{"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":"# Big Data Derby 2022 - Strategy analysis for drafting and speed\n","metadata":{}},{"cell_type":"markdown","source":"# Introduction","metadata":{}},{"cell_type":"markdown","source":"Choice of pacing strategy and the benefit of aerodynamic drafting are thought to be key determinants of racing  performance.[1]. Therefore, we would like to take a detailed analysis for drafting the effect of a specific winner's game strategy on aerodynamic drafting for the same type of competition.","metadata":{}},{"cell_type":"markdown","source":"We want to start with two parts: the specific type of competition and the players' strategy. First of all, there is a general analysis of the situation of the competition and the situation of the players.The first part analyzes the situation of the competition, from the competition venue and the distance of the competition:\n\n \n In terms of competition cities, there were 825 matches from Aqueduct, 772 from Belmont and 403 from Saratoga. In terms of race distance, there are 271 races of 600 meters in Aqueduct and 221 in Belmont ; There are about 240 races of 800m at Aqueduct and 126 at Belmont . Judging by the race track conditions, the race track is mostly on dirt.\n\nTo sum up, in order to take into account the number of samples and more detailed race conditions, dirt race tracks of 800 and 600 were selected for the main analysis in this report.","metadata":{}},{"cell_type":"markdown","source":"In addition to considering the total number of races, this report would like to analyze some racers who are good at horse racing. Analyze the ranking information of all players in the competition data:\nAs you can see from the chart, Manuel Franco, Jose Lecano and Junior Alvarado have by far the most number of first-place finishes on Dirt tracks of any racer.\n![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110315969.png)","metadata":{}},{"cell_type":"markdown","source":"Similarly, from the point of view of race distance, the same three runners Manuel Franco, Jose Lecano, Junior Alvarado, they are very good at 600m, 800m distance horse races. Therefore, from the perspective of the players, The Times of the three players' matches are mainly selected for analysis.\n\n![2](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110315962.png)","metadata":{}},{"cell_type":"markdown","source":"# PART1 Motivation","metadata":{}},{"cell_type":"markdown","source":"In order to have a more intuitive understanding of the impact of aerodynamic drafting, quantitative formulation for drafting is given here:\n$$ draft = \\sum\\limits_{i=1}^n \\frac{\\overrightarrow{p} \\cdot \\overrightarrow{q_{i}}}{\\left| \\left| p \\right| \\right| \\cdot {\\left| \\left| q_{i} \\right| \\right|}^2 } $$\nWhere p represents the velocity vector at that moment, and $q_{i}$ represents the distance vector between the horse and the horse.","metadata":{}},{"cell_type":"markdown","source":"In order to obtain more general conclusions from the data of large samples, I selected the four categories of races with the most courses for analysis mainly from the perspectives of track_id,distance_id,course_type and track_condtion\nThe drafting result for the four types of competition is shown as below respectively:\n# ![image.png](attachment:05947314-dccf-4beb-9233-16361960596c.png)\nIn summary, it can be seen that the tendency for first place drafting is to have a similar nature. The overall drafting process for a Category 3 winner (first place) is in a drastically decreased course from the first 10% of the race, is in a relatively stable state at 15%-50% of the race, and is in a drastically decreased course from the second half of the race. The winner of a race is more likely to be in a mid-course position in the early and middle stages of the race, keeping a high drafting level; Instead, they chose to excel at the end of the race.","metadata":{},"attachments":{"05947314-dccf-4beb-9233-16361960596c.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"The following is an overall situation analysis for specific players:\n\nFirstly, I would like to take in the drafting process for Manuel Franco, the winner with the most number of times, in the Dirt course in AQU. By taking an analysis for the different race distances, his/her overall drafting trend is shown in the chart below.\n\n# ![image.png](attachment:a33ebf2f-782f-4ed2-a30e-151ee9419e8e.png)\nAs could be seen, Manuel Franco's general drafting strategy for a winning round was to keep his drafting in the centre or in the forward range in the first half of the race, always keeping his drafting in some way and then taking in his drafting when he was close to the end 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"}}},{"cell_type":"markdown","source":"# PART2 Analysis","metadata":{}},{"cell_type":"markdown","source":"We conducted classified statistical analysis on the players who generally won the first place and the players who generally ranked lower, analyzed the phenomenon that the players who ranked higher have a greater probability of surpassing at a key-surpassing point, they tend to control the speed better.\n\nWe will conduct a deeper analysis on how to maintain an effective drafting state, which requires not only maintaining a reasonable ranking (not far behind), but also selecting an appropriate point to surpass (not too late or too early).We discuss how to achieve effective drafting and specifically discussed the key surpassing points of horse racing. \n\nFirst, we define some functions to calculate the position and distance. Then we defines the key exceeding point: at the index of this point, the horse race surpasses other horses (the real-time ranking of the horse race changes), and returns the current index and an index range. At last, we take the competition data of a special finishing position and analysis his historical data, taking a sight in his key surpassing points. Race condition is limited by choosing the track_id, distance_id, track condition and course type. We count all the key surpassing points, recording their location and ranks.\n\nWe draw a histogram graph to visualize the key surpassing points.Each line represents a distance range from the end point. For example, 0.2 means 20% of the distance from the end point. Each color represents a different ranking of the current point.\n\nWe choose the special jockey named \"Manuel Franco\", counted his data as champion for AQU/800/FT/D. (Why we choose \"Manuel Franco\": He has participated in the most matches of this type and often won the first place.)","metadata":{}},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110320228.png)","metadata":{}},{"cell_type":"markdown","source":"It can be seen that the main surpassing points of \"Manuel Franco\" are concentrated in the first 10% and the last 20% of the race. Also, we got following findings.  \n(1)Speeding up when the race is half way is not a proper way.  \n(2)The better acceleration route is around 20%, starting from the third place. \n\nWe also want to see some of the commonalities of all the first place winners in the competition type.  \n\nWe choose the 1st horse's trakus info as the data for champion, recording its key surpassing points as below.","metadata":{}},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110317293.png)","metadata":{}},{"cell_type":"markdown","source":"We choose the 4th horse as the data for non-first place, recording its key surpassing points as below.","metadata":{}},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110318893.png)","metadata":{}},{"cell_type":"markdown","source":"In the comparison between the first place data and the fourth place data, it is easy to find that the first place horse often rush to the first place in the last race（about 10%～20% race distance left）, where we consider a suitable place to accelerate and take the first place.\n\nToo early acceleration will lead to the disadvantage of the whole race.","metadata":{}},{"cell_type":"markdown","source":"# PART3 Methods\n\nThrough the comparative data analysis of first and non-first habits beyond the moment and the relevant research results of some cutting-edge papers [1], we can find that under the same average speed of all horses, riders are very important to control the moment of speed change. Therefore, we designed an algorithm to find the speed strategy for people who often come first throughout the game to help those who lose in a game analyze the reasons for losing at some point.","metadata":{}},{"cell_type":"markdown","source":"First, after screening the competition data, we defined the dependent variables of the station S variable group and the action A variable group at some point (all variables in the following formula are variables at time t, and hide the subscript t for the formula simplicity):","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:f7b48678-42dd-4a37-b365-62f600d4c3a6.png)","metadata":{},"attachments":{"f7b48678-42dd-4a37-b365-62f600d4c3a6.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"Among them, we explain some incomprehensible variables: ∆variable represents the difference of speed at time t, ∆variable_angle represents the difference of speed angle at time t, and the A at time t is determined by these two difference variables. Suppose a game number of n, S by the global curvature, players related variables, match other players related variables, including 〖ranking〗_i represents the real-time ranking of player i at time t, progress indicates the extent to which the game is played at time t, 〖distance〗_j said the distance between player j and player i at time t, 〖angle〗_j said the angle from which player j connects to player i at time t. In the formula, we estimate the discrete curvature by fitting quadratic curves to adjacent points. We also design a physical strength representation of the pending state variable ——the horse's physical strength is expressed as an integral of speed over the distance, which we can compute it for each horses and use it in the process of one game.","metadata":{}},{"cell_type":"markdown","source":"Next, the algorithm is defined as shown in the following figure below. The core significance of the algorithm is that it can help players analyze the correctness or error of their decisions at each moment during the game after the game.","metadata":{}},{"cell_type":"markdown","source":"![2](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110316776.png)","metadata":{}},{"cell_type":"markdown","source":"To prove that our algorithm is efficient and useful, in the ideal algorithm can fit the ranking relationship of a game by the output total loss distance —— the higher the game-ranked player, the lower the total loss distance, which means that the smaller the mistakes and the decision is close to the optimal decision. In the algorithm credibility experiment, our fitting rate was 41.472% (the limitation analysis is below), and the probability was only 0.138% in the random fitting, which represents that our algorithm improved the understanding of the strategy during the competition by using the information from the metadata. The following figure shows the fitting of our algorithm to the match results in a class of matches. The first column is the basic information of the game, that ('AQU', '2019-01-18', 6) means race 6 to be held at AQU on 18 January 2019. The second column is the real ranking, the third column is the fitting ranking, and the fourth column is the fitting accuracy. Obviously, we can see that the algorithm achieves 100 percent fitting accuracy in many games, such as ('AQU', '2019-01-18', 6), ('AQU', '2019-02-17', 3), ('AQU', '2019-02-17', 7), etc.","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:d77f68e7-51c9-4b1a-8b2a-290a3069a07a.png)","metadata":{},"attachments":{"d77f68e7-51c9-4b1a-8b2a-290a3069a07a.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"To visualize the image display, we made a dynamic video of the process of a race suggestion strategy, where each point in the video represents a horse, the arrow direction represents the speed direction, and the length of the arrow represents the speed size. Through the video, we can see that the action obtained from matching metadata from dynamic distance can help a horse (blue one) get the first place.","metadata":{}},{"cell_type":"code","source":"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport matplotlib.animation as animation\nfrom geopy.distance import geodesic\nimport matplotlib.gridspec as gridspec\n\n\ndef CreateMP4(position: np.array, velocity: np.array, first_index: int, mask: list, savepath: str,\n              mode: str = 'overall', interval: int = 100) -> None:\n    \"\"\"\n    :param position: 一场比赛人数N * 位置索引数index * 2矩阵\n    :param velocity: 一场比赛人数N * (位置索引数index矩阵-1),没有最后一点的速度\n    :param first_index: 这场比赛中第一名索引 -1 < i < N\n    :param mask: 两元列表，index开始的位置和结束的位置\n    :param savepath: 视频保存的路径\n    :param mode: 绘图模式，overall 或者 local\n    :param interval: 视频播放速度\n    \"\"\"\n    position *= 10000\n    colors = ['#0000FF', '#A52A2A', '#00FFFF', '#FF1493', '#FFD700', '#FFD700', '#778899', '#778899', '#778899',\n              '#778899', '#778899', '#778899', '#778899', '#778899']\n\n    MaskPosition = position\n    MaskVelocity = velocity\n\n    fig = plt.figure()  # 创建画布\n    grid = gridspec.GridSpec(5, 5)\n    ax = [fig.add_subplot(grid[0:2, 0:2]), fig.add_subplot(grid[2:6, 2:6])]\n\n    x_min, x_max = min(MaskPosition[first_index, :, 0]) - 10, max(MaskPosition[first_index, :, 0]) + 10\n    y_min, y_max = min(MaskPosition[first_index, :, 1]) - 10, max(MaskPosition[first_index, :, 1]) + 10\n    plt.xlim(x_min, x_max)\n    plt.ylim(y_min, y_max)\n\n    arrow_end = []\n    for index, data in enumerate(MaskPosition):\n        now_pos = data[:data.shape[0] - 1, ]\n        next_pos = data[1:, ]\n        diff = next_pos - now_pos\n        arrow_end.append(\n            np.column_stack(\n                (MaskVelocity[index, :diff.shape[0]] * diff[:, 0] / ((diff[:, 0] ** 2 + diff[:, 1] ** 2) ** 0.5),\n                 MaskVelocity[index, :diff.shape[0]] * diff[:, 1] / ((diff[:, 0] ** 2 + diff[:, 1] ** 2) ** 0.5))))\n\n    def update(frame):\n        ax[0].cla()\n        ax[1].cla()\n\n        # 拟赛道\n        for i in range(MaskPosition.shape[0]):\n            ax[0].plot(MaskPosition[i, :, 0], MaskPosition[i, :, 1], \"-\", linewidth=20, alpha=0.03, color='b')\n            ax[1].plot(MaskPosition[i, :, 0], MaskPosition[i, :, 1], \"-\", linewidth=20, alpha=0.03, color='b')\n\n        # 画点\n        for i in range(MaskPosition.shape[0]):\n            ax[0].plot(MaskPosition[i, frame, 0], MaskPosition[i, frame, 1], \"o\", color=colors[i], markersize=1)\n            ax[1].plot(MaskPosition[i, frame, 0], MaskPosition[i, frame, 1], \"o\", color=colors[i], markersize=5)\n\n        # 画箭头\n        for i in range(MaskPosition.shape[0]):\n            ax[1].quiver(MaskPosition[i, frame, 0], MaskPosition[i, frame, 1], arrow_end[i][frame, 0],\n                         arrow_end[i][frame, 1],\n                         angles='xy', scale=1, scale_units='xy', width=0.003, color=colors[i], alpha=0.5)\n\n        ax[1].set(xlim=(x_min, x_max), ylim=(y_min, y_max))\n\n\n        for i in range(MaskPosition.shape[0]):\n            ax[0].quiver(MaskPosition[i, frame, 0], MaskPosition[i, frame, 1], arrow_end[i][frame, 0],\n                         arrow_end[i][frame, 1],\n                         angles='xy', scale=9, scale_units='xy', width=0.003, color=colors[i], alpha=0.5)\n        ax[0].set(xlim=(MaskPosition[0, frame, 0] - 3, MaskPosition[0, frame, 0] + 3),\n                  ylim=(MaskPosition[0, frame, 1] - 3, MaskPosition[0, frame, 1] + 3))\n\n    ani = animation.FuncAnimation(fig=fig, func=update, frames=MaskPosition.shape[1] - 1, interval=interval, )\n    # plt.show()\n\n    # 保存视频的时间可以调fps来改变\n    FFwriter = animation.FFMpegWriter(fps=15, extra_args=['-vcodec', 'libx264'])\n    # 要有ffmpeg.exe才能保存为MP4\n    ani.save(savepath, writer=FFwriter)\n\n\ndef GetDate():\n    data_table = pd.read_csv(\"../input/big-data-derby-2022/nyra_2019_complete.csv\")\n    data_table.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'trakus_index',\n                          'latitude', 'longitude', 'distance_id', 'course_type', 'track_condition',\n                          'run_up_distance', 'race_type', 'purse', 'post_time', 'weight_carried',\n                          'jockey', 'odds', 'position_at_finish']\n\n    track_id = \"BEL\"\n    race_date = \"2019-10-23\"\n    race_number = 4\n\n    race_tracking = data_table.query(\n        \"track_id == @track_id & race_date == @race_date & race_number == @race_number\").sort_values(\"trakus_index\")\n\n    horse_id_set = set(race_tracking['program_number'])  # horse唯一编号\n    horse_id_list = list(horse_id_set)\n\n    jockey = []\n    first_jockey = 0\n    for i, horse_id in enumerate(horse_id_list):\n        horse_data = race_tracking.query(\"program_number == @horse_id\").sort_values(\"trakus_index\")\n\n        horse_data = horse_data.loc[:, ['trakus_index', 'latitude', 'longitude', 'position_at_finish', 'jockey']]\n\n        lat = np.array(horse_data[\"latitude\"])\n        lon = np.array(horse_data[\"longitude\"])\n\n        if np.array(horse_data['position_at_finish'])[0] == 1:\n            first_jockey = i\n\n        # 调用geopy包中的方法 计算经纬度距离\n        t = 0.25  # 以0.25秒为时间长度算平均速度、平均加速度\n        N = int(t / 0.25)  # N表示每N个点采样一次，对相邻点计算平均速度\n\n        v = []  # 速度数组\n\n        for j in range(len(lat) - N):\n            if j % N == 0:\n                s = geodesic((lat[j], lon[j]), (lat[j + N], lon[j + N])).m\n                vel = s * 1.0 / t\n                v.append(vel)\n\n        jockey.append({'lat': lat, 'lon': lon, 'jockey': np.array(horse_data['jockey'])[0],\n                       'position': np.array(horse_data['position_at_finish'])[0], 'v': v})\n\n    velocity = []\n    position = []\n    for i in jockey:\n        velocity.append(i['v'])\n        position.append(np.column_stack((i['lat'], i['lon'])))\n    velocity = np.array(velocity)\n    position = np.array(position)\n\n    mask = [0, 300]\n    return position, velocity, first_jockey, mask\n\n\n\nposition, velocity, first_jockey, mask = GetDate()\nCreateMP4(position, velocity, first_jockey, mask, mode='local', savepath='TIME.mp4', interval=100)\n##################################################################\n\nimport numpy as np\nfrom IPython.display import Video, Image\npath_data = '../input/bdb2022plots/'\n\nfrom IPython.display import HTML\nfrom base64 import b64encode\n\ndef play(filename):\n    html = ''\n    video = open(filename,'rb').read()\n    src = 'data:video/mp4;base64,' + b64encode(video).decode()\n    html += '<video width=1000 controls autoplay loop><source src=\"%s\" type=\"video/mp4\"></video>' % src \n    return HTML(html)\n\nplay('./TIME.mp4')","metadata":{"execution":{"iopub.status.busy":"2022-11-10T19:31:46.318593Z","iopub.execute_input":"2022-11-10T19:31:46.319044Z","iopub.status.idle":"2022-11-10T19:33:34.702536Z","shell.execute_reply.started":"2022-11-10T19:31:46.318992Z","shell.execute_reply":"2022-11-10T19:33:34.700797Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"For the reasons for the performance limitations of the algorithm, we summarize the following two points:\n* 1、Due to some outliers in the index during the competition, there are some errors in the estimation of the curvature and velocity.\n* 2、In some multi- jockey competitions, the index data of the players is true, that is, only six jockey may have position information data in a ten-jockey game, resulting in certain errors in calculating the variables of other jockey.\n","metadata":{}},{"cell_type":"markdown","source":"# Summary","metadata":{}},{"cell_type":"markdown","source":"In this competition, we started from the basic data of Big Data Derby 2022 and found that there were some players with extremely high victories in specific conditions. Therefore, from the perspective of these players, we conducted a classified statistical analysis of the players who generally won the first place and the players who generally ranked at the bottom, and found that the top players have a greater probability of surpassing the phenomenon at a fixed race point, that is, the players who often win always have a better control of the speed. Further, we focus on analyzing the process of the station and action of these players during the competition, propose a model to help the players to analyze the correctness or error of each moment during the competition, and analyze the rationality and validity of the model. Finally, we share some of the innovative small horse-racing strategies found when analyzing the data, called drafting, which may largely affect the runner ranking and the physical strength of the horse.\n\nFor the future work, there are two main aspects:\n* We will explore the influence factor of each component of the distance function using either the Shapley value method or the machine learning method to better fit the ranking data and improve the confidence of the model. At the same time, we hope to analyze the model with tips such as drafting.\n* We will explore the more appropriate mathematical expression of a horse's physical strength to build horse racing agents using reinforcement learning to further analyze the relationship between state and behavior during the ra\n","metadata":{}},{"cell_type":"markdown","source":"# Reference ","metadata":{}},{"cell_type":"markdown","source":"[1] Mercier Q, Aftalion A. Optimal speed in Thoroughbred horse racing[J]. Plos one, 2020, 15(12): e0235024.","metadata":{}},{"cell_type":"markdown","source":"# Code and Detailed Analysis\n# Headers for import and drawing function","metadata":{}},{"cell_type":"markdown","source":"Headers for import","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom statistics import mean\nimport math\nfrom math import radians,sin,cos,asin,sqrt,pi\nimport cmath\nimport seaborn as sns\nimport pyarrow.parquet as pq\nimport pyarrow as pa\nfrom haversine import haversine\nfrom geopy.distance import geodesic\nfrom geographiclib.geodesic import Geodesic\nfrom tqdm import tqdm\n\nimport warnings \nwarnings.filterwarnings( \"ignore\")","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:26.780753Z","iopub.execute_input":"2022-11-10T16:39:26.781231Z","iopub.status.idle":"2022-11-10T16:39:27.565475Z","shell.execute_reply.started":"2022-11-10T16:39:26.781196Z","shell.execute_reply":"2022-11-10T16:39:27.56408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"## data loading and pre-processing \n## start_table\\race_table\\tracking_table\n\nstart_table = pd.read_csv(r'/kaggle/input/big-data-derby-2022/nyra_start_table.csv',header = None)\nstart_table.columns = ['track_id','race_date','race_number','program_number','weight_carried','jockey','odds','position_at_finis']\nstart_table[\"program_number\"] = start_table[\"program_number\"].str.strip()\n\nrace_table = pd.read_csv(r'/kaggle/input/big-data-derby-2022/nyra_race_table.csv')\nrace_table['track_condition'] = race_table['track_condition'].str.strip()\n\ntracking_table = pd.read_csv(r'/kaggle/input/big-data-derby-2022/nyra_tracking_table.csv')\n\ndata_table = pd.read_csv(r\"/kaggle/input/big-data-derby-2022/nyra_2019_complete.csv\")\ndata_table.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'trakus_index',\n                          'latitude', 'longitude', 'distance_id', 'course_type', 'track_condition',\n                          'run_up_distance', 'race_type', 'purse', 'post_time', 'weight_carried',\n                          'jockey', 'odds', 'position_at_finish']\ntype_dict = {\"D\":\"Dirt\",\"T\":\"Turf\",\"I\":\"Inner_turf\",\"O\":\"Outer_turf\",\"M\":\"Hurdle\"}\ncity_dict = {\"AQU\":\"Aqueduct\", \"BEL\":\"Belmont\", \"SAR\":\"Saratoga\"}\ncondition_dict = {\"YL\": \"Yielding\", \"FM\" : \"Firm\", \"SY\":\"Sloppy\", \"GD\":\"Good\", \"FT\":\"Fast\",\"MY\":\"Muddy\",\"SF\":\"Soft\"}","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:27.567759Z","iopub.execute_input":"2022-11-10T16:39:27.568343Z","iopub.status.idle":"2022-11-10T16:39:55.408143Z","shell.execute_reply.started":"2022-11-10T16:39:27.568306Z","shell.execute_reply":"2022-11-10T16:39:55.406838Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def stacked_barh(data, figsize=(15, 14), color_set='b', \n                 sum_col='sum', caption='', sub_title='',\n                 labels=[]):\n    fig, ax = plt.subplots(figsize = figsize)\n    plt.style.use('seaborn-whitegrid')\n    \n    if color_set=='b': #blue\n        colors = [\"LightBlue\",\"MediumBlue\", \"RoyalBlue\", \"CornflowerBlue\",\n        \"DodgerBlue\", \"DeepskyBlue\", \"LightSkyBlue\", \"SkyBlue\", \"#00009C\",\n        \"#3232CD\", \"#4D4DFF\", \"#5959AB\", \"#007FFF\"]\n    elif color_set=='g': #green\n        colors = ['teal', 'lightseagreen', 'powderblue', 'lightcyan',  'azure','darkslategray']\n    \n    \n    for i in range(data.shape[1]-1): \n        if i>1:\n            fc = 'dimgray'\n        else:\n            fc = 'white'\n        if i<0:\n            ax.barh(y = data.index.values, width = data.iloc[:, i].values, height=0.8, color=colors[i], label=labels[i])\n        else:\n            ax.barh(y = data.index.values, width = data.iloc[:, i].values, height=0.8, left=data.iloc[:, :(i)].sum(axis=1).values, color=colors[i], label=labels[i])\n        ax.bar_label(ax.containers[i], label_type='center', color=fc, fontsize=10, fontweight=\"bold\")\n        \n        \n    ax.bar_label(ax.containers[i], labels=data[sum_col].map('{:.0f}'.format), label_type='edge', color=colors[-1], fontsize=10, fontweight=\"bold\")\n\n\n    ax.set_ylabel('')\n    ax.set_title(caption, fontsize=10, loc='left')\n    ax.legend(loc=4)\n\n\n    plt.xlabel('', fontsize=13)\n    plt.xticks([])\n    plt.grid(visible=True, which='major', axis='both', color='lightgray', linestyle='-', linewidth=0.2)\n    plt.gca().spines[['top', 'right']].set_visible(False)\n    plt.gca().spines[['bottom', 'left']].set_edgecolor('whitesmoke')\n    #plt.rc('ytick', labelsize=13, ) \n    plt.suptitle(caption,fontsize=16, y=0.89, x=0.3)\n    plt.title(sub_title,fontsize=12, y=0.96, loc='left')\n    plt.show() ","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.41042Z","iopub.execute_input":"2022-11-10T16:39:55.411241Z","iopub.status.idle":"2022-11-10T16:39:55.428424Z","shell.execute_reply.started":"2022-11-10T16:39:55.411193Z","shell.execute_reply":"2022-11-10T16:39:55.427089Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Part I Analyzing Drafting","metadata":{}},{"cell_type":"code","source":"# 定义公用的距离，速度，以及向量表示函数\n\ndef horse_distance(lat1,lon1,lat2,lon2):\n    \"\"\"\n    输入两匹马在同一个时间的位置信息，返回两个地点之间的距离以及角度，单位为m\n    \"\"\"\n    return Geodesic.WGS84.Inverse(lat1,lon1,lat2,lon2)['s12'],Geodesic.WGS84.Inverse(lat1,lon1,lat2,lon2)['azi1']*pi/180\n\ndef horse_speed(lat1,lon1,lat2,lon2,dt = 0.25):\n    \"\"\"\n    输入同一个马两个时间的位置信息，返回两个地点的速度以及角度，间隔时间默认为0.25s\n    \"\"\"\n    dis,ang = horse_distance(lat1,lon1,lat2,lon2)\n    \n    return dis/dt, ang","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.43012Z","iopub.execute_input":"2022-11-10T16:39:55.43098Z","iopub.status.idle":"2022-11-10T16:39:55.449859Z","shell.execute_reply.started":"2022-11-10T16:39:55.430934Z","shell.execute_reply":"2022-11-10T16:39:55.448539Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def draft_pd(test_track_id,test_race_date,test_race_number, test_program_number, negetive_ = False, Test = False ,Test_index_ = 1):\n    # 是否考虑为负数的情况（即在别人前面则视为劣势，默认为不考虑）\n    print(\"track_id:{},race_date:{},race_number:{}, program_number:{}\".format(test_track_id,test_race_date,test_race_number, test_program_number))\n    spe_horse_series = tracking_table[(tracking_table[\"track_id\"] == test_track_id) & (tracking_table[\"race_date\"] == test_race_date) & (tracking_table[\"race_number\"] == test_race_number)].replace(\" \",\"\")\n\n    spe_horse_series.replace('\\s+','',regex=True,inplace=True) # 去掉空格\n\n    spe_horse_series.sort_values(by = ['program_number','trakus_index'],inplace = True)\n    spe_horse_series['program_number'] = spe_horse_series['program_number'].str.strip()\n#     spe_horse_series[['program_number']] = spe_horse_series[['program_number']].astype('int')\n    person_speed = spe_horse_series[(spe_horse_series[\"program_number\"]==test_program_number)]\n    person_speed['speed'] = np.nan\n    person_speed['speed_angle'] = np.nan\n    person_speed['draft'] = np.nan\n    person_speed['rank'] = np.nan\n    person_speed.set_index('trakus_index',inplace=True)\n    for i in range(1,len(person_speed.index)):\n        # 计算所有时刻的速度以及该时刻速度的方向\n        sp,an = horse_speed(person_speed['latitude'][i],person_speed['longitude'][i],person_speed['latitude'][i+1],person_speed['longitude'][i+1])\n        person_speed['speed'][i] = sp\n        person_speed['speed_angle'][i] = an   \n\n    for index_ in tqdm(pd.unique(spe_horse_series['trakus_index'])):\n        # 先对处于相同时间的距离进行计算\n#         print(index_)\n        if person_speed['speed'][index_] == np.nan:\n            break\n        sum_draft = 0\n        rank = 1\n        df_index = spe_horse_series.loc[spe_horse_series['trakus_index'] == index_]\n        lat1 = df_index.loc[spe_horse_series['program_number']== test_program_number, 'latitude'].values\n        lon1 = df_index.loc[spe_horse_series['program_number']== test_program_number, 'longitude'].values\n        speed_angle = person_speed['speed_angle'][index_]\n        for program_number_ in pd.unique(spe_horse_series['program_number']):\n            lat2 = df_index.loc[spe_horse_series['program_number']== program_number_, 'latitude'].values\n            lon2 = df_index.loc[spe_horse_series['program_number']== program_number_, 'longitude'].values\n#         print(lat1,lat2,lon1,lon2)\n        # 对属于index_的所有的马与目标马的draft都进行求和\n        # 求两个马之间的距离以及角度关系\n            dis, dis_angle = horse_distance(lat1,lon1,lat2,lon2)\n            if dis == 0: continue\n            draft = math.cos(dis_angle - speed_angle)/ dis\n            if draft > 0 :\n                rank += 1\n            if not negetive_ :\n                if draft < 0 : \n                    continue\n            sum_draft += draft\n        person_speed['rank'][index_] = rank\n        person_speed['draft'][index_] = sum_draft\n    person_speed.reset_index(inplace=True)\n    person_speed['percentage'] = np.nan\n    for index_ in person_speed['trakus_index']:\n        person_speed['percentage'][index_-1] = (index_/max(person_speed['trakus_index'])*100)\n    return person_speed","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.452973Z","iopub.execute_input":"2022-11-10T16:39:55.453482Z","iopub.status.idle":"2022-11-10T16:39:55.47374Z","shell.execute_reply.started":"2022-11-10T16:39:55.453418Z","shell.execute_reply":"2022-11-10T16:39:55.47268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Part II Analyzing Key Surpassing Point","metadata":{}},{"cell_type":"markdown","source":"In the analysis of drafting in the previous section, we concluded that maintaining the drafting state is helpful to reduce the air resistance, thus saving the horse's physical strength.     \nIn Part II, We will conduct a deeper analysis on how to maintain an effective drafting state, which requires not only maintaining a reasonable ranking (not far behind), but also selecting an appropriate point to surpass (not too late or too early).    \nWe discuss how to achieve effective drafting and specifically discussed the key surpassing points of horse racing. Let's first define some basic functions.  \n\nFirst, we define some functions to calculate the position and distance.  ","metadata":{}},{"cell_type":"code","source":"def find_pass_index(test_track_id,test_race_date,test_race_number, test_program_number, point_lat, point_lon):\n    ''' \n    Calculate when the horse passes the point\n    test_track_id,test_race_date,test_race_Number choose one race\n    test_program_Number choose a horse\n    (point_lat, point_lon) choose a point\n\n    Return the index of the horse passing the point\n    '''\n    horse_data = tracking_table[(tracking_table[\"track_id\"] == test_track_id) & (tracking_table[\"race_date\"] == test_race_date) & (tracking_table[\"race_number\"] == test_race_number)].replace(\" \",\"\")\n    horse_data.replace('\\s+','',regex=True,inplace=True) \n    \n    horse_data = horse_data[horse_data[\"program_number\"] == test_program_number]\n\n    horse_data.sort_values(by = ['trakus_index'],inplace = True)\n\n    lat = np.array(horse_data[\"latitude\"])\n    lon = np.array(horse_data[\"longitude\"])\n    trakus_index = np.array(horse_data[\"trakus_index\"])\n        \n    dis = 100\n    index = 0\n    for i in range(len(lat)-1):\n        s1,a1 = horse_distance(lat[i], lon[i], point_lat,point_lon)\n        s2,a2 = horse_distance(point_lat,point_lon, lat[i + 1], lon[i + 1])\n        s = s1 + s2\n        if s<dis:\n            dis = s\n            index = trakus_index[i+1]\n\n    return index                  ","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.475271Z","iopub.execute_input":"2022-11-10T16:39:55.47591Z","iopub.status.idle":"2022-11-10T16:39:55.493633Z","shell.execute_reply.started":"2022-11-10T16:39:55.475876Z","shell.execute_reply":"2022-11-10T16:39:55.49248Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def find_end_index(test_track_id,test_race_date,test_race_number, race_end_lat, race_end_lon):\n    '''\n    calculate when the horse pass the end point\n    '''\n    print(\"track_id:{},race_date:{},race_number:{}\".format(test_track_id,test_race_date,test_race_number))\n    horse_series = tracking_table[(tracking_table[\"track_id\"] == test_track_id) & (tracking_table[\"race_date\"] == test_race_date) & (tracking_table[\"race_number\"] == test_race_number)].replace(\" \",\"\")\n    horse_series.replace('\\s+','',regex=True,inplace=True) \n    horse_series.sort_values(by = ['program_number','trakus_index'],inplace = True)\n    for program_number in pd.unique(horse_series['program_number'].values):\n        index = find_pass_index(test_track_id,test_race_date,test_race_number, program_number, race_end_lat, race_end_lon)\n        print(\"program_number:{},index:{}\".format(program_number,index))","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.49484Z","iopub.execute_input":"2022-11-10T16:39:55.495869Z","iopub.status.idle":"2022-11-10T16:39:55.51277Z","shell.execute_reply.started":"2022-11-10T16:39:55.495831Z","shell.execute_reply":"2022-11-10T16:39:55.511592Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_track_id = \"AQU\"\ntest_race_date = \"2019-01-04\"\ntest_race_number = 6\ntest_program_number = \"3\"\nrace_end_lat = 40.67197767\nrace_end_lon = -73.83176067\nfind_end_index(test_track_id,test_race_date,test_race_number, race_end_lat, race_end_lon)","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:39:55.514679Z","iopub.execute_input":"2022-11-10T16:39:55.515178Z","iopub.status.idle":"2022-11-10T16:40:02.68321Z","shell.execute_reply.started":"2022-11-10T16:39:55.515131Z","shell.execute_reply":"2022-11-10T16:40:02.682156Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def dis_index(test_track_id,test_race_date,test_race_number, test_program_number, index1, index2):\n    '''\n    Calculate the distance between two trakus index for one horse\n    test_track_id,test_race_date,test_race_Number choose one race\n    test_program_Number choose a horse\n    (index1, index2) choose two trakus index\n\n    Return the distance the horse passed\n    '''\n    horse_data = tracking_table[(tracking_table[\"track_id\"] == test_track_id) & (tracking_table[\"race_date\"] == test_race_date) & (tracking_table[\"race_number\"] == test_race_number)].replace(\" \",\"\")\n    horse_data.replace('\\s+','',regex=True,inplace=True) \n    \n    horse_data = horse_data[horse_data[\"program_number\"] == test_program_number]\n\n    horse_data.sort_values(by = ['trakus_index'],inplace = True)\n\n    lat = np.array(horse_data[\"latitude\"])\n    lon = np.array(horse_data[\"longitude\"])\n    trakus_index = np.array(horse_data[\"trakus_index\"])\n        \n    dis = 0\n    for i in range(index1,index2):\n        s,a = horse_distance(lat[i], lon[i], lat[i+1], lon[i+1])\n        dis += s\n\n    return dis      ","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:40:05.233887Z","iopub.execute_input":"2022-11-10T16:40:05.234285Z","iopub.status.idle":"2022-11-10T16:40:05.243261Z","shell.execute_reply.started":"2022-11-10T16:40:05.234253Z","shell.execute_reply":"2022-11-10T16:40:05.242387Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def cal_dis_end(test_track_id,test_race_date,test_race_number,test_program_number, index, race_end_lat, race_end_lon):\n    '''\n    Calculate the distance of the remaining race and percentage of it in total race\n    test_track_id,test_race_date,test_race_Number choose one race\n    test_program_Number choose a horse\n    index choose a special time\n\n    Return the remaining distance and its percentage\n    '''\n    #print(\"track_id:{},race_date:{},race_number:{},program_number:{}\".format(test_track_id,test_race_date,test_race_number,test_program_number))\n    end_index = find_pass_index(test_track_id,test_race_date,test_race_number, test_program_number, race_end_lat, race_end_lon)\n    #print(end_index)\n    dis_end = dis_index(test_track_id,test_race_date,test_race_number, test_program_number, index, end_index)\n    race_long = dis_index(test_track_id,test_race_date,test_race_number, test_program_number, 0, end_index)\n    #print(dis_end)\n    #print(race_long)\n    dis_end_per = dis_end * 1./race_long\n    \n    return dis_end, dis_end_per\n","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:40:05.66271Z","iopub.execute_input":"2022-11-10T16:40:05.663556Z","iopub.status.idle":"2022-11-10T16:40:05.671165Z","shell.execute_reply.started":"2022-11-10T16:40:05.663507Z","shell.execute_reply":"2022-11-10T16:40:05.670308Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cal_dis_end(test_track_id,test_race_date,test_race_number,test_program_number, 260, race_end_lat, race_end_lon)","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:40:06.502453Z","iopub.execute_input":"2022-11-10T16:40:06.502913Z","iopub.status.idle":"2022-11-10T16:40:08.952629Z","shell.execute_reply.started":"2022-11-10T16:40:06.502878Z","shell.execute_reply":"2022-11-10T16:40:08.951399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here is the function we defines the key exceeding point: at the index of this point, the horse race surpasses other horses (the real-time ranking of the horse race changes), and returns the current index and an index range.","metadata":{}},{"cell_type":"code","source":"def match_process(test_track_id,test_race_date,test_race_number, test_program_number):\n    '''\n    Choose the key surpassing points of horse racing\n    test_track_id,test_race_date,test_race_Number choose one race\n    test_program_Number choose a horse\n\n    Return time_choose(the key index), time_cut(key index section)\n    '''\n    df = draft_pd(test_track_id,test_race_date,test_race_number, test_program_number)\n    time_choose = []\n    for index_ in tqdm(pd.unique(df['trakus_index'])): \n        if np.isnan(df['speed'][index_ + 1]): break\n        if df['rank'][index_] > df['rank'][index_+ 1]:\n            time_choose.append(index_)\n    time_cut = []\n    t_last = 0\n    for index,time_ in enumerate(time_choose):\n        t = []\n        if index > 0:\n            if t_last + 5 > time_:\n                continue\n        #t_last = time_ +5       \n        t.append(max(time_-5,0))\n        t.append(time_+5)\n        time_cut.append(t)        \n    #return GetDate(test_track_id,test_race_date,test_race_number), time_cut\n    return time_choose, time_cut","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:40:10.242218Z","iopub.execute_input":"2022-11-10T16:40:10.243726Z","iopub.status.idle":"2022-11-10T16:40:10.254641Z","shell.execute_reply.started":"2022-11-10T16:40:10.243665Z","shell.execute_reply":"2022-11-10T16:40:10.253206Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Here is the function we take the competition data of a special person and analysis his historical champion data, taking a sight in his key surpassing points.\n\nRace condition is limited by choosing the track_id, distance_id, track condition and course type.\n\nCount all the key surpassing points, record their location and ranks.","metadata":{}},{"cell_type":"code","source":"def person_time_choose_info(person_name,track_id,distance_id = None,track_condition = None ,course_type = None):\n    '''\n    Analysis of the situdation of key surpassing points for specific jockeys and competitions\n    Record the jockey's surpassing points of which race he finished as the champion.\n    \n    Return \n    dis_count(distance of remaining distance of total race length), \n    n_count(current horse ranking)\n    '''\n    start_race_table = pd.merge(start_table,race_table,how='inner')\n    start_race_table[\"program_number\"] = start_race_table[\"program_number\"].str.strip()\n    person_table = start_race_table[(start_race_table[\"jockey\"] == person_name) & (start_race_table[\"track_id\"] == track_id)]\n    if distance_id is not None:\n        person_table = person_table[(person_table[\"distance_id\"] == distance_id)]\n    if track_condition is not None:\n        person_table = person_table[(person_table[\"track_condition\"] == track_condition)]\n    if course_type is not None:\n        person_table = person_table[(person_table[\"course_type\"] == course_type)]\n    \n    dis_count=[]\n    n_count=[]\n    for index_ in person_table.index:\n        time_choose, time_cut = match_process(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_], person_table[\"program_number\"][index_])\n        per_df = draft_pd(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_],person_table[\"program_number\"][index_])\n        for t_index in time_choose:\n            dis_end, dis_end_per=cal_dis_end(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_],person_table[\"program_number\"][index_], t_index, race_end_lat, race_end_lon)\n            n = per_df['rank'][t_index]\n            n_count.append(n)\n            dis_count.append(dis_end_per) \n    \n    return dis_count, n_count","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:40:11.757756Z","iopub.execute_input":"2022-11-10T16:40:11.758237Z","iopub.status.idle":"2022-11-10T16:40:11.770365Z","shell.execute_reply.started":"2022-11-10T16:40:11.7582Z","shell.execute_reply":"2022-11-10T16:40:11.769119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Drawing description: In the following code, we draw a histogram graph to visualize the key surpassing points.  \n\nEach line represents a distance range from the end point. For example, 0.2 means 20% of the distance from the end point.    \n\nEach color represents a different ranking of the current point.  \n\nWhy we choose \"Manuel Franco\": He has participated in the most matches of this type and often won the first place.","metadata":{}},{"cell_type":"code","source":"dis_count, n_count=person_time_choose_info(\"Manuel Franco\", \"AQU\", 800, \"FT\", \"D\")\ndis_count_round = []\nfor dis in dis_count:\n    dis_count_round.append(round(dis,1))\n    \ndraw_surpass_data = {'dis_count':dis_count_round,\n                     'n_count':n_count}\ndraw_surpass_data = pd.DataFrame(draw_surpass_data)\n\ncs=pd.pivot_table(draw_surpass_data, # values='race_date', \n               index=['dis_count'], columns=['n_count'], aggfunc=np.size)\n\ncs['sum'] = cs.sum(axis=1)\n\ncs.index = [str(v) for v in cs.index.values]\n\n\nstacked_barh(cs.tail(10), figsize=(14, 10), color_set='b', sum_col='sum', \n             caption='key-surpassing-points for Manuel Franco in AQU/800/FT/D', \n             sub_title = '',\n             labels=['2', '3', '4', '5', '6', '7','8','9','10','11','12'], \n             )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110320228.png)","metadata":{}},{"cell_type":"markdown","source":"It can be seen that the main surpassing points of \"Manuel Franco\" are concentrated in the first 10% and the last 20% of the race. Also, we got following findings.  \n(1)Speeding up when the race is half way is not a proper way.  \n(2)The better acceleration route is around 20%, starting from the third place. \n\nWe also want to see some of the commonalities of all the first place winners in the competition type.  ","metadata":{}},{"cell_type":"markdown","source":"Here is the function we take the competition data of a special position and analysis historical data, taking a sight in key surpassing points. \n\nRace condition is limited by choosing the track_id, distance_id, track condition and course type.\n\nCount all the key surpassing points, record their location and ranks.","metadata":{}},{"cell_type":"code","source":"def race_time_choose_info(track_id,distance_id = None,track_condition = None ,course_type = None, position_at_finis=1):\n    '''\n    Analysis of the situdation of key surpassing points for competitions\n    Record the surpassing points of jockeys in a specific ranking\n    \n    Return \n    dis_count(distance of remaining distance of total race length), \n    n_count(current horse ranking)\n    '''\n    start_race_table = pd.merge(start_table,race_table,how='inner')\n    start_race_table = start_race_table[(start_race_table['position_at_finis'] == position_at_finis)]\n    start_race_table[\"program_number\"] = start_race_table[\"program_number\"].str.strip()\n    person_table = start_race_table[(start_race_table[\"track_id\"] == track_id)]\n    if distance_id is not None:\n        person_table = person_table[(person_table[\"distance_id\"] == distance_id)]\n    if track_condition is not None:\n        person_table = person_table[(person_table[\"track_condition\"] == track_condition)]\n    if course_type is not None:\n        person_table = person_table[(person_table[\"course_type\"] == course_type)]\n    \n    dis_count=[]\n    n_count=[]\n    for index_ in person_table.index:\n        time_choose, time_cut = match_process(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_], person_table[\"program_number\"][index_])\n        per_df = draft_pd(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_],person_table[\"program_number\"][index_])\n        for t_index in time_choose:\n            dis_end, dis_end_per=cal_dis_end(track_id,person_table[\"race_date\"][index_],person_table[\"race_number\"][index_],person_table[\"program_number\"][index_], t_index, race_end_lat, race_end_lon)\n            n = per_df['rank'][t_index]\n            n_count.append(n)\n            dis_count.append(dis_end_per) \n    \n    return dis_count, n_count","metadata":{"execution":{"iopub.status.busy":"2022-11-10T16:16:43.673353Z","iopub.execute_input":"2022-11-10T16:16:43.675Z","iopub.status.idle":"2022-11-10T16:16:43.686445Z","shell.execute_reply.started":"2022-11-10T16:16:43.674933Z","shell.execute_reply":"2022-11-10T16:16:43.685276Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"We choose the 1st horse's trakus info as the data for champion, recording its key surpassing points as below.  ","metadata":{}},{"cell_type":"code","source":"dis_count, n_count=race_time_choose_info(\"AQU\", 800, \"FT\", \"D\", 1)\n\ndis_count_round = []\nfor dis in dis_count:\n    dis_count_round.append(round(dis,1))\n\ndraw_surpass_data = {'dis_count':dis_count_round,\n                     'n_count':n_count}\ndraw_surpass_data = pd.DataFrame(draw_surpass_data)\n\ncs=pd.pivot_table(draw_surpass_data, \n               index=['dis_count'], columns=['n_count'], aggfunc=np.size)\n\ncs['sum'] = cs.sum(axis=1)\n\ncs.index = [str(v) for v in cs.index.values]\n\n\nstacked_barh(cs.tail(10), figsize=(14, 10), color_set='b', sum_col='sum', \n             caption='key-surpassing-points for Champion in AQU/800/FT/D', \n             sub_title = '',\n             labels=['2', '3', '4', '5', '6', '7','8','9','10','11','12'], \n             )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110317293.png)","metadata":{}},{"cell_type":"markdown","source":"We choose the 4th horse as the data for non first place, recording its key surpassing points as below.","metadata":{}},{"cell_type":"code","source":"dis_count, n_count=race_time_choose_info(\"AQU\", 800, \"FT\", \"D\", 4)\n\ndis_count_round = []\nfor dis in dis_count:\n    dis_count_round.append(round(dis,1))\ndraw_surpass_data = {'dis_count':dis_count_round,\n                     'n_count':n_count}\ndraw_surpass_data = pd.DataFrame(draw_surpass_data)\n\ncs=pd.pivot_table(draw_surpass_data,\n               index=['dis_count'], columns=['n_count'], aggfunc=np.size)\n\ncs['sum'] = cs.sum(axis=1)\n\ncs.index = [str(v) for v in cs.index.values]\n\n\nstacked_barh(cs.tail(10), figsize=(14, 10), color_set='b', sum_col='sum', \n             caption='key-surpassing-points for 5th place in AQU/800/FT/D', \n             sub_title = '',\n             labels=['2', '3', '4', '5', '6', '7','8','9','10','11','12'], \n             )","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![1](https://raw.githubusercontent.com/JiuZhouu/ImageBed/main/mac/202211110318893.png)","metadata":{}},{"cell_type":"markdown","source":"In the comparison between the first place data and the fourth place data, it is easy to find that the first place horse often rush to the first place in the last race（about 10%～20% race distance left）, where we consider a suitable place to accelerate and take the first place.\n\nToo early acceleration will lead to the disadvantage of the whole race.","metadata":{}},{"cell_type":"markdown","source":" we focus on analyzing the process of the station and action of these players during the competition, propose a model to help the players to analyze the correctness or error of each moment during the competition, and analyze the rationality and validity of the model. ","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport matplotlib.animation as animation\nfrom geopy.distance import geodesic\nimport math\n\ndata_table = pd.read_csv(\"../input/bigdatadebry/nyra_2019_complete.csv\")\nstart_table = pd.read_csv(\"../input/bigdatadebry/nyra_start_table.csv\")\ndata_table.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'trakus_index',\n                      'latitude', 'longitude', 'distance_id', 'course_type', 'track_condition',\n                      'run_up_distance', 'race_type', 'purse', 'post_time', 'weight_carried',\n                      'jockey', 'odds', 'position_at_finish']\nstart_table.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried',\n                      'jockey', 'odds', 'position_at_finish']\n\n\ndef EuclideanDistances(A, B):\n    BT = B.transpose()\n    vecProd = np.dot(A, BT)\n    SqA = A ** 2\n    sumSqA = np.matrix(np.sum(SqA, axis=1))\n    sumSqAEx = np.tile(sumSqA.transpose(), (1, vecProd.shape[1]))\n\n    SqB = B ** 2\n    sumSqB = np.sum(SqB, axis=1)\n    sumSqBEx = np.tile(sumSqB, (vecProd.shape[0], 1))\n    SqED = sumSqBEx + sumSqAEx - 2 * vecProd\n    SqED[SqED < 0] = 0.0\n    ED = np.sqrt(SqED)\n    return ED\n\n\n# 特征归一化\ndef normalization(feature):\n    rows, cols = feature.shape\n    for i in range(cols):\n        if feature[:, i].std() == 0:\n            new_col = feature[:, i] - feature[:, i].mean()\n        else:\n            new_col = (feature[:, i] - feature[:, i].mean()) / feature[:, i].std()\n        feature[:, i] = new_col\n\n    return feature, (feature.mean(axis=0), feature.std(axis=0))\n\n\ndef ComputeCurvity(pos: np.array) -> int:\n    \"\"\"\n    :param pos: 三个点的坐标\n    拟合二次曲线，返回估计的曲率\n    \"\"\"\n    x = pos[:, 0]\n    y = pos[:, 1]\n    t_a = ((x[1] - x[0]) ** 2 + (y[1] - y[0]) ** 2) ** 0.5\n    t_b = ((x[2] - x[1]) ** 2 + (y[2] - y[1]) ** 2) ** 0.5\n    M = np.array([[1, -t_a, t_a ** 2], [1, 0, 0], [1, t_b, t_b ** 2]])\n    try:\n        _M = np.linalg.inv(M)\n    except:\n        M = M + np.identity(3)\n        _M = np.linalg.inv(M)\n    else:\n        _M = np.linalg.inv(M)\n\n    A = np.matmul(_M, x.reshape(3, 1)).reshape(-1)\n    B = np.matmul(_M, y.reshape(3, 1)).reshape(-1)\n    return 2 * (A[2] * B[1] - A[1] * B[2]) / ((A[1] ** 2 + B[1] ** 2) ** 1.5)\n\n\ndef ComputeCurvityMean(positions: np.array) -> np.array:\n    CurvityMean = []\n    for position in positions:\n        pos1 = position[:position.shape[0] - 2, ]\n        pos2 = position[1:position.shape[0] - 1, ]\n        pos3 = position[2:, ]\n        Curvity = [0]\n        for i in range(pos1.shape[0]):\n            Curvity.append(ComputeCurvity(np.row_stack([pos1[i], pos2[i], pos3[i]])))\n        Curvity.append(0)\n\n        CurvityMean.append(Curvity)\n\n    return np.array(CurvityMean).mean(axis=0)\n\n\ndef ComputeStationAction(data, choice_index):\n    choice = data[choice_index]\n    others = np.row_stack([data[0:choice_index], data[choice_index + 1:]])\n\n    # 曲率\n    CurvityMean = ComputeCurvityMean(data)  # 掐头去尾了\n\n    # 速度、角度\n    self_speed_horse, self_speed_angle = np.zeros(choice.shape[0]), np.zeros(choice.shape[0])\n    for i in range(1, choice.shape[0] - 1):\n        self_speed_horse[i], self_speed_angle[i] = horse_speed(choice[i, 0], choice[i, 1], choice[i + 1, 0],\n                                                               choice[i + 1, 1])\n\n    # 角度速度变化\n    self_speed_angle_change = np.zeros(choice.shape[0])\n    self_speed_angle_change[1:self_speed_horse.shape[0]] = self_speed_angle[\n                                                           1:self_speed_horse.shape[0]] - self_speed_angle[\n                                                                                          :self_speed_horse.shape[\n                                                                                               0] - 1]\n\n    self_speed_horse_change = np.zeros(choice.shape[0])\n    self_speed_horse_change[1:self_speed_horse.shape[0]] = self_speed_horse[\n                                                           1:self_speed_horse.shape[0]] - self_speed_horse[\n                                                                                          :self_speed_horse.shape[\n                                                                                               0] - 1]\n\n    # 位次，和别人的距离、角度\n    other_distance, other_angle = np.zeros((others.shape[0], choice.shape[0])), np.zeros(\n        (others.shape[0], choice.shape[0]))\n    oder = np.ones(choice.shape[0])\n    for j in range(1, choice.shape[0] - 1):\n        for i in range(others.shape[0]):\n            A = horse_distance(choice[j, 0], choice[j, 1], others[i, j, 0],\n                               others[i, j, 1])\n            other_distance[i, j], other_angle[i, j] = horse_distance(choice[j, 0], choice[j, 1], others[i, j, 0],\n                                                                     others[i, j, 1])\n            if math.cos(other_angle[i, j] - self_speed_angle[j]) > 0:\n                oder[j] += 1\n\n    # 别人的速度、速度角度\n    other_speed_horse, other_speed_angle = np.zeros((others.shape[0], choice.shape[0])), np.zeros(\n        (others.shape[0], choice.shape[0]))\n    for i in range(others.shape[0]):\n        for j in range(1, choice.shape[0] - 1):\n            other_speed_horse[i, j], other_speed_angle[i, j] = horse_speed(others[i, j, 0], others[i, j, 1],\n                                                                           others[i, j + 1, 0], others[i, j + 1, 1])\n\n    # 别人的加速度\n    other_acceleration = np.zeros((others.shape[0], choice.shape[0]))\n    for i in range(others.shape[0]):\n        for j in range(1, choice.shape[0] - 1):\n            other_acceleration[i, j] = (other_speed_horse[i, j + 1] - other_speed_horse[i, j]) * 4\n\n    # 加速度0.25秒间隔  掐1去2\n    self_acceleration = np.zeros(choice.shape[0])\n    for i in range(1, choice.shape[0] - 1):\n        self_acceleration[i] = (self_speed_horse[i + 1] - self_speed_horse[i]) * 4\n\n    station = np.zeros([choice.shape[0] - 3, 30])\n    action = np.zeros([choice.shape[0] - 3, 2])\n    for i in range(1, choice.shape[0] - 2):\n        station[i - 1] = np.concatenate([np.array([CurvityMean[i], oder[i], self_speed_horse[i], self_acceleration[i],\n                                                   math.ceil(10 * i / choice.shape[0])]),\n                                         other_distance[:, i].reshape(-1),\n                                         other_speed_angle[:, i].reshape(-1), other_speed_horse[:, i].reshape(-1),\n                                         other_angle[:, i].reshape(-1), other_acceleration[:, i].reshape(-1)], axis=0)\n\n        action[i - 1] = np.array([self_speed_angle_change[i], self_speed_horse_change[i]])\n\n    return station, action\n\n\ndef CreateMuteDate(track_id, track_condition, course_type, distance_id, jockey_num):\n    position_at_finish = 1\n    jockey = 'Manuel Franco'\n\n    race_tracking = data_table.query(\n        \"track_id == @track_id & track_condition == @track_condition & course_type == @course_type & distance_id == \"\n        \"@distance_id &  position_at_finish == @position_at_finish \")\n\n    groups = race_tracking.groupby(['track_id', 'race_date', 'race_number']).groups\n\n    station_action = []\n    for i, key in enumerate(groups):\n        print(key)\n        sign = False\n        # 查找这场比赛所有选手\n        jockeys = data_table.query(\"track_id == @key[0] & race_date == @key[1] & race_number == @key[2]\").groupby(\n            ['jockey']).groups\n\n        if len(jockeys) == jockey_num:\n            one_race_data = np.zeros([jockey_num, groups[key].size, 2])\n            first_index = 0\n\n            for index, people in enumerate(jockeys):\n                # print(index)\n                temp = data_table.iloc[jockeys[people]].sort_values(\"trakus_index\").values\n\n                if temp.shape[0] != groups[key].size:\n                    sign = True\n                    break\n\n                one_race_data[index] = temp[:, 5:7]\n                if temp[0, 17] == 1:\n                    first_index = index\n\n            if sign:\n                continue\n            station_action.append(ComputeStationAction(one_race_data, first_index))\n\n    station = station_action[0][0]\n    action = station_action[0][1]\n    for i in range(1, len(station_action)):\n        print(i)\n        station = np.concatenate([station, station_action[i][0]], axis=0)\n        action = np.concatenate([action, station_action[i][1]], axis=0)\n\n    np.save('station.npy', station)\n    np.save('action.npy', action)\n\n    return station, action\n\n\ndef Compare(track_id, track_condition, course_type, distance_id, jockey_num, station, action):\n    position_at_finish = 1\n    jockey = 'Manuel Franco'\n\n    race_tracking = data_table.query(\n        \"track_id == @track_id & track_condition == @track_condition & course_type == @course_type & distance_id == \"\n        \"@distance_id &  position_at_finish == @position_at_finish\")\n\n    groups = race_tracking.groupby(['track_id', 'race_date', 'race_number']).groups\n\n    sum = 0\n    num = 0\n    for i, key in enumerate(groups):\n        sign = False\n        # 查找这场比赛所有选手\n        # real_num = start_table.query(\"track_id == @key[0] & race_date == @key[1] & race_number == @key[2]\").shape[0]\n        jockeys = data_table.query(\"track_id == @key[0] & race_date == @key[1] & race_number == @key[2]\").groupby(\n            ['jockey']).groups\n\n\n        if len(jockeys) == jockey_num:\n            num += 1\n\n            one_race_data = np.zeros([jockey_num, groups[key].size, 2])\n            position = {}\n\n            for index, people in enumerate(jockeys):\n                temp = data_table.iloc[jockeys[people]].sort_values(\"trakus_index\").values\n\n                if temp.shape[0] != groups[key].size:\n                    sign = True\n                    break\n\n                one_race_data[index] = temp[:, 5:7]\n                position[index] = [people, temp[0, 17]]\n\n            if sign:\n                continue\n\n            # 针对每个人与最佳策略进行比对\n            for j in range(one_race_data.shape[0]):\n                j_station, j_action = ComputeStationAction(one_race_data, j)\n\n                # 比对最佳策略\n                sim_sco = 0\n                for act_i, one_station in enumerate(j_station):\n                    \"\"\"\n                    CurvityMean[i], oder[i], self_speed_horse[i], self_acceleration[i],math.ceil(10 * i / choice.shape[0])]),\n                    other_distance[:, i], 5-10\n                    other_speed_angle[:, i], other_speed_horse[:, i],\n                    other_angle[:, i], other_acceleration[:, i]\n                    \"\"\"\n\n                    t = np.tile(one_station, (station.shape[0], 1)) - station\n                    w = np.ones([30]).reshape([1, -1])\n\n                    w[0, 0] = 0.001\n                    w[0, 1] = 1\n                    w[0, 2] = 1\n                    w[0, 3] = 1\n                    w[0, 4] = 1\n                    w[0, 5:10] *= 2\n                    w[0, 10:15] *= 2\n                    w[0, 15:20] *= 2\n                    w[0, 20:25] *= 2\n                    w[0, 25:30] *= 2\n\n                    dist = np.linalg.norm(w * t, ord=None, axis=1, keepdims=False)\n                    # sim = 1.0 / (1.0 + dist)\n\n                    max_index = np.argmax(dist)  # 此时刻最优策略索引\n                    sim_sco += np.linalg.norm(j_action[act_i].reshape(1, -1) -\n                                              action[max_index].reshape(1, -1), ord=None, axis=1, keepdims=False)\n\n                position[j].append(sim_sco)\n\n            auc = 0\n            finish_position = []\n            sco = []\n            for r_key in position:\n                temp = position[r_key]\n                finish_position.append(temp[1])\n                sco.append(temp[2])\n\n            real_num = max(finish_position)\n            sco = sorted(range(len(sco)), key=lambda k: sco[k], reverse=False)\n            finish_position = sorted(range(len(finish_position)), key=lambda k: sco[k], reverse=False)\n            for j in range(len(finish_position)):\n                if finish_position[j] == sco[j]:\n                    auc += 1\n\n            print(real_num, key, finish_position, sco, auc / 6)\n            sum += (auc / 6)\n\n    print(sum / num, num)\n\n\ntrack_id = \"AQU\"\ntrack_condition = \"FT \"\ncourse_type = 'D'\ndistance_id = 800\njockey_num = 6  # 每场比赛有index的人数0.0.0.0.3.23.43.30.21.19.6.5.2.\n\nstation, action = CreateMuteDate(track_id, track_condition, course_type, distance_id, jockey_num)\n\n#station = np.load('station.npy')\n# action = np.load('action.npy')\nCompare(track_id, track_condition, course_type, distance_id, jockey_num, station, action)","metadata":{},"execution_count":null,"outputs":[]}]}