├── LICENSE
├── functions.py
├── game_result_by_evaluation.ipynb
├── position_sharpness.ipynb
└── queries.py


/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2018 Chris Goldammer
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/functions.py:
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 1 | CONNSTRING_PROD = "host=localhost port=5434 user=postgres dbname=chess_prod"
 2 | CONNSTRING_DEV = "host=localhost port=5434 user=postgres dbname=chess_dev"
 3 | 
 4 | names = {
 5 |    0: "number_moves_own"
 6 | ,  1: "number_moves_opp"
 7 | ,  2: "number_checks_own"
 8 | ,  3: "number_checks_opp"
 9 | ,  4: "number_takes_own"
10 | ,  5: "number_takes_opp"
11 | ,  6: "number_takes_pawn_own"
12 | ,  7: "number_takes_pawn_opp"
13 | ,  8: "opp_kings"
14 | ,  9: "piece_values_own"
15 | , 10: "piece_values_opp"
16 | , 11: "kings_pawn_own"
17 | , 12: "kings_pawn_opp"
18 | , 13: "king_row_own"
19 | , 14: "king_row_opp"
20 | , 15: "queens_own"
21 | , 16: "queens_opp"
22 | , 17: "rooks_own"
23 | , 18: "rooks_opp"
24 | , 19: "in_check"
25 | }
26 | 
27 | # data StatType = NumberMovesOwn                                                            
28 | #               | NumberMovesOpp                                                            
29 | #               | NumberChecksOwn                                                           
30 | #               | NumberChecksOpp                                                           
31 | #               | NumberTakesOwn                                                            
32 | #               | NumberTakesOpp                                                            
33 | #               | NumberTakesPawnOwn                                                        
34 | #               | NumberTakesPawnOpp                                                        
35 | #               | OppKings                                                                  
36 | #               | PieceValuesOwn                                                            
37 | #               | PieceValuesOpp                                                            
38 | #               | KingPawnsOwn                                                              
39 | #               | KingPawnsOpp                                                              
40 | #               | KingRowOwn                                                                
41 | #               | KingRowOpp                                                                
42 | #               | QueensOwn                                                                 
43 | #               | QueensOpp                                                                 
44 | #               | RooksOwn                                                                  
45 | #               | RooksOpp deriving (Eq, Enum)
46 | 
47 | def top_code(x, val):
48 |   x[x > val] = val
49 |   return x
50 | 
51 | def bottom_code(x, val):
52 |   x[x < val] = val
53 |   return x
54 | 
55 | def top_and_bottom(x, bottom, top):
56 |   return top_code(bottom_code(x, bottom), top)
57 | 
58 | 


--------------------------------------------------------------------------------
/game_result_by_evaluation.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Overview\n",
   8 |     "\n",
   9 |     "In this analysis, we measure how well players convert an advantage, as measured by the computer evaluation.\n",
  10 |     "\n",
  11 |     "For instance, we would expect a player to obtain roughly 50% of all available points if they have an equal position against a similarly-rated player. But this percentage will increase if they are higher-rated than their opponent.\n",
  12 |     "\n",
  13 |     "I find that, if you are playing against someone who is 300 points lower-rated than you, and you are in a losing position (-2), then you're still expected to get more than 50% of points!\n",
  14 |     "\n",
  15 |     "The impact of the rating on the win percentage is not strongly affected by player quality, the effects are similarly strong for a sample of strong players (Elo > 2000) and super tournaments."
  16 |    ]
  17 |   },
  18 |   {
  19 |    "cell_type": "markdown",
  20 |    "metadata": {},
  21 |    "source": [
  22 |     "# Setup"
  23 |    ]
  24 |   },
  25 |   {
  26 |    "cell_type": "code",
  27 |    "execution_count": 5,
  28 |    "metadata": {},
  29 |    "outputs": [],
  30 |    "source": [
  31 |     "import pandas as pd\n",
  32 |     "import numpy as np\n",
  33 |     "import psycopg2\n",
  34 |     "import pandas.io.sql as sqlio\n",
  35 |     "import statsmodels.formula.api as smf\n",
  36 |     "import imp"
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "code",
  41 |    "execution_count": 6,
  42 |    "metadata": {},
  43 |    "outputs": [
  44 |     {
  45 |      "data": {
  46 |       "text/plain": [
  47 |        "<module 'queries' from '/Users/cg/Dropbox/code/Python/chess-analysis/queries.py'>"
  48 |       ]
  49 |      },
  50 |      "execution_count": 6,
  51 |      "metadata": {},
  52 |      "output_type": "execute_result"
  53 |     }
  54 |    ],
  55 |    "source": [
  56 |     "import queries\n",
  57 |     "import functions as f\n",
  58 |     "imp.reload(f); imp.reload(queries)"
  59 |    ]
  60 |   },
  61 |   {
  62 |    "cell_type": "code",
  63 |    "execution_count": 7,
  64 |    "metadata": {},
  65 |    "outputs": [],
  66 |    "source": [
  67 |     "pd.set_option('precision', 2) \n",
  68 |     "pd.set_option('display.width', 200)\n",
  69 |     "\n",
  70 |     "%matplotlib inline\n",
  71 |     "import matplotlib\n",
  72 |     "import numpy as np\n",
  73 |     "import matplotlib.pyplot as plt"
  74 |    ]
  75 |   },
  76 |   {
  77 |    "cell_type": "code",
  78 |    "execution_count": 13,
  79 |    "metadata": {},
  80 |    "outputs": [],
  81 |    "source": [
  82 |     "use_dev = False\n",
  83 |     "connstring = f.CONNSTRING_DEV if use_dev else f.CONNSTRING_PROD\n",
  84 |     "conn = psycopg2.connect(connstring)"
  85 |    ]
  86 |   },
  87 |   {
  88 |    "cell_type": "markdown",
  89 |    "metadata": {},
  90 |    "source": [
  91 |     "# Data cleaning"
  92 |    ]
  93 |   },
  94 |   {
  95 |    "cell_type": "markdown",
  96 |    "metadata": {},
  97 |    "source": [
  98 |     "We look only at moves between move 10 and 40 of a game. Thus we exclude moves in which most players are still in book and also long endgames which provide a lot of observations, but little change in the status of the game."
  99 |    ]
 100 |   },
 101 |   {
 102 |    "cell_type": "code",
 103 |    "execution_count": 18,
 104 |    "metadata": {},
 105 |    "outputs": [],
 106 |    "source": [
 107 |     "params = {\n",
 108 |     "    'move_number_start': 10,\n",
 109 |     "    'move_number_end': 40\n",
 110 |     "}"
 111 |    ]
 112 |   },
 113 |   {
 114 |    "cell_type": "code",
 115 |    "execution_count": 24,
 116 |    "metadata": {},
 117 |    "outputs": [
 118 |     {
 119 |      "data": {
 120 |       "text/plain": [
 121 |        "(572665, 6)"
 122 |       ]
 123 |      },
 124 |      "execution_count": 24,
 125 |      "metadata": {},
 126 |      "output_type": "execute_result"
 127 |     }
 128 |    ],
 129 |    "source": [
 130 |     "df = sqlio.read_sql_query(queries.q_rating, conn, params=params) \n",
 131 |     "df.shape"
 132 |    ]
 133 |   },
 134 |   {
 135 |    "cell_type": "markdown",
 136 |    "metadata": {},
 137 |    "source": [
 138 |     "## Summary statistics"
 139 |    ]
 140 |   },
 141 |   {
 142 |    "cell_type": "markdown",
 143 |    "metadata": {},
 144 |    "source": [
 145 |     "### Number of observations "
 146 |    ]
 147 |   },
 148 |   {
 149 |    "cell_type": "code",
 150 |    "execution_count": 25,
 151 |    "metadata": {},
 152 |    "outputs": [
 153 |     {
 154 |      "data": {
 155 |       "text/plain": [
 156 |        "kingbase_random                       532836\n",
 157 |        "Rejkjavik Open 2018                    23716\n",
 158 |        "Candidates 2011-2018                    5916\n",
 159 |        "Wijk An Zee (Tata Steel) 2012-2018      5193\n",
 160 |        "Supertournaments 2017                   4461\n",
 161 |        "World Championships 1886-2014            543\n",
 162 |        "Name: db_name, dtype: int64"
 163 |       ]
 164 |      },
 165 |      "execution_count": 25,
 166 |      "metadata": {},
 167 |      "output_type": "execute_result"
 168 |     }
 169 |    ],
 170 |    "source": [
 171 |     "df.db_name.value_counts()"
 172 |    ]
 173 |   },
 174 |   {
 175 |    "cell_type": "code",
 176 |    "execution_count": 26,
 177 |    "metadata": {},
 178 |    "outputs": [],
 179 |    "source": [
 180 |     "df['difference'] = df.rating_own - df.rating_opponent \n",
 181 |     "df['result'] = df.win + 0.5 * df.draw"
 182 |    ]
 183 |   },
 184 |   {
 185 |    "cell_type": "markdown",
 186 |    "metadata": {},
 187 |    "source": [
 188 |     "### Standard deviation of rating difference\n",
 189 |     "The standard deviation is much smaller in elite tournaments than in opens:"
 190 |    ]
 191 |   },
 192 |   {
 193 |    "cell_type": "code",
 194 |    "execution_count": 27,
 195 |    "metadata": {},
 196 |    "outputs": [
 197 |     {
 198 |      "data": {
 199 |       "text/plain": [
 200 |        "db_name\n",
 201 |        "Candidates 2011-2018                   35.01\n",
 202 |        "Rejkjavik Open 2018                   321.70\n",
 203 |        "Supertournaments 2017                  61.70\n",
 204 |        "Wijk An Zee (Tata Steel) 2012-2018     87.20\n",
 205 |        "World Championships 1886-2014          82.67\n",
 206 |        "kingbase_random                       173.47\n",
 207 |        "Name: difference, dtype: float64"
 208 |       ]
 209 |      },
 210 |      "execution_count": 27,
 211 |      "metadata": {},
 212 |      "output_type": "execute_result"
 213 |     }
 214 |    ],
 215 |    "source": [
 216 |     "df.groupby('db_name').difference.std()"
 217 |    ]
 218 |   },
 219 |   {
 220 |    "cell_type": "markdown",
 221 |    "metadata": {},
 222 |    "source": [
 223 |     "## Data cleaning"
 224 |    ]
 225 |   },
 226 |   {
 227 |    "cell_type": "markdown",
 228 |    "metadata": {},
 229 |    "source": [
 230 |     "Creating a grouped variable for the rating difference"
 231 |    ]
 232 |   },
 233 |   {
 234 |    "cell_type": "code",
 235 |    "execution_count": 28,
 236 |    "metadata": {},
 237 |    "outputs": [],
 238 |    "source": [
 239 |     "df['difference_group'] = round(df.difference / 200)\n",
 240 |     "diff_max = 3\n",
 241 |     "df.loc[df.difference_group < -diff_max, 'difference_group'] = -diff_max\n",
 242 |     "df.loc[df.difference_group > diff_max, 'difference_group'] = diff_max\n",
 243 |     "\n",
 244 |     "df['eval_group'] = round(df.ev / 100)"
 245 |    ]
 246 |   },
 247 |   {
 248 |    "cell_type": "markdown",
 249 |    "metadata": {},
 250 |    "source": [
 251 |     "# Summarizing the data"
 252 |    ]
 253 |   },
 254 |   {
 255 |    "cell_type": "code",
 256 |    "execution_count": 35,
 257 |    "metadata": {},
 258 |    "outputs": [],
 259 |    "source": [
 260 |     "df_king = df.query('db_name == \"kingbase_random\"')"
 261 |    ]
 262 |   },
 263 |   {
 264 |    "cell_type": "code",
 265 |    "execution_count": 36,
 266 |    "metadata": {},
 267 |    "outputs": [],
 268 |    "source": [
 269 |     "means = pd.DataFrame(\n",
 270 |     "     df_king\n",
 271 |     "    .query('-3 <= eval_group <= 3')\n",
 272 |     "    .groupby(['difference_group', 'eval_group'])\n",
 273 |     "    .result\n",
 274 |     "    .mean()\n",
 275 |     ")\n",
 276 |     "counts = (df_king\n",
 277 |     "  .query('-3 <= eval_group <= 3')\n",
 278 |     "  .groupby(['difference_group', 'eval_group'])\n",
 279 |     "  .result\n",
 280 |     "  .count())\n",
 281 |     "\n",
 282 |     "means['counts'] = counts  \n",
 283 |     "means = means.query('counts >= 10 and difference_group >= 0')\n",
 284 |     "del means['counts']"
 285 |    ]
 286 |   },
 287 |   {
 288 |    "cell_type": "code",
 289 |    "execution_count": 42,
 290 |    "metadata": {},
 291 |    "outputs": [],
 292 |    "source": [
 293 |     "table = means.unstack(0) * 100 \n",
 294 |     "table.index = [int(v) for v in table.index]\n",
 295 |     "def group_name(v):\n",
 296 |     "    return '%s to %s' % (v*200-100, v*200+100)\n",
 297 |     "table.columns = [group_name(v) for v in table.columns.get_level_values(1)]"
 298 |    ]
 299 |   },
 300 |   {
 301 |    "cell_type": "code",
 302 |    "execution_count": 43,
 303 |    "metadata": {},
 304 |    "outputs": [
 305 |     {
 306 |      "data": {
 307 |       "text/plain": [
 308 |        "<AxesSubplot:title={'center':'Result percentage as function of evaluation and rating difference'}, xlabel='Evaluation', ylabel='Result percentage'>"
 309 |       ]
 310 |      },
 311 |      "execution_count": 43,
 312 |      "metadata": {},
 313 |      "output_type": "execute_result"
 314 |     },
 315 |     {
 316 |      "data": {
 317 |       "image/png": 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\n",
 318 |       "text/plain": [
 319 |        "<Figure size 720x720 with 1 Axes>"
 320 |       ]
 321 |      },
 322 |      "metadata": {
 323 |       "needs_background": "light"
 324 |      },
 325 |      "output_type": "display_data"
 326 |     }
 327 |    ],
 328 |    "source": [
 329 |     "fig, ax = plt.subplots()\n",
 330 |     "ax.set_xlabel(\"Evaluation\", fontsize=20)\n",
 331 |     "ax.set_ylabel(\"Result percentage\", fontsize=20)\n",
 332 |     "settings = {\n",
 333 |     "    'title': \"Result percentage as function of evaluation and rating difference\",\n",
 334 |     "    'fontsize': 20,\n",
 335 |     "    'legend': True,\n",
 336 |     "    'figsize': (10, 10),\n",
 337 |     "    'lw': 4,\n",
 338 |     "    'ax': ax\n",
 339 |     "}\n",
 340 |     "table.plot(**settings)"
 341 |    ]
 342 |   },
 343 |   {
 344 |    "cell_type": "markdown",
 345 |    "metadata": {},
 346 |    "source": [
 347 |     "The graph shows the expected result percentage as a function of the position evaluation and the rating difference to the opponent.\n",
 348 |     "\n",
 349 |     "For instance, we find that, if you play against someone who is 400 points lower-rated then you and your evaluation is -3, then you are still obtaining more than 40% of the points. This is among players with Elo above 2000.\n",
 350 |     "\n",
 351 |     "Note, however, that this is just one point on this graph. To quantify this by summarizing all the data, we work with a linear regression, below. "
 352 |    ]
 353 |   },
 354 |   {
 355 |    "cell_type": "markdown",
 356 |    "metadata": {},
 357 |    "source": [
 358 |     "The same data in table format"
 359 |    ]
 360 |   },
 361 |   {
 362 |    "cell_type": "code",
 363 |    "execution_count": 44,
 364 |    "metadata": {},
 365 |    "outputs": [
 366 |     {
 367 |      "data": {
 368 |       "text/html": [
 369 |        "<div>\n",
 370 |        "<style scoped>\n",
 371 |        "    .dataframe tbody tr th:only-of-type {\n",
 372 |        "        vertical-align: middle;\n",
 373 |        "    }\n",
 374 |        "\n",
 375 |        "    .dataframe tbody tr th {\n",
 376 |        "        vertical-align: top;\n",
 377 |        "    }\n",
 378 |        "\n",
 379 |        "    .dataframe thead th {\n",
 380 |        "        text-align: right;\n",
 381 |        "    }\n",
 382 |        "</style>\n",
 383 |        "<table border=\"1\" class=\"dataframe\">\n",
 384 |        "  <thead>\n",
 385 |        "    <tr style=\"text-align: right;\">\n",
 386 |        "      <th></th>\n",
 387 |        "      <th>-100.0 to 100.0</th>\n",
 388 |        "      <th>100.0 to 300.0</th>\n",
 389 |        "      <th>300.0 to 500.0</th>\n",
 390 |        "      <th>500.0 to 700.0</th>\n",
 391 |        "    </tr>\n",
 392 |        "  </thead>\n",
 393 |        "  <tbody>\n",
 394 |        "    <tr>\n",
 395 |        "      <th>-3</th>\n",
 396 |        "      <td>7.34</td>\n",
 397 |        "      <td>15.98</td>\n",
 398 |        "      <td>34.40</td>\n",
 399 |        "      <td>NaN</td>\n",
 400 |        "    </tr>\n",
 401 |        "    <tr>\n",
 402 |        "      <th>-2</th>\n",
 403 |        "      <td>15.38</td>\n",
 404 |        "      <td>29.41</td>\n",
 405 |        "      <td>39.17</td>\n",
 406 |        "      <td>NaN</td>\n",
 407 |        "    </tr>\n",
 408 |        "    <tr>\n",
 409 |        "      <th>-1</th>\n",
 410 |        "      <td>30.78</td>\n",
 411 |        "      <td>50.37</td>\n",
 412 |        "      <td>56.41</td>\n",
 413 |        "      <td>79.41</td>\n",
 414 |        "    </tr>\n",
 415 |        "    <tr>\n",
 416 |        "      <th>0</th>\n",
 417 |        "      <td>52.58</td>\n",
 418 |        "      <td>71.15</td>\n",
 419 |        "      <td>81.50</td>\n",
 420 |        "      <td>95.54</td>\n",
 421 |        "    </tr>\n",
 422 |        "    <tr>\n",
 423 |        "      <th>1</th>\n",
 424 |        "      <td>66.27</td>\n",
 425 |        "      <td>82.82</td>\n",
 426 |        "      <td>90.79</td>\n",
 427 |        "      <td>98.02</td>\n",
 428 |        "    </tr>\n",
 429 |        "    <tr>\n",
 430 |        "      <th>2</th>\n",
 431 |        "      <td>87.72</td>\n",
 432 |        "      <td>94.11</td>\n",
 433 |        "      <td>95.96</td>\n",
 434 |        "      <td>100.00</td>\n",
 435 |        "    </tr>\n",
 436 |        "    <tr>\n",
 437 |        "      <th>3</th>\n",
 438 |        "      <td>93.54</td>\n",
 439 |        "      <td>97.12</td>\n",
 440 |        "      <td>97.35</td>\n",
 441 |        "      <td>100.00</td>\n",
 442 |        "    </tr>\n",
 443 |        "  </tbody>\n",
 444 |        "</table>\n",
 445 |        "</div>"
 446 |       ],
 447 |       "text/plain": [
 448 |        "    -100.0 to 100.0  100.0 to 300.0  300.0 to 500.0  500.0 to 700.0\n",
 449 |        "-3             7.34           15.98           34.40             NaN\n",
 450 |        "-2            15.38           29.41           39.17             NaN\n",
 451 |        "-1            30.78           50.37           56.41           79.41\n",
 452 |        " 0            52.58           71.15           81.50           95.54\n",
 453 |        " 1            66.27           82.82           90.79           98.02\n",
 454 |        " 2            87.72           94.11           95.96          100.00\n",
 455 |        " 3            93.54           97.12           97.35          100.00"
 456 |       ]
 457 |      },
 458 |      "execution_count": 44,
 459 |      "metadata": {},
 460 |      "output_type": "execute_result"
 461 |     }
 462 |    ],
 463 |    "source": [
 464 |     "table"
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "markdown",
 469 |    "metadata": {},
 470 |    "source": [
 471 |     "## Quantifying the rating-evaluation tradeoff\n",
 472 |     "We know try to quantify these effects and calculate the tradeoff between player rating and the position evaluation. We do this using a linear regression."
 473 |    ]
 474 |   },
 475 |   {
 476 |    "cell_type": "markdown",
 477 |    "metadata": {},
 478 |    "source": [
 479 |     "Creating helper variables that are scaled, which gives us nicer regression coefficients."
 480 |    ]
 481 |   },
 482 |   {
 483 |    "cell_type": "code",
 484 |    "execution_count": 45,
 485 |    "metadata": {},
 486 |    "outputs": [],
 487 |    "source": [
 488 |     "df['rating_own_small'] = df.rating_own / 100\n",
 489 |     "df['rating_opponent_small'] = df.rating_opponent / 100\n",
 490 |     "df['diff_small'] = df.difference / 100\n",
 491 |     "df['eval_small'] = df.ev / 100"
 492 |    ]
 493 |   },
 494 |   {
 495 |    "cell_type": "code",
 496 |    "execution_count": 47,
 497 |    "metadata": {},
 498 |    "outputs": [
 499 |     {
 500 |      "data": {
 501 |       "text/html": [
 502 |        "<table class=\"simpletable\">\n",
 503 |        "<caption>OLS Regression Results</caption>\n",
 504 |        "<tr>\n",
 505 |        "  <th>Dep. Variable:</th>         <td>result</td>      <th>  R-squared:         </th>  <td>   0.362</td>  \n",
 506 |        "</tr>\n",
 507 |        "<tr>\n",
 508 |        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.362</td>  \n",
 509 |        "</tr>\n",
 510 |        "<tr>\n",
 511 |        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.453e+05</td> \n",
 512 |        "</tr>\n",
 513 |        "<tr>\n",
 514 |        "  <th>Date:</th>             <td>Sat, 01 Jan 2022</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
 515 |        "</tr>\n",
 516 |        "<tr>\n",
 517 |        "  <th>Time:</th>                 <td>12:55:14</td>     <th>  Log-Likelihood:    </th> <td>-1.4711e+05</td>\n",
 518 |        "</tr>\n",
 519 |        "<tr>\n",
 520 |        "  <th>No. Observations:</th>      <td>511532</td>      <th>  AIC:               </th>  <td>2.942e+05</td> \n",
 521 |        "</tr>\n",
 522 |        "<tr>\n",
 523 |        "  <th>Df Residuals:</th>          <td>511529</td>      <th>  BIC:               </th>  <td>2.943e+05</td> \n",
 524 |        "</tr>\n",
 525 |        "<tr>\n",
 526 |        "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>      <td> </td>     \n",
 527 |        "</tr>\n",
 528 |        "<tr>\n",
 529 |        "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
 530 |        "</tr>\n",
 531 |        "</table>\n",
 532 |        "<table class=\"simpletable\">\n",
 533 |        "<tr>\n",
 534 |        "       <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
 535 |        "</tr>\n",
 536 |        "<tr>\n",
 537 |        "  <th>Intercept</th>  <td>    0.5119</td> <td>    0.000</td> <td> 1122.939</td> <td> 0.000</td> <td>    0.511</td> <td>    0.513</td>\n",
 538 |        "</tr>\n",
 539 |        "<tr>\n",
 540 |        "  <th>eval_small</th> <td>    0.1280</td> <td>    0.000</td> <td>  336.778</td> <td> 0.000</td> <td>    0.127</td> <td>    0.129</td>\n",
 541 |        "</tr>\n",
 542 |        "<tr>\n",
 543 |        "  <th>diff_small</th> <td>    0.0875</td> <td>    0.000</td> <td>  325.704</td> <td> 0.000</td> <td>    0.087</td> <td>    0.088</td>\n",
 544 |        "</tr>\n",
 545 |        "</table>\n",
 546 |        "<table class=\"simpletable\">\n",
 547 |        "<tr>\n",
 548 |        "  <th>Omnibus:</th>       <td>7838.850</td> <th>  Durbin-Watson:     </th> <td>   0.076</td>\n",
 549 |        "</tr>\n",
 550 |        "<tr>\n",
 551 |        "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5068.977</td>\n",
 552 |        "</tr>\n",
 553 |        "<tr>\n",
 554 |        "  <th>Skew:</th>           <td>-0.108</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
 555 |        "</tr>\n",
 556 |        "<tr>\n",
 557 |        "  <th>Kurtosis:</th>       <td> 2.562</td>  <th>  Cond. No.          </th> <td>    1.84</td>\n",
 558 |        "</tr>\n",
 559 |        "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
 560 |       ],
 561 |       "text/plain": [
 562 |        "<class 'statsmodels.iolib.summary.Summary'>\n",
 563 |        "\"\"\"\n",
 564 |        "                            OLS Regression Results                            \n",
 565 |        "==============================================================================\n",
 566 |        "Dep. Variable:                 result   R-squared:                       0.362\n",
 567 |        "Model:                            OLS   Adj. R-squared:                  0.362\n",
 568 |        "Method:                 Least Squares   F-statistic:                 1.453e+05\n",
 569 |        "Date:                Sat, 01 Jan 2022   Prob (F-statistic):               0.00\n",
 570 |        "Time:                        12:55:14   Log-Likelihood:            -1.4711e+05\n",
 571 |        "No. Observations:              511532   AIC:                         2.942e+05\n",
 572 |        "Df Residuals:                  511529   BIC:                         2.943e+05\n",
 573 |        "Df Model:                           2                                         \n",
 574 |        "Covariance Type:            nonrobust                                         \n",
 575 |        "==============================================================================\n",
 576 |        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
 577 |        "------------------------------------------------------------------------------\n",
 578 |        "Intercept      0.5119      0.000   1122.939      0.000       0.511       0.513\n",
 579 |        "eval_small     0.1280      0.000    336.778      0.000       0.127       0.129\n",
 580 |        "diff_small     0.0875      0.000    325.704      0.000       0.087       0.088\n",
 581 |        "==============================================================================\n",
 582 |        "Omnibus:                     7838.850   Durbin-Watson:                   0.076\n",
 583 |        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5068.977\n",
 584 |        "Skew:                          -0.108   Prob(JB):                         0.00\n",
 585 |        "Kurtosis:                       2.562   Cond. No.                         1.84\n",
 586 |        "==============================================================================\n",
 587 |        "\n",
 588 |        "Notes:\n",
 589 |        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
 590 |        "\"\"\""
 591 |       ]
 592 |      },
 593 |      "execution_count": 47,
 594 |      "metadata": {},
 595 |      "output_type": "execute_result"
 596 |     }
 597 |    ],
 598 |    "source": [
 599 |     "df_king = df.query('db_name == \"kingbase_random\"')\n",
 600 |     "reg = smf.ols('result ~ eval_small + diff_small', data=df_king.query('-5 <= eval_small <= 5')).fit()\n",
 601 |     "params = reg.params\n",
 602 |     "reg.summary()"
 603 |    ]
 604 |   },
 605 |   {
 606 |    "cell_type": "markdown",
 607 |    "metadata": {},
 608 |    "source": [
 609 |     "To check for a non-linear effect, I include a squared term in the regression. The coefficient is small."
 610 |    ]
 611 |   },
 612 |   {
 613 |    "cell_type": "code",
 614 |    "execution_count": 50,
 615 |    "metadata": {},
 616 |    "outputs": [
 617 |     {
 618 |      "data": {
 619 |       "text/html": [
 620 |        "<table class=\"simpletable\">\n",
 621 |        "<caption>OLS Regression Results</caption>\n",
 622 |        "<tr>\n",
 623 |        "  <th>Dep. Variable:</th>         <td>result</td>      <th>  R-squared:         </th>  <td>   0.362</td>  \n",
 624 |        "</tr>\n",
 625 |        "<tr>\n",
 626 |        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.362</td>  \n",
 627 |        "</tr>\n",
 628 |        "<tr>\n",
 629 |        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>9.687e+04</td> \n",
 630 |        "</tr>\n",
 631 |        "<tr>\n",
 632 |        "  <th>Date:</th>             <td>Sat, 01 Jan 2022</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
 633 |        "</tr>\n",
 634 |        "<tr>\n",
 635 |        "  <th>Time:</th>                 <td>12:55:38</td>     <th>  Log-Likelihood:    </th> <td>-1.4709e+05</td>\n",
 636 |        "</tr>\n",
 637 |        "<tr>\n",
 638 |        "  <th>No. Observations:</th>      <td>511532</td>      <th>  AIC:               </th>  <td>2.942e+05</td> \n",
 639 |        "</tr>\n",
 640 |        "<tr>\n",
 641 |        "  <th>Df Residuals:</th>          <td>511528</td>      <th>  BIC:               </th>  <td>2.942e+05</td> \n",
 642 |        "</tr>\n",
 643 |        "<tr>\n",
 644 |        "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>      <td> </td>     \n",
 645 |        "</tr>\n",
 646 |        "<tr>\n",
 647 |        "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
 648 |        "</tr>\n",
 649 |        "</table>\n",
 650 |        "<table class=\"simpletable\">\n",
 651 |        "<tr>\n",
 652 |        "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
 653 |        "</tr>\n",
 654 |        "<tr>\n",
 655 |        "  <th>Intercept</th>                  <td>    0.5129</td> <td>    0.000</td> <td> 1038.282</td> <td> 0.000</td> <td>    0.512</td> <td>    0.514</td>\n",
 656 |        "</tr>\n",
 657 |        "<tr>\n",
 658 |        "  <th>eval_small</th>                 <td>    0.1281</td> <td>    0.000</td> <td>  336.586</td> <td> 0.000</td> <td>    0.127</td> <td>    0.129</td>\n",
 659 |        "</tr>\n",
 660 |        "<tr>\n",
 661 |        "  <th>I(eval_small * eval_small)</th> <td>   -0.0007</td> <td>    0.000</td> <td>   -5.364</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.000</td>\n",
 662 |        "</tr>\n",
 663 |        "<tr>\n",
 664 |        "  <th>diff_small</th>                 <td>    0.0875</td> <td>    0.000</td> <td>  325.726</td> <td> 0.000</td> <td>    0.087</td> <td>    0.088</td>\n",
 665 |        "</tr>\n",
 666 |        "</table>\n",
 667 |        "<table class=\"simpletable\">\n",
 668 |        "<tr>\n",
 669 |        "  <th>Omnibus:</th>       <td>7840.481</td> <th>  Durbin-Watson:     </th> <td>   0.076</td>\n",
 670 |        "</tr>\n",
 671 |        "<tr>\n",
 672 |        "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5098.351</td>\n",
 673 |        "</tr>\n",
 674 |        "<tr>\n",
 675 |        "  <th>Skew:</th>           <td>-0.110</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
 676 |        "</tr>\n",
 677 |        "<tr>\n",
 678 |        "  <th>Kurtosis:</th>       <td> 2.564</td>  <th>  Cond. No.          </th> <td>    4.33</td>\n",
 679 |        "</tr>\n",
 680 |        "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
 681 |       ],
 682 |       "text/plain": [
 683 |        "<class 'statsmodels.iolib.summary.Summary'>\n",
 684 |        "\"\"\"\n",
 685 |        "                            OLS Regression Results                            \n",
 686 |        "==============================================================================\n",
 687 |        "Dep. Variable:                 result   R-squared:                       0.362\n",
 688 |        "Model:                            OLS   Adj. R-squared:                  0.362\n",
 689 |        "Method:                 Least Squares   F-statistic:                 9.687e+04\n",
 690 |        "Date:                Sat, 01 Jan 2022   Prob (F-statistic):               0.00\n",
 691 |        "Time:                        12:55:38   Log-Likelihood:            -1.4709e+05\n",
 692 |        "No. Observations:              511532   AIC:                         2.942e+05\n",
 693 |        "Df Residuals:                  511528   BIC:                         2.942e+05\n",
 694 |        "Df Model:                           3                                         \n",
 695 |        "Covariance Type:            nonrobust                                         \n",
 696 |        "==============================================================================================\n",
 697 |        "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
 698 |        "----------------------------------------------------------------------------------------------\n",
 699 |        "Intercept                      0.5129      0.000   1038.282      0.000       0.512       0.514\n",
 700 |        "eval_small                     0.1281      0.000    336.586      0.000       0.127       0.129\n",
 701 |        "I(eval_small * eval_small)    -0.0007      0.000     -5.364      0.000      -0.001      -0.000\n",
 702 |        "diff_small                     0.0875      0.000    325.726      0.000       0.087       0.088\n",
 703 |        "==============================================================================\n",
 704 |        "Omnibus:                     7840.481   Durbin-Watson:                   0.076\n",
 705 |        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5098.351\n",
 706 |        "Skew:                          -0.110   Prob(JB):                         0.00\n",
 707 |        "Kurtosis:                       2.564   Cond. No.                         4.33\n",
 708 |        "==============================================================================\n",
 709 |        "\n",
 710 |        "Notes:\n",
 711 |        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
 712 |        "\"\"\""
 713 |       ]
 714 |      },
 715 |      "execution_count": 50,
 716 |      "metadata": {},
 717 |      "output_type": "execute_result"
 718 |     }
 719 |    ],
 720 |    "source": [
 721 |     "reg = smf.ols('result ~ eval_small + I(eval_small*eval_small) + diff_small', data=df_king.query('-5 <= eval_small <= 5')).fit()\n",
 722 |     "params = reg.params\n",
 723 |     "reg.summary()"
 724 |    ]
 725 |   },
 726 |   {
 727 |    "cell_type": "code",
 728 |    "execution_count": 51,
 729 |    "metadata": {},
 730 |    "outputs": [
 731 |     {
 732 |      "name": "stdout",
 733 |      "output_type": "stream",
 734 |      "text": [
 735 |       "Value of 100 rating points: 0.68\n"
 736 |      ]
 737 |     }
 738 |    ],
 739 |    "source": [
 740 |     "value_of_100_rating = params['diff_small'] / params['eval_small']\n",
 741 |     "print(\"Value of 100 rating points: %0.2f\" % value_of_100_rating)"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": 52,
 747 |    "metadata": {},
 748 |    "outputs": [],
 749 |    "source": [
 750 |     "df_value = pd.DataFrame()\n",
 751 |     "df_value['rating_difference'] = np.arange(0, 400, 100)\n",
 752 |     "df_value['evaluation'] = -(df_value.rating_difference * value_of_100_rating)/100"
 753 |    ]
 754 |   },
 755 |   {
 756 |    "cell_type": "markdown",
 757 |    "metadata": {},
 758 |    "source": [
 759 |     "### Evaluation to get result of 50% against a player\n",
 760 |     "Note that this is a linear interpolation from the linear regression."
 761 |    ]
 762 |   },
 763 |   {
 764 |    "cell_type": "code",
 765 |    "execution_count": 55,
 766 |    "metadata": {},
 767 |    "outputs": [
 768 |     {
 769 |      "data": {
 770 |       "text/html": [
 771 |        "<div>\n",
 772 |        "<style scoped>\n",
 773 |        "    .dataframe tbody tr th:only-of-type {\n",
 774 |        "        vertical-align: middle;\n",
 775 |        "    }\n",
 776 |        "\n",
 777 |        "    .dataframe tbody tr th {\n",
 778 |        "        vertical-align: top;\n",
 779 |        "    }\n",
 780 |        "\n",
 781 |        "    .dataframe thead th {\n",
 782 |        "        text-align: right;\n",
 783 |        "    }\n",
 784 |        "</style>\n",
 785 |        "<table border=\"1\" class=\"dataframe\">\n",
 786 |        "  <thead>\n",
 787 |        "    <tr style=\"text-align: right;\">\n",
 788 |        "      <th></th>\n",
 789 |        "      <th>evaluation</th>\n",
 790 |        "    </tr>\n",
 791 |        "    <tr>\n",
 792 |        "      <th>rating_difference</th>\n",
 793 |        "      <th></th>\n",
 794 |        "    </tr>\n",
 795 |        "  </thead>\n",
 796 |        "  <tbody>\n",
 797 |        "    <tr>\n",
 798 |        "      <th>0</th>\n",
 799 |        "      <td>-0.00</td>\n",
 800 |        "    </tr>\n",
 801 |        "    <tr>\n",
 802 |        "      <th>100</th>\n",
 803 |        "      <td>-0.68</td>\n",
 804 |        "    </tr>\n",
 805 |        "    <tr>\n",
 806 |        "      <th>200</th>\n",
 807 |        "      <td>-1.37</td>\n",
 808 |        "    </tr>\n",
 809 |        "    <tr>\n",
 810 |        "      <th>300</th>\n",
 811 |        "      <td>-2.05</td>\n",
 812 |        "    </tr>\n",
 813 |        "  </tbody>\n",
 814 |        "</table>\n",
 815 |        "</div>"
 816 |       ],
 817 |       "text/plain": [
 818 |        "                   evaluation\n",
 819 |        "rating_difference            \n",
 820 |        "0                       -0.00\n",
 821 |        "100                     -0.68\n",
 822 |        "200                     -1.37\n",
 823 |        "300                     -2.05"
 824 |       ]
 825 |      },
 826 |      "execution_count": 55,
 827 |      "metadata": {},
 828 |      "output_type": "execute_result"
 829 |     }
 830 |    ],
 831 |    "source": [
 832 |     "df_value.set_index('rating_difference')"
 833 |    ]
 834 |   },
 835 |   {
 836 |    "cell_type": "markdown",
 837 |    "metadata": {},
 838 |    "source": [
 839 |     "# Comparison to super-tournaments"
 840 |    ]
 841 |   },
 842 |   {
 843 |    "cell_type": "code",
 844 |    "execution_count": 57,
 845 |    "metadata": {},
 846 |    "outputs": [
 847 |     {
 848 |      "data": {
 849 |       "text/html": [
 850 |        "<table class=\"simpletable\">\n",
 851 |        "<caption>OLS Regression Results</caption>\n",
 852 |        "<tr>\n",
 853 |        "  <th>Dep. Variable:</th>         <td>result</td>      <th>  R-squared:         </th>  <td>   0.362</td>  \n",
 854 |        "</tr>\n",
 855 |        "<tr>\n",
 856 |        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.362</td>  \n",
 857 |        "</tr>\n",
 858 |        "<tr>\n",
 859 |        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>9.687e+04</td> \n",
 860 |        "</tr>\n",
 861 |        "<tr>\n",
 862 |        "  <th>Date:</th>             <td>Sat, 01 Jan 2022</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
 863 |        "</tr>\n",
 864 |        "<tr>\n",
 865 |        "  <th>Time:</th>                 <td>12:56:34</td>     <th>  Log-Likelihood:    </th> <td>-1.4709e+05</td>\n",
 866 |        "</tr>\n",
 867 |        "<tr>\n",
 868 |        "  <th>No. Observations:</th>      <td>511532</td>      <th>  AIC:               </th>  <td>2.942e+05</td> \n",
 869 |        "</tr>\n",
 870 |        "<tr>\n",
 871 |        "  <th>Df Residuals:</th>          <td>511528</td>      <th>  BIC:               </th>  <td>2.942e+05</td> \n",
 872 |        "</tr>\n",
 873 |        "<tr>\n",
 874 |        "  <th>Df Model:</th>              <td>     3</td>      <th>                     </th>      <td> </td>     \n",
 875 |        "</tr>\n",
 876 |        "<tr>\n",
 877 |        "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
 878 |        "</tr>\n",
 879 |        "</table>\n",
 880 |        "<table class=\"simpletable\">\n",
 881 |        "<tr>\n",
 882 |        "               <td></td>                 <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
 883 |        "</tr>\n",
 884 |        "<tr>\n",
 885 |        "  <th>Intercept</th>                  <td>    0.5129</td> <td>    0.000</td> <td> 1038.282</td> <td> 0.000</td> <td>    0.512</td> <td>    0.514</td>\n",
 886 |        "</tr>\n",
 887 |        "<tr>\n",
 888 |        "  <th>eval_small</th>                 <td>    0.1281</td> <td>    0.000</td> <td>  336.586</td> <td> 0.000</td> <td>    0.127</td> <td>    0.129</td>\n",
 889 |        "</tr>\n",
 890 |        "<tr>\n",
 891 |        "  <th>I(eval_small * eval_small)</th> <td>   -0.0007</td> <td>    0.000</td> <td>   -5.364</td> <td> 0.000</td> <td>   -0.001</td> <td>   -0.000</td>\n",
 892 |        "</tr>\n",
 893 |        "<tr>\n",
 894 |        "  <th>diff_small</th>                 <td>    0.0875</td> <td>    0.000</td> <td>  325.726</td> <td> 0.000</td> <td>    0.087</td> <td>    0.088</td>\n",
 895 |        "</tr>\n",
 896 |        "</table>\n",
 897 |        "<table class=\"simpletable\">\n",
 898 |        "<tr>\n",
 899 |        "  <th>Omnibus:</th>       <td>7840.481</td> <th>  Durbin-Watson:     </th> <td>   0.076</td>\n",
 900 |        "</tr>\n",
 901 |        "<tr>\n",
 902 |        "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>5098.351</td>\n",
 903 |        "</tr>\n",
 904 |        "<tr>\n",
 905 |        "  <th>Skew:</th>           <td>-0.110</td>  <th>  Prob(JB):          </th> <td>    0.00</td>\n",
 906 |        "</tr>\n",
 907 |        "<tr>\n",
 908 |        "  <th>Kurtosis:</th>       <td> 2.564</td>  <th>  Cond. No.          </th> <td>    4.33</td>\n",
 909 |        "</tr>\n",
 910 |        "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
 911 |       ],
 912 |       "text/plain": [
 913 |        "<class 'statsmodels.iolib.summary.Summary'>\n",
 914 |        "\"\"\"\n",
 915 |        "                            OLS Regression Results                            \n",
 916 |        "==============================================================================\n",
 917 |        "Dep. Variable:                 result   R-squared:                       0.362\n",
 918 |        "Model:                            OLS   Adj. R-squared:                  0.362\n",
 919 |        "Method:                 Least Squares   F-statistic:                 9.687e+04\n",
 920 |        "Date:                Sat, 01 Jan 2022   Prob (F-statistic):               0.00\n",
 921 |        "Time:                        12:56:34   Log-Likelihood:            -1.4709e+05\n",
 922 |        "No. Observations:              511532   AIC:                         2.942e+05\n",
 923 |        "Df Residuals:                  511528   BIC:                         2.942e+05\n",
 924 |        "Df Model:                           3                                         \n",
 925 |        "Covariance Type:            nonrobust                                         \n",
 926 |        "==============================================================================================\n",
 927 |        "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
 928 |        "----------------------------------------------------------------------------------------------\n",
 929 |        "Intercept                      0.5129      0.000   1038.282      0.000       0.512       0.514\n",
 930 |        "eval_small                     0.1281      0.000    336.586      0.000       0.127       0.129\n",
 931 |        "I(eval_small * eval_small)    -0.0007      0.000     -5.364      0.000      -0.001      -0.000\n",
 932 |        "diff_small                     0.0875      0.000    325.726      0.000       0.087       0.088\n",
 933 |        "==============================================================================\n",
 934 |        "Omnibus:                     7840.481   Durbin-Watson:                   0.076\n",
 935 |        "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             5098.351\n",
 936 |        "Skew:                          -0.110   Prob(JB):                         0.00\n",
 937 |        "Kurtosis:                       2.564   Cond. No.                         4.33\n",
 938 |        "==============================================================================\n",
 939 |        "\n",
 940 |        "Notes:\n",
 941 |        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
 942 |        "\"\"\""
 943 |       ]
 944 |      },
 945 |      "execution_count": 57,
 946 |      "metadata": {},
 947 |      "output_type": "execute_result"
 948 |     }
 949 |    ],
 950 |    "source": [
 951 |     "df_super = df.query('db_name == \"Supertournaments 2017\"')\n",
 952 |     "reg_super = smf.ols('result ~ eval_small + diff_small', data=df_super.query('-5 <= eval_small <= 5')).fit()\n",
 953 |     "params_super = reg_super.params\n",
 954 |     "reg.summary()"
 955 |    ]
 956 |   },
 957 |   {
 958 |    "cell_type": "code",
 959 |    "execution_count": 60,
 960 |    "metadata": {},
 961 |    "outputs": [
 962 |     {
 963 |      "name": "stdout",
 964 |      "output_type": "stream",
 965 |      "text": [
 966 |       "Value of 100 rating points (Kingbase): 0.68\n"
 967 |      ]
 968 |     }
 969 |    ],
 970 |    "source": [
 971 |     "value_of_100_rating = params['diff_small'] / params['eval_small']\n",
 972 |     "print(\"Value of 100 rating points (Kingbase): %0.2f\" % value_of_100_rating)"
 973 |    ]
 974 |   },
 975 |   {
 976 |    "cell_type": "code",
 977 |    "execution_count": 61,
 978 |    "metadata": {},
 979 |    "outputs": [
 980 |     {
 981 |      "name": "stdout",
 982 |      "output_type": "stream",
 983 |      "text": [
 984 |       "Value of 100 rating points (Supertournaments): 0.59\n"
 985 |      ]
 986 |     }
 987 |    ],
 988 |    "source": [
 989 |     "value_of_100_rating = params_super['diff_small'] / params_super['eval_small']\n",
 990 |     "print(\"Value of 100 rating points (Supertournaments): %0.2f\" % value_of_100_rating)"
 991 |    ]
 992 |   },
 993 |   {
 994 |    "cell_type": "markdown",
 995 |    "metadata": {},
 996 |    "source": [
 997 |     "For elite players, the impact of rating on the result percentage is only a little smaller (0.6 vs 0.7) compared to a sample of games from players with rating above 2000 (Kingbase)."
 998 |    ]
 999 |   }
1000 |  ],
1001 |  "metadata": {
1002 |   "kernelspec": {
1003 |    "display_name": "Python 3",
1004 |    "language": "python",
1005 |    "name": "python3"
1006 |   },
1007 |   "language_info": {
1008 |    "codemirror_mode": {
1009 |     "name": "ipython",
1010 |     "version": 3
1011 |    },
1012 |    "file_extension": ".py",
1013 |    "mimetype": "text/x-python",
1014 |    "name": "python",
1015 |    "nbconvert_exporter": "python",
1016 |    "pygments_lexer": "ipython3",
1017 |    "version": "3.8.3"
1018 |   }
1019 |  },
1020 |  "nbformat": 4,
1021 |  "nbformat_minor": 2
1022 | }
1023 | 


--------------------------------------------------------------------------------
/queries.py:
--------------------------------------------------------------------------------
 1 | q_rating = """
 2 | SELECT 
 3 |   db_name
 4 | , rating_own
 5 | , rating_opponent
 6 | , eval as ev
 7 | , (result=1) :: Int as win
 8 | , (result=0) :: Int as draw
 9 | FROM (
10 |   SELECT 
11 |     game_result as result
12 |   , is_white
13 |   , rating1.rating as rating_own
14 |   , rating2.rating as rating_opponent
15 |   , eval
16 |   , move_number
17 |   , database.name as db_name
18 |   FROM game
19 |   JOIN player_rating as rating1 ON 
20 |         game.player_white_id=rating1.player_id
21 |     AND extract(year from game.date)=rating1.year
22 |     AND extract(month from game.date)=rating1.month
23 |   JOIN player_rating as rating2 ON 
24 |         game.player_black_id=rating2.player_id
25 |     AND extract(year from game.date)=rating2.year
26 |     AND extract(month from game.date)=rating2.month
27 |   JOIN move_eval on game.id=move_eval.game_id
28 |   JOIN database on game.database_id=database.id
29 |   WHERE 
30 |         move_number>=%(move_number_start)s and move_number<=%(move_number_end)s
31 |     AND eval is not null
32 |     AND database.is_public OR name='kingbase_random'
33 | ) values
34 | WHERE is_white
35 | """
36 | 
37 | q_games = """SELECT * from game"""
38 | q_db = """SELECT * from database WHERE is_public OR name='kingbase_random'"""
39 | q_players = """SELECT * from player"""
40 | q_tournaments = """SELECT * from tournament"""
41 | 
42 | q_positions = """
43 | SELECT
44 |   g.id as game_id
45 | , me.move_number, me.is_white
46 | , me.fen, me.eval as eval_played, me.eval_best
47 | , g.game_result as result
48 | , pa.typ, pa.value
49 | FROM move_eval me
50 | JOIN game g on me.game_id = g.id
51 | JOIN position p on me.fen = p.fen
52 | JOIN position_attribute pa on pa.position_id = p.id
53 | WHERE eval_best is not null
54 | LIMIT {limit}
55 | """
56 | 


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