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	<title>
	Comments on: Previewing the Conference Championship Games	</title>
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	<link>http://www.footballperspective.com/previewing-the-conference-championship-games/</link>
	<description>NFL History and Stats</description>
	<lastBuildDate>Mon, 03 Dec 2012 16:30:05 +0000</lastBuildDate>
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		<title>
		By: Chase Stuart		</title>
		<link>http://www.footballperspective.com/previewing-the-conference-championship-games/#comment-4195</link>

		<dc:creator><![CDATA[Chase Stuart]]></dc:creator>
		<pubDate>Mon, 03 Dec 2012 16:30:05 +0000</pubDate>
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					<description><![CDATA[NIU won by 7 in OT...Stanford by 3....Tulsa by 6 in OT....Bama by 4....FSU by 6....Wisconsin by 49.

So I went 4-2, but I&#039;d say NIU and Tulsa were both very lucky.]]></description>
			<content:encoded><![CDATA[<p>NIU won by 7 in OT&#8230;Stanford by 3&#8230;.Tulsa by 6 in OT&#8230;.Bama by 4&#8230;.FSU by 6&#8230;.Wisconsin by 49.</p>
<p>So I went 4-2, but I&#8217;d say NIU and Tulsa were both very lucky.</p>
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		<title>
		By: George		</title>
		<link>http://www.footballperspective.com/previewing-the-conference-championship-games/#comment-4115</link>

		<dc:creator><![CDATA[George]]></dc:creator>
		<pubDate>Fri, 30 Nov 2012 22:53:34 +0000</pubDate>
		<guid isPermaLink="false">http://www.footballperspective.com/?p=4727#comment-4115</guid>

					<description><![CDATA[Great and informative post as usual. I decided to rig up an Elo based sheet (but working on scores rather than win/losses) so that I could rank College Football (because of the number of teams, I can&#039;t do any of the options that I usually use on other sports involving Excel&#039;s Solver, and don&#039;t want to do a 739x739 team matrix to try and do something like Massey&#039;s thesis method - it would be easy once it is set up but difficult to set up).

In short I&#039;m not that convinced on the numbers that I got at the end of it (or how to interpret them but some seem reasonable and I think my workings are sound) but here&#039;s what it came out with (using a K of 32, H of 15, and a logistic parameter of 1000 - might try and solve for one of these to find an optimal value if I can to minimise the difference between the Elo ratings and the actual game results);

Kent State 39
Northen Illinois 68.55

Alabama 104
Georgia 66.9

FSU 75.84
Georgia Tech 14.62

Stanford 44.4
UCLA 27.36

Central Florida 35.10
Tulsa 30.84

Wisconsin 26.86
Nebraska 42.71]]></description>
			<content:encoded><![CDATA[<p>Great and informative post as usual. I decided to rig up an Elo based sheet (but working on scores rather than win/losses) so that I could rank College Football (because of the number of teams, I can&#8217;t do any of the options that I usually use on other sports involving Excel&#8217;s Solver, and don&#8217;t want to do a 739&#215;739 team matrix to try and do something like Massey&#8217;s thesis method &#8211; it would be easy once it is set up but difficult to set up).</p>
<p>In short I&#8217;m not that convinced on the numbers that I got at the end of it (or how to interpret them but some seem reasonable and I think my workings are sound) but here&#8217;s what it came out with (using a K of 32, H of 15, and a logistic parameter of 1000 &#8211; might try and solve for one of these to find an optimal value if I can to minimise the difference between the Elo ratings and the actual game results);</p>
<p>Kent State 39<br />
Northen Illinois 68.55</p>
<p>Alabama 104<br />
Georgia 66.9</p>
<p>FSU 75.84<br />
Georgia Tech 14.62</p>
<p>Stanford 44.4<br />
UCLA 27.36</p>
<p>Central Florida 35.10<br />
Tulsa 30.84</p>
<p>Wisconsin 26.86<br />
Nebraska 42.71</p>
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