range(32)]\nfor team in team_abbr:\n    avg_acc[tidx[team]] = sum(kicker_approx[team].values)/61\n\nest_acc = pd.DataFrame(avg_acc, index=team_abbr, columns=['accuracy']).sort_values(by='accuracy')\n\nres =  []\nfor _, row in kicker_approx.iterrows():\n    vals = row.values\n    diff = [vals[tidx[team]]-est_acc.loc[team].values[0] for team in est_acc.index]\n    avg = [sum(diff)/32]\n    res.append(diff+avg)\n\nfinal = pd.DataFrame(res, columns=list(est_acc.index) + ['avgAcc'], index=kicker_approx.index).sort_values(by='avgAcc')\n\nfinal_idx = list(final.index)\nacc_idx = list(acc.index)\n\nmovements = []\nmv= []\nfor kicker in final.index:\n    move = final_idx.index(kicker)-acc_idx.index(kicker)\n#     print(kicker, move)\n    movements.append((kicker,move))\n    mv.append(move)\n\n# print('\\n', 'Biggest Mover: ', max(movements, key=lambda x: abs(x[1])))\n# print('Avg movement: ', sum([abs(move[1]) for move in movements])/len(movements), '\\n')\n","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:04.828244Z","iopub.execute_input":"2022-02-08T17:32:04.828695Z","iopub.status.idle":"2022-02-08T17:32:04.944315Z","shell.execute_reply.started":"2022-02-08T17:32:04.828657Z","shell.execute_reply":"2022-02-08T17:32:04.943726Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"kicker_devs = []\nkicker_counts = []\n\nfor k, row in kicker_dev.iterrows():\n    tot = 0\n    count = 0\n    for team in team_abbr:\n        if kicker_att[k][tidx[team]] != 0:\n            tot += row[team]\n            count += 1\n    kicker_devs.append(tot)\n    kicker_counts.append(count)\n\navg_kicker_dev = {k: kicker_devs[i]/kicker_counts[i] for i, k in enumerate(kicker_dev.index)}\n\ndevoa = []\nfor k, row in kicker_dev.iterrows():\n    avg = avg_kicker_dev[k]\n    d = [0 for _ in range(32)]\n    for team in team_abbr:\n        if kicker_att[k][tidx[team]] != 0:\n            d[tidx[team]] = row[team] - avg\n    devoa.append(d)\n    \nkicker_devoa = pd.DataFrame(devoa, index=kicker_dev.index)\n\nstadium_devoa = []\nfor team in team_abbr:\n    col = kicker_devoa.loc[:,[tidx[team]]]\n    stadium_devoa.append(100*float(col.sum())/np.count_nonzero(col, axis=1).sum())\nstadium_devoa = pd.DataFrame(stadium_devoa, index=team_abbr, columns=['DevOA']).sort_values(by='DevOA')\n\n# colors = [team_to_color[name] for name in stadium_devoa.index]\n# plt.figure(figsize=(16,4))\n# plt.scatter(stadium_devoa.index,stadium_devoa, c=colors)\n# plt.savefig('stadium_devs.png')\n# plt.show()\n\nleage_avg = sum(made_per_stadium.values())/sum(att_per_stadium.values())\naccoa = pd.DataFrame([100*(row['accuracy']-leage_avg) for i, row in acc_per_stadium.iterrows()], index=acc_per_stadium.index, columns=['AccOA'])\n","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:04.945607Z","iopub.execute_input":"2022-02-08T17:32:04.946023Z","iopub.status.idle":"2022-02-08T17:32:05.007896Z","shell.execute_reply.started":"2022-02-08T17:32:04.945985Z","shell.execute_reply":"2022-02-08T17:32:05.007304Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# from matplotlib.offsetbox import OffsetImage, AnnotationBbox\n# from matplotlib.pyplot import figure\n\n# x = stadium_devoa.sort_index()\n# y = accoa.sort_index()\n\n# fig, ax = plt.subplots(figsize=(12,12))\n# x_ = [-5.05+i*3.7 for i in range(4)]\n# y_ = [6.10-i*4.7 for i in range(4)]\n# for i in range(4):\n#     for j in range(4):\n#         if j < i: continue\n#         plt.plot([x_[i], x_[j]], [y_[j],y_[i]], color='k')\n# ax.scatter(x,y,c='white')\n# ax.invert_yaxis()\n# logos = [logo[name] for name in x.index]\n\n# for xi, yi, path in zip(x.to_numpy(),y.to_numpy(),logos):\n#     ab = AnnotationBbox(OffsetImage(plt.imread(path), zoom=0.15), (float(xi), float(yi)), frameon=False)\n#     ax.add_artist(ab)","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:05.010986Z","iopub.execute_input":"2022-02-08T17:32:05.011289Z","iopub.status.idle":"2022-02-08T17:32:05.01499Z","shell.execute_reply.started":"2022-02-08T17:32:05.011248Z","shell.execute_reply":"2022-02-08T17:32:05.014251Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stadium_data = pd.concat([x, y], axis=1).to_numpy()\ntheta = -np.arccos(np.dot([111,141]/np.linalg.norm([111,141]),[0,1]))\nc, s = np.cos(theta), np.sin(theta)\nR = np.array(((c, -s), (s, c)))\nrotated = pd.DataFrame(np.matmul(stadium_data, R), index=x.index)\nrotated = rotated.sort_values(by=0, ascending=False)\n\n\n","metadata":{"jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2022-02-08T17:32:05.015988Z","iopub.execute_input":"2022-02-08T17:32:05.016447Z","iopub.status.idle":"2022-02-08T17:32:05.030963Z","shell.execute_reply.started":"2022-02-08T17:32:05.016412Z","shell.execute_reply":"2022-02-08T17:32:05.030427Z"},"trusted":true},"execution_count":null,"outputs":[]}]}