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How 'Should' Tech Do in Recruiting Rank - Analysis
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<blockquote data-quote="BCJacket" data-source="post: 507661" data-attributes="member: 2332"><p>I hate myself some days. <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" class="smilie smilie--sprite smilie--sprite59" alt=":facepalm:" title="Facepalm :facepalm:" loading="lazy" data-shortname=":facepalm:" /> <img src="data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7" class="smilie smilie--sprite smilie--sprite44" alt=":banghead:" title="Bang Head :banghead:" loading="lazy" data-shortname=":banghead:" /></p><p></p><p>So, here's what I did; I looked up the number of 4 and 5 star recruits for each state for the past five years. I compared that to the total population to get a blue chips per million population per year. which is interesting enough by itself to share. Really makes you wonder how much geographic bias is in the rankings. Are there really 10x more top talent kids relative to population in FL, GA, AL than MI and WI??</p><p></p><p>[ATTACH=full]4686[/ATTACH]</p><p></p><p></p><p>Using that, I listed out every state by the states that border it and their scores (that was a bear). The best info I could find indicated that, on average, P5 schools have about 50% of their roster in state. So I used 50% of the school's state's score and 50% the average of the bordering states. Matched each school with its state and viola: a regional recruiting power score for each school:</p><p></p><p>Plugged that data into my model. It has a Pearson correlation coefficient of .5182 with recruiting results. Which is a moderately strong correlation. It's basically the third best indicator behind spending and attendance. Plugging it into the model brought the overall correlation of my model to the real world rankings up to .8979. Which isn't much of an improvement. Doesn't change much boosts the expected rank of southern schools, hurts the northern schools. Makes GT look a little worse.</p><p></p><p>[ATTACH=full]4687[/ATTACH]</p><p></p><p>And you might ask, "Hey, BCJacket, don't you have more important things to be doing?" </p><p></p><p>Yes, yes, I do.</p></blockquote><p></p>
[QUOTE="BCJacket, post: 507661, member: 2332"] I hate myself some days. :facepalm: :banghead: So, here's what I did; I looked up the number of 4 and 5 star recruits for each state for the past five years. I compared that to the total population to get a blue chips per million population per year. which is interesting enough by itself to share. Really makes you wonder how much geographic bias is in the rankings. Are there really 10x more top talent kids relative to population in FL, GA, AL than MI and WI?? [ATTACH=full]4686[/ATTACH] Using that, I listed out every state by the states that border it and their scores (that was a bear). The best info I could find indicated that, on average, P5 schools have about 50% of their roster in state. So I used 50% of the school's state's score and 50% the average of the bordering states. Matched each school with its state and viola: a regional recruiting power score for each school: Plugged that data into my model. It has a Pearson correlation coefficient of .5182 with recruiting results. Which is a moderately strong correlation. It's basically the third best indicator behind spending and attendance. Plugging it into the model brought the overall correlation of my model to the real world rankings up to .8979. Which isn't much of an improvement. Doesn't change much boosts the expected rank of southern schools, hurts the northern schools. Makes GT look a little worse. [ATTACH=full]4687[/ATTACH] And you might ask, "Hey, BCJacket, don't you have more important things to be doing?" Yes, yes, I do. [/QUOTE]
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How 'Should' Tech Do in Recruiting Rank - Analysis
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