{"id":837,"date":"2021-11-19T07:00:40","date_gmt":"2021-11-19T13:00:40","guid":{"rendered":"https:\/\/wollen.org\/blog\/?p=837"},"modified":"2024-05-15T03:36:26","modified_gmt":"2024-05-15T08:36:26","slug":"how-does-matt-amodio","status":"publish","type":"post","link":"https:\/\/wollen.org\/blog\/2021\/11\/how-does-matt-amodio\/","title":{"rendered":"How does Matt Amodio’s Jeopardy streak compare?"},"content":{"rendered":"

If you’re reading this blog there’s a good chance you heard about Matt Amodio’s incredible run on Jeopardy<\/em>. After winning 38 consecutive games he walked away with over $1.5 million dollars. I thought I’d take a look at how his streak fits into Jeopardy<\/em> history.<\/p>\n

I’d like to visualize both total winnings<\/strong> and consecutive games won<\/strong>. I’ll use these variables to plot average daily winnings<\/strong>, which will check both boxes and hopefully provide some new information you haven’t seen a dozen times before.<\/p>\n

Conveniently, the show maintains a Hall of Fame<\/a> with most of the data we need. I put the data into a CSV file you can download at the bottom of this post.<\/p>\n

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1. Prepare the data.<\/h4>\n

Start by reading the dataset into a pandas Dataframe.<\/p>\n

df = pd.read_csv(\"jeopardy_top_winners.csv\")<\/pre>\n
The show uses ALL CAPS<\/strong> in their text:<\/p>\n
\"\"<\/a><\/p>\n
So we’ll follow suit. Convert the name column with upper()<\/code>.<\/p>\n
df[\"name\"] = df[\"name\"].str.upper()<\/pre>\n
Now we can generate the data we intend to plot: average daily winnings. It’s as simple as dividing total winnings by number of games won. Create a new column to hold this information.<\/p>\n
df.loc[:, \"average\"] = df[\"total_won\"] \/ df[\"games_won\"]<\/pre>\n
Before jumping into Matplotlib I want to double-check the Dataframe.<\/p>\n
Although the primary objective is to plot average daily winnings, I’d like to order contestants by their number of games won. This should convey a second dimension of the data while also highlighting Amodio’s longevity.<\/p>\n
Notice that I pass a list into the sort_values<\/code> method. Some contestants have won an equal number of games so I’ll use average winnings as the “tiebreaker.” You could include more tiebreakers if you wanted, but that won’t be necessary with this data.<\/p>\n
df = df.sort_values([\"games_won\", \"average\"]).reset_index(drop=True)\r\n\r\nprint(df)<\/pre>\n
The output:<\/p>\n
                 name  games_won  total_won       average\r\n0     JONATHAN FISHER         11     246100  22372.727273\r\n1          ARTHUR CHU         11     297200  27018.181818\r\n2         SETH WILSON         12     265002  22083.500000\r\n3       AUSTIN ROGERS         12     411000  34250.000000\r\n4        MATT JACKSON         13     411612  31662.461538\r\n5        DAVID MADDEN         19     430400  22652.631579\r\n6   JASON ZUFFRANIERI         19     532496  28026.105263\r\n7       JULIA COLLINS         20     428100  21405.000000\r\n8     JAMES HOLZHAUER         32    2462216  76944.250000\r\n9         MATT AMODIO         38    1518601  39963.184211\r\n10       KEN JENNINGS         74    2520700  34063.513514<\/pre>\n
The sorted values appear upside down right now, but notice the longest win streak (Ken Jennings) corresponds to the highest index (10). This will work because we’ll plot the data on a horizontal bar chart. It would be awkward and unnecessary to read the name text sideways. Let’s make it easy and turn the bars sideways instead.<\/p>\n
I’ve created a Jeopardy<\/em>-themed Matplotlib style for this plot. The colors and text are designed to mimic the style of the show. For reference:<\/p>\n
\"\"<\/a><\/p>\n
I also found a font<\/a> that copies clue text fairly well. We can use Matplotlib’s Path Effects<\/em> to create a drop shadow and get even closer.<\/p>\n
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2. Plot the data.<\/h4>\n
Begin in the usual way by referencing an mplstyle<\/code> and retrieving fig<\/code> and ax<\/code> objects.<\/p>\n
plt.style.use(\"jeopardy.mplstyle\")\r\n\r\nfig, ax = plt.subplots()<\/pre>\n
Horizontal bar charts are a little tricky\u2014at least for me\u2014because it’s easy to mix up the independent and dependent variables. I can never remember what Matplotlib considers x<\/code> and y<\/code> or which argument should be passed first.<\/p>\n
Confessions out of the way, the independent (vertical) iterable is passed first. This is df.index<\/code> (0-10, seen above). Remember we’re plotting names so we’ll have to replace integer tick labels with text in a moment. The dependent variable, average winnings, is passed second.<\/p>\n
On a regular bar chart the bar width parameter is intuitively called width<\/code>. On a barh<\/code> chart, somewhat less obviously, it becomes height<\/code>.<\/p>\n
ax.barh(df.index, df[\"average\"], height=0.65)<\/pre>\n
I don’t want to turn this into a patheffects<\/code> tutorial so I’ll just link to the documentation here<\/a>. The module is capable of much more but we’ll use it to create a nice text drop shadow.<\/p>\n