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<!DOCTYPE html>
<meta charset="utf-8">
<html>
<head>
<link rel="stylesheet" href="style.css"/>
<title>2014 Baseball Hall of Fame Predictions</title>
</head>
<body>
<h1>
2014 Baseball Hall of Fame Predictions
</h1>
<p>
Last January, after the 2013 Baseball Hall of Fame voting results were announced,
my colleague Carlos Scheidegger and I created an
<a href='http://http://cscheid.net/static/mlb-hall-of-fame-voting/'>
interactive visualization</a> of historical Baseball Hall of Fame
voting results, showing the voting trajectories of every player (over 1000 of them) who has
been listed on the ballot since Hall of Fame voting began in 1936.
<!--
We call them voting 'trajectories'
because a player can remain on the ballot for up to 15 years if his voting percentage remains
between 5% and 75% each year, and thus a player's year-by-year voting results trace out a trajectory over
time.
-->
Our visualization allows you to filter on the career statistics of players
(i.e. HR > 400, or BA > .300 and SB > 200, etc.) in order to display only a subset of
the trajectories. This way you can interactively explore the relationship between a player's
statistics and his Hall of Fame voting results. It was a lot of fun to create, and we even got
some recognition in December from
<a href=http://www.http://deadspin.com/the-12-best-sports-infographics-of-2013-1484953458/>
Deadspin</a> as one of the 12 best sports infographics of the year!
</p>
<p>
The logical next step, to me, was to see if we could predict future voting results
based solely on a player's statistics. I started with a simple 'baseline' statistical model, and backtested it
over the past 15 or so years to see how accurate its predictions were. Then, I refined the model
and improved the predictive accuracy until I couldn't find any ways to improve it (or until
any potential improvements would be too much work).
</p>
<p>
To be finished within a few days...
</p>
</body>
</html>