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	<title>Comments on: Cashing in Big: A Look at Contract Year Effects</title>
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	<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/</link>
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		<title>By: Phil Curry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79566</link>
		<dc:creator><![CDATA[Phil Curry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 23:04:14 +0000</pubDate>
		<guid isPermaLink="false">http://www.depthockeyanalytics.com/?p=383#comment-79566</guid>
		<description><![CDATA[Thanks, Henry, that&#039;s a very kind comment. I&#039;m glad we got that sorted out - and I apologize for any looseness in the writing that caused the confusion. But, I do have to admit that it really is Mark that deserves all the credit (and I deserve the blame for the write up here). I know he&#039;ll be happy to hear that you think so highly of his work!]]></description>
		<content:encoded><![CDATA[<p>Thanks, Henry, that's a very kind comment. I'm glad we got that sorted out - and I apologize for any looseness in the writing that caused the confusion. But, I do have to admit that it really is Mark that deserves all the credit (and I deserve the blame for the write up here). I know he'll be happy to hear that you think so highly of his work!</p>
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		<title>By: henry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79563</link>
		<dc:creator><![CDATA[henry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 22:55:52 +0000</pubDate>
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		<description><![CDATA[Thanks Phil. If the performance was taken for each year instead of across all years, then you and Mark have on your hands the most convincing evidence for contract year effect I&#039;ve seen.]]></description>
		<content:encoded><![CDATA[<p>Thanks Phil. If the performance was taken for each year instead of across all years, then you and Mark have on your hands the most convincing evidence for contract year effect I've seen.</p>
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		<title>By: Phil Curry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79552</link>
		<dc:creator><![CDATA[Phil Curry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 22:03:52 +0000</pubDate>
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		<description><![CDATA[I&#039;m not sure I&#039;m following. First off, what do you mean by &quot;the entire sample&quot;? Do you mean all years, or all players? If you mean all years, then there&#039;s no problem, because we&#039;re not doing that.

The Pts/60 measures are how that player did in that given year. So, from above, players in the top quartile average 2.4 pt/60 in their contract year and their contract year only, and those same players averaged 1.9 pts/60 the year before. These are not career averages - just averages across players for that year.

There is no survivorship bias, because it is the same players being examined in all the years. There is no any look-ahead bias, because players are aware of when their contract year is, and we&#039;re only looking at performance in a specific year and seeing how it changes. You are correct, however, that we could be capturing something like an age profile. This is discussed in the article above. I&#039;m sure we&#039;re capturing the effect of an age profile for RFAs. However, UFAs are generally past their peak. There is no way to reconcile a flat trend before the contract year and the spike up in the contract year with an age profile.

Again, if I&#039;ve misunderstood what you&#039;ve said, let me know.]]></description>
		<content:encoded><![CDATA[<p>I'm not sure I'm following. First off, what do you mean by "the entire sample"? Do you mean all years, or all players? If you mean all years, then there's no problem, because we're not doing that.</p>
<p>The Pts/60 measures are how that player did in that given year. So, from above, players in the top quartile average 2.4 pt/60 in their contract year and their contract year only, and those same players averaged 1.9 pts/60 the year before. These are not career averages - just averages across players for that year.</p>
<p>There is no survivorship bias, because it is the same players being examined in all the years. There is no any look-ahead bias, because players are aware of when their contract year is, and we're only looking at performance in a specific year and seeing how it changes. You are correct, however, that we could be capturing something like an age profile. This is discussed in the article above. I'm sure we're capturing the effect of an age profile for RFAs. However, UFAs are generally past their peak. There is no way to reconcile a flat trend before the contract year and the spike up in the contract year with an age profile.</p>
<p>Again, if I've misunderstood what you've said, let me know.</p>
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		<title>By: henry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79542</link>
		<dc:creator><![CDATA[henry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 21:42:22 +0000</pubDate>
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		<description><![CDATA[By taking the pts/60 over the entire sample for each player, sorting them into quartiles, then looking at the YoY difference of each quartile, you are introducing look ahead bias. pts/60 should only include CY - 1 and before, not the entire sample. 

If you don&#039;t do this, you are merely looking at the YoY trend. Top quartile players may show an uptrend for reasons of age (entering their primes) and not related to CY effects at all. These other factors either need to be controlled for or performance should not be based on the entire sample.]]></description>
		<content:encoded><![CDATA[<p>By taking the pts/60 over the entire sample for each player, sorting them into quartiles, then looking at the YoY difference of each quartile, you are introducing look ahead bias. pts/60 should only include CY - 1 and before, not the entire sample. </p>
<p>If you don't do this, you are merely looking at the YoY trend. Top quartile players may show an uptrend for reasons of age (entering their primes) and not related to CY effects at all. These other factors either need to be controlled for or performance should not be based on the entire sample.</p>
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		<title>By: Phil Curry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79387</link>
		<dc:creator><![CDATA[Phil Curry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 15:26:50 +0000</pubDate>
		<guid isPermaLink="false">http://www.depthockeyanalytics.com/?p=383#comment-79387</guid>
		<description><![CDATA[Hi Henry,

Your concern, as I understand it, is that we might be observing survivorship bias because there are players dropping out of the dataset in their contract year and the year after, and these players are below average for their quartile, thereby bringing the average up after they drop out.

If I haven&#039;t got it right, let me know.

But, there are no players dropping out in these data. We are only looking at players who signed a contract, and then compare the performance of those players in their contract year (the last year of their old contract), to the years leading up to it, and the year after.

So, if there were 12 players who signed a contract in the data, we sorted them by how well they did in the last year of their old contract. The top 3 would then be in the top quartile. We then looked at how those 3 players did, on average, in the years leading up to the new contract, and in the first year of the new contract. So, there is no survivorship bias. Nobody drops out of the data - the quartiles contain the same players in the CY year as well as the CY+1, CY-1, etc years.

I hope this answers your question. As I said, if I&#039;ve got your question wrong somehow, let me know.]]></description>
		<content:encoded><![CDATA[<p>Hi Henry,</p>
<p>Your concern, as I understand it, is that we might be observing survivorship bias because there are players dropping out of the dataset in their contract year and the year after, and these players are below average for their quartile, thereby bringing the average up after they drop out.</p>
<p>If I haven't got it right, let me know.</p>
<p>But, there are no players dropping out in these data. We are only looking at players who signed a contract, and then compare the performance of those players in their contract year (the last year of their old contract), to the years leading up to it, and the year after.</p>
<p>So, if there were 12 players who signed a contract in the data, we sorted them by how well they did in the last year of their old contract. The top 3 would then be in the top quartile. We then looked at how those 3 players did, on average, in the years leading up to the new contract, and in the first year of the new contract. So, there is no survivorship bias. Nobody drops out of the data - the quartiles contain the same players in the CY year as well as the CY+1, CY-1, etc years.</p>
<p>I hope this answers your question. As I said, if I've got your question wrong somehow, let me know.</p>
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		<title>By: Henry</title>
		<link>http://www.depthockeyanalytics.com/uncategorized/cashing-in-big-a-look-at-contract-year-effects/#comment-79248</link>
		<dc:creator><![CDATA[Henry]]></dc:creator>
		<pubDate>Tue, 11 Nov 2014 09:54:44 +0000</pubDate>
		<guid isPermaLink="false">http://www.depthockeyanalytics.com/?p=383#comment-79248</guid>
		<description><![CDATA[How was performance measured to avoid look ahead and survivorship bias? Top quartile players are the ones that earn the big contracts, so of course they would display a boost in pts/game in CY and CY+1.]]></description>
		<content:encoded><![CDATA[<p>How was performance measured to avoid look ahead and survivorship bias? Top quartile players are the ones that earn the big contracts, so of course they would display a boost in pts/game in CY and CY+1.</p>
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