{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# League of Legends win rates by tier\n", "\n", "An exploration of champion win rates by tier. Hypothesis: certain high skill champions like Nidalee win more when played by Platinum players compared to Silver players. Conversely certain cheese champions like Amumu win more in Silver players, where they are mostly against lower skill players.\n", "\n", "Data comes from http://na.op.gg/statistics/champion/ and represents approximately 15 million games played in the month ending September 12 2016.\n", "\n", "By Nelson Minar " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas, collections, numpy, seaborn\n", "from IPython.core.display import display, HTML\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import seaborn as sns;\n", "sns.set_palette('deep')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tier_names = ['Bronze', 'Silver', 'Gold', 'Platinum', 'Diamond', 'Master', 'Challenger']\n", "\n", "right_align = [{'selector': 'td', 'props': [('text-align', 'right')]}] # for DataFrame.style" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load and prepare the data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Create a bunch of DataFrames, one per CSV file\n", "tiers = {}\n", "for tier in tier_names:\n", " tiers[tier] = pandas.read_csv('data/Champion win rates by tier - %s.tsv' % tier,\n", " sep='\\t', header=0,\n", " names=['N', 'X', 'champion', 'winrate', 'games', 'kda', 'cs', 'gold'],\n", " thousands = ',',\n", " index_col = 2)\n", " # Remove unneeded columns\n", " del tiers[tier]['X']\n", " del tiers[tier]['N']\n", " # Parse a couple of columns down to simple numbers\n", " tiers[tier]['winrate'] = tiers[tier]['winrate'].apply(lambda s: float(s[:-1]))\n", " tiers[tier]['kda'] = tiers[tier]['kda'].apply(lambda s: float(s[:-2]))\n", "\n", "# Smoosh all the DataFrames into a single Panel\n", "data = pandas.Panel(tiers, items = tier_names)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sample data for Nidalee" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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BronzeSilverGoldPlatinumDiamondMasterChallenger
winrate41.4544.5948.1451.0053.9661.4557.21
games17466.0058051.0052953.0039436.0012505.00664.00208.00
kda2.042.382.702.923.133.593.97
cs107.19122.93133.71138.80142.22144.76146.22
gold11625.0012232.0012708.0012806.0012767.0012992.0012950.00
\n", "
" ], "text/plain": [ " Bronze Silver Gold Platinum Diamond Master \\\n", "winrate 41.45 44.59 48.14 51.00 53.96 61.45 \n", "games 17466.00 58051.00 52953.00 39436.00 12505.00 664.00 \n", "kda 2.04 2.38 2.70 2.92 3.13 3.59 \n", "cs 107.19 122.93 133.71 138.80 142.22 144.76 \n", "gold 11625.00 12232.00 12708.00 12806.00 12767.00 12992.00 \n", "\n", " Challenger \n", "winrate 57.21 \n", "games 208.00 \n", "kda 3.97 \n", "cs 146.22 \n", "gold 12950.00 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.xs('Nidalee')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Average statistics by tier" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " winrate\n", " \n", " games\n", " \n", " kda\n", " \n", " cs\n", " \n", " gold\n", " \n", "
\n", " Bronze\n", " \n", " \n", " 45.93%\n", " \n", " \n", " 31,248\n", " \n", " \n", " 2.27\n", " \n", " \n", " 126\n", " \n", " \n", " 11,774\n", " \n", "
\n", " Silver\n", " \n", " \n", " 48.21%\n", " \n", " \n", " 89,143\n", " \n", " \n", " 2.44\n", " \n", " \n", " 142\n", " \n", " \n", " 12,058\n", " \n", "
\n", " Gold\n", " \n", " \n", " 49.42%\n", " \n", " \n", " 61,990\n", " \n", " \n", " 2.53\n", " \n", " \n", " 151\n", " \n", " \n", " 12,205\n", " \n", "
\n", " Platinum\n", " \n", " \n", " 49.90%\n", " \n", " \n", " 33,342\n", " \n", " \n", " 2.56\n", " \n", " \n", " 156\n", " \n", " \n", " 12,116\n", " \n", "
\n", " Diamond\n", " \n", " \n", " 50.05%\n", " \n", " \n", " 7,429\n", " \n", " \n", " 2.55\n", " \n", " \n", " 156\n", " \n", " \n", " 11,714\n", " \n", "
\n", " Master\n", " \n", " \n", " 54.45%\n", " \n", " \n", " 321\n", " \n", " \n", " 2.70\n", " \n", " \n", " 156\n", " \n", " \n", " 11,494\n", " \n", "
\n", " Challenger\n", " \n", " \n", " 55.38%\n", " \n", " \n", " 94\n", " \n", " \n", " 3.11\n", " \n", " \n", " 152\n", " \n", " \n", " 11,361\n", " \n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = []\n", "for t, df in data.iteritems():\n", " d.append((df['winrate'].mean(), \n", " df['games'].mean(), \n", " df['kda'].mean(), \n", " df['cs'].mean(), \n", " df['gold'].mean()))\n", "\n", "averages = pandas.DataFrame(d, index = data.items, columns=('winrate', 'games', 'kda', 'cs', 'gold'))\n", "(averages.style\n", " .format({'cs': \"{:.0f}\", 'games': '{:,.0f}', 'gold': '{:,.0f}', 'kda': '{:.2f}', 'winrate': \"{:.2f}%\"})\n", " .set_table_styles(right_align))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Win rates by tier, alternate calculation\n", "\n", "It seems odd that the total win rate across all data is < 50%. Perhaps they are including games that didn't complete?" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " win rate for tier\n", " \n", " wins in tier\n", " \n", " games in tier\n", " \n", "
\n", " Bronze\n", " \n", " \n", " 46.39%\n", " \n", " \n", " 1,913,643\n", " \n", " \n", " 4,124,704\n", " \n", "
\n", " Silver\n", " \n", " \n", " 48.50%\n", " \n", " \n", " 5,707,026\n", " \n", " \n", " 11,766,902\n", " \n", "
\n", " Gold\n", " \n", " \n", " 49.53%\n", " \n", " \n", " 4,052,918\n", " \n", " \n", " 8,182,712\n", " \n", "
\n", " Platinum\n", " \n", " \n", " 50.01%\n", " \n", " \n", " 2,200,874\n", " \n", " \n", " 4,401,080\n", " \n", "
\n", " Diamond\n", " \n", " \n", " 50.37%\n", " \n", " \n", " 493,929\n", " \n", " \n", " 980,689\n", " \n", "
\n", " Master\n", " \n", " \n", " 53.46%\n", " \n", " \n", " 20,766\n", " \n", " \n", " 38,843\n", " \n", "
\n", " Challenger\n", " \n", " \n", " 54.48%\n", " \n", " \n", " 4,101\n", " \n", " \n", " 7,528\n", " \n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = []\n", "for t, df in data.iteritems():\n", " winsPerChamp = df.games * df.winrate / 100\n", " d.append((100 * winsPerChamp.sum() / df.games.sum(), winsPerChamp.sum(), df.games.sum()))\n", "tier_stats = pandas.DataFrame(d, index=data.items, columns=('win rate for tier', 'wins in tier', 'games in tier'))\n", "(tier_stats.style\n", " .format({'games in tier': '{:,.0f}', 'win rate for tier': '{:.2f}%', 'wins in tier': '{:,.0f}'})\n", " .set_table_styles(right_align)\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Champion win rates from Silver to Diamond\n", "\n", "Which champions do the most better in the hands of skilled players? Which champions' win rates fall off in higher tiers?\n", "\n", "It turns out Nidalee has the most improvement for player skill; she goes from 44.59% win rate for Silver players to 53.96% in Diamond, a gain of 9.37%. Conversely Amumu loses 4.10% win rate.\n", "\n", "The column \"Silver to Diamond\" is simply the difference in win rates in the two tiers. \"Max Spread\" is the difference between maximum and minimum win rate. It's uesful for champs like Blitzcrank that are strongest in Gold (+2.09%), not Platinum (+0.87%)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
\n", " \n", " Silver to Diamond\n", " \n", " Max Spread\n", " \n", " Silver\n", " \n", " Gold\n", " \n", " Platinum\n", " \n", " Diamond\n", " \n", "
\n", " Nidalee\n", " \n", " \n", " +9.37%\n", " \n", " \n", " 9.37%\n", " \n", " \n", " 44.59%\n", " \n", " \n", " 48.14%\n", " \n", " \n", " 51.00%\n", " \n", " \n", " 53.96%\n", " \n", "
\n", " Pantheon\n", " \n", " \n", " +7.50%\n", " \n", " \n", " 7.50%\n", " \n", " \n", " 48.01%\n", " \n", " \n", " 51.33%\n", " \n", " \n", " 52.84%\n", " \n", " \n", " 55.51%\n", " \n", "
\n", " Riven\n", " \n", " \n", " +6.07%\n", " \n", " \n", " 6.07%\n", " \n", " \n", " 47.05%\n", " \n", " \n", " 49.26%\n", " \n", " \n", " 50.63%\n", " \n", " \n", " 53.12%\n", " \n", "
\n", " Twisted Fate\n", " \n", " \n", " +5.99%\n", " \n", " \n", " 5.99%\n", " \n", " \n", " 46.75%\n", " \n", " \n", " 49.52%\n", " \n", " \n", " 51.70%\n", " \n", " \n", " 52.74%\n", " \n", "
\n", " Aurelion Sol\n", " \n", " \n", " +5.77%\n", " \n", " \n", " 5.77%\n", " \n", " \n", " 49.09%\n", " \n", " \n", " 50.19%\n", " \n", " \n", " 52.31%\n", " \n", " \n", " 54.86%\n", " \n", "
\n", " Rengar\n", " \n", " \n", " +5.57%\n", " \n", " \n", " 5.57%\n", " \n", " \n", " 45.68%\n", " \n", " \n", " 47.25%\n", " \n", " \n", " 49.28%\n", " \n", " \n", " 51.25%\n", " \n", "
\n", " Ryze\n", " \n", " \n", " +5.53%\n", " \n", " \n", " 5.53%\n", " \n", " \n", " 41.48%\n", " \n", " \n", " 42.92%\n", " \n", " \n", " 43.95%\n", " \n", " \n", " 47.01%\n", " \n", "
\n", " Kindred\n", " \n", " \n", " +5.22%\n", " \n", " \n", " 5.22%\n", " \n", " \n", " 45.64%\n", " \n", " \n", " 48.64%\n", " \n", " \n", " 50.41%\n", " \n", " \n", " 50.86%\n", " \n", "
\n", " Urgot\n", " \n", " \n", " +4.98%\n", " \n", " \n", " 4.98%\n", " \n", " \n", " 45.08%\n", " \n", " \n", " 48.46%\n", " \n", " \n", " 49.00%\n", " \n", " \n", " 50.06%\n", " \n", "
\n", " Evelynn\n", " \n", " \n", " +4.79%\n", " \n", " \n", " 4.79%\n", " \n", " \n", " 46.65%\n", " \n", " \n", " 49.29%\n", " \n", " \n", " 50.62%\n", " \n", " \n", " 51.44%\n", " \n", "
\n", " Nasus\n", " \n", " \n", " -1.13%\n", " \n", " \n", " 1.94%\n", " \n", " \n", " 47.69%\n", " \n", " \n", " 48.50%\n", " \n", " \n", " 46.91%\n", " \n", " \n", " 46.56%\n", " \n", "
\n", " Ziggs\n", " \n", " \n", " -1.18%\n", " \n", " \n", " 2.72%\n", " \n", " \n", " 49.37%\n", " \n", " \n", " 50.91%\n", " \n", " \n", " 50.36%\n", " \n", " \n", " 48.19%\n", " \n", "
\n", " Brand\n", " \n", " \n", " -1.48%\n", " \n", " \n", " 1.48%\n", " \n", " \n", " 51.66%\n", " \n", " \n", " 51.64%\n", " \n", " \n", " 51.31%\n", " \n", " \n", " 50.18%\n", " \n", "
\n", " Kalista\n", " \n", " \n", " -1.95%\n", " \n", " \n", " 3.31%\n", " \n", " \n", " 42.63%\n", " \n", " \n", " 43.99%\n", " \n", " \n", " 43.57%\n", " \n", " \n", " 40.68%\n", " \n", "
\n", " Sion\n", " \n", " \n", " -1.96%\n", " \n", " \n", " 1.96%\n", " \n", " \n", " 52.06%\n", " \n", " \n", " 51.47%\n", " \n", " \n", " 52.03%\n", " \n", " \n", " 50.10%\n", " \n", "
\n", " Aatrox\n", " \n", " \n", " -2.23%\n", " \n", " \n", " 4.21%\n", " \n", " \n", " 46.78%\n", " \n", " \n", " 47.98%\n", " \n", " \n", " 48.76%\n", " \n", " \n", " 44.55%\n", " \n", "
\n", " Dr. Mundo\n", " \n", " \n", " -2.36%\n", " \n", " \n", " 2.97%\n", " \n", " \n", " 46.82%\n", " \n", " \n", " 47.43%\n", " \n", " \n", " 47.15%\n", " \n", " \n", " 44.46%\n", " \n", "
\n", " Garen\n", " \n", " \n", " -2.56%\n", " \n", " \n", " 2.83%\n", " \n", " \n", " 49.27%\n", " \n", " \n", " 49.54%\n", " \n", " \n", " 49.20%\n", " \n", " \n", " 46.71%\n", " \n", "
\n", " Yorick\n", " \n", " \n", " -3.88%\n", " \n", " \n", " 3.88%\n", " \n", " \n", " 46.24%\n", " \n", " \n", " 45.16%\n", " \n", " \n", " 43.84%\n", " \n", " \n", " 42.36%\n", " \n", "
\n", " Amumu\n", " \n", " \n", " -4.10%\n", " \n", " \n", " 4.10%\n", " \n", " \n", " 52.85%\n", " \n", " \n", " 52.52%\n", " \n", " \n", " 51.91%\n", " \n", " \n", " 48.75%\n", " \n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "spreads = {}\n", "for name in data.major_axis:\n", " champ_data = data.major_xs(name)\n", " # Consider only Silver -> Diamond data\n", " reduced = champ_data.transpose()[1:-2]\n", " spreads[name] = (\n", " reduced.winrate[-1] - reduced.winrate[0], \n", " max(reduced.winrate) - min(reduced.winrate),\n", " data.Silver.loc[name].winrate,\n", " data.Gold.loc[name].winrate,\n", " data.Platinum.loc[name].winrate,\n", " data.Diamond.loc[name].winrate,\n", " )\n", "win_rates = pandas.DataFrame.from_records(spreads, \n", " index=('Silver to Diamond', 'Max Spread', 'Silver', 'Gold', 'Platinum', 'Diamond')).transpose()\n", "win_rates.sort_values('Silver to Diamond', ascending=False, inplace=True)\n", "df_disp = pandas.concat([win_rates.head(10), win_rates.tail(10)])\n", "display(df_disp.style\n", " .format({'Silver to Diamond': '{:+.2f}%', 'Max Spread': '{:.2f}%',\n", " 'Silver': '{:.2f}%', 'Gold': '{:.2f}%', 'Platinum': '{:.2f}%', 'Diamond': '{:.2f}%'})\n", " .set_table_styles(right_align)\n", " .background_gradient(cmap='coolwarm', low = 0.5, high= 0.5,\n", " subset=['Silver', 'Gold', 'Platinum', 'Diamond'])\n", ")\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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49Sjqq0spLw1nuTSLi3dpDI1HlFwpkqcURIgUCM/z6Bl0LRFrGnNjwa3FNPoj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\nuUQi2aUgQmQB337wEKcHJwF480u3U1t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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.distplot(win_rates['Silver to Diamond'], bins=15)\n", "g.set(title='Win rate differences from Silver to Diamond')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Champion popularity by tier\n", "\n", "How popular are champions at various tiers? Which champions get more popular at higher tiers?\n", "\n", "It turns out Janna has the most increase in usage in higher tiers. She's picked in only 0.79% of Silver examples (93,510 games out of 11.8M) but she's picked 2.92% of the time in Diamond examples (28,644 games out of 1M). Conversely Leona has the biggest drop in usage, from 1.63% to 0.63%.\n", "\n", "Note that the raw numbers reported in Silver/Gold/Platinum/Diamond are not strictly pick rate, although they are mostly correlated. Janna represents 0.79% of all the Silver data we have. The report is sorted by the column \"Silver to Diamond\", the difference in pick rates from Silver to Diamond." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", " \n", " \n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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\n", " Jhin\n", " \n", " \n", " +1.27\n", " \n", " \n", " 2.01\n", " \n", " \n", " 2.46\n", " \n", " \n", " 2.96\n", " \n", " \n", " 3.28\n", " \n", "
\n", " Ezreal\n", " \n", " \n", " +0.99\n", " \n", " \n", " 1.93\n", " \n", " \n", " 2.50\n", " \n", " \n", " 2.72\n", " \n", " \n", " 2.93\n", " \n", "
\n", " Bard\n", " \n", " \n", " +0.95\n", " \n", " \n", " 0.77\n", " \n", " \n", " 0.97\n", " \n", " \n", " 1.20\n", " \n", " \n", " 1.72\n", " \n", "
\n", " Graves\n", " \n", " \n", " +0.93\n", " \n", " \n", " 0.97\n", " \n", " \n", " 1.36\n", " \n", " \n", " 1.73\n", " \n", " \n", " 1.90\n", " \n", "
\n", " Nidalee\n", " \n", " \n", " +0.78\n", " \n", " \n", " 0.49\n", " \n", " \n", " 0.65\n", " \n", " \n", " 0.90\n", " \n", " \n", " 1.28\n", " \n", "
\n", " Karma\n", " \n", " \n", " +0.71\n", " \n", " \n", " 0.79\n", " \n", " \n", " 0.91\n", " \n", " \n", " 1.05\n", " \n", " \n", " 1.50\n", " \n", "
\n", " Rek'Sai\n", " \n", " \n", " +0.71\n", " \n", " \n", " 0.44\n", " \n", " \n", " 0.56\n", " \n", " \n", " 0.74\n", " \n", " \n", " 1.15\n", " \n", "
\n", " Elise\n", " \n", " \n", " +0.56\n", " \n", " \n", " 0.45\n", " \n", " \n", " 0.56\n", " \n", " \n", " 0.70\n", " \n", " \n", " 1.01\n", " \n", "
\n", " Annie\n", " \n", " \n", " -0.67\n", " \n", " \n", " 1.31\n", " \n", " \n", " 1.09\n", " \n", " \n", " 0.82\n", " \n", " \n", " 0.64\n", " \n", "
\n", " Miss Fortune\n", " \n", " \n", " -0.69\n", " \n", " \n", " 1.15\n", " \n", " \n", " 0.81\n", " \n", " \n", " 0.64\n", " \n", " \n", " 0.47\n", " \n", "
\n", " Garen\n", " \n", " \n", " -0.69\n", " \n", " \n", " 0.88\n", " \n", " \n", " 0.51\n", " \n", " \n", " 0.28\n", " \n", " \n", " 0.19\n", " \n", "
\n", " Xin Zhao\n", " \n", " \n", " -0.71\n", " \n", " \n", " 0.89\n", " \n", " \n", " 0.54\n", " \n", " \n", " 0.34\n", " \n", " \n", " 0.17\n", " \n", "
\n", " Master Yi\n", " \n", " \n", " -0.73\n", " \n", " \n", " 1.09\n", " \n", " \n", " 0.83\n", " \n", " \n", " 0.66\n", " \n", " \n", " 0.37\n", " \n", "
\n", " Vayne\n", " \n", " \n", " -0.78\n", " \n", " \n", " 1.87\n", " \n", " \n", " 1.89\n", " \n", " \n", " 1.58\n", " \n", " \n", " 1.09\n", " \n", "
\n", " Amumu\n", " \n", " \n", " -0.91\n", " \n", " \n", " 1.25\n", " \n", " \n", " 0.98\n", " \n", " \n", " 0.68\n", " \n", " \n", " 0.34\n", " \n", "
\n", " Lux\n", " \n", " \n", " -0.95\n", " \n", " \n", " 1.51\n", " \n", " \n", " 1.16\n", " \n", " \n", " 0.89\n", " \n", " \n", " 0.56\n", " \n", "
\n", " Jinx\n", " \n", " \n", " -0.97\n", " \n", " \n", " 2.25\n", " \n", " \n", " 2.04\n", " \n", " \n", " 1.87\n", " \n", " \n", " 1.28\n", " \n", "
\n", " Leona\n", " \n", " \n", " -1.00\n", " \n", " \n", " 1.63\n", " \n", " \n", " 1.11\n", " \n", " \n", " 0.79\n", " \n", " \n", " 0.63\n", " \n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pick_rates = 100 * data.minor_xs('games') / data.minor_xs('games').sum()\n", "del pick_rates['Bronze']\n", "del pick_rates['Master']\n", "del pick_rates['Challenger']\n", "pick_rates.insert(0, 'Silver to Diamond', pick_rates.Diamond - pick_rates.Silver)\n", "pick_rates.sort_values('Silver to Diamond', ascending=False, inplace=True)\n", "df_disp = pandas.concat([pick_rates.head(10), pick_rates.tail(10)])\n", "(df_disp.style\n", " .format({'Silver': '{:,.2f}', 'Gold': '{:,.2f}', 'Platinum': '{:,.2f}', 'Diamond': '{:,.2f}',\n", " 'Silver to Diamond': '{:+.2f}'})\n", " .set_table_styles(right_align)\n", " .background_gradient(cmap='coolwarm', low = 0.5, high= 0.5,\n", " subset=['Silver', 'Gold', 'Platinum', 'Diamond']))\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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9RHef60/QVJOfWoKUVBNCT/8Ix073TbG2iMj0KCkQydKB411jfzcuym9ScPHa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\nn1DATyjoI5eHfC5PNp8nm82TymTdN/10lnhygL5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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "g = sns.distplot(pick_rates['Silver to Diamond'], bins=15)\n", "g.set(title='Pick rate differences from Silver to Diamond')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatterplot of Platinum win rate vs pick rate\n", "\n", "Are high win rate champs more popular in platinum? Not particularly..." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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AAIcITgAAAA4RnAAAABwiOAEAADhEcAIAAHCI4AQAAOAQwQkAAMAhghMAAIBD\nBCcAAACHCE4AAAAOEZwAAAAcIjgBAAA4RHACAABwiOAEAADgEMEJAADAIYITAACAQwQnAAAAhwhO\nAAAADlV63QBJMsaskvRdSW+RNCbpPkkXWmtjac59r6RLJbVKspIutdb+pojNBQAAZcovI06/ktQn\n6SBJ6yQdIenqmScZY94o6SZJn5HULOk7kjYZY1YWraUAAKBseR6cjDGLJD0q6XPW2hFrbY+km5Uc\nfZrpI5J+ba29z1o7Zq39qaROSe8vXosBAEC58nyqzlo7IOmfZxxeJenlNKcfJenuGcf+LOlNBWga\nAABACs+D00zGmHWSPinpnWkeXixpz4xjfZIOz+Y1Kio8H2iDA1P9RH/5H30VLPRXsOTbT+GwVFlJ\nX7slp+BkjIlKOkVSq7X23yaPtVhru/NpjDHmOEl3SfqMtfZ3Dp8WkpTI5nUikZpsmwYP0V/BQV8F\nC/1VHpqa6tTUVOd1M0pG1sHJGPN3kn6rZH1UraR/M8YcLKnTGHOitfaPuTTEGPNOSbdIWm+t/a8M\np+1Usih8uujkccdisRFNTMSzbySKqqIirEikhv4KAPoqWOivYJnqr1zt2TOsBQuGXWxRaXIaLnMZ\ncfqmpB9JukTSkCRZa58zxnxB0pVKX9Q9J2PMsUoWhJ9urX1gjlMfU7LOabo3Sbo1m9ebmIhrfJxf\nFkFBfwUHfRUs9Fd5iMdFP7sol0nPN0u6zFo7odQpsg2aHWrmZYypkPRDSZ9NF5qMMb81xpw5+eUP\nJb3NGHOyMabaGHOupNVKjlQBAAAUVC4jTnuVvqYoIimXSHuMpDWSrjHGXDt57am6pTWSDpbUJEnW\n2ieNMecouVnmKklPSXqHtXZHDq8LAACQlVyC02OSviTpc1MHJvdiukZS1vVN1tqHJVXMccrBM87/\npaRfZvs6AAAA+colOH1W0u8mp8mqjTGdSoabAUlvd7NxAAAgP319ux2dF41GFQ6zbcF8sg5O1ton\njDGvk3SOJCNpRMl7xv2XtZayfQAAfOSxZ3aqvn5sznOGhgZ0YtsaNTfPXLiOmXLZjuDL1trLJP37\njOP1xphrrbXnu9Y6AACQl/r6RYo0Rr1uRslwHJyMMWFJCyT9qzHma0oWcE+3WtK/SCI4AQCAkpTN\nZOYlSk7LVUt6dfLf0//3mKQn3W4gAACAXzgecbLWXmGM+ZWkLUqOLM00rOSO4gAAACUpqxona22n\nMeY0a+098g/GAAAgAElEQVTd6R43xvyzpBtcaRkAAIDP5LKq7m5jTJOktZIWTntolZJ7ORGcAJSt\nrt6YNnd0KzY8pkhdldrbWtS6IuJ1swC4JJdVdW+TtFFSnVJ3+ZayvGccAJSSrt6YNmzsVN/g6GvH\nemJaf9pawhNQInLZ6eoKJUeWXidpn6RDJX1A0l2SLnCvaQAQLJs7ulNCkyT1DY5qc0e3Ry0C4LZc\ndg4/TNIx1tpxY0zCWvucpOeMMbskXS/pzLmfDgClKTacfpPBTMcBBE8uI04JJfdzkqQRY8ziyX8/\nKOkfXGkVAARQpK4qq+MAgieXEaeHJN1sjPmQpL9K+oIx5nJJx0vizyqUDIp8ka32thZ19cRSpuui\nDdVqb2vxsFUA3JRLcPqUpDsm//1VSXfrtdqmL7vRKMBrFPmWtkKF4tYVEa0/ba3u6ejWAIEbKEm5\nbEewTdLrJ798wBhzpKR1kp611m5xs3GAV+Yq8l1/6lqPWgU3FDoUt66I6Dx+RoCSlUuNUwpr7d+s\ntbdZa7cYYxrcaBTgNYp8Sxcr3wDkI6sRJ2PMRZI+qOS96W6w1t447bHjJP1E0sGuthDwAEW+pYtQ\nDCAfjoOTMWa9kjVNt0mqkrTBGLNP0i2SLpN0qZIbYwKBR5Fv6SIUA8hHNiNO/yzpbGvtXZJkjLlX\n0mckfUzSGkkfsNayczhKAkW+peuNhy7WX5/drX0T8f3HCMUAnMomOB0s6d5pX29Scmru15KOsNZu\nd7NhgNco8i09Xb0xbXqoKyU0VVWGdepbWgnFABzJpjh8gbV2fxGAtXavpFFr7SmEJgBBkK4wfGw8\nrsef3e1RiwAETb6r6hLznwIA/kBhOIB85b0dAQAEBYXhAPKVTY1TlTHmp/Mds9aenX+zAMB9rJZE\nOdqxo1dDw8NznrN3OKa+vuQfENFoVOEw4yqZZBOcHpa0YsaxP6Q5BgC+xGpJlKP4+D4lJuaejq5Z\nuFBPvfSq9j6zVSe2rVFzc3ORWhc8joOTtfb4ArYDAFwz173onKyW5AbPKCXLV65SpDHq6FxGmuaX\ny01+AcC38r0XHTd4BjAXoiWAkpLvvei4lx2AuRCcAJSUfLccYMsCAHMhOAEoKfluOcCWBQDmknVw\nMsa8aY7Hzs+vOQDKSVdvTBs2derKW7Zow6ZOdfXG8r5me1uLog3VKcey2XIg3+cDKG25FIf/1hjz\nTmvtH6YOGGMaJP2npJMkXetW4wCUrkIVYee75QBbFgCYSy7B6SJJdxtjzrTW3m+M+TtJv5A0Iulo\nV1sHoGTNVYS9Ps+bK+d7g2Zu8Awgk6yn6qy1P5L0AUk/N8ZcK+mPkh6SdLS19mmX2wegRFGEDSCI\nctrHyVp7pzHmXZLulHSLtfaj7jYLQKmjCBtAEDkKTsaYKzI89EdJ5xhj+iTFJclae6lLbQPKUrns\nWs194wAEkdMRp/fN8dgOSe+d/HdCEsEJyFE57VpNETaAIHIUnKy1rYVuCIDCFkz7EUXYAIImpw0w\njTGnG2PeMO3rk4wxZ7rXLKA8UTANAP6WywaYH5P0Y0nLph2ulXSDMebjbjUMKEcUTAOAv+Uy4vT/\nSmq31t4/dcBau0nS2ycfA5Ajdq0GAH/LZTuCAyX9Ic3xxyQdlF9zgPJGwTQA+FsuwalLyVur3DPj\n+GmSXsq7RUCZo2AaAPwrl+B0paSNxpj7JD0nKSTpcElvlXSWi20DAADwlayDk7X2VmPMLkmfkPQ2\nSROSnpF0krX2dy63DyhL5bIJJgAETa63XPmNpN+43BYAKq9NMAEgaJzecuXL1trLJv+d6fYrkrjl\nCpCvctsEEwCCxOmI03slXTb577luv8ItV4A8sQkmADft2NGroeHhtI/V1dQqFA7t/3poaEDSiiK1\nLJic3nJlzbR/c/sVoIDYBBOAm+Lj+5SYmP2H1/DQoNatblI0unja0RWKRqPFa1wAZVXjZIxpkHSs\npDFJj1hrR+d5CoAstbe1qKsnljJdxyaYAHK1fOUqRRpnh6FYf5+i0cVqbm72oFXB5Tg4GWMOk/Sw\npKl3eKsx5kRrLXs3AS5iE0wA8K9sRpy+ouSO4RdKWiDp3yV9VdKH822EMeYkSTdLetBae/Yc5/2n\npPdL2jd5KCRpxFrLuCJKCptgAoA/ZROc/kHSG6dGmIwxF0h6MN8GGGP+VdK5Su4F5cRXrbVfyfd1\nAQAAspXNTX7rpk/LWWufl7TchTaMSDpa0t9cuBYAAEDBZDPilChEA6y110mSMcbpU04wxrxb0qGS\nnpJ0nrX2z4VoGwAAwHTZjDj5wd8kbZN0sqSVShar/8YY0+RpqwAAQFnIZsSpyhjz0/mOzVXcnS9r\n7demf22M+YySG3K+W9J/Or1ORUXQ8mJ5muon+sv/6Ktgob+CJd9+CoVCqpi2yeWUcDikysqQKiv5\nOchGNsHpYc3eTvQPaY4VjbU2box5UcnRJ8cikZoCtQiFQH8FB30VLPRXeaitXaDa2upZx8dGq9TY\nWKempjoPWhVcjoOTtfb4ArbDEWPMtyTdZK3tnPy6UtIhkp7L5jqx2IgmJuIFaCHcVFERViRSQ38F\nAH0VLPRXsEz1V6727t2nqurZ+1WPjIypv39YlZW1+TSvZDgNkFntHF5sxpiVkh6Q9HZrbbekVkkb\njDFnSYopuY/UmKRfZnPdiYm4xsf5ZREU9Fdw0FfBQn+Vh0QioYn47PVd8XhC4+MJfgay5HlwMsaM\nKLlib8Hk16dKSlhrayePHSZpaozxXEnfkrRFUoOkP0l6q7V2pNjtBgAA5cfz4GStzTj+ODnKVDHt\n635JHylGuwAAAGbyPDgBKE9dvTFt7uhWjPvxAQgQghOAouvqjWnDxk71Db5WsNrVE9P609b6IjwR\n6gBkwuYNAIpuc0d3SmiSpL7BUW3u6PaoRa+ZCnVb7E5te2lAW+xObdjYqa7emNdNA+ADBCcARRcb\nHsvqeDH5OdQB8B7BCUDRReqqsjpeTH4OdQC8R3ACUHTtbS2KNqTuZBxtqFZ7W4tHLXqNn0MdAO9R\nHA6gKGYWXJ/6llb95dndGvBZAXZ7W4u6emIp03V+CXUAvEdwAlBwfl9FN13riojWn7ZW93R0+y7U\nAfAewQlAwc1VcL3+1LUetSqz1hURnefDdgG52LGjV0PDw7OO7x2Oqa9v9hR0NBpVOEwlTyYEJwAF\nR8E14J34+D4lJmb/t1azcKGeeulVhXt69x8bGhrQiW1r1NzcXMwmBgrBCUDBUXANeGf5ylWKNEa9\nbkbJYCwOQMH5eRUdAGSDEScABUfBNYBSQXACUBQUXAMoBUzVAQAAOERwAgAAcIjgBAAA4BDBCQAA\nwCGCEwAAgEMEJwAAAIfYjgBA0XT1xrS5o1sx9nICEFAEJwBF0dUb04aNnSk3++3qiWn9aWsJTwAC\ng6k6AEWxuaM7JTRJUt/gqDZ3dHvUIgDIHsEJQFHEhmffnX2u4wDgRwQnAEURqavK6jgA+BE1TgBm\nKUQRd3tbi7p6YinTddGGarW3teTbXAAoGoITgBSFKuJuXRHR+tPW6p6Obg2wqg5AQBGcAKSYq4h7\n/alr87p264qIzsvzGgDgJYIT4FNe7XnkhyJu9nsC4FcEJ8CHvNzzqJBF3E4CEfs9AfAzVtUBPuTl\nnkftbS2KNlSnHHOjiHsqEG2xO7XtpQFtsTu1YWOnunpjKeex3xMAP2PECfAhL6fLClXE7bR2yg9T\nhblgehF+tWNHr4aGhx2du3c4pr6+7EeXo9GowuHyGIshOAE+5PWeR4Uo4nYaiLz+3nPB9CL8LD6+\nT4kJZ3941CxcqKdeelXhnl7H1x8aGtCJbWvU3NycaxMDheCEsufHkYJS3PPIaSAK4vdeyJWIQL6W\nr1ylSGPU62aUDIITyppfRwpKcc+jNx66WH9+ZqcSideOhULJ49MF8XsP6vQigOwRnFDW/DxSUGp7\nHj3+7O6U0CRJiUTy+HFrV6YcD9r3HsTpRQC5KY9KLiADRgqKp5Tf60KtRATgP4w4oawxUlA8pfxe\nB3F6EUBuCE4oa0EsRA6qUn+vgza9CCA3BCeUNUYKiseN99rtFZB+XFEJwN8ITih7jBQUTzbv9cxQ\n88ZDF2vTQ12urYD064pKAP5GcTgA30l3e5af3PeMq7di4dYuAHJBcALgO+lCzdh4PO25ua7KK+VV\nfgAKh+AEwHeyCS+5rsor5VV+AAqH4ATAdzKFlwUVqb+y8lmVx95LAHJBcThQZKzkml+mrQtOfUur\n/vLsbldWQLKiEkAuCE5AEbGSy5m5Qs3M27Pk+zqsqASQDYITUET53huvnEarghhqyql/gHJFcAKK\nKJ+VXIxW+Rv9A5QHisOBIspnJRf7Dvkb/QOUB0acgCJyer+2dFM+7Dvkb/QPUB58EZyMMSdJulnS\ng9bas+c593JJZ0lqlPQnSeuttV2FbyWQPycruTJN+Sxtqk17TfYd8gf2hQLKg+fByRjzr5LOlfSM\ng3PPVzI0nSzpZUlXStok6Y2FbCPgpvmKnjNN+SxtqlG0oXre0Sp4w+loIoBg8zw4SRqRdLSkayRV\nz3PuRyV921r7jCQZYy6V1GeMOdpa+z+FbSZQHJmmdibiCfYd8jH2hQLKg+fByVp7nSQZY+Y8zxiz\nUNLhkv7PtOcOGWO2SXqTJIITSsJcUz5BXKJfTugfoPR5Hpyy0CQpJGnPjON9kpqL3xygMJjyAeCm\nHTt6NTQ8XLDr7x2Oqa8v+QdfNBpVOFzaC/aDFJwyCUlKZPOEiorS7tRSMdVP5dZfqw9q1AVnvkG/\nfuR5DQyPaVHdAr3j2FYdvNK/Uz7l2ldBRX8FS779lJjYJ8X3udSa2WprarS1Z1Qjz1qddOwaNTcv\nKdhr+UGQglOfpLhmjy5FJe3M5kKRSI1bbUIRlGN/HdVUp6OOWOF1M7JWjn0VZPRXeTj40EPV2FT4\niZn+PbvV2Finpqa6gr+WlwITnKy1o8aYJyQdJekPkmSMaZR0qJLbEjgWi41oYiLufiPhqoqKsCKR\nGvorAOirYKG/gmWqv3K1d+8+VVWPzn9inkZGxtTfP6zKyvRbp/id08Dn6+BkjFkp6QFJb7fWdkv6\nvqRLjDH3KrkdwTckbbHW/jmb605MxDU+zi+LoKC/gsOPfcX94zLzY3/BfYlEQhPxrCpachKPJzQ+\nnij5nynPg5MxZkTJGqUFk1+fKilhra2dPHaYJrcpsNZeb4xZLum/JdVL+p2k0z1oNoAA4P5xANzm\neXCy1mYcf5wcZaqYcezLkr5c6HYBCL657h+3nm0DAOSAJRUAShb3jwPgNoITgJLF/eMAuI3gBKBk\ntbe1KNqQeicnNhMFkA/Pa5wAoFC4fxwAtxGcAJQ07h8HwE0EJ6BEBX3/oqC3H0BpIjgBJSjo+xcF\nvf0AShfF4UAJmmv/oiAIevsBlC5GnAAfcHtaKuj7F+Xbfqb5ABQKwQnwWCGmpYK+f1E+7WeaD0Ah\nMVUHeKwQ01JB379ovvZ39ca0YVOnrrxlizZs6lRXb2z/eUzzASgkRpwAjxViWi1I+xdlmlbL1P75\nRpSCPk0JwN8IToDHCjWtFoT9i+YLQSe3tewPVZs7utU++fVcN+4N+jQlAH8jOAEea29rUVdPLCUM\n+GlaLd2IkKSUY6cc16qjmuqyvvZcIai9rSVtqKpbmP7X1tSIkt/fTwDBRnACPObnabV0I0LbXtgj\nhcKK7X1t6uv53pg+H1mo5vrsRnXmmlbLFKriiUTa50yNKGXzfrL6DkC2CE6AD/h1Wi1deImNjM86\nb3dsVLc/uE0ff9cRWV1/rmm1TKGqobZK4VBozhElJ+9nIVbfEcSA0kdwAspMNh/u2RRU988IWE7M\nNa2WaRXc0qYatZ+8Ju8RuvlqpbLFNgjwqx07ejU0PFzw19k7HFNfnze1hNFoVOFwcTYKIDgBJWS+\nUJTth3s2BdWNM7YPcGKuabW5QpUbI3Rur75zO4gBbomP71NiovCrSmsWLtRTL72qcE9vwV9ruqGh\nAZ3YtkbNzc1FeT2CE1AinISibD/c04WXSE3lrBqnxZFqnXHC6pzanSkEFbr2y+3Vd2yDAL9avnKV\nIo1Rr5tRMghOQIlwEoqcfrhPH7la2lSrpU01mognUlbVTQ80pxzXqtUHNWnPHnenAwpZ++X26ju2\nQQDKA8EJKBFOQpGTD/d0I1fRhupZ03nTA01lZfBuQuD2iBbbIADlgeAElAgnocjJh3s51eq4OaLl\n520lALiH4AQUWLGWqDsJRU4+3MutVsfN/vHrthIA3ENwAgqomEvUnY54zPfhXk61OmwhACBbBCeg\ngIo97eXGiEc51eqU07QkAHcQnIACyjS9Zbv3qKs35stRjULV6vhxV+1ym5YEkD+CE1BAmaa3hl4d\n14aNnb6dEnK7VsevU2LlNC0JwB3BW0MMBEh7W4uiGXbUnpoSKgdzTYl5KV3/lOq0JAB3MOIEFNjS\nplr1D40qnpj9mJ+mhAo5lebXKbFcpyX9OO0IoDgITkCBpJueminbKaFCfWAXeirNz1Ni2U5L+nXa\nEUBxMFUHFEi66anpsp0SmvrA3mJ3attLA9pid2rDxk519cYK0lY3p9JKaUrMr9OOAIqDESegQDJN\nQy2sqtARrdGsR4sKuXR+R9/etMfdmkrzw67abo3W+XXaEUBxEJyAAsk0DXVEazSnoFOoD+yu3phe\n2TOS9rHnemI6/7sP6aClDTrzrYdIUs7hw8tdtd2cXvPztCOAwiM4AQXi9kaShfrA3tzRrbHxeNrH\nJuIJDb86rq0v7NG3b3tcFSEpNjK+//Gp8LH6oMa82lBobo7WpevXSG2Vhkf26cpbtlAsDpQ4ghNQ\nIG5PTxVqR2+nI1bDr47POjYVPi5ME5z8tPLMzdG6mf1aWRHWyzsGtfWF/v3nUCwOlC6CE1BAbk5P\nZQpikrRhU2fOAaUinN8akXThI93U2F+f3a0PvP0wHbd2ZV6vlwu3R+um9+uGTZ0po3ASt20BShnB\nCQiQmUHMndqdNBtMZSFd+Eg3NbZvIq6f3PeMVjbXF30kxs3RupkjaYUurAfgL2xHAASYG0vjJ9Lt\nzJlG3cJKRWpS/9bKFD4yhYax8bgny/anRuvWmSVafeAiHWWW5DSVlm5LiEyF9RSLA6WJEScgwPKt\n3enqjWlnf/oP/oaaSiUSkkLSQUsbdMbxyVV1Tmq25goNXo3EuDFtmi6ojo3HtaAirH0TrxXYB3WP\nKgDzIzghcPxUdOy1fGp3pkZP+odmB5loQ3XGERkn4eONhy7WFrsz57b5VabQt3xxrZY11Xi2RxWA\n4iE4IVC43UWqfGp3Mu1s3lhflff7+fizu9MeX1AR9nwkJp/gnSn0De4d04dOXlOWP4Pwvx07ejU0\nPOx1Mwpm73BMfX2Z/yCLRqMK57kIZjqCEwKlkLtnB1G2Wx5MDw09u9L/Il3SWJN3AJhrZMatcJFL\nAMo3eKcLqpLUPzSmDRs7yzbAw9/i4/uUmCjdxQo1CxfqqZdeVbind9ZjQ0MDOrFtjZqbm117PYIT\nAqWcb3eRKSg4rd1xctNhyZ2ptEzXWNpU4/gacwWjXANQvsF7Kqhed8dftWfGFGc5B3j42/KVqxRp\njHrdjJJBcEKglOvtLtyYopzvpsOSe0XN+S7/n+/7zTUAuRG8W1dE1NxYMys4ZXsdAMFEcEKgFGr3\nbL9zY4oy04d6/cJKrWiuy7reZ/qI0KL6Kp399tepuT4ZYPPdNX2+7zfXAORW8C7XAA+A4ISAcfs2\nJkHhxkhJpg9109KU9fRSuhGh7u1DWn/aWq1aWi8pv+X/832/uQYXt4J3uQZ4AAQnBJCbtzEJCjdG\nONz8sE83IrSzf0S/fqRLn3h3/n0z3/eb6/fiVvAu1wAPgOAEFE0+y+DdCD1ufthnGhEaGN7n+Bpz\nvR/zfb/5fC9uBe9yDPAACE5AUeRb3O3mSIkbH/aZRoQW1S1w9Pz53g8n3y/BBYAXCE5AEbhR3O2n\noJBuRGhJY43ecWyro+c7eT/89P0CwBSCE1AEpbb/1MwRocb6Kr1vclXd+Hh8zud29cZku/ekfcy+\nsEddvTFqhQD4li+CkzGmRdIGSW2SBiXdZq29JM15l0n6oqSpT5uQpISkFmsz3BgLyINb98UrxeXr\n00eEKivDamqq0549c9/W4Y+dPfrxvc+k3BB3uqGR8Xl34OZehQC85IvgJOkOSY9KOkvSMkmbjTHb\nrbXfTXPuj6215xa1dShLbt4Xj+XryffzJ/dlDk1T+gZHde0df9WSxhrXdgwHALd4HpyMMeskvV7S\nCdbaIUlDxphvS7pQUrrgBBSFm/fFY/l68v0cm2cab0r/0Jj6J3fmdrJj+O3//TfVLqxkFApAwXke\nnCT9L0nPW2tj0479WZIxxtRPhqnp3mCM+aOkIyW9IOlia+1vitRWlBG365IKUezs1bTVzNc95bhW\nHdVUN+dzcn3fnOwYvu3Ffo3HE6+1j1E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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wr_vs_pick_platinum = pandas.concat((win_rates['Platinum'], pick_rates['Platinum']), axis=1)\n", "wr_vs_pick_platinum.columns = ('Win Rate', 'Pick Rate')\n", "g = sns.jointplot(x='Win Rate', y='Pick Rate', ylim=(0,3.5), xlim=(40,60), data=wr_vs_pick_platinum, kind=\"scatter\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatterplot of Win Rate improvement vs Pick Rate change\n", "\n", "Are champions that have a bigger Silver-to-Diamond win rate change also likely to have a higher pick rate Silver-to-Diamond? If there were a correlation you'd expect the dots below to fall on the line `x=y`. They don't really, but there is a correlation" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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UKWmxMeYcSQFJN0jqkPRoLMft6gqqs9N9/7h6UX9qUX/quLl2ifpTze31xysY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\nRK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wr_vs_pick_sd = pandas.concat((win_rates['Silver to Diamond'], pick_rates['Silver to Diamond']), axis=1)\n", "wr_vs_pick_sd.columns = ('Win Rate', 'Pick Rate')\n", "g = sns.jointplot(x='Win Rate', y='Pick Rate', data=wr_vs_pick_sd, kind=\"scatter\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.3" } }, "nbformat": 4, "nbformat_minor": 1 }