tag:blogger.com,1999:blog-38600807.post643422047134906687..comments2009-03-14T15:58:39.760-04:00Brian Burkenoreply@blogger.comBlogger13125tag:blogger.com,1999:blog-38600807.post-41753640243939729502009-03-14T15:58:00.000-04:002009-03-14T15:58:00.000-04:00I think you would find a wider gap by studying how quickly and/or often teams with 4 or fewer wins become playoff and/or Super Bowl teams in a 3-5-year span, before and after the salary cap.<BR/><BR/>What any study like this is really telling us is that the salary FLOOR is the most important difference between NFL parity (which didn't make it impossible to create a dynasty, as evidenced by the Patriots, Steelers, and maybe even the Rams). There have been no Johnny Damon/Carlos Beltran fire sales in the NFL.<BR/><BR/>Some have misinterpreted this sort of data (including the bigcatcountry blogger and many of his readers) as suggesting that the loss of the NFL salary cap will be no big deal and nothing will change significantly (+/-1 win to bad teams is never going to sound significant to anyone, even if it is statistically significant). But if the cap/floor are no more, then the NFL won't be worth watching soon afterward, many small market teams will see their owners line their pockets with revenue sharing checks rather than use franchise and transition tags at all. 1/3 of the NFL will be like the past 15 years of Kansas City Royals history.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-5328947165712372212009-03-13T12:18:00.000-04:002009-03-13T12:18:00.000-04:00Thanks for the heads up. Yeah, I noticed that a couple days ago. I think the writer just isn't aware of the rules more than deliberately ripping me off. Further down in the comments, he freely links to this article as "one of the sites I used to research the post," so I assume he just neglected to cite the source of the graph. <BR/><BR/>Just to be clear, I don't mind if people use my stuff as long they properly attribute it. In fact, anyone can literally copy and paste an entire article if they want, as long as they attribute it. It's when guys repeatedly rip off my articles/ideas and don't link to them that really ticks me off.Brian Burkehttp://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-2300671777641473472009-03-13T11:30:00.000-04:002009-03-13T11:30:00.000-04:00Were you plagiarized?<BR/><BR/>http://www.bigcatcountry.com/2009/3/10/787605/the-nfl-salary-cap-facts-mAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-41113190551180680682009-03-05T21:23:00.000-05:002009-03-05T21:23:00.000-05:00At this point, FA and the cap go hand in hand regarding parity. FA without the cap would make for a very unstable system--i.e. "rich get richer," just like the MLB now.<BR/><BR/>I can rerun the pre-cap regression without 89-92 and compare the slopes. But that creates another problem. That period would have fewer years, which makes luck a larger part of the variance, which in turn would make the slope appear shallower than it should.Brian Burkehttp://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-19300966145167227322009-03-05T21:06:00.000-05:002009-03-05T21:06:00.000-05:00My memory may be failing me as I get older, but this source<BR/> <BR/>http://bleacherreport.com/articles/18183-nfl-history-the-road-to-free-agency<BR/> <BR/>claims a "plan B" free agency (with each team protecting 37 players) from 1989 to 1992 with free agency as we know it today starting in 1993. Close enough to the dates in your analysis to confound the analysis. How do you separate the salary cap effects from the free agency effects?jarheadnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-32372859948604224962009-03-05T17:16:00.000-05:002009-03-05T17:16:00.000-05:00I think you mean 1987.Brian Burkehttp://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-45905220293640721272009-03-05T17:10:00.000-05:002009-03-05T17:10:00.000-05:00I think free agency started in 1994, so free agency may confound the analysis. Free agency and the salary cap makes the pro scouting and the general manager more important.jarheadnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-35097958078939425362009-03-02T20:32:00.000-05:002009-03-02T20:32:00.000-05:00You can see even more of an increase in churn if you look at several consecutive years. I ran a regression a while ago predicting NFL teams' Year X performance (winning percentage, I believe) based on performance in Year X-1, Year X-2, and Year X-3. Year X-1 and Year X-2 both had significant coefficients for the early seasons, but for recent seasons only Year X-1 was significant. In other words, teams used to be stable enough for multiple years of previous data to be predictive, but they aren't any more.<BR/><BR/>I could try to find the data to give you more details, or maybe you could run the analysis on your data set.<BR/><BR/>-VinceAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-40917196717344067722009-03-02T19:37:00.000-05:002009-03-02T19:37:00.000-05:00Thanks for the reply, Brian. I guess we better hope for the salary cap to stay put...Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-76912774122563931102009-03-02T15:55:00.000-05:002009-03-02T15:55:00.000-05:00Ian-That's amazing about the Portuguese teams. <BR/><BR/>You're a little off on interpreting r-squared. R^2 is the amount of statistical variance shared by two variables. It's not correct to say 62.5% of goals scored depend on previous year goals.<BR/><BR/>R, the correlation coefficient, is about .8 if the r^2 is .625. So it would be more precise to say 'for every standard deviation above average a team is any year, we'd expect it to be .8 standard deviations above average the next year.' <BR/><BR/>I think that's a better real world interpretation. In this case, the units of R^2 is "goals squared." I like using both the regression slope and R^2 to think about the relationship, but in a univariate regression, they're both reporting the same information. <BR/><BR/>Either way you interpret it, it seems the Premier League doesn't have much year-to-year parity at all.<BR/><BR/>By the way, I just read a great article on the subject of correlation and regression today:<BR/><BR/><A HREF="http://www.hardballtimes.com/main/article/statistical-shenanigans/" REL="nofollow">http://www.hardballtimes.com/main/article/statistical-shenanigans/</A>Brian Burkehttp://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-19138305702747602702009-03-02T15:25:00.000-05:002009-03-02T15:25:00.000-05:00I just did a similar regression for English Premiership football, although rather than looking for year on year change I looked to see whether we can predict year X+1 points from year X points. R-squared is 0.625, which if I remember stats correctly, means that 62.5% of the points a team will score depend on how well they did the previous season.<BR/><BR/>That would be disparity (although wikipedia has a better example. Portuguese football has had 3 teams win 71 of the past 73 championships)Ianhttp://www.blogger.com/profile/01518825067469269377noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-15479406404471930962009-03-02T15:16:00.000-05:002009-03-02T15:16:00.000-05:00Paul-Yes, the difference is statistically significant. The standard errors for both line slopes is .5, so they&#39;re about 3 and a half SEs apart.<BR/><BR/>So it&#39;s doubtful it&#39;s random, but nothing I&#39;ve done here proves that it&#39;s the CBA that causes the change. For now, that has to be inferred from the timing: <BR/><BR/>&#39;87 Free agency...&#39;94 CBA --&gt; greater parityBrian Burkehttp://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-56805577552606301152009-03-02T14:30:00.000-05:002009-03-02T14:30:00.000-05:00Interesting post. Do you have any idea of the statistical significance of the difference in regression estimates? I agree that there is a difference, but I am not sure if it is actually a meaningful difference or just random variation, possibly attributable to other causes. Thoughts?<BR/><BR/>Cheers, PaulAnonymousnoreply@blogger.com