{"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.5"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Setup Project","metadata":{}},{"cell_type":"code","source":"library(tidyverse)\nlibrary(tidymodels)\nlibrary(lubridate)\nlibrary(RColorBrewer)\ninstall.packages(\"poissonreg\")\nlibrary(poissonreg)\ninstall.packages(\"ggpubr\")\nlibrary(ggpubr)\ninstall.packages(\"patchwork\")\nlibrary(patchwork)\ninstall.packages(\"easystats\")\nlibrary(easystats)\n\n\nload(\"../input/big-data-derby-data/summary_data.rda\")\nsummary_data <- summary_data %>%\n  mutate(first_place = if_else(position_at_finish == 1, 1,0))\n\nsummary_data$first_place <- factor(summary_data$first_place,\n                                   levels = c(0, 1),\n                                   labels = c(\"No\", \"Yes\"))","metadata":{"execution":{"iopub.status.busy":"2022-11-10T19:21:55.680546Z","iopub.execute_input":"2022-11-10T19:21:55.682180Z","iopub.status.idle":"2022-11-10T19:23:05.448638Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Objective","metadata":{}},{"cell_type":"markdown","source":"The objective of this analysis is to determine whether racing tactics: acceleration and distance from center of track impact winning the race. These tactics will be evaluated using classification models with `first_place` as the outcome of interest. Additional covariate variables include: `track_id`, `track_id`, `program_number`, `distance_id`, `course_type`, `track_condition`, and `run_up_distance`, `ytd_races`, `day_mean_performance`. \n\n`ytd_races` = number of races the jockey has completed in 2019 up to that race\n`day_mean_performance` = the average place of the jockey in the races that day, up to the particular race","metadata":{}},{"cell_type":"markdown","source":"## Primary variables of interest\n`median_speed_kmh` = the median speed (km/h) across the entire race. Positive slopes indicate speed is progressively increassing over the course of the track, negative slopes indicate speed is decreasing over the course of the track\n\n`var_accel_kms` = variance in acceleration across the entire race\n\n`prop_inside` = the proportion of time the horse is the horse closest to the inside/center of the track\n\n`slope_center_dist_order` = the slope of the order of the horse relative to the inside of the center of the track. Positive slopes indicate the horse is progressively moving to the lateral/outside of the track. Negative slopes indicate the horse is progressively moving towards the medial/center of the track\n","metadata":{}},{"cell_type":"markdown","source":"> These variables were created to provide summary metrics for the machine learning models but also provide interpretable information to guide racing strategy.","metadata":{}},{"cell_type":"code","source":"p1 <- ggboxplot(summary_data, x = \"first_place\", y = \"median_speed_kmh\", fill = \"first_place\") + xlab(\"First Place\") + ylab(\"Median Speed (km/h)\")\np2 <- ggboxplot(summary_data, x = \"first_place\", y = \"var_accel_kms\", fill = \"first_place\") + xlab(\"First Place\") + ylab(\"Variation in Acceleration\")\np3 <- ggboxplot(summary_data, x = \"first_place\", y = \"prop_inside\", fill = \"first_place\") + xlab(\"First Place\") + ylab(\"Proporion Inside\")\np4 <- ggboxplot(summary_data, x = \"first_place\", y = \"slope_center_dist_order\", fill = \"first_place\") + xlab(\"First Place\") + ylab(\"Slope of Center Order\")\n\n(p1 + p2) / (p3 + p4)","metadata":{"execution":{"iopub.status.busy":"2022-11-10T19:25:54.239524Z","iopub.execute_input":"2022-11-10T19:25:54.241089Z","iopub.status.idle":"2022-11-10T19:25:55.820654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Development\n\nThe model formula will be:\n`first_place` = `track_id` + `program_number` + `distance_id` + `course_type` + `track_condition` + `run_up_distance` + `ytd_races` + `day_mean_performance` + `slope_speed_kmh` + `var_accel_kms` + `prop_inside` + `slope_center_dist_order`\n\nUsing the tidymodels framework we will test multiple classificaiton models. The best model will be selected based on the the area under the curve. ","metadata":{}},{"cell_type":"markdown","source":"### Methods\n-  Split the data 60/40 into training and test datasets stratified by first place so that an equal distribution of first place finishes would be in the training and test sets\n-  Created a 10-fold cross validation object from the training dataset to tune models\n-  All numeric predictors were normalized\n-  All categorical predictors were dummy coded\n-  Models tested: \n      - Logistic regression using brulee engine - tune the penalty and mixture arguments\n      - Decision Tree using rpart enging - tune cost complexity, tree depth, and min n arguments\n- The best model was selected based on the area under the curve","metadata":{}},{"cell_type":"markdown","source":"# Results","metadata":{}},{"cell_type":"markdown","source":"## Selected Model","metadata":{}},{"cell_type":"markdown","source":"The best model was the logistic regression model.\n\n- AUC: 0.792\n\n\n![image.png](attachment:bbf088ea-50fc-4a0a-ad0c-201cb1a26d9f.png)","metadata":{},"attachments":{"bbf088ea-50fc-4a0a-ad0c-201cb1a26d9f.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"The predicted position, calculated based on pre-race odds, was the most useful prediction parameter. The next most usefule metrics were related to how inside the horse ran the race. We can interpret how this impacts first place finishes by looking at the odds ratios.  ","metadata":{}},{"cell_type":"markdown","source":"## Odds 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H37Y9INH9e/Xr58L3vr06eOyqE6ffPKJ2xih6WI97kUGW2+9tVtrpxFGPS5F1/3www8uaFZQLDftEo6XYtsXnUeBpdYAavOKAk+tV9SGlYEDB9rhhx8ef61a9A189LpmZO1irVtutbzIdHZeZDq57L/I+1o33WS1LrzQR7WlKgggkG4B/YGvJzJEz06ku0zuj0BFAoGfotVonEbDFCxpKlbPe9Nu2Ngp0WgIbYS47LLL7Omnn3YBjka0tKngwsgvZu86PRNO/8Mqj9ahaRRLz5FTAJco6fEe2kmrkS49705Bku7ZunVrd4k2FChI0jEFX+mqh1c/Pbrk1ltvdcGURtD0Wl5K+jQNBcdPPPGEC3RloM0oaqsenaKk5wmqntqVrKlVtUe7gRWQxUux7YvNow0a8tEuXo30aa2dAk49Wy9oqWZkLWKNiENJJIAviYz+5kfWN+ZHguM8PqooaF1JfRHYZAE9I/SmyB93JAT8JJAX+UUb8zh+P1Wv/LooGNGUrB43oo0TClI0ipRsUtP1+A4FGgpe4iXl0cYLbQaI3okbL693TKNz2jSgUT3vocHeOd1PgZUCRi+luh5ffvml6SHHGnHUpg+NJsbbUKHyFYjqeYJaNyi/eEkbHGSg9XsVGcRrX+w95bMwshvVW+sXe76y7999913r3bu3TY+MjurzIEkIIIAAAgjkukDgR/C8DozdeekdL++rRv4qeuq48iiwqUxSsOiN2sVep/tFB3c6n6566N4K2hIFdzqvgK2iQEsjoxXl0b2U4rXv9zN//iufZO/351W8QgABBBBAAIFkBQK/Bi/ZhpIPAQQQQACBdAhoBkkfban1zCQE/CIQ6BE8fSqCFuuTNhTQA4n1OJPyRu42vIJ3CCCAAAJVFRg9erR70oA+29zbqFfVe3EdAqkSCHSA5z0kN1UYYbmP1iFq0S8JAQQQQCD9AvqosjFjxrAGOP3UlFAJgUAHeJVoJ1kRQAABBBBIi4Ae/h79EYtpKYSbIlBJAdbgVRKM7AgggAACCCCAgN8FCPD83kPUDwEEEEAAAQQQqKQAAV4lwciOAAIIIIBAtMCkSZPs6quvtt9++y36MK8RyKoAAV5W+SkcAQQQQCDoAvoIx1dffdU9xD7obaH+4RFgk0V4+pKWIIAAAghkQaB79+7u87yzUDRFIpBQgBG8hDScQAABBBBAAAEEgilAgBfMfqPWCCCAAAIIIIBAQgECvIQ0nEAAAQQQQAABBIIpQIAXzH6j1ggggAACPhH44osvrF27djZ9+nSf1IhqIGBGgMd3AQIIIIAAApsg0KhRIzvggAOsTp06m3AXLkUgtQLsok2tJ3dDAAEEEMgxAY3ePfLIIznWaprrdwFG8PzeQ9QPAQQQQAABBBCopAABXiXByI4AAggggAACCPhdgADP7z1E/RBAAAEEfC2wcuVK++mnn6yoqMjX9aRyuSVAgJdb/U1rEUAAAQRSLKBdtO3bt7dp06al+M7cDoGqCxDgVd2OKxFAAAEEELBu3brZ0KFDrXXr1mgg4BsBdtH6piuoCAIIIIBAEAU233xz69WrVxCrTp1DLMAIXog7l6YhgAACCCCAQG4KEODlZr/TagQQQAABBBAIsQABXog7l6YhgAACCKRfQB9Rdvfdd9vixYvTXxglIJCkAAFeklBkQwABBBBAIJ7AlClT7NZbb7WFCxfGO80xBLIiwCaLrLBTKAIIIIBAWAR69uxpK1asCEtzaEdIBBjBC0lH0gwEEEAAAQQQQMATIMDzJPiKAAIIIIAAAgiERIAALyQdSTMQQAABBBBAAAFPgADPk+ArAggggAACVRAYPXq07bXXXjZr1qwqXM0lCKRHgAAvPa7cFQEEEEAgRwRq1aplW221lRUWFuZIi2lmEATYRRuEXqKOCCCAAAK+FejUqZO9/PLLvq0fFctNAUbwcrPfaTUCCCCAAAIIhFiAAC/EnUvTEEAAAQQQQCA3BQjwcrPfaTUCCCCAQIoE1q1bZwsWLLDi4uIU3ZHbILDpAgR4m27IHRBAAAEEclhg2LBh1rRpU9NHlpEQ8IsAAZ5feoJ6IIAAAggEUqBz5842aNAga9myZSDrT6XDKcAu2nD2K61CAAEEEMiQQLNmzeyYY47JUGkUg0ByAozgJedELgQQQAABBBBAIDACBHiB6SoqigACCCCAAAIIJCdAgJecE7kQQAABBBCIKzB79mx7+umnbdmyZXHPcxCBbAgQ4GVDnTIRQAABBEIjMH78ePu///s/mzdvXmjaREOCL8Ami+D3IS1AAAEEEMiiQK9evWzNmjVWvXr1LNaCohHYUIAAb0MP3iGAAAIIIFApgfz8fKtRo0alriEzAukWYIo23cLcHwEEEEAAAQQQyLAAAV6GwSkOAQQQQAABBBBItwABXrqFuT8CCCCAQKgFxo4da4cddpjNmTMn1O2kccESIMALVn9RWwQQQAABnwmUlpZacXGx6SsJAb8IsMnCLz1BPRBAAAEEAinQtWtX+89//hPIulPp8AowghfevqVlCCCAAAIIIJCjAgR4OdrxNBsBBBBAAAEEwitAgBfevqVlCCCAAAIZEPDW4GWgKIpAIGkBArykqciIAAIIIIDAxgJDhw61atWq2c8//7zxSY4gkCUBArwswVMsAggggEA4BDp06GBPPPGENWvWLBwNohWhEGAXbSi6kUYggAACCGRLoHXr1nbuuedmq3jKRSCuACN4cVk4iAACCCCAAAIIBFeAAC+4fUfNEUAAAQQQQACBuAIEeHFZOIgAAggggEByAr/++qu99dZbtmLFiuQuIBcCGRAgwMsAMkUggAACCIRX4Ntvv7W+ffvyWbTh7eJAtoxNFoHsNiqNAAIIIOAXgf32288Fd02bNvVLlagHAkaAxzcBAggggAACmyBQo0YNa9GixSbcgUsRSL0AU7SpN+WOCCCAAAIIIIBAVgUI8LLKT+EIIIAAAggggEDqBQjwUm/KHRFAAAEEckjghx9+sFNPPdXmzZuXQ62mqX4XIMDzew9RPwQQQAABXwusXLnSJkyYYOvWrfN1PalcbgnkVID32Wef2b777msLFy7MrV6uQmtvuukmu+SSSxJeec8999iZZ56Z8HxVTnz55Zeuf+bOnVuVy7kGAQQQyIrA7rvvbqNHj7a2bdtmpXwVumZ9qb33Y5Fd++9V9n+DVtodQ1fbmF/WZ60+FJx9AXbRZr8PqAECCCCAAAJVFvh1RYkdO2ClLVxZYkvX/H6b/DyzF79aZz22q2aPHVXb8vIiB0g5JZBTI3g51bM0FgEEEEAg9AJLV5farvctt6kL/wzu1OiSUrNVRWZDJ663k19aaaWlkQOknBII3QiePi7mjTfeMH10TOvWre3QQw+1o48+OmGnfvfdd/bUU0/ZpEmTrGHDhqYHVp511llWWFjorrnwwgvtmGOOsU8++cQNwW+xxRZ22GGH2SGHHLLBPd977z1788037ZdffjHlOe2002zvvffeIE/0G63ZeOihh2zUqFG2fv1622abbezcc8+1jh07umwDBgywNWvWWKNGjdx99T+nppc1LVqrVq2yW02dOtUeeeQRmzhxotWrV8969Ojh8lSr9mfXVpSnpKTEnn/+eddG1Utt07HKJJV/7733Ws+ePe24444z1b+oqMhatmxpr732mi1dutTZXnrppTZ48GDXRwUFBc7y2GOP3aCoWbNmmaaAtaZFlqecckq5lhtczBsEEEAghwRu/u9qK4gMzhUniN/WFZuNm11sH/y03g5u//vvtRziyemm/hkFhIDhiy++sAceeMBOOukk69Chg+njYx5++GGrU6fORgGZmvvzzz/bRRddZHvssYf985//dDugnnnmGZs2bZoLMJRHgd+dd95pu+22m9122202duxYu/vuu61+/fplQcfAgQPtiSeesP3339/tpFK511xzjd1+++3WvXt33WajpHrqXgoEda+3337b1UXBj95rN9bw4cNd0HbxxRfb2rVrXUCoQEl1VZoxY4adffbZtuWWW9oFF1zgFvg+/fTT7tobb7wx6TwvvviivfTSS+5ezZo1s5dfftkUFHbq1Mndo6J/FJBdccUVLv9RRx3lsqv+I0eOdPb6CB/dT8G36qzg+4gjjrDp06fbo48+al27drXtttuurBi17/DDD3cB4ZAhQ+z666831bFVq1ZleXiBAAII+EXg448/tj59+tiYMWPcH+ux9Vq1rtRGz0z9erjiyDDd+5F1d4mCO68ey9aaPfrZGquV4fhOk8L7bZvhQr1G8zVcn2ShwEqjduecc47rWo14aZQoPz/+TLRGvrbddlu76667ytYn6Gnk//jHP+ybb76xXXbZxd1HHz+jTQdaw7Dzzjvb/Pnz7cEHH3QBnj5cWqNVBx54YFngpVFAbeRQ0JcowFNdDzroIDvyyCNdGZ07d7Ynn3zSFi1a5AI8HdS9H3/8cdtqq61cntq1a9uVV17pPvOwXbt21r9/f9MT1JVHX5UaNGhgN9xwg51wwgkuaKooT5MmTezZZ581jVR6I53dunVz9UpmSH/BggX297//3XbYYQe7+eabLXrkUG3RKKUCUKWvvvrKuSog9oI1bawYMWLEBgGe6n766ae7axSoa9RSI4TeNe4E/yCAAAI+EdDP6KuuusrNAsWr0i9LS+zUl1fFO5WxYz/MzU4dZtxYP2NtpKANBUI1gqegR8GDgiAFXBqZO//88zds8R/vFLxoClDTsdGLTxUUKpD6/vvvywK8Aw44YIM8Ctref/99W758uU2ePNk0rdm+fXsXhHiFKajRtO6SJUtc0OUd974qIBo0aJCbhlWZGi277rrrvNPuq0a1vOBOB9Se6tWru5FHtVVBYpcuXdxImHehppnVHtVf11eUR/XUdGx0IFq3bl03Yqn2lZfU7ssuu8y1/9Zbby2b1vauUbCp+3tp6623dgFgdKDWuHFj0whgdPICax1TGzQlzc7aaCFeI4CAnwT0c9qbWYlXr7zIQf2X6pRgVjZhMemoQ8LCIiciv4pIWRQIVYCn9V+aGtQUo9a2afROQZGmDxVIRCeNLmnaUyNYsUnHNDLlpdg8m2++uTulwMR7sKWmXOMlnVegE5s0hauRQ63be/31191UrEbzNHLljTjG++Bqla1ytT5PwaMe/aL/YpNGGZPJIyMlrfWLTmpzRQHe7NmzrU2bNm6k8YMPPnBTFLH3iH6vdjVv3jz6kOujDQ5E3kT3lYJVjQoWF0cWkpAQQACBAAps16TApqdhJKskMlCx+wPL7dfl5Yd6irP67FRoD/SpHUA9qlxVgVAFeEI48cQTTYv2x40bZ59//rm9++67bg2Xpkujk0aplOIFMcuWLXObA7z8q1ZtOLSu80qazlWgqPTYY4/Z9ttv715H/6MRt3hJ5WuNnu6ltWofffSRm+rVcW3qUNIIWWxSfpWrKVndW2vZtA4vNikoUvBWUR49u0lJDhr989Lq1au9lwm/Kijs16+f2+Sh6WWNREYHZ16gmvAGCU5Ej6gmyMJhBBBAIOcF8iN/AJ+ya3V79NO1kefgJeZQ+HfqbvF/FyW+ijNBF4i/OC2grdKUpxbtK7jRNJ8e1KsF/lq/FbsrVIGPpgq10SE6aYPF4sWLN1goO378+Ogspp23GklTQKRdnkoKJmvWrFn2nxbdalepdsjGJo0car2aRt6081Vr8bRxQztONW3spSlTprhROO+9poMVbGrdoIIgPVRT94guVwGnNlhoA0kyebzNDQqIvaTp6x9//NF7m/CrAjwFpJoGVzB5//33J8zLCQQQQCCsAvqdMWzYMEvmD+NUG1ywTw3bulH5v8pvObimdWkVuvGcVFOG7n7lf1cErLkKeBTk/fe//3WjX9oBq6laPXok3mjSySef7NbJaeeqpjNnzpzpdsxqrVj0DtIPP/zQja55o22aUtWjO5Q0RalHk+gxKdolqpEwbSbQJgxNc8YbwdPomx7DotEvfYahgjYFalpnttNOO5Wp614K1hR0zpkzx+3s1Y5TbTxQUh10jQIrTZdqh6p2/CqvN5pYUR7VsVevXm5DiIJCTe3qESXTIztck02aglaQpzZ8+umnyV5GPgQQQCAUAvqZr98DseuJM9E4jeL959y6dnK36lY3steuQa0897Vh5GvjOnl23xE1I6N3v2/Cy0R9KMM/AqEK6ffcc0+3e/S5555zjzTRCJZ2p3qPDIll1/PeFERp+lZBkoIx5dfaOD1axUt6/IkCOD1CRY8w0RSqRga9pE0d2i2qR7JoLZ5GthQ0nRZ5BEqipJ2hyq9rtVtWQd/xxx/vHg/iXaOAVW1QkKYRMu1u1Q5Zb6eqfqBok4MejaIgVfXXyKV2xFYmj/dIl/POO889akXBrUYVo9chenVK9FXPG9TGEzlGb5JIlJ/jCCCAQFgE9tprL/fH+pZbbpm1Jt12aC274i81bOwvxbZ8bam1qJdvnVtFlunoIXmknBTIi0zHlb86M6Asetaapg+jHwqcqCmavtVmCK0fix1xU6Bz6qmnusBRo1sa8Yo3Gqh768G+Coq0Rk6BWTJJmwd0X20+iL6vgkxNyeoxJ9pMoXppd2+8pC5UezWS5j0uJTZfMnk0daxgM3bDRey9/PZe6yx79+7tRh29KXO/1ZH6IIAAAgggkEmBUI3gRcPF24EafT76tQIrrX+rKOkhwOUlTbsmc5/oe2hkrqJr4u3Cjb6HgsmK6pZMHgWHiQLE6PJ4jQACCCCAAAL+FgjVGjx/U1M7BBBAAAEEEEAgMwKhHcFLFZ8e/1HRCFqqyoq+zxlnnOHWw0Uf4zUCCCCAgP8E9KQG/a7QpyBpGQ8JAT8IMIJXQS/oCeXRz4erIHvKTmuKWR+7RkIAAQQQ8LeA1l7/+9//jvvsUn/XnNqFWYARvDD3Lm1DAAEEEEi7wD777OM2xaW9IApAoBICjOBVAousCCCAAAIIIIBAEAQI8ILQS9QRAQQQQAABBBCohAABXiWwyIoAAggggAACCARBgAAvCL1EHRFAAAEEfCugj2nUR1zqYyVJCPhFgADPLz1BPRBAAAEEAimgB8336dPHfXpSIBtApUMpwC7aUHYrjUIAAQQQyJTA9ttvb/fdd1+miqMcBJISYAQvKSYyIYAAAggggAACwREgwAtOX1FTBBBAAAEEEEAgKQECvKSYyIQAAggggEB8gRUrVth3333Hx0vG5+FolgQI8LIET7EIIIAAAuEQGDFihHXu3NmmT58ejgbRilAIEOCFohtpBAIIIIBAtgR23XVXGz58uLVp0yZbVaBcBDYSYBftRiQcQAABBBBAIHmBhg0bWvfu3ZO/gJwIZECAEbwMIFMEAggggAACCCCQSQECvExqUxYCCCCAAAIIIJABAQK8DCBTBAIIIIBAeAWmTp1qt956qy1atCi8jaRlgRMgwAtcl1FhBBBAAAE/CcyYMcPuv/9+W7x4sZ+qRV1yXIBNFjn+DUDzEUAAAQQ2TaBHjx4Ed5tGyNVpEGAELw2o3BIBBBBAAAEEEMimAAFeNvUpGwEEEEAAAQQQSIMAAV4aULklAggggAACCCCQTQECvGzqUzYCCCCAQOAFRo0aZd26dbOZM2cGvi00IDwCBHjh6UtaggACCCCQBYG6detap06drEaNGlkonSIRiC/ALtr4LhxFAAEEEEAgKYGOHTvac889l1ReMiGQKQFG8DIlTTkIIIAAAggggECGBAjwMgRNMQgggAACCCCAQKYECPAyJU05CCCAAAKhFFi7dq3Nnj3b1q9fH8r20ahgChDgBbPfqDUCCCCAgE8Ehg8fbq1btzZ9Ji0JAb8IEOD5pSeoBwIIIIBAIAW6dOligwcPtlatWgWy/lQ6nALsog1nv9IqBBBAAIEMCTRt2tSOOOKIDJVGMQgkJ8AIXnJO5EIAAQQQQAABBAIjQIAXmK6ioggggAACCCCAQHICBHjJOZELAQQQQACBuAKzZs2yxx9/3JYuXRr3PAcRyIYAAV421CkTAQQQQCA0AhMmTLALLrjA5s+fH5o20ZDgC7DJIvh9SAsQQAABBLIocOCBB1pJSYnl5eVlsRYUjcCGAozgbejBOwQQQAABBCotQHBXaTIuSLMAAV6agbk9AggggAACCCCQaQECvEyLUx4CCCCAAAIIIJBmAQK8NANzewQQQACBcAt88803pnV4+jxaEgJ+ESDA80tPUA8EEEAAgUAKFBQUWM2aNdlkEcjeC2+l2UUb3r6lZQgggAACGRDQZ9EOGTIkAyVRBALJCzCCl7wVORFAAAEEEEAAgUAIEOAFopuoJAIIIIAAAgggkLwAAV7yVuREAAEEEEBgIwE95Hj16tVWWlq60TkOIJAtAQK8bMlTLgIIIIBAKAQ+/PBDq127tk2aNCkU7aER4RAgwAtHP9IKBBBAAIEsCXTs2NGeeeYZa968eZZqQLEIbCzALtqNTTiCAAIIIIBA0gKtWrWyM844I+n8ZEQgEwKM4GVCmTIQQAABBBBAAIEMChDgZRCbohBAAAEEEEAAgUwIEOBlQpkyEEAAAQRCKzBv3jwbOHCgLV++PLRtpGHBEyDAC16fUWMEEEAAAR8JfPfdd3b88cfb3LlzfVQrqpLrAmyyyPXvANqPAAIIILBJAj169LDffvvNGjZsuEn34WIEUilAgJdKTe6FAAIIIJBzAoWFhdaoUaOcazcN9rcAU7T+7h9qhwACCCCAAAIIVFqAAK/SZFyAAAIIIIAAAgj4W4AAz9/9Q+0QQAABBHwu4G2y0G5aEgJ+ESDA80tPUA8EEEAAgUAKrF271mbNmmVFRUWBrD+VDqcAmyzC2a+0CgEEEEAgQwK77rqrff755xkqjWIQSE6AEbzknMiFAAIIIIAAAggERoAALzBdRUURQAABBBBAAIHkBAjwknMiFwIIIIAAAgggEBgBArzAdBUVRQABBBDwo8BHH31ktWrVssmTJ/uxetQpRwXYZJGjHZ9Ms2+55RbTtv/HH388bvZ77rnHJk6caM8880zc8xxEAAEEgi5QumqVFY0aZcU//milkd2yBdtua4X772/59euXNW2bbbYx/bzk0yzKSHjhAwECPB90AlVAAAEEEPCfwPoJE2zFxRdZ6cqVZsuW/V7BOnXMbrnZ6tx9j1Xv1csd23LLLe3KK6/0XwOoUU4LMEWb091P4xFAAAEE4gmURGYvlh99lJXOnftncKeMCvYiI3krr7jc1gwaFO9SjiHgCwFG8JLshqeeesrmzJljN9100wZX3HnnndawYUM799xz3UMuNV05cuRImz17tjVp0sT22GMPO+ecc6xmzZqmp51ruvPMM8+0O+64w1q3bm2333671atXb4N76o3Keuyxx9wU6LLIX45t2rSx4447zg444ICyvD/99JO98sorNnbsWNNfkH/5y1/s8MMPt/z83+P2is5PnTrVHnnkEVeG6tCjRw9Xt2rVqvZtoenae++913r27OnqOmDAAGfSsmVLe+2112zp0qW233772aWXXmqDBw+2N954wwoKCuywww6zY489tqxdb731ljv366+/OqNDDz3Ujj766LLzvEAAAQTSKVBaUmLLzz+v/CLWr7c1Dz9k1SM/d/MjP+tJCPhNoGq/yf3WigzUZ7vttrMXX3zRBXLNmzd3JS5evNg++OADu+6669z72267zb755hs7/vjjTUHNqMi6DQUxdevWtTPOOMNWrFhhEyJD/nfffbd16tQp8kfg2rjB3crIX4hnnXWWbbHFFnb66adbcXGxvffee26Nx7aR9R9bbbWVzY38VakpgR122MF9/fnnn+2hhx6yzTbbzAWBFZ2fMWOGnX322S4wvOCCC2zdunX29NNPuzV3N954Y6VF9RT3K664wrXrqKOOctdr/Z6C3TqRKY2+ffuaAkoFbypbwdsRRxxh06dPt0cffdS6du1qMv7iiy/sgQcesJNOOsk6dOhg3377rT388MPuHoccckil68UFCCDgb4HSyKc/FPtsc0LxzJlWEvkjvaJUunq1rXn5JVu9+x7uZ3uXnbtE/pivVdFlG53Pb9HC8hs02Og4BxDYFAECvCT19tprLxdkfPLJJy6A02XDhg2zGjVq2L777mtr1qyxadOmuUDuyCOPdHfViJhGtb7//vuyUkoifxn27t3bTj311LJjsS+UX0GRAi0vmNx5553thBNOcPdSgKcRRQWO2uigEbvu3bubAk4FUBrlq+h8//79Xd01oqg2KDWI/IC54YYbXDkKtpJNCxYssL///e8u2Lz55pstegRw0aJFLvDUCKPSV1995YLggQMHWqtWrdyxL7/80kaMGOECPAV0GtnUqKeSbDXK541KuoP8gwACoREoifz80FRoIFPkD+O1kZ+l+ZH/OkYaoA8qq8qHldWOzAzVOIpZikB+D/i40gR4SXaOgiBNL0YHeB9//LELrDT9qvTCCy+4r6WlpW6ETVvm9VrBX3RSsFZe0rTu66+/7rJ4n3E4fvx4y8vLc6N+OqF7K6iLDnw0gqY8yZxXINWlSxebHhlB85KmmnW9AsxkAzyNNl522WWRZSkr7dZbb7XCwkLvdu6rgsYt/wjudGDrrbd2AaAX3OlY48aN3ec46nW7du1MwZ9GJw888EA3xX3++efrFAkBBBBAAAEEkhQgwEsSStkOOuggu+SSS1zwpkBm3Lhx9q9//avsDpp+1TSnAiQFdVo3p696PlJ0atq0afTbuK/feecdN707MzJVoCCuffv2LlhUwKhRQE2Jxm7J94K7is6rTkuWLLHPPvvM/Rdbgfnz58ceSvheaw3VTk0/a7q6T58+G+TVOsTopLZ4o5LecY3QeUnr9zS1+/LLL7spbp1TwKvgVYEgCQEEwiWQH1nyUv/Tz3zVqOJZM22FZhEiP9fKTZGf7bUuvsSqR9YJb0rK085cEgIpFiDAqwSoRrwUsAwfPtyNQml0qlu3bu4OCxcudCNZWjd39dVXu9ExBSR6rc0F0Sl61C36uPdagZI2K2iNmgLKHXfc0Y2s9YpsyVeAp+sVNGrULDpptG99ZOGvpnfLO1+7dm2rXr26u7/W4cWm6CnW2HOx7xVk9uvXz23WePLJJ92UanQgVlFbY++n9yeeeKLbdKEAWh/g/e6779r1119vTzzxRLzsHEMAgQAL5EV+nuVtvrmvWpAX+dleEHm2XXFkY1zkh27iukX+WFZwl++z+ieuMGdySYDHpFSitxWsaH2bgo5PP/3U7Rb1Rp805alRLAV0CsQU5GgkTVOg+lqZpJE1TWtefvnlLoDUFPCUKVPcLbx7tW3b1jRtG500raudtgr0yjuvDRU6r3J0b+8/rZfTuj9t2Eg2KcDTWkBNo8ri/vvvT/bSuPkGRR47oE0XCjJ32WUXF+Bqg4bWMnptj3shBxFAAIEUCSjo1HPuyg3uIj+jNnvhRYK7FJlzm9QLEOBV0lTrwn744Qf3yBO99lLHjh3dKJvW6CnA+u2339wGCD3uJHYNnndNoq/aYaspWAVwCmomTZpk2qGr5N1Lo1zapatRM5U1ZswYt8FCO3i1XrCi86eccoqbalZApmlW7WzVI19U3+233z5R1RIe12imgjwFjQp+q5oUeCrI++9//+tGKNV2tVO+VRkNrGo9uA4BBHJboCCy2avByFFWEFkek6dHWWl3bGTmIy/yCRZ5kWU2mz36mBX+sZ5ay3P0NAI9HYCEgF8EmKKtZE/oI2k0DVsU2dqvdXFe0royPQpFa+f0OBUl7ZbV//Sawly+fLmXtcKveu6bnpl38cUXuylZjZDpOXtDhw51W/F1A22w0PStdstqvZqSdpx6a+AqOq8dvtocoTWDeiadpmw1YnbhhRdusAvW3TjJf1Tv999/343i6V5VSXvuuafbxfvcc8+5oFbrCjt37uxGFqtyP65BAAEEqiqQF/nZW/fVgVYcmUEojjx3NPKD3/IjsyvVIj/f8qI2lOkJBvrj/qqrrqpqUVyHQMoF8iJruspZYJDy8kJ/Q3HqGXTaSFGZtWzxYDRap4ccl7cpQyN82pSgIFD/xaaKzqu++qtTI3De41Ji75Gt96qX2hS7SSW2Plqjp2Ba0+EKvkkIIIAAAgjkugAjeCn+DtCIkx5ynIrkrY0r716atiyvvIrOq77NmjUrr4isnSsvsM1apSgYAQQQQACBAAiwBi8AnUQVEUAAAQQQQACByggQ4FVGi7wIIIAAAggggEAABAjwAtBJVBEBBBBAwL8CenKAPmJRH1dJQsAvAgR4fukJ6oEAAgggEEiBFi1a2Mknnxx3o1sgG0SlQyHAJotQdCONQAABBBDIloA+u1vPESUh4CcBRvD81BvUBQEEEEAAAQQQSIEAAV4KELkFAggggAACCCDgJwECPD/1BnVBAAEEEAicgB5I/80337iPqQxc5alwaAUI8ELbtTQMAQQQQCATAvq87G7durnP9M5EeZSBQDICBHjJKJEHAQQQQACBBAK77767jRw50tq2bZsgB4cRyLwAu2gzb06JCCCAAAIhEqhfv77tscceIWoRTQmDACN4YehF2oAAAggggAACCEQJEOBFYfASAQQQQAABBBAIgwABXhh6kTYggAACCGRNYMqUKXb99dfbwoULs1YHCkYgVoAAL1aE9wgggAACCFRC4JdffrGnnnrKli5dWomryIpAegXYZJFeX+6OAAIIIBBygf3228/mz58f8lbSvKAJMIIXtB6jvggggAACCCCAQAUCBHgVAHEaAQQQQAABBBAImgABXtB6jPoigAACCCCAAAIVCBDgVQDEaQQQQAABBMoT0KdY7LTTTnxUWXlInMu4AAFexskpEAEEEEAgTAL6JIs999zTatWqFaZm0ZaAC7CLNuAdSPURQAABBLIr0KFDB+vXr192K0HpCMQIMIIXA8JbBBBAAAEEEEAg6AIEeEHvQeqPAAIIIIAAAgjECBDgxYDwFgEEEEAAgcoIrF692qZNm2br16+vzGXkRSCtAgR4aeXl5ggggAACYRf47LPPbOutt7apU6eGvam0L0ACBHgB6iyqigACCCDgP4GuXbvae++9Z61atfJf5ahRzgqwizZnu56GI4AAAgikQqBx48Z2yCGHpOJW3AOBlAkwgpcySm6EAAIIIIAAAgj4Q4AAzx/9QC0QQAABBBBAAIGUCRDgpYySGyGAAAII5KLAzJkz7cEHH7QlS5bkYvNps08FCPB82jFUCwEEEEAgGAITJ060K6+80hYsWBCMClPLnBBgk0VOdDONRAABBBBIl8ABBxxgRUVF6bo990WgSgKM4FWJjYsQQAABBBBAAAH/ChDg+bdvqBkCCCCAAAIIIFAlAQK8KrFxEQIIIIAAAggg4F8BAjz/9g01QwABBBAIgMDXX39tPXr0sF9++SUAtaWKuSJAgJcrPU07EUAAAQTSIlBYWGiNGjWygoKCtNyfmyJQFQF20VZFjWsQQAABBBD4Q6Bz5872xhtv4IGArwQYwfNVd1AZBBBAAAEEEEBg0wUI8DbdkDsggAACCCCAAAK+EiDA81V3UBkEEEAAgaAJrF+/3pYuXWolJSVBqzr1DbEAAV6IO5emIYAAAgikX+Djjz+2Bg0a2OTJk9NfGCUgkKQAAV6SUGRDAAEEEEAgnkCnTp3shRdesBYtWsQ7zTEEsiLALtqssFMoAggggEBYBBTYnXzyyWFpDu0IiQAjeCHpSJqBAAIIIIAAAgh4AgR4ngRfEUAAAQQQQACBkAgQ4IWkI2kGAggggEB2BObOnevW4C1btiw7FaBUBOIIEODFQeEQAggggAACyQp8//33duqpp9q8efOSvYR8CKRdgE0WaSemAAQQQACBMAv07NnTNHpXp06dMDeTtgVMgAAvYB1GdRFAAAEE/CVQUFBgdevW9VelqE3OCzBFm/PfAgAggAACCCCAQNgECPDC1qO0BwEEEEAAAQRyXoAAL+e/BQBAAAEEENgUgXHjxlnfvn1Nu2lJCPhFgADPLz1BPRBAAAEEAilQVFRkixcvtuLi4kDWn0qHU4BNFuHsV1qFAAIIIJAhgW7dutnHH3+codIoBoHkBBjBS86JXAgggAACCCCAQGAECPAC01VUFAEEEEAAAQQQSE6AAC85J3IhgAACCCCAAAKBESDAC0xXUVEEEEAAAT8KDB061KpVq2aTJk3yY/WoU44KEODlaMfTbAQQQACB1AjssMMOdt9991mTJk1Sc0PugkAKBNhFmwJEboEAAgggkLsCbdu2tUsuuSR3AWi5LwUYwfNlt1ApBBBAAAEEEECg6gIEeFW340oEEEAAAQQQQMCXAgR4vuwWKoUAAgggEBSB3377zd59911buXJlUKpMPXNAgAAvBzo52SYefPDB9vLLLyebvcJ899xzj5155pnl5rvppptYu1KuECcRQMDvAmPGjLHevXvb7Nmz/V7VnK7ftIXFNnDMOus3Yq29/f06W7CiJNQebLIIdfdWrnEHHHCAbb311pW7qJzcnTp1ssaNG5eTg1MIIIBA8AW6d+9u06ZNs9atWwe/MSFswbriUrvkzVX22dT1VlJqtma9We3C3xt62u417IoeNUPYajMCvFB2a9Uaddlll1XtwgRXaUSQhAACCIRdoGbNmrbllluGvZmBbF9Jaant+cByW7K61NZHDdgtX/t7c56KjObNX1Zi/zq8diDbV16lCfDK00njuQEDBtiaNWusUaNG9uabb1pp5Jtw3333dVOatWrVciU/++yzVlJSYkVFRfb+++/bIYccYuedd54tWrTInnzySRs9erStX7/edtxxR7v44outZcuW7rrx48fbAw88YFdccYU9+OCDNmvWLNOHYZ944om2/fbbJ2zVRRdd5MpQYKb6rVu3zrbaait75ZVXbN68edaxY0d3z+bNm7t7aL3JQw89ZKNGjXL12Gabbezcc891+ZTh+eeft5kzZ9r111/v8qstOvbJJ5+4tSpqj45Fp9WrV9vjjz/u7qnXGgW89NJLrVmzZtHZeI0AAggggECFAg9/utaWrdkwuIu+aG1kNO+DCUV2cIci+8t2fwzrRWcI8GsCvCx1ngKm4cOHW7169VxwtnbtWhcsLV261P75z3+6Ws2dO9e+/vpry8vLcwGa8iqgU8Cj4Orss882/eX46quvusDwxRdfdFOiK1assIkTJ9qVV17pgrrtttvOBWnXXnutPffcc67MeM2ePHmyCx51TvVT4FZQUOCCvjp16rigT8Ha008/7S5XEDl27Fg77bTTrH79+vb222+bgsTBgwe79/Pnz7fp06e7vPpH9XvppZdcvRWwab3f1KlTXRCn8wpy1TatYzn88MNt2223tddff93OOussV/+6desqGwkBBBBAIEQC85dv+Id+Kpv20tfrbF1x+XdcFhnNezoyktexeUH5GTfxbL2aeVarMG8T75L85QR4yVulPKcCMY1WaZRMqXbt2i4o69u3r7Vr184d0+4sBUVbbLGFe//WW2+5oEnH9HBNpT333NOOPvpoF7wpqPPSkUceaccdd5x7q1G+o446ygVVGgVMJi1evNiV3aZNm7Lsjz32mC1fvtwUbH377bd20EEHmcpR6ty5sxtZ1AijAr7otGTJEtOI5IUXXujqqnMaVdS1CuyUNLL3448/2t1332177bWXO6a2HXbYYTZw4EAXGLqD/IMAAgj4SEA/t/QH72233cZsQyX7ZX1kUdxu9y+v5FWpzz5ienHa63H7oTXtpG41Ul/5BHckwEsAk4nDGlnzgjuVt8cee1j16tXt559/LgvwNt9887LgTnn0g6R9+/ZlwZ2OKTDU9O4PP/ygt2XpwAMPLHtdo0YN23333d29yw5W8EIfuxMd3HmvV61a5QI8fTzPoEGD3FSzytd06nXXXRf3rhqp03SsFiN7SUHibrvt5gJGHVPAqJHCBg0auBFIL5+mlb/77jvvLV8RQAABXwlo5uWrr75yPwt9VTEqk9MCBHhZ7P6mTZtuVLoCOq2Z81Jsnjlz5sT9vEMFY59++ql3mfsa+7mIureCqGSTAq3oVFj4+/qE4uLfx7uvueYau+uuu9waQk2lagpZI3Knn3665edv+AQer01acxidVEeNCCppWlhTz+ecc050FvfaW/e30QkOIIAAAlkW0ExDZX62Zrm6viq+Wn6e/e+8zdJWp+OeX2mLVv0+S1ReId3aFNgdh/2+/r28fJtyrnndDX8vbsq9krmWAC8ZpTTlifdQzGXLllmLFi3KSowNlDT16QVEZZkiL3Sdt8nCO66RNo0Iekl5UhkoaQTu9ttvd2WPHDnSPvroI7dOT8ePOeYYr1j31Qs2VfeGDRuWndNGCi9tttlmrn4vvPCCW3foHddXrUMkIYAAAgiET2CHpulb+3ZMl0IbMHqdezRKIjn9djlrzxqWznokKjudxzMbTqazJQG895QpUzYY0tcmBwVl2lyQKGkrvqZwo4NDrWHTX4+xz7DTblovKY+mOTUtnIqkTSE333yzffbZZ27kTmvxtHZOQeaECRM2KsIrd9y4cWXnVCdNOXtJ09UaxdPGDG0e0X+aWtZO3SFDhnjZ+IoAAggggEBSApfsV9OaRUbOqiWIdgojseVJu1a3g9uHawetcBI0OSk3Mm2igEazbrzxRveATE296pMfunbtah06dEh4Z22U0BTpnXfeadqAoY0aTzzxhP3yyy9ut2v0hQ8//LANGzbMBYP9+vUzbZro06dPdJYqv1bgpSlb3Vdr/xSYKtjTzt+ddtppo/tqBK9Xr16urgpQtcNW7VUw5yXtnNXonwI6PQJG61q06/e9994r22nr5eUrAggggAACFQnUrp5nwy7azNo1y7d6kf0NhX9EPTUj85d637dzod16MA86rsiR85UU0C5YTT2ecsop7nEk2lV6ww03WLVqiWfOtYbt3nvvdaNlCtZ0vXbYaqq0S5cuG9Rg7733tltvvdU9z04jf3rt7bzdIGMV3+hjyBREaueuAk0Ffccff7x7xEm8W2rNnuqpXbx6xp42ZWjkb8GCBS671vBpJ5rW9V1++eXumDZY6LW3qzjefTmGAAIIZFNAf0hrWYoeLRU7k5LNelH27wL5kd+T7569mY2bU2xfzii2hStLrEW9fOuxXTXbcvP0TQ9n2z8vMk1W8erDbNcyhOUriNGUbP/+/U2PENFaOe2GrUzSiJxS9Jo2vf/yyy/dA4lfe+019yBljYR5a+B0PtVJI4oakdP6vtg1g/HK0vSuAsLYDRfReTW6qSCwvDxefn3Itz4HUqOB3uNkxspF5gAAPDVJREFUvHN8RQABBNItoOU2WjusB84n8zMr3fXh/ghIIPFQET4ZE4jdrZpswbGBXbzrFDimM7hTmXoYcuwGj3h18Y5ppE//lZc0VUtCAAEEgiCgT/HRmmQSAn4SYA2en3qDuiCAAAIIIIAAAikQYAQvBYhVucUZZ5zhpiCrcm1F12ht24ABA9I+cldRPTiPAAIIIIAAAtkRYAQvO+6mBxi3bt06LaVrLZ+mDMrbrJGWgrkpAgggkIMCWuesZ4GuWbMmB1tPk/0qQIDn156hXggggAACgRDQxjZ9fvbMmTMDUV8qmRsCBHi50c+0EgEEEEAgTQL6qLJvvvmGXfxp8uW2VRNgDV7V3LgKAQQQQAABJ6Bd/3pIPQkBPwkwguen3qAuCCCAAAIIIIBACgQI8FKAyC0QQAABBBBAAAE/CRDg+ak3qAsCCCCAQOAEJk2aZPooRn0+OAkBvwgQ4PmlJ6gHAggggEAgBebOnWsvv/yy6SMWSQj4RYBNFn7pCeqBAAIIIBBIge7du9usWbMCWXcqHV4BRvDC27e0DAEEEEAAAQRyVIAAL0c7nmYjgAACCCCAQHgFCPDC27e0DAEEEEAAAQRyVIAAL0c7nmYjgAACCKRG4IsvvrB27drZ9OnTU3ND7oJACgQI8FKAyC0QQAABBHJXoFGjRtarVy+rU6dO7iLQct8JsIvWd11ChRBAAAEEgiSg0btHH300SFWmrjkgwAheDnQyTUQAAQQQQACB3BIgwMut/qa1CCCAAAIIIJADAgR4OdDJNBEBBBBAIH0Cq1atsokTJ1pRUVH6CuHOCFRSgACvkmBkRwABBBBAIFrg888/d7top02bFn2Y1whkVYAAL6v8FI4AAgggEHSBbt262dChQ61169ZBbwr1D5EAu2hD1Jk0BQEEEEAg8wKbb765e0xK5kumRAQSCzCCl9iGMwgggAACCCCAQCAFCPAC2W1UGgEEEEAAAQQQSCxAgJfYhjMIIIAAAghUKKCPKLvnnnts8eLFFeYlAwKZEiDAy5Q05SCAAAIIhFJgypQpdvPNN9vChQtD2T4aFUwBNlkEs9+oNQIIIICATwR69uxpK1eu9EltqAYCvwswgsd3AgIIIIAAAgggEDIBAryQdSjNQQABBBBAAAEECPD4HkAAAQQQQAABBEImQIAXsg6lOQgggAACmRUYPXq07b333jZr1qzMFkxpCJQjQIBXDg6nEEAAAQQQqEigVq1atsUWW1hhYWFFWTmPQMYE2EWbMWoKQgABBBAIo0CnTp3slVdeCWPTaFOABRjBC3DnUXUEEEAAAQQQQCCeAAFePBWOIYAAAggggAACARYgwAtw51F1BBBAAIHsCxQVFdmCBQusuLg4+5WhBgj8IUCAx7cCAggggAACmyDwySefWNOmTU0fWUZCwC8CBHh+6QnqgQACCCAQSIHOnTvbwIEDrWXLloGsP5UOpwC7aMPZr7QKAQQQQCBDAs2aNbNjjz02Q6VRDALJCTCCl5wTuRBAAAEEEEAAgcAIEOAFpquoKAIIIIAAAgggkJwAAV5yTuRCAAEEEEAgrsDs2bOtf//+tmzZsrjnOYhANgQI8LKhTpkIIIAAAqERGD9+vJ199tk2b9680LSJhgRfgE0Wwe9DWoAAAgggkEWBXr162erVq61GjRpZrAVFI7ChAAHehh68QwABBBBAoFIC+fn5VrNmzUpdQ2YE0i3AFG26hbk/AggggAACCCCQYQECvAyDUxwCCCCAAAIIIJBuAQK8dAtzfwQQQACBUAuMHTvWevfubXPmzAl1O2lcsAQI8ILVX9QWAQQQQMBnAqWlpVZUVGT6SkLALwJssvBLT1APBBBAAIFACnTt2tU++OCDQNadSodXgBG88PYtLUMAAQQQQACBHBUgwMvRjqfZCCCAAAIIIBBeAQK88PYtLUMAAQQQyJBASUlJhkqiGASSEyDAS86JXAgggAACCMQV+N///mcFBQX2888/xz3PQQSyIUCAlw11ykQAAQQQCI1A+/bt7fHHH7dmzZqFpk00JPgC7KINfh/SAgQQQACBLAq0adPGzjvvvCzWgKIR2FiAEbyNTTiCAAIIIIAAAggEWoAAL9DdR+URQAABBBBAAIGNBQjwNjbhCAIIIIAAAkkL/PrrrzZ48GBbsWJF0teQEYF0CxDgpVuY+yOAAAIIhFrg22+/tSOPPJLPog11LwevcWyyCF6fUWMEEEAAAR8J7LfffjZ79mx20fqoT6iKGQEe3wUIIIAAAghsgkCNGjWsZcuWm3AHLkUg9QJM0abelDsigAACCCCAAAJZFSDAyyo/hSOAAAIIIIAAAqkXIMBLvWlW7zh+/Hj77rvvyq3D8OHDbd9997WlS5eWm4+TCCCAAAIVC/zwww926qmn2vz5823lulIbPrnI3hy3zj6eVGRLV5dWfANyIJAGAdbgpQE1W7dcv369nXvuuXbttdfaTjvtlK1qUC4CCCCQUwIrV660H3/80Z79qtRe/XG55VupFRVHFrkX5FlxSamd2K26XdurVk6Z0NjsCxDgZb8PqAECCCCAQIAFdt99d7vg0c/tpg/WREK76BG7318/+cU6m/RriT17Qp0At5KqB02AAC8gPVZSUmKXXXaZ7bbbbnbCCSeU1Xrt2rV28cUX27HHHmsvv/yyO/7cc8/ZmDFj7LrrrnPvR4wYYW+++aZNmDDBjezph1F5SVO8+uDsM8880+644w5r3bq13X777VarVi175plnbOTIke6RAE2aNLE99tjDzjnnHKtZs6a75apVq+zpp5+20aNHW3FxsfXo0cM9H0p5lVavXu3uPWrUKPe6U6dOdumll27weIG33nrL3njjDdPDQ1X2oYceakcffbS7nn8QQAABvwl8OLHIbowEd+WlUTPW28Axa+24rjXKy8Y5BFImwBq8lFGm90b5+fnWvHlz97T06JIUKE2cONEFbn369HGndtllFxdY6c3kyZPt+uuvt4YNG7qp20aNGtkjjzwSfYuNXutp7AoG7777blMAVrt2batXr57ddttt9u6779qBBx5o//znP12ZCsReeeUVdw8FoTfffLNpjZ+C0JNOOsk++eQTu//++9350tJSF8zpmO5x+eWXu3WAZ511li1fvtzl+eKLL+yBBx4wPVfqxhtvNLXl4Ycftvfff3+jenIAAQQQqKqAfh6VpOi//qPWVliNlevM+o9al7IyvbpXWDAZclaAEbwAdb2Covfee88FX+3bt3c1/+ijj6xbt27WuHFj++tf/+qCMq2/23vvvd35xx57zHbYYYey0Txtrli0aJF9/vnn5bZcwVrv3r3dwmFlXLNmjU2bNs3OOOMMNyKnYxqdU3D5/fff6627p0YL+/fv78rUsRYtWtg111xjM2fOdMGm1qkocNxrr7102vbcc0877LDDbODAgXb22WebngivUTuNCiqpvgUFBaYAl4QAAgikSmDPB5fb3GXR06mpunPi+0xaUGJb3bIscYYqnHnq2Np2ULvCKlzJJWEX4LdmgHq4S5cupqnOjz/+2NVa050a8TrggAMStmLSpEnWvXv3Dc737Nlzg/eJ3uy8885lpzQF+8ILL7jgTn/5zpkzxz799FPTawV/Shot1MM+FVB6SSNw//vf/6xt27YueKtTp441aNDABYYKDmfMmGHbb7992c7fdu3a2S+//GJXXnmlDR061I3snX/++S549e7JVwQQQAABBBAoX4ARvPJ9fHVWo1i9evVy054XXHCBGzHLy8tzo1zxKrps2TI3Bapp2ejkrYeLPhbvddOmTTc4rGlbra/TiJ2CujZt2rivWpunpGAttiwdVx2V5s2bZ9pt5o3OuYN//KPpZyUFn8qn9YSaftbondb5XXHFFW6U8o/sfEEAAQQ2SeCRvrVt7fpNukXZxVcOXmZzVhSUvU/0omGtPHv0qNqJTlfpePtmjNNUCS4HLiLAC1gna5r21VdftZ9++smGDRtm++yzj1sjF68Zm222mdv84K1v8/Jo5C+ZFD0tunDhQrfJY4sttrCrr77aNJqoaWG99p6np/I0jRubFi9e7EbtdF6BnEYCvaDPyxv9/sQTT3SbRsaNG+eCWK370zrCJ554wsvOVwQQQGCTBHZtm7pff+d1r2N3f7jGVkTW2SVKhZH4T49L2Wfr1JWbqCyOIyABQv+AfR9su+22ttVWW7np0a+++iru9KymTV3nRkb8lF+BUnTSOrjKJq2N0+YLBXQaRVRwp3V606dPd191P03D6gO3vYBPx2bNmmV/+9vf7Msvv3T11uicrtGUr/7TZzg+9NBDNmTIEGW3QYMG2aOPPmrVqlVzGywuueQS69u3r5vSVXkkBBBAwG8Cx3Wtbts0LrDCBL9R8yOTGHou3kX71vBb1alPiAUSfDuGuMUhaNpBBx3kHiOiICj6kSd6X1hYaGPHjnXr4dTU4447zu1q1eYMTY/qESSvvfZapRU6duzoRt20A1aPZvntt9/snnvucWvxvDV4epyJgjbtftWT3RXsadRN6+o0zXr44Ydb3bp1XUCnx6goENQjXVQ37dZVUpCoIO+///2vq6/WEGqqVuVHjyhWugFcgAACCKRJoHrkgcaDz6xjrRrk22aRGE4BnZca1MyzHVsU2A/X1LOahVEnvAx8RSBNAnmR0Z7MbiNKU0Ny6bb6OJyjjjrKjYxpM0J00iNFFMQpkPr3v//tTr3++utuTZumWfW4EwV9Tz31lHvkSf369aMvd6+1E1YjdXp2XvQ6vAEDBtg777xjS5Yscfm0y1abKvr16+dG4FSm1unp2XkapVPSjtgbbrjBvF2/2lhx1113lQWg2mChET4Ff15SUKhHrShA1NRt586dXdCoUcN4SVO4qovK1BQyCQEEEMikgJah6Pmhu+66q32/oLp9/HOR/bK01JrVzbPu21Rz07LVoqO+TFaOsnJWgAAvgF2vac5jjjnG9AgUb+QruhkaYdN0prf5QecUxyswVMC2KSNhus/cuXPdfTRimCjpB966detcvuj1dV5+rQvU+XibMrw8etCxgsbodnjnor8S4EVr8BoBBDItoCcFaGZFf8Dqj1YSAn4QSPwb2g+1ow4bCGgqVJ8OoefMbb311nGDO12gdW2xSUGWt1M19lxl3us+GrWrKOnByuUlBW4VpejRw4rych4BBBDIloCe66mnC2y55ZbZqgLlIrCRAAHeRiT+PaC1ag8++KBVr169wk+j8G8rqBkCCCAQLgE9IWDHHXcMV6NoTeAFCPAC1IV6RpxGtbSeLdF6tAA1h6oigAACCCCAQJoECPDSBJuO2+oTIPTRXSQEEEAAAQQQQKA8AR6TUp4O5xBAAAEEEKhAQJsrLrvsMluwYEEFOTmNQOYECPAyZ01JCCCAAAIhFFBgp0dI6VmjJAT8IsAUrV96gnoggAACCARSQB8ZOWXKlEDWnUqHV4ARvPD2LS1DAAEEEEAAgRwVIMDL0Y6n2QgggAACCCAQXgECvPD2LS1DAAEEEEAAgRwVIMDL0Y6n2QgggAACqRH4/PPP3acLeZ/BnZq7chcENk2AAG/T/LgaAQQQQCDHBZo0aWJHHHGE1alTJ8claL6fBNhF66feoC4IIIAAAoET2GGHHez+++8PXL2pcLgFGMELd//SOgQQQAABBBDIQQECvBzsdJqMAAIIIIAAAuEWIMALd//SOgQQQACBNAusWLHCvv/+e1u3bl2aS+L2CCQvQICXvBU5EUAAAQQQ2EhgxIgRttNOOxm7aDei4UAWBQjwsohP0QgggAACwRfYddddbdiwYdamTZvgN4YWhEaAXbSh6UoaggACCCCQDYGGDRvafvvtl42iKROBhAKM4CWk4QQCCCCAAAIIIBBMAQK8YPYbtUYAAQQQQAABBBIKEOAlpOEEAggggAACFQtMnTrVbrvtNlu0aFHFmcmBQIYECPAyBE0xCCCAAALhFJgxY4bdd999tnjx4nA2kFYFUoBNFoHsNiqNAAIIIOAXgR49ehDc+aUzqEeZACN4ZRS8QAABBBBAAAEEwiFAgBeOfqQVCCCAAAIIIIBAmQABXhkFLxBAAAEEEEAAgXAIEOCFox9pBQIIIIBAlgS+/PJL06dZzJw5M0s1oFgENhYgwNvYhCMIIIAAAggkLVCnTh3r2LGj1ahRI+lryIhAugXYRZtuYe6PAAIIIBBqgR133NEGDBgQ6jbSuOAJMIIXvD6jxggggAACCCCAQLkCBHjl8nASAQQQQAABBBAIngABXvD6jBojgAACCPhIYO3atTZnzhwrLi72Ua2oSq4LEODl+ncA7UcAAQQQ2CSB4cOHW6tWrWzKlCmbdB8uRiCVAgR4qdTkXggggAACOSfQpUsXe+utt6xly5Y513Ya7F8BdtH6t2+oGQIIIIBAAASaNm1qffr0CUBNqWIuCTCCl0u9TVsRQAABBBBAICcECPByoptpJAIIIIAAAgjkkgABXi71Nm1FAAEEEEi5wKxZs+zxxx+3pUuXpvze3BCBqgoQ4FVVjusQQAABBBCICEyYMMEuuOACmz9/Ph4I+EaATRa+6QoqggACCCAQRIEDDzzQPQMvP58xkyD2X1jrzHdjWHuWdiGAAAIIZEyA4C5j1BSUpAABXpJQZEMAAQQQQAABBIIiQIAXlJ6inggggAACCCCAQJICBHhJQpENAQQQQACBeAJjxoyxgw46yGbPnh3vNMcQyIoAAV5W2CkUAQQQQCAsAnl5eVajRg3TVxICfhFgF61feoJ6IIAAAggEUmDnnXe2IUOGBLLuVDq8AozghbdvaRkCCCCAAAII5KgAAV6OdjzNRgABBBBAAIHwChDghbdvaRkCCCCAQAYESkpKbM2aNVZaWpqB0igCgeQECPCScyIXAggggAACcQU+/PBDq1Wrlk2aNCnueQ4ikA0BArxsqFMmAggggEBoBDp27Gj9+/e35s2bh6ZNNCT4AuyiDX4f0gIEEEAAgSwKtGrVys4888ws1oCiEdhYgBG8jU04ggACCCCAAAIIBFqAAC/Q3UflEUAAAQQQQACBjQUI8DY24QgCCCCAAAJJC8ybN88GDRpky5cvT/oaMiKQbgECvHQLc38EEEAAgVALfPfdd3bcccfZ3LlzQ91OGhcsATZZBKu/qC0CCCCAgM8EevToYQsWLLCGDRv6rGZUJ5cFCPByufdpOwIIIIDAJgsUFhZa48aNN/k+3ACBVAowRZtKTe6FAAIIIIAAAgj4QIAAzwedQBUQQAABBBBAAIFUChDgpVKTeyGAAAII5JzA999/byeccIJpNy0JAb8IEOD5pSeoBwIIIIBAIAVWr15tM2bMsKKiokDWn0qHU4BNFuHsV1qFAAIIIJAhgd12282++OKLDJVGMQgkJ8AIXnJO5EIAAQQQQAABBAIjQIAXmK76vaLz58+3hQsXBqzWVBcBBBBAAAEEMilQpSnam266yRYvXmwPPfRQJuua02UpsLvgggtMXzfffHN75513ctqDxiOAAALpFChdu9aKf/rJSpcvs/xmzS1/m20sL58xkXSac+/UClQpwEttFbhbMgJDhgyx3377zR599FFr2bJlMpeQBwEEEECgCgKr+z1pa194wSwvr+zqvNp1rNa111r1yKdWxKaPPvrIevfubfrIsm233Tb2NO8RyIoAf45khb3yhWrEdKuttrLOnTtbkyZNKn8DrkAAAQQQqFBg9SMP25onnrDSpUutdMmSsv9K5sy2lRddaGsjf2zHpm0io3ua2WrUqFHsKd4jkDWBCkfwSkpK7Pnnn7dPPvnEVq5caYcccojpWHTS1vBnnnnGRo4cabNnz3YByB577GHnnHOO1axZ016I/CU0btw4u++++6Ivs8cee8xtK7/00ks3OK43AwYMcOc0WvXaa6/Z0sj/bPvtt58p7+DBg+2NN96wgoICO+yww+zYY48tu17b1R9//HEbNWqU6XWnTp3cNc2aNSvLM2fOHFf2xIkTbdmyZdamTRv3QdEHHHCAyzN+/Hg3/XzDDTfY/fffbz/++KM1b97czjjjDOvevXvZfWJfXHjhhXbMMcc4q9GjR9sWW2zh6iczL1VUP/0FqPqfeeaZdscdd1jr1q2tVq1arg5r1qyxs846y0466STbf//93V+LTz31lE2aNMl9BqJ8dF4fm6P07LPPur5S/7z//vuu71T/hx9+2K655hr3VQY77LCDXXnllbZ8+XI3Qqjt/vpsRZXjBZMV9bHKU/tPOeUUt5vss88+0yH7y1/+Yueee65Vq/b7t9qqVavs6aefNvkUFxe7co488siycirycTflHwQQQCANAmsGvmpr+vUr986r77zDCiIBXbWOHcvybbnllnbVVVeVvecFAn4QKIj81XFTeRVRcPbiiy+6AEi/rBUoKFhTwHTwwQe7S2+55RYbPny4HXHEEXbooYe6wExrxPRLfeeddzYFJs8995ztueeeZb/I10bWN9x444120EEHuQAjtg4ffPCBffzxx/ZTZA1Enz59rG7duvb222+7oOarr75yZdWoUcNeeukl23fffd1fTqWlpS7IUJCkeij4GzNmjMuj4XPlV5B68sknu9cKDHfddVebMmWKCxgV1OjDoqdNm2avvPKKC1j1P66uVZ5BgwbZX//6V1eX2PrqvdYkaqu8gtLzzz/f8iPrNRTEbr/99ta2bVtLpn4qZ+DAgTZ27Fjbcccd3T3UDgXOCqyPP/5456W1eOedd55tvfXWLhhUkPrqq6+aglMvUFVfqV+mTp1qXbt2tRYtWthmm23m2vb555+7/thll13s008/dQGxzNVfe++9t2trXmR6olu3bq6pFfWx134FbupbBW21a9e2t956y+rUqeMCbdX/+uuvd31y6qmn2k477WSaelYf9+rVKykfV5mYf37++WfXdgX/DRo0iDnLWwQQQKBiAf18XnXVlVYa+aO/3KRn3UV+p1WP/MwiIeBngXJH8JZEhqc1CqSRmaOPPtq1Q7/w9ctb/zMoKXhTQKTRLR1XUqCkkSE93VtJ1yhw0ihg+/bt3bERI0a4QFAjUYnSokWLXNCkIEtJgd0333zjAqBWrVq5Y19++aXpXtttt527v0bb7r77bttrr73ceQWVCpAUNJ199tmuTgo4FFxqVE5JQY2eQq76ahrUS3/729/sxBNPdG/1nKOjjjrKvv76a9PxRKlp06ammFnBke6rQOzBBx90QZPaX1H9dF8FQgoqFQR5SaOj69atc6NwOnbRRRe5tR533XWXK0vHFMD94x//cEYK3JS0bk9BsEYTleSlvlMbTjvtNHdMo5gKaDXiqlE7JdVbZepYMn3sLor8ow0gGoFU+5UUsCroU2CqoFJ91b9//7KgXnXWaOLMmTNt8uTJSfm4G/MPAgjkvMCKSy629ZE/4lORSjUzFZkpqjBFfn6ue+89K4r8PEt1qhuZuSrYZttU35b75ahAuQGeRn4UbERPS2okTcGOpvOUvClYvVbgMHfuXPeLWq8VGChpKrVnz54uANPIlpIWpe6+++5Wr1499z7ePxqN8YI7nddolUYFveBOxxo3bmyzZs3SS/v222/daJGuU4DpJY2gaVRPSVPHr7/+unutkSZdq1EvBSR6H50UoHlJI5bVq1c3TTGWlzR65gU3yic7jaTJK5n6efeOLts75n2V7YQJE9x0bHRZGsnUqJkCVS/AU8DlBXfe9fqqtXxekquSbLwU7ZpMH3vXqd7RddIUs9c/CuA0uqkpYS+pnv/73//cNZp2V/BdXv951/EVAQQQKI38XC2NrE/OeIr8XkxHuaXrizPeFAoMr0C5AZ73izl24ajWZXkBnmgUbGhdlQILBXWaLtRXrR3zkqZi9QtcI1gK2jQ6pNGm8pK3/svLoylPb9TNO6bg0Uv6HEBNwWrUKTZFX6fpY9VFo0a6p0YVFTTpv+gUO92n4FLrxspLsXVWgKUky2Trp/waCUyUNLKpYDS2LOXXsQULFpRdmug+0dfKQEmjaV6KdtWxZPpY+WLNFBTrjwQlre2L/V7ScS8grIyPriMhgEBuC9T8v3Os5I+Zo02V0Aje6ptvtsgP1wpvlR9ZclPzj8EKZdbvPs0c3fuve615i99nhiq8SZwM+VE/g+Oc5hAClRIoN8DzggAFc5pi9ZIWwntJD9297LLL3CjR1VdfbV26dHGjanqtjRFeateunQv8tCZMmxwUQOyzzz7e6bhfvcAj7sk4B7W+TIGc1g16QYOXzXuvdWb33nuvW8N3ySWXuHVuOuetAfPy66t3TfSxil7HjvBp+lPJW/9WUf28+5fXdo2iKkUH2d51Ki/6MSqJ7hMbwHnXx/uabB/r2vLM1D+azo9N2iGswDCZ/ou9lvcIIJC7AoVRsw6pUCiOLCFZ95//WOQv+cS3i6zlrnHmWVbjsN5leTr8pafd3vcoaxlZ4uNtcis7yQsEsiTw+9BNgsK1rk1Jmyq8pFEujcJ5SdOOK1asMAV0CpI0tacRm+nTp5eN3Hh5NX2pNVja4aqpS216SGXaKvI/l0aBVLamFfWfytDmBy3mV9LuTo0gXn755W5toPJonZiSN9Lk3lTxH033RidNDWsUTwFyMvWLvjbRa42KaZpaGzGik4InBUvasp/KVJk+Lq9cbTTRZpHowF8jm1oPqLWBqfIprw6cQwABBBIJ1Lr071prlOh05C/YyK/MyFMKakQ2FEYnLY3RUiCCu2gVXmdboNwATyN4CtqeiDwTSDsVtfD+nnvucQGUV/GOka3iGrXRBgJNG2pRv/JolM5bg+flPfDAA9212rWp+6Y6HX744W6HqwI6LexXIKHdu+9FFsTqcSlK+qqgQoGYAjo9YuS2225z52Lr6w5W8p8PP/zQPeJFI2mahtZ6Pz06RCmZ+iVbnHYCy1yPjFG9Nd185513unWKXluTvVdF+SrTx+XdSzubFVBrg8sPP/zggj19b2l0V+v/UulTXj04hwACCMQTyI/MANUfNvz3U5GgLTrlRdaLF3TpbA0+/czyopYGRefhNQJ+Eih3ilYV1Q7H22+/3T2SQ7s4FTxoPZ23zktTjtpBq3VtepyKknaA6mO1+kWeJ6RpRG9KUaNOHTp0cMGf9/gNd0GK/tGGjQceeMC0s1QjdEr6q0qvFUQoKcjQqNrFF1/s1typbnpO29ChQ906M5dpE/7RrmAFlHouYP369d1z8fr27evumEz9ki1az9aTrQIkPatPo3raOKG2a6NCKlNl+ri8cjUFq+lxPd9Pj3hR0iYMPW9QKZU+7ob8gwACCFRSID8y49IgMkCw7v3/WNGnw60k8jSJgtZtrDAyA1UYmXkiuKskKNmzJpAXmXItZzz6z3ppdE5TsfEWySuXbqMdtFrU7z3U9s+r/3ylDRAaEVKAlc6k4EcBaaL6atRLo2yJNiFUpW4KfPVoEz1yRaOdGgFNtAauovolW75GITUtralxBXnpTMn2cTJ10FSy+kf+8dbtVcbn3XffdX9UaGo+3o7hZOpDHgQQQKCqAnqWpz5GUn+spvJ3SlXrw3UISKDcKdpoIq1lSxQsKZ9+SWtxf7zgbv369e5TJbznwGkqLt1JI3Pl1VdThen8H1GPVUkU3KntFdUvWR+VIfd0B3eqT3l9nGx9vXxakyijeMGd8qTKxyuPrwgggEC6BLQRTY/+it1kl67yuC8CyQhUOEWbzE0qyqORMi+o03o0RlkqEuM8AggggEBQBPTpP3qUFAkBPwlkJMDTLlJtvNDX6Ifc+gkiFXV58sknN3oOXCruyz0QQAABBBBAAIHKCGQkwFOF9JFhYU96zAcJAQQQQAABBBDItkDSa/CyXVHKRwABBBBAAAEEEEhOgAAvOSdyIYAAAgggEFdAz3bVR3TG+6SeuBdwEIEMCBDgZQCZIhBAAAEEwiugj6I88cQTy575Gt6W0rIgCWRsDV6QUKgrAggggAACyQroYz31kHkSAn4SYATPT71BXRBAAAEEEEAAgRQIEOClAJFbIIAAAggggAACfhIgwPNTb1AXBBBAAIHACehh/t98843pIz1JCPhFgADPLz1BPRBAAAEEAikwatQo69atm82YMSOQ9afS4RQgwAtnv9IqBBBAAIEMCey+++42YsQIa9u2bYZKpBgEKhZgF23FRuRAAAEEEEAgoUD9+vVz4tOaEgJwwpcCjOD5sluoFAIIIIAAAgggUHUBAryq23ElAggggAACCCDgSwECPF92C5VCAAEEEAiKwJQpU+yGG26whQsXBqXK1DMHBAjwcqCTaSICCCCAQPoEZs2aZf369bOlS5emrxDujEAlBdhkUUkwsiOAAAIIIBAtsP/++9v8+fOjD/EagawLMIKX9S6gAggggAACCCCAQGoFCPBS68ndEEAAAQQQQACBrAsQ4GW9C6gAAggggAACCCCQWgECvNR6cjcEEEAAgRwTGDlypHXu3JmPKsuxfvd7cwnw/N5D1A8BBBBAwNcC+iQLfVxZrVq1fF1PKpdbAuyiza3+prUIIIAAAikW6NChgz311FMpviu3Q2DTBBjB2zQ/rkYAAQQQQAABBHwnQIDnuy6hQggggAACCCCAwKYJEOBtmh9XI4AAAgjkuMDq1att+vTptn79+hyXoPl+EiDA81Nv/H975wEkRfG28Zecc5accyyCKMkSEEEQiaKCSBQTUaFI/hEQFCWIQkGBARQQhBIFUYIIShQk55xESZIlz9dP+804u7d7N3uLd7M7z1t1NzM9PT3dv96be7b7fXtYFxIgARIggYgj8PPPP0vhwoXl8OHDEVd3Vjh6CVDgRW/fsmUkQAIkQAIJQKBKlSqyaNEiyZs3bwLcjbcgAWcEGEXrjBNzkQAJkAAJkEBAAtmzZ5cmTZoEPMdEEkgsAhzBSyzyvC8JkAAJkAAJkAAJ/EcEKPD+I7AslgRIgARIgARIgAQSiwAFXmKR531JgARIgASigsDx48dlwoQJcvHixahoDxsRHQQo8KKjH9kKEiABEiCBRCKwb98+6du3r5w9ezaRasDbkkBMAgyyiMmEKSRAAiRAAiTgmECDBg24Bp5jWsyYUAQ4gpdQpHkfEiABEiABEiABEkggAhR4CQSatyEBEiABEiABEiCBhCJAgZdQpHkfEiABEiABEiABEkggAvTBSyDQvM1/T+DGjRuCd0LSSIAESCAhCWzevFkGDhwo06dPl3z58iXkrXkvDxNIliyZpEyZMigBCrygaHgiUgjkyZNHvweyVKlSkVJl1pMESCAKCZQoUSIKW8UmuZVAs2bNZOHChUGrl8RQFvQsT5BAhBBYu3at7NmzJ0Jqy2qSAAmQAAmQQHgEChYsKPXr1w9aCAVeUDQ8QQIkQAIkQAIkQAKRSYBBFpHZb6w1CZAACZAACZAACQQlQIEXFA1PkAAJkAAJkAAJkEBkEqDAi8x+Y61JgARIgARIgARIICgBCrygaHgisQnEN/7n3r17iV31qLh/fPk7afx/WbaT+0dKHnJK3J66e/eu3L59O6RKOOkzJ3lCummUZo4Pfz7///0wMMjiXxbccwmB/fv3y+zZs2XdunWSMWNGady4sXTs2DHW2h05ckQ++ugj2b59u34nZJEiRaR79+5SrVo167qffvpJpk6dah2bO926dZN69eqZh57fxoe/E7bXr1+Xzz77TFatWiWXLl2SypUrS//+/SVTpkyeZ24HECqnvXv3yrBhw+xF+Oy/++67kj9/fnHSRz4XevwAQuGNN96QLFmyyKBBg+KksWTJEvnuu+9k9+7dgudPjx49pEqVKj7XOcnjc4GHD0Llv2zZMpkxY4acOHFC0qRJo5/9r7zyiuTMmVNTxBqpXbp0EX9xXbFiRf0cikbUXAcvGns1gtuExYqHDBkiZcqUkYkTJ8rBgwfl/fff1y0KJvIuX74svXv3luzZs+sHcubMmWXevHny+uuvy5QpU6RkyZL6+p07d8rNmzelSZMmPoQeeOABn2MvH8SHP3g5YQtxvX79et0vKVKkkHHjxknPnj3lk08+kSRJkngZu0/bQ+WEzztedu9v33zzjSRNmlQLFJxz0kf+ZXj1+NatWzJ+/HjZsGGDNGrUKE4MW7duFQjpV199VX+mwd58/hQrVkxf7yRPnDfySIZQ+a9Zs0beeustPRjQr18/LfJmzpyp+2DatGmC583hw4fl+PHj0rZtW0mbNq1FMm/evNZ+1O0oNUsjAdcQUH+MRsOGDQ0lNKw6KQFgKFFmKHFmpdl3Fi1aZNSqVctQ35yt5GvXrhnqn54xduxYK61Xr17GyJEjrWPuxCQQH/4oJS62Bw4cMGrXrm2sXr3auunRo0d1v6mRWivN6zv3i5MS0kbdunUNJeospHH1kZXR4ztqRNR49tlnDSXsjCeeeMIYMWJEnESQX42i+uRr37698fbbb1tpTvJYmT28Ex/+ffr0MVq3bu1DbcWKFfr5smXLFp2uFgTW/xPUyKBPvmg+oA9e1En2yG7Qxo0bpUaNGpIqVSqrIUq86Sk9TEUFsuLFi4v6A5fSpUtbp/ENDVN/V69etdIwGoi8MPVHbaVz518C8eGPq+Nii1c54Vv0gw8+aN0Mi3Ri6hCjerR/CNwPTpiKeu+99+TJJ5+UsmXLWmjj6iMro8d3vv32W8mRI4d+7ZiT0f0zZ87IsWPHpE6dOj7k1Bca67PtJI/PxR4+CJU/UDVt2lT69u3rQw1vOIKpL/t6i88/RlMxW+CV5z+naHXX85dbCJw6dUrKly/vUx1MvcLOnz/vk24e4PVA/q8I2rFjh/zxxx/Srl07nQ3XXrx4UQ/dq5EM7asHPxlMEfrfzyzXi9v48HfC9uTJk4KpRIg8u2XLlk0uXLhgT/L0/v3g9PHHH+svRPA3Ms1JH5l5vb7t1KmTZM2a1TEG/M3AIArths82njnwJXOSB9PpNJFQ+YNZIB/qpUuXCt7VarroHDp0SB+PGjVK+wGnS5dOWrVqJU8//XTUuojwE8W/KFcRwLetDBky+NQpffr0+vivv/7ySQ92gBEMNTWrX/qtplh0Nvxxw9Rwvf6mraZL5OzZs9pnBgEatH8IxIe/E7YIHPDvV9wRaU771Qt9FC4n+JgqlwXtk2fn7aSPvMDXSRtDEXcozxwhQkCY3cAf4g4BRU7y2K/18n6o/AOx2rVrl8APEgLOHCDA3wBeZ4njl156SSDAJ02apAMzApURDWkcwYuGXoyiNuAbF37sZjrgO1muAP8gBwwYoL8xI0gjZcqUuihMBSIQA+/tMx/EmMJq0aKFdvKHgy5NNPtQ+Tthi9GJQCMU6Fsn/eqVvgmXE5zN4ZbgH0jkpI+8wvh+t9P8e/H/fJvHd+7csZ5pZppZB/MYeWj3h8C+fft0sB1mdbp27aoLBd/XXntNChQoIOXKldNpzZo1E0TZIvIWo3h2t6D7U5PEL4UjeInfB6yBjQC+VV25csWWIpYfHYbUYzP8Y4OIU47qegTPHJrHNfDHgJgzxR3S8E0O07PIT/uHQHz4O2EbqFzcEX0WV796qW/C5fT999/rJToQhW43J31kz8995wTMESf/55Z5DH9gJ3mc35E5gxHAMlkQcvDvHTNmjCXakidPriNsTXFnXv/II48IInaxtEo0GgVeNPZqBLcJ/+D8fbLMY9NpNlDzMAWCQIvTp0/LBx98YH1LM/OiDDjZ+hvEhfkt2v+cF4/jw98JW5SLqSpMWdkN1+bOndue5On9cDhhuSAEyfiP3gGokz7yNPgwGo8+g4Gx3XCMaVo8Y5zksV/L/dAJwO8aS6RAxMFFx3TtQUlY/gnPf7jv2M1cLsWcJbKfi4Z9Crxo6MUoagMWv8U/KbsQwFpUqVOntpxl/ZuLiCgsSIpINSx2bEbK2vPNnz9fO+/Cid00iMJNmzbFCNAwz3txGx/+TtiiXDxct23bZmFFEIxaKkUqVapkpXl9JxxO8DHCyv/2yFmTp5M+MvNyGxoBiDdM/flHg+PY/Gw7yRPaXZnbTgDr2yGKtmrVqvLOO+/o/xf283juv/DCC7JgwQJ7sg62wKLI+fLl80mPlgMKvGjpyShpR/PmzfVIz+TJkwX+dHCWnTVrlnTu3Nn6Rvb777/L6NGjdSQsmo3V4zE0j0gqLKWCFc3NHwRVwLAQLCI44ZeHhwG+zak18bSQRMAF7R8C8eHvhC2WJ8A/OwhwjLIisAJLeUCMwC+S9g8BJ5z8P/8mO3yuYYUKFdJb+y8nfWTPz/3YCeD5NHfuXJ0Joz8tW7YUvKVi7dq1ejF1nIPgxtssYE7y6Iz85YiAnT8uwKwNvtzUrFlTVq5caT3/8X8Afy9FixaVUqVK6QXwMWCA5w/eqoPBBKy0EI3+d+DCIAtQoLmGAJYaUAuLCkLZ58yZo6c44CehFrG06oilBxYvXqyH4itUqKD3cRKjFPixW/Xq1fUrsfBPb/jw4fqtGKagw5TvhAkT9NpI9mu8vB8f/k7ZDh48WIYOHSpt2rTRTucYrcKryqJ1eiS+n6O4OPl//s37YC029F8gn0anfWSWxW3sBJYvXy5gis8yDA778OMaOHCgFhrwAcN0IYJbTHOSx8zLbewE7PzPnTun3ziCK/A2EX/DMwarKeD/CgYG0C8wjNw9//zz0qFDB/9LouaY76KNmq6MroZg2hUjPXiPIBxk75ehXEzlokzTL+Z+lR1N5cSHv1O2+PaMyEN7wEs0sbtfbfkvODnto/vVBq+Vg2VqIMBz5coVtOlO8gS9mCfCJoDgF/gDYxHraPe/psAL++PCAkiABEiABEiABEjAXQTog+eu/mBtSIAESIAESIAESCBsAhR4YSNkASRAAiRAAiRAAiTgLgIUeO7qD9aGBEiABEiABEiABMImQIEXNkIWQAIkQAIkQAIkQALuIkCB567+YG1IgARIgARIgARIIGwCFHhhI2QBJEACJEACJEACJOAuAhR47uoP1oYESIAESIAESIAEwiZAgRc2QhZAAiRAAiRAAiRAAu4iQIHnrv5gbUiABEiABEiABEggbAIUeGEjZAEkQAIkQAIkQAIk4C4CFHju6g/WhgRIgARIgARIgATCJkCBFzZCFkACJEACJEACJEAC7iJAgeeu/mBtSIAESIAESIAESCBsAhR4YSNkASRAAiRAAiRAAiTgLgIUeO7qD9aGBEiABEiABEiABMImQIEXNkIWQAIkQAIkQAIkQALuIkCB567+YG1IgARIgARIgARIIGwCFHhhI2QBJEACJEACJEACJOAuAhR47uoP1oYESIAESIAESIAEwiZAgRc2QhZAAiRAAiRAAiRAAu4iQIHnrv5gbUiABEiABEiABEggbAIUeGEjZAEkQAIkQAIkQAIk4C4CFHju6g/WhgRIgARIgARIgATCJkCBFzZCFkACJEACJEACJEAC7iJAgeeu/mBtSIAESIAESIAESCBsAhR4YSNkASRAAiRAAiRAAiTgLgIUeO7qD9aGBEiABEiABEiABMImQIEXNkIWQAIkQAIkQAIkQALuIpDcXdVhbUiABEiABPwJ7Ny5U9avXy87duyQ1KlTS/ny5aVly5aSJk0a/6wBj1euXCmnTp2S5557LuB5JF65ckW++OILqVevnpQqVSpovrhO/Pjjj7J//36fbEmSJJEcOXJIkSJFpESJEpI2bVqf86Ec3L17V5IlS6YvmTNnjmTNmlUaNmwYShHMSwKeIJDEUOaJlrKRJEACJBBhBP7++2/p37+/fPjhh5I0aVIpWrSonDx5Uq5fvy7ZsmWTJUuWSLVq1eJsVceOHWXFihVy4sSJoHmPHj0qhQsXlmnTpknnzp2D5ovrRPv27eXzzz+PIT7RFliBAgVk8eLFUq5cubiK8jl/584d6devnxQvXlxefvllfa5kyZJa7H711Vc+eXlAAiQgwilafgpIgARIwKUEmjdvLpMnT5aBAwfKpUuXZN++fXLhwgVZtmyZFniNGzeWvXv3urL2165d00IUYhQ/EJBoy59//inNmjULuc5o/4QJEwRCz7Rx48ZJ7969zUNuSYAEbAQo8GwwuEsCJEACbiEwf/58Wbp0qfzvf/+TESNGSLp06XTVUqVKJfXr19ejYJiAeeyxx+T27dsxqn327Fm5fPlyjHR7wr1797Twim0iB+IMwvLq1av2S0PeL1iwoLz44ovSrl07OXLkSIxpXBSIe+BeuKcTg8B9+OGHY2TFNO6hQ4fk4sWLMc4xgQS8QoACzys9zXaSAAlEFIExY8ZI7ty59bRkoIoXK1ZMBg0aJMePH5ft27dbWTZs2CA1atTQ12bJkkVatWoVQ5xB0PXs2VNy5cqlp2WxxbSp3SAaO3XqJBkzZtQ+edhWrVrV5172/E73M2XKpLPaRRxGJOH3lyFDBr1Nnz69PPTQQ3LgwAGdd8uWLZIzZ069jxE7U+yWKVNG2rRpY90adX7zzTcF9wAftL9SpUry66+/Wnm4QwJeIUCB55WeZjtJgAQiisCePXukQoUKghG7YAbBBUMABuz06dPy1FNPaf+3TZs2ybZt2/ToHkYD7TZs2DCZMmWKDB8+XI+mjR07Vk8D2/Ng+nPGjBkyffp0OXz4sBaA58+f9xFU9vxx7WOKdfbs2bo8+M6VLVtWX4KADIxCIm316tWya9cugbjduHGj9OnTR+eB+IMIhCFtzZo1eh8jdRiFNA3tGTlypAwYMEAOHjwoa9eu1Sxq166tj8183JKAFwgwitYLvcw2kgAJRBQB+KlhehVRp7FZxYoV9WlzhGr8+PF6WnLu3LnWiNeXX36pR+nMcuAbByHUq1cvPWWK9EKFCsm5c+d8/NkwKpg3b15p27atjtxFAMann34qiOi9ceOGTjPLDLRFEIhp8JtDlC4MI2qzZs2SFClS6GP4EDZq1EimTp2qRxSRiJE5jChCoMIQLWy2NX/+/LoMfcL2C8xGjRolXbt2lcGDB+szCEpZtGiR5og0RN3SSMArBCjwvNLTbCcJkEDEEMAUJJYCsQcUBKo8hBYse/bsegtBVLlyZUvcIRHLqmCEDFG0MCy1glEviCq7IaDDHrDQtGlTvWxK6dKl9ajg448/LnXq1JG6devaLwu6DwGJ5VEgKBcsWKBFIUQcInrthoAL/GA0DqN5EHyo41EVlGFG3trzB9uH8AQvLB9jNwhNLP2CEU0aCXiJAKdovdTbbCsJkEBEEIAPGqZnEXAQm2EaEgZ/NRgCC0xfNZ3w/7/gy2ca8sD88+XJk8fMorcYucNIIPzzMDKIteawj+lcJzZkyBDBz+jRo+W3337Ty7l0795dfvnlF5/L4TcHf0D4zWGaFmv1LV++3PKz88kcy8GxY8f0WXtbzexIO3PmjHnILQl4ggAFnie6mY0kARKINAIQbfCti20ZFPjHwWrWrKm3WEQYU5X+hqVVTEMemH8++Nf5W+vWrXUdII6wCDIEWN++fQULJ4diEKyYKsYCxy1atNBr+ZnXQwRinT8sBYO2wlcP5WN6OrboXvN6c4vpZJh/u5AG30RM+9JIwEsEKPC81NtsKwmQQMQQwHQp3tLQrVs3LXr8Kw4fNfjEdejQQczRNwRdYLQMiyGbhmlLvF3CNIwMpkyZUhYuXGgm6e0PP/zgcwxfNkzbwjAF/Mwzz8ikSZP0MQJAQrV8+fLJxIkTBcu39OjRw7ocU8cQnRB4EJCY1kWELdphn6LGQs8we5pViNrB2z2SJ0+up4Pt6RC3q1at0lPX9nTuk0DUE1DfkGgkQAIkQAIuJLB582ZDLR1iqDXkDBXVaiihZqhpU6NLly6GEkKG8jczlOCxaq5G2gwVDGFUr17dwLVqCtdQb5YwlDgylMCy8qkoVUOJIV2mEoPG119/bajpV7zVyFBvstD51Nsh9PHQoUON3bt3Gyoi1VCCT1+nli+xyvLfUVOs+jrl5+d/Sh8rf0B9Xo3o6WO1zp9uixohNNS6dca6desMtc6frjPqaJZz69YtQ/klGsqfzpg3b56+VglDzcC8kYqw1fVT08KGmrI1VPCJUatWLUMJVEP56JnZuCUBTxDAEDiNBEiABEjApQQgeB599FEtiiDAVPSpoaZvDQgjiB5/U8uMaFEDMYT8DRo0MNSrvXwEHq5RUaWWqIOIVK8X0/lNgYc8aqRN3w/l4EdF2xpqxA2nglpcAk8tcmyoqVp9bzW6ZqgpVUOtZWeoaVx9D5xTo3nGzJkz9TGEpWlqetiqD4Spv8ADDwhSFXWrrwUrtRCysXXrVrMIbknAMwT4Llr11KKRAAmQgNsJ4C0PWNQYb4QwF/qNrc7wqUO0bI4cOYJmU//p9Bp3WCYFUbuBDNOleIctpotjKyvQtaGk3bx5U08tYzkWczo20PXIh+jazJkzBzqt09BurN2HqWsnrIIWxBMkEMEEKPAiuPNYdRIgARIgARIgARIIRIBBFoGoMI0ESIAESIAESIAEIpgABV4Edx6rTgIkQAIkQAIkQAKBCFDgBaLCNBIgARIgARIgARKIYAIUeBHceaw6CZAACZAACZAACQQiQIEXiArTSIAESIAESIAESCCCCVDgRXDnseokQAIkQAIkQAIkEIgABV4gKkwjARIgARIgARIggQgmQIEXwZ3HqpMACZAACZAACZBAIAL/Bzou/MH/UpWrAAAAAElFTkSuQmCC"}}},{"cell_type":"markdown","source":"We will focus on the `prop_inside` and `slope_speed_kmh` variables as they had the highest variable importance besides predicted position.\n-  For each 1% increase of the time the horse spent closest to the inside rail the odds of a first place finish increased ~25%\n-  For each 1 unit increase in slope, meaning the horse was progresively moving faster, the odds of a first place finish increased ~10%","metadata":{}},{"cell_type":"markdown","source":"# Conclusions ","metadata":{}},{"cell_type":"markdown","source":"Staying closer to the inside of the track, closer to the rail is associated with better race outcomes. Additionally, progressively increasing speed, rather than static or decreasing speed is also associated with better race outcomes.","metadata":{}}]}