{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![Callysto.ca Banner](https://github.com/callysto/curriculum-notebooks/blob/master/callysto-notebook-banner-top.jpg?raw=true)\n", "\n", "\"Open" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Stats Project - Soccer\n", "\n", "#### by Flor Nightgale\n", "\n", "For this project we used secondary data about [Premier League (Soccer)](https://www.premierleague.com/tables).\n", "\n", "## Team Statistics" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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2020-2021GPWDLFAGDP
01LEILeicester City22007256
12EVEEverton22006246
23ARSArsenal22005146
34LIVLiverpool22006336
45CRYCrystal Palace22004136
56TOTTottenham Hotspur21015323
67MNCManchester City11003123
78BHABrighton & Hove Albion21014313
89AVLAston Villa11001013
910LEELeeds United21017703
1011CHEChelsea21013303
1112WOLWolverhampton Wanderers21013303
1213NEWNewcastle United210123-13
1314BURBurnley100124-20
1415MANManchester United100113-20
1516WHUWest Ham United200214-30
1617SHUSheffield United200203-30
1718FULFulham200237-40
1819SOUTSouthampton200226-40
1920WBAWest Bromwich Albion200228-60
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" ], "text/plain": [ " 2020-2021 GP W D L F A GD P\n", "0 1LEILeicester City 2 2 0 0 7 2 5 6\n", "1 2EVEEverton 2 2 0 0 6 2 4 6\n", "2 3ARSArsenal 2 2 0 0 5 1 4 6\n", "3 4LIVLiverpool 2 2 0 0 6 3 3 6\n", "4 5CRYCrystal Palace 2 2 0 0 4 1 3 6\n", "5 6TOTTottenham Hotspur 2 1 0 1 5 3 2 3\n", "6 7MNCManchester City 1 1 0 0 3 1 2 3\n", "7 8BHABrighton & Hove Albion 2 1 0 1 4 3 1 3\n", "8 9AVLAston Villa 1 1 0 0 1 0 1 3\n", "9 10LEELeeds United 2 1 0 1 7 7 0 3\n", "10 11CHEChelsea 2 1 0 1 3 3 0 3\n", "11 12WOLWolverhampton Wanderers 2 1 0 1 3 3 0 3\n", "12 13NEWNewcastle United 2 1 0 1 2 3 -1 3\n", "13 14BURBurnley 1 0 0 1 2 4 -2 0\n", "14 15MANManchester United 1 0 0 1 1 3 -2 0\n", "15 16WHUWest Ham United 2 0 0 2 1 4 -3 0\n", "16 17SHUSheffield United 2 0 0 2 0 3 -3 0\n", "17 18FULFulham 2 0 0 2 3 7 -4 0\n", "18 19SOUTSouthampton 2 0 0 2 2 6 -4 0\n", "19 20WBAWest Bromwich Albion 2 0 0 2 2 8 -6 0" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "data = pd.read_html('https://www.espn.com/soccer/table/_/league/eng.1')\n", "teams = data[0].join(data[1]) # join the two data tables together\n", "teams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Columns in the data set are:\n", "* GP: Games Played\n", "* W: Wins\n", "* D: Draws\n", "* L: Losses\n", "* F: Goals For\n", "* A: Goals Against\n", "* GD: Goal Difference\n", "* P: Points\n", "\n", "Notice that the ranking (index values) start at zero. As well, the team names got combined with their ranks and abbreviations, let's cut those out and leave just the team names.\n", "\n", "For each team name, the second character is a lowercase letter, so we'll find the first lowercase letter then take just the characters from one before that until the end of the name.\n", "\n", "We'll also rename the columns." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TeamGames PlayedWinsDrawsLossesGoals ForGoals AgainstGoal DifferencePoints
0Leicester City22007256
1Everton22006246
2Arsenal22005146
3Liverpool22006336
4Crystal Palace22004136
5Tottenham Hotspur21015323
6Manchester City11003123
7Brighton & Hove Albion21014313
8Aston Villa11001013
9Leeds United21017703
10Chelsea21013303
11Wolverhampton Wanderers21013303
12Newcastle United210123-13
13Burnley100124-20
14Manchester United100113-20
15West Ham United200214-30
16Sheffield United200203-30
17Fulham200237-40
18Southampton200226-40
19West Bromwich Albion200228-60
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" ], "text/plain": [ " Team Games Played Wins Draws Losses Goals For \\\n", "0 Leicester City 2 2 0 0 7 \n", "1 Everton 2 2 0 0 6 \n", "2 Arsenal 2 2 0 0 5 \n", "3 Liverpool 2 2 0 0 6 \n", "4 Crystal Palace 2 2 0 0 4 \n", "5 Tottenham Hotspur 2 1 0 1 5 \n", "6 Manchester City 1 1 0 0 3 \n", "7 Brighton & Hove Albion 2 1 0 1 4 \n", "8 Aston Villa 1 1 0 0 1 \n", "9 Leeds United 2 1 0 1 7 \n", "10 Chelsea 2 1 0 1 3 \n", "11 Wolverhampton Wanderers 2 1 0 1 3 \n", "12 Newcastle United 2 1 0 1 2 \n", "13 Burnley 1 0 0 1 2 \n", "14 Manchester United 1 0 0 1 1 \n", "15 West Ham United 2 0 0 2 1 \n", "16 Sheffield United 2 0 0 2 0 \n", "17 Fulham 2 0 0 2 3 \n", "18 Southampton 2 0 0 2 2 \n", "19 West Bromwich Albion 2 0 0 2 2 \n", "\n", " Goals Against Goal Difference Points \n", "0 2 5 6 \n", "1 2 4 6 \n", "2 1 4 6 \n", "3 3 3 6 \n", "4 1 3 6 \n", "5 3 2 3 \n", "6 1 2 3 \n", "7 3 1 3 \n", "8 0 1 3 \n", "9 7 0 3 \n", "10 3 0 3 \n", "11 3 0 3 \n", "12 3 -1 3 \n", "13 4 -2 0 \n", "14 3 -2 0 \n", "15 4 -3 0 \n", "16 3 -3 0 \n", "17 7 -4 0 \n", "18 6 -4 0 \n", "19 8 -6 0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for i, row in teams.iterrows():\n", " for character in row[0]:\n", " if character.islower(): # we've found the first lowercase letter\n", " start_here = row[0].index(character)-1\n", " team_name = row[0][start_here:]\n", " break # stop looking through the team name\n", " teams.iloc[i,0] = team_name\n", "teams.columns = ['Team','Games Played','Wins','Draws','Losses','Goals For','Goals Against','Goal Difference','Points']\n", "teams" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical Calculations\n", "\n", "The `describe()` method does some statisical calculations for us." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Games PlayedWinsDrawsLossesGoals ForGoals AgainstGoal DifferencePoints
count20.00000020.00000020.020.00000020.00000020.00000020.00000020.000000
mean1.8000000.9000000.00.9000003.3500003.3500000.0000002.700000
std0.4103910.7880690.00.7880692.0844032.1588253.0779352.364207
min1.0000000.0000000.00.0000000.0000000.000000-6.0000000.000000
25%2.0000000.0000000.00.0000002.0000002.000000-2.2500000.000000
50%2.0000001.0000000.01.0000003.0000003.0000000.0000003.000000
75%2.0000001.2500000.01.2500005.0000004.0000002.2500003.750000
max2.0000002.0000000.02.0000007.0000008.0000005.0000006.000000
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" ], "text/plain": [ " Games Played Wins Draws Losses Goals For Goals Against \\\n", "count 20.000000 20.000000 20.0 20.000000 20.000000 20.000000 \n", "mean 1.800000 0.900000 0.0 0.900000 3.350000 3.350000 \n", "std 0.410391 0.788069 0.0 0.788069 2.084403 2.158825 \n", "min 1.000000 0.000000 0.0 0.000000 0.000000 0.000000 \n", "25% 2.000000 0.000000 0.0 0.000000 2.000000 2.000000 \n", "50% 2.000000 1.000000 0.0 1.000000 3.000000 3.000000 \n", "75% 2.000000 1.250000 0.0 1.250000 5.000000 4.000000 \n", "max 2.000000 2.000000 0.0 2.000000 7.000000 8.000000 \n", "\n", " Goal Difference Points \n", "count 20.000000 20.000000 \n", "mean 0.000000 2.700000 \n", "std 3.077935 2.364207 \n", "min -6.000000 0.000000 \n", "25% -2.250000 0.000000 \n", "50% 0.000000 3.000000 \n", "75% 2.250000 3.750000 \n", "max 5.000000 6.000000 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "team_stats = teams.describe()\n", "team_stats" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also find the median values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Games Played 2.0\n", "Wins 1.0\n", "Draws 0.0\n", "Losses 1.0\n", "Goals For 3.0\n", "Goals Against 3.0\n", "Goal Difference 0.0\n", "Points 3.0\n", "dtype: float64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "teams.median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `Goal Difference` column is probably the most interesting, since it has the largest range and highest standard deviation. The top teams scored a lot more than they were scored on, and the bottom teams were scored on a lot more than they scored.\n", "\n", "\n", "\n", "Since we are looking at data for all of the teams, we see that the mean number of wins is equal to the mean number of losses. The same goes for goals scored and goals scored against.\n", "\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "hovertemplate": "index=%{x}
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\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import plotly_express as px\n", "fig = px.bar(team_stats.iloc[3:], y='Goal Difference', title='')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to see which teams scored more than the mean value of \"Goals For\", we can use the following code." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TeamGames PlayedWinsDrawsLossesGoals ForGoals AgainstGoal DifferencePoints
0Leicester City22007256
1Everton22006246
2Arsenal22005146
3Liverpool22006336
4Crystal Palace22004136
5Tottenham Hotspur21015323
7Brighton & Hove Albion21014313
9Leeds United21017703
\n", "
" ], "text/plain": [ " Team Games Played Wins Draws Losses Goals For \\\n", "0 Leicester City 2 2 0 0 7 \n", "1 Everton 2 2 0 0 6 \n", "2 Arsenal 2 2 0 0 5 \n", "3 Liverpool 2 2 0 0 6 \n", "4 Crystal Palace 2 2 0 0 4 \n", "5 Tottenham Hotspur 2 1 0 1 5 \n", "7 Brighton & Hove Albion 2 1 0 1 4 \n", "9 Leeds United 2 1 0 1 7 \n", "\n", " Goals Against Goal Difference Points \n", "0 2 5 6 \n", "1 2 4 6 \n", "2 1 4 6 \n", "3 3 3 6 \n", "4 1 3 6 \n", "5 3 2 3 \n", "7 3 1 3 \n", "9 7 0 3 " ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gf_mean = teams['Goals For'].mean()\n", "teams[teams['Goals For'] > gf_mean]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In general, but not always, the top teams scored more than the average number of goals.\n", "\n", "Mean is probably the best measure of central tendency here, since using the median would just give us the top half of the teams. Mode wouldn't be useful because there aren't a lot of repeated values in the column.\n", "\n", "Let's see if the top teams had fewer than the mean number of goals scored against them." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TeamGames PlayedWinsDrawsLossesGoals ForGoals AgainstGoal DifferencePoints
0Leicester City22007256
1Everton22006246
2Arsenal22005146
3Liverpool22006336
4Crystal Palace22004136
5Tottenham Hotspur21015323
6Manchester City11003123
7Brighton & Hove Albion21014313
8Aston Villa11001013
10Chelsea21013303
11Wolverhampton Wanderers21013303
12Newcastle United210123-13
14Manchester United100113-20
16Sheffield United200203-30
\n", "
" ], "text/plain": [ " Team Games Played Wins Draws Losses Goals For \\\n", "0 Leicester City 2 2 0 0 7 \n", "1 Everton 2 2 0 0 6 \n", "2 Arsenal 2 2 0 0 5 \n", "3 Liverpool 2 2 0 0 6 \n", "4 Crystal Palace 2 2 0 0 4 \n", "5 Tottenham Hotspur 2 1 0 1 5 \n", "6 Manchester City 1 1 0 0 3 \n", "7 Brighton & Hove Albion 2 1 0 1 4 \n", "8 Aston Villa 1 1 0 0 1 \n", "10 Chelsea 2 1 0 1 3 \n", "11 Wolverhampton Wanderers 2 1 0 1 3 \n", "12 Newcastle United 2 1 0 1 2 \n", "14 Manchester United 1 0 0 1 1 \n", "16 Sheffield United 2 0 0 2 0 \n", "\n", " Goals Against Goal Difference Points \n", "0 2 5 6 \n", "1 2 4 6 \n", "2 1 4 6 \n", "3 3 3 6 \n", "4 1 3 6 \n", "5 3 2 3 \n", "6 1 2 3 \n", "7 3 1 3 \n", "8 0 1 3 \n", "10 3 0 3 \n", "11 3 0 3 \n", "12 3 -1 3 \n", "14 3 -2 0 \n", "16 3 -3 0 " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ga_mean = teams['Goals Against'].mean()\n", "teams[teams['Goals Against'] < gf_mean]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, it is generally true that the top teams that had fewer goals scored against them.\n", "\n", "### Teams Visualizations\n", "\n", "Let's create some plots of `Wins`, `Losses`, `Draws` versus team rank." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ "Leicester City" ], [ "Everton" ], [ "Arsenal" ], [ "Liverpool" ], [ "Crystal Palace" ], [ "Tottenham Hotspur" ], [ "Manchester City" ], [ "Brighton & Hove Albion" ], [ "Aston Villa" ], [ "Leeds United" ], [ "Chelsea" ], [ "Wolverhampton Wanderers" ], [ "Newcastle United" ], [ "Burnley" ], [ "Manchester United" ], [ "West Ham United" ], [ "Sheffield United" ], [ "Fulham" ], [ "Southampton" ], [ "West Bromwich Albion" ] ], "hovertemplate": "index=%{x}
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "columns = ['Wins', 'Losses', 'Draws']\n", "for column in columns:\n", " fig = px.scatter(teams, x=teams.index, y=column, title=column+' vs Rank', hover_data=['Team'])\n", " fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Player Data\n", "\n", "We are also going to look at individual player data for scoring and assists. We'll download both and then look first at the top 10, `head(10)`, of the `scorers` data table." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RKNameTeamPG
01.0Dominic Calvert-LewinEverton24
1NaNSon Heung-MinTottenham Hotspur24
23.0Mohamed SalahLiverpool23
3NaNWilfried ZahaCrystal Palace23
45.0Hélder CostaLeeds United22
5NaNAleksandar MitrovicFulham22
6NaNNeal MaupayBrighton & Hove Albion22
7NaNSadio ManéLiverpool22
8NaNPatrick BamfordLeeds United22
9NaNRaúl JiménezWolverhampton Wanderers22
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" ], "text/plain": [ " RK Name Team P G\n", "0 1.0 Dominic Calvert-Lewin Everton 2 4\n", "1 NaN Son Heung-Min Tottenham Hotspur 2 4\n", "2 3.0 Mohamed Salah Liverpool 2 3\n", "3 NaN Wilfried Zaha Crystal Palace 2 3\n", "4 5.0 Hélder Costa Leeds United 2 2\n", "5 NaN Aleksandar Mitrovic Fulham 2 2\n", "6 NaN Neal Maupay Brighton & Hove Albion 2 2\n", "7 NaN Sadio Mané Liverpool 2 2\n", "8 NaN Patrick Bamford Leeds United 2 2\n", "9 NaN Raúl Jiménez Wolverhampton Wanderers 2 2" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats = pd.read_html('https://www.espn.com/soccer/stats/_/league/ENG.1/view/scoring')\n", "scorers = stats[0]\n", "assists = stats[1]\n", "scorers.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Columns:\n", "* RK: Ranking\n", "* P: Games played\n", "* G: Goals scored\n", "* A: Assists\n", "\n", "There are quite a few missing (`NaN`) values, which means that player is tied with the player above them, so we can use `fillna(method='ffill')` which means \"forward fill\" values to replace missing values." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RKNameTeamPG
01.0Dominic Calvert-LewinEverton24
11.0Son Heung-MinTottenham Hotspur24
23.0Mohamed SalahLiverpool23
33.0Wilfried ZahaCrystal Palace23
45.0Hélder CostaLeeds United22
55.0Aleksandar MitrovicFulham22
65.0Neal MaupayBrighton & Hove Albion22
75.0Sadio ManéLiverpool22
85.0Patrick BamfordLeeds United22
95.0Raúl JiménezWolverhampton Wanderers22
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" ], "text/plain": [ " RK Name Team P G\n", "0 1.0 Dominic Calvert-Lewin Everton 2 4\n", "1 1.0 Son Heung-Min Tottenham Hotspur 2 4\n", "2 3.0 Mohamed Salah Liverpool 2 3\n", "3 3.0 Wilfried Zaha Crystal Palace 2 3\n", "4 5.0 Hélder Costa Leeds United 2 2\n", "5 5.0 Aleksandar Mitrovic Fulham 2 2\n", "6 5.0 Neal Maupay Brighton & Hove Albion 2 2\n", "7 5.0 Sadio Mané Liverpool 2 2\n", "8 5.0 Patrick Bamford Leeds United 2 2\n", "9 5.0 Raúl Jiménez Wolverhampton Wanderers 2 2" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scorers = scorers.fillna(method='ffill')\n", "scorers.head(10)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RKNameTeamPA
01.0Harry KaneTottenham Hotspur24
12.0Daniel PodenceWolverhampton Wanderers22
22.0WillianArsenal22
32.0RicharlisonEverton22
45.0Tariq LampteyBrighton & Hove Albion21
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" ], "text/plain": [ " RK Name Team P A\n", "0 1.0 Harry Kane Tottenham Hotspur 2 4\n", "1 2.0 Daniel Podence Wolverhampton Wanderers 2 2\n", "2 2.0 Willian Arsenal 2 2\n", "3 2.0 Richarlison Everton 2 2\n", "4 5.0 Tariq Lamptey Brighton & Hove Albion 2 1" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "assists = assists.fillna(method='ffill')\n", "assists.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create histograms for these two data sets." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "alignmentgroup": "True", "bingroup": "x", "hovertemplate": "G=%{x}
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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Histogram of Assists by Top Players" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "A" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "count" } } } }, "text/html": [ "
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig1 = px.histogram(scorers, x='G', title='Histogram of Goals Scored by Top Players')\n", "fig1.show()\n", "fig2 = px.histogram(assists, x='A', title='Histogram of Assists by Top Players')\n", "fig2.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both of these histograms show that there are many more players that scored (or assisted) fewer goals, so the data are not normally distributed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Research Question\n", "\n", "**Does having more top scoring or top assisting players on a team mean that team has a higher standing?**\n", "\n", "To answer this question, we will need to group the player data by team and merge the two data tables together. We'll also drop the columns that we don't need." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TeamGAGoals and Assists
0Leeds United5510
1Brighton & Hove Albion459
2Arsenal448
3Leicester City538
4Crystal Palace347
5Everton347
6Chelsea325
7Liverpool325
8Manchester City325
9Newcastle United235
10Southampton235
11West Bromwich Albion235
12Fulham224
13Tottenham Hotspur224
14Wolverhampton Wanderers224
15Burnley213
16West Ham United123
17Aston Villa112
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" ], "text/plain": [ " Team G A Goals and Assists\n", "0 Leeds United 5 5 10\n", "1 Brighton & Hove Albion 4 5 9\n", "2 Arsenal 4 4 8\n", "3 Leicester City 5 3 8\n", "4 Crystal Palace 3 4 7\n", "5 Everton 3 4 7\n", "6 Chelsea 3 2 5\n", "7 Liverpool 3 2 5\n", "8 Manchester City 3 2 5\n", "9 Newcastle United 2 3 5\n", "10 Southampton 2 3 5\n", "11 West Bromwich Albion 2 3 5\n", "12 Fulham 2 2 4\n", "13 Tottenham Hotspur 2 2 4\n", "14 Wolverhampton Wanderers 2 2 4\n", "15 Burnley 2 1 3\n", "16 West Ham United 1 2 3\n", "17 Aston Villa 1 1 2" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# group the data by team\n", "scorers_team = scorers.groupby('Team').count().drop(columns=['RK', 'Name', 'P'])\n", "assists_team = assists.groupby('Team').count().drop(columns=['RK', 'Name', 'P'])\n", "# merge the players data tables\n", "players = scorers_team.merge(assists_team, on='Team')\n", "# create a column that adds goals and assists\n", "players['Goals and Assists'] = players['G']+players['A']\n", "# sort the values, create an index column, and display the data\n", "players = players.sort_values('Goals and Assists', ascending=False).reset_index()\n", "players" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to merge this data table with the `Teams` data table from earlier." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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TeamGames PlayedWinsDrawsLossesGoals ForGoals AgainstGoal DifferencePointsGAGoals and Assists
0Leicester City220072565.03.08.0
1Everton220062463.04.07.0
2Arsenal220051464.04.08.0
3Liverpool220063363.02.05.0
4Crystal Palace220041363.04.07.0
5Tottenham Hotspur210153232.02.04.0
6Manchester City110031233.02.05.0
7Brighton & Hove Albion210143134.05.09.0
8Aston Villa110010131.01.02.0
9Leeds United210177035.05.010.0
10Chelsea210133033.02.05.0
11Wolverhampton Wanderers210133032.02.04.0
12Newcastle United210123-132.03.05.0
13Burnley100124-202.01.03.0
14Manchester United100113-20NaNNaNNaN
15West Ham United200214-301.02.03.0
16Sheffield United200203-30NaNNaNNaN
17Fulham200237-402.02.04.0
18Southampton200226-402.03.05.0
19West Bromwich Albion200228-602.03.05.0
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" ], "text/plain": [ " Team Games Played Wins Draws Losses Goals For \\\n", "0 Leicester City 2 2 0 0 7 \n", "1 Everton 2 2 0 0 6 \n", "2 Arsenal 2 2 0 0 5 \n", "3 Liverpool 2 2 0 0 6 \n", "4 Crystal Palace 2 2 0 0 4 \n", "5 Tottenham Hotspur 2 1 0 1 5 \n", "6 Manchester City 1 1 0 0 3 \n", "7 Brighton & Hove Albion 2 1 0 1 4 \n", "8 Aston Villa 1 1 0 0 1 \n", "9 Leeds United 2 1 0 1 7 \n", "10 Chelsea 2 1 0 1 3 \n", "11 Wolverhampton Wanderers 2 1 0 1 3 \n", "12 Newcastle United 2 1 0 1 2 \n", "13 Burnley 1 0 0 1 2 \n", "14 Manchester United 1 0 0 1 1 \n", "15 West Ham United 2 0 0 2 1 \n", "16 Sheffield United 2 0 0 2 0 \n", "17 Fulham 2 0 0 2 3 \n", "18 Southampton 2 0 0 2 2 \n", "19 West Bromwich Albion 2 0 0 2 2 \n", "\n", " Goals Against Goal Difference Points G A Goals and Assists \n", "0 2 5 6 5.0 3.0 8.0 \n", "1 2 4 6 3.0 4.0 7.0 \n", "2 1 4 6 4.0 4.0 8.0 \n", "3 3 3 6 3.0 2.0 5.0 \n", "4 1 3 6 3.0 4.0 7.0 \n", "5 3 2 3 2.0 2.0 4.0 \n", "6 1 2 3 3.0 2.0 5.0 \n", "7 3 1 3 4.0 5.0 9.0 \n", "8 0 1 3 1.0 1.0 2.0 \n", "9 7 0 3 5.0 5.0 10.0 \n", "10 3 0 3 3.0 2.0 5.0 \n", "11 3 0 3 2.0 2.0 4.0 \n", "12 3 -1 3 2.0 3.0 5.0 \n", "13 4 -2 0 2.0 1.0 3.0 \n", "14 3 -2 0 NaN NaN NaN \n", "15 4 -3 0 1.0 2.0 3.0 \n", "16 3 -3 0 NaN NaN NaN \n", "17 7 -4 0 2.0 2.0 4.0 \n", "18 6 -4 0 2.0 3.0 5.0 \n", "19 8 -6 0 2.0 3.0 5.0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "combined_data = teams.merge(players, on='Team', how='left') # left means keep the order from the teams data table\n", "combined_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see if there is a relationship between `Goals and Assists` and team rank, let's create another scatterplot." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "customdata": [ [ "Leicester City" ], [ "Everton" ], [ "Arsenal" ], [ "Liverpool" ], [ "Crystal Palace" ], [ "Tottenham Hotspur" ], [ "Manchester City" ], [ "Brighton & Hove Albion" ], [ "Aston Villa" ], [ "Leeds United" ], [ "Chelsea" ], [ "Wolverhampton Wanderers" ], [ "Newcastle United" ], [ "Burnley" ], [ "Manchester United" ], [ "West Ham United" ], [ "Sheffield United" ], [ "Fulham" ], [ "Southampton" ], [ "West Bromwich Albion" ] ], "hovertemplate": "index=%{x}
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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "title": { "text": "Goals and Assists vs Team Rank" }, "xaxis": { "anchor": "y", "domain": [ 0, 1 ], "title": { "text": "index" } }, "yaxis": { "anchor": "x", "domain": [ 0, 1 ], "title": { "text": "Goals and Assists" } } } }, "text/html": [ "
\n", " \n", " \n", "
\n", " \n", "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = px.scatter(combined_data, y='Goals and Assists', x=combined_data.index, hover_data=['Team'], title='Goals and Assists vs Team Rank')\n", "fig.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion\n", "\n", "It looks like higher ranked teams (lower $x$ values) tend to have more players with more goals and assists, although there is a fair amount of variation in the data.\n", "\n", "Perhaps we could look at a similar analysis using a larger data set from a league such as the [National Hockey League](https://www.nhl.com) where there are more games played by more teams." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Callysto.ca License](https://github.com/callysto/curriculum-notebooks/blob/master/callysto-notebook-banner-bottom.jpg?raw=true)](https://github.com/callysto/curriculum-notebooks/blob/master/LICENSE.md)" ] } ], "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": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 4 }