I'm not sure when this started happening, but all of my visualizations (mostly line charts) have stopped working in my blogger blog posts. Instead, they display the following error (in browser, not the javascript console):
Object #<Ap> has no method 'G'× When I take the HTML/Javascript code that I submitted as a blogpost and just create a standalone page locally, the chart works fine. It seems like there is something that Blogger is wrapping around my post that is mucking things up. I tried googling the error above with little success. Here is an example page that is not working: http://www.inpredictable.com/2013/08/home-underdogs-are-no-longer-good-bet.html Here is the code submitted through the blogger interface: <script src="http://www.google.com/jsapi"; type="text/javascript"></script> <script type="text/javascript"> google.load('visualization', '1', {packages: ['corechart']}); </script> <script type="text/javascript"> function drawHun() { // Create and populate the data table. var dhun = new google.visualization.DataTable(); dhun.addColumn('string', 'season'); dhun.addColumn('number', '% covered'); dhun.addColumn({type:'string', role:'annotation'}); dhun.addColumn('number', 'break even'); dhun.addRows([ ['1989',0.500,null,0.5238], ['1990',0.500,null,0.5238], ['1991',0.537,null,0.5238], ['1992',0.531,null,0.5238], ['1993',0.534,null,0.5238], ['1994',0.516,null,0.5238], ['1995',0.533,null,0.5238], ['1996',0.609,null,0.5238], ['1997',0.522,null,0.5238], ['1998',0.545,null,0.5238], ['1999',0.565,null,0.5238], ['2000',0.486,null,0.5238], ['2001',0.553,null,0.5238], ['2002',0.593,null,0.5238], ['2003',0.486,null,0.5238], ['2004',0.469,'Levitt publishes paper',0.5238], ['2005',0.373,null,0.5238], ['2006',0.556,null,0.5238], ['2007',0.483,null,0.5238], ['2008',0.451,null,0.5238], ['2009',0.454,null,0.5238], ['2010',0.494,null,0.5238], ['2011',0.529,null,0.5238], ['2012',0.479,null,0.5238] ]); // Create and draw the visualization. new google.visualization.LineChart(document.getElementById('dhun')). draw(dhun, {width: 600, height: 400, legend:{position:'top',fontName:'Verdana'}, hAxis:{maxAlternation:1,title:'season',titleTextStyle:'Verdana',slantedText:false}, series:{0:{pointSize:4},1:{pointSize:0,color:'gray'}}, chartArea: {left: 60, top: 40, width:"80%", height: "70%"}, title:'NFL Home Underdogs Against the Spread', titleTextStyle:{fontName:'Verdana'}} ); } google.setOnLoadCallback(drawHun); </script> <a href="http://commons.wikimedia.org/wiki/File%3AUnderdog-Macys-1979.jpg"; style="clear: left; float: left; margin-bottom: 1em; margin-right: 1em;" title="By Jon Harder (User JonHarder) [GFDL (http://www.gnu.org/copyleft/fdl.html), CC-BY-SA-3.0 (http://creativecommons.org/licenses/by-sa/3.0/) or CC-BY-2.5 (http://creativecommons.org/licenses/by/2.5)], via Wikimedia Commons"><img alt="Underdog-Macys-1979" height="273" src="//upload.wikimedia.org/wikipedia/commons/thumb/b/b3/Underdog-Macys-1979.jpg/512px-Underdog-Macys-1979.jpg" width="320" /></a>In an <a href="http://pricetheory.uchicago.edu/levitt/Papers/LevittWhyAreGamblingMarkets2004.pdf"; target="_blank">oft-cited 2004 paper</a>, Steven Levitt (of <a href="http://freakonomics.com/"; target="_blank">Freakonomics</a> fame) investigated the behavior of gambling markets. Using a unique dataset comprised of bets made in an NFL handicapping contest, Levitt concluded that, contrary to conventional wisdom, bookmakers do not set point spreads to equalize the amount of money placed on either side of the spread. Instead, Levitt postulated that bookmakers willingly accept these imbalances in an effort to maximize their <i>own</i> profits.<br /> <br /> A bookmaker can increase profits beyond their nominal "<a href="http://en.wikipedia.org/wiki/Vigorish"; target="_blank">vig</a>" by exploiting bettor biases. Levitt cited home underdogs as one example of this bettor bias that bookmakers could exploit. Using data from the 1980-2001 seasons, there were 1,483 games in which the home team was the underdog. Of those games, the home team covered 53.3% of the time (significant at the 0.5% level).<br /> <br /> <!--more--><br /> <h3> Results from the 1989-2012 Seasons</h3> <div> I don't have data going back to 1980, but my dataset (from <a href="http://sportsdatabase.com/";>sportsdatabase.com</a>) does go back to 1989, and the results from 1989-2003 (Levitt published in 2004) are consistent with the 1980-2001 data. From 1989-2003, there were 1,121 games in which the home team was the underdog. Of those games, the home team covered 53.5% of the time (significant at the 0.8% level).</div> <div> <br /></div> <div> So how have home underdogs fared since Levitt published his 2004 paper? Not so well. From 2004-2012, there were 766 games in which the home team was the underdog. They covered just 47.8% of the time, nearly significant at the 10% level (but in the wrong direction). The graph below summarizes the results by season and compares them to the "breakeven" percentage of 52.38% (how often a bettor would have to win in order to break even against the standard vig).</div> <div> <br /></div> <div id="dhun"> <br /></div> The above chart illustrates why gambling on sports can be so challenging to do successfully. In 2004, you had a betting strategy with a plausible rationale and statistical significance out the wazoo. And you would have gone broke following it. -- You received this message because you are subscribed to the Google Groups "Google Visualization API" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. Visit this group at http://groups.google.com/group/google-visualization-api. For more options, visit https://groups.google.com/groups/opt_out.
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