tag:blogger.com,1999:blog-5204092591876211047.post5873088152702102654..comments2016-07-29T03:02:20.310-04:00Comments on Advanced NFL Stats Community: Final "BigWin%" Ratings for 2010, Looking to the PlayoffsUnknownnoreply@blogger.comBlogger10125tag:blogger.com,1999:blog-5204092591876211047.post-44549956470793951112011-01-26T15:57:41.621-05:002011-01-26T15:57:41.621-05:00And for the big game, BigW% says it's as even ...And for the big game, BigW% says it's as even a match as one could hope for:<br /><br />______________ spreads by<br />___rating_____BigW___Vegas<br /><br />GBAY .694 __ -0.5 __ -2.0<br />PITT .678 <br /><br />Bruce: Enjoy the game or find my mistakes, I've been so quick and dirty on this I'm sure they are there. But I have found this an interesting proof-of-concept. In the off-season I'll check the data, best values to use, follow some implications, fix the howlers I've made ... if I have the time.Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-15664412461272213722011-01-23T09:13:58.926-05:002011-01-23T09:13:58.926-05:00Jim,
You've rendered multiple man-hours of pr...Jim,<br /><br />You've rendered multiple man-hours of programming and analysis mute, now what do I do?<br /><br />(Been lazy, haven't finished checking the various point differentials YET.)Bruce D.http://i60200nfl.clanteam.comnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-54796113647875208712011-01-23T04:17:06.130-05:002011-01-23T04:17:06.130-05:00Hey, through two rounds this is picking pretty dar...Hey, through two rounds this is picking pretty darn well for entertainment purposes -- plus a free mortgage payment!<br /><br />Just for the record, this week via BigW%, rating and spread giving 2.5 to the home team...<br /><br />______________ spreads by<br />___rating_____BigW___Vegas<br /><br />NYJ .648 <br />PIT .680 __ -3.5 __ -3.5<br /><br />GBY .699 __ -1.5 __ -3.5<br />CHI .591Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-84193703528913362012011-01-13T23:26:36.963-05:002011-01-13T23:26:36.963-05:00Just for the record and heck of it (and to procras...Just for the record and heck of it (and to procrastinate away from some real-world work) I estimated point spreads for this week's games using "reverse Pythagorean" -- going from win probability to point differential, instead of the normal other way around, using the Pythagorean formula. <br /><br />FWIW, for three of the games the total net point spreads by this method and from Vegas were exactly the same, 22 (with slight differences per game). So for this tiny example they approximate each other surprisingly well. The fourth game with the biggest difference of course was the Atlanta game. People are taking Atlanta's close wins seriously, looks like.<br /><br />Using 2.5 pts for home field advantage, the "BigW% spread" (rough, chunky style) and smooth Vegas spread (as per today's NY Post) are ...<br /><br />____________BigW___Vegas<br /><br /><br />NEWE__0.613 -6 ... -8.5<br />NYJT__0.387<br /> <br />PITT__0.551 -4 ... -3.5<br />BALT__0.449<br /> <br />CHIC__0.767 -12 ... -10<br />SEAT__0.233<br /> <br />GBAY__0.604 -1 ... +2.5<br />ATLA__0.396 <br /><br />I'm rushing out now to bet this month's mortgage payment on the Packers. For the entertainment of it only.Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-65878883677946820632011-01-13T22:13:27.291-05:002011-01-13T22:13:27.291-05:00If you can give a quick example of how you factore...<em>If you can give a quick example of how you factored SoS in, I'll rerun the program for various point differentials to see which is the most "entertaining". </em><br /><br />If you are proposing to test break points (other than 10) as in my 15-year playoff run, the SoS adjustment is easy and standard, I think. At the risk of telling what you already know (in an exercise like describing a spiral staircase without using my hands)...<br /><br />Count BigWs and BigLs with close games as ties, to give each team a normal W-L%. Call that its WinPct(0). Then all its opponents' combined average win% (their average WinPct(0)) is computed. Figure the original team's adjusted win% against that strength of schedule, and the result is its WinPct(1), and do for all teams. Then substitute all the WinPct(1)s for the WinPct(0)s in figuring average opponent strength for each team, etc. Rinse and repeat until the numbers stabilize.<br /><br />For instance, using the simple arithmetic method, say Team A has a 60% winning% against opponents with an average 46% winning%. As 46% is 4 below 50% (league average) drop Team A's adjusted winning% by 4 points to 56% -- winning 60% against .460 teams is equivalent to winning 56% against .500 teams. If Team B has a 48% winning% against teams with an average 65% winning%, it's adjusted winning% becomes 63% (higher than Team A's even though it has a worse regular W-L record). That's the first cut at expected performance against a league-average .500 schedule for Teams A, B, C, etc. Except that now each has a changed strength of opposition, so the process has to be repeated until the numbers stop changing. (Then hope all the combined team strengths and SoSes still average out to .500.)<br /><br />At the end you get the expected record for each team against league-average .500 opposition, so they are all on the same scale, plus a list of opponents for each team by final adjusted "BigW-L" strenght, which (purportedly) gives an improved SoS measure for the opposition against which for instance KC got its regular 10 wins.<br /><br />Now ... the simple arithmetic method is very easy and fine as long as team win%s are clustered around 50%, say from 35% to 65% (as in MLB). But it is a linear appoximation of a non-linear reality, so at the extremes errors become significant -- NE projects to win 124% of games against CAR. <br /><br />So instead I use Log5 in the iterations, which is complex for pencil-and-paper but easy for a spreadsheet. The little extra coding is a cheap price to pay to get out of having to convert a 124% chance of winning into a point spread.<br /><br />Have fun!Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-21598638768161313402011-01-13T21:42:42.004-05:002011-01-13T21:42:42.004-05:00Bruce, dang it, I haven't been able to get a c...Bruce, dang it, I haven't been able to get a comment posted in response to yours -- and then when I did get one up here, after a while it disappeared. So let me break it up, rewrite to make the gods of Blogger and comment moderation (?) happy, and try again...<br />~~~<br /><br />Bruce: My 15-year playoff database is a kludge, but the 2010 BigW-L% one runs easy. Here are the post-wild card games BigW% ratings for the remaining playoff teams:<br /><br />(I know this can't line up right)<br /><br />_________BW____BL____pct<br />NEWE___11.8___4.2___0.739<br />PITT___11.0___5.0___0.686<br />GBAY___11.5___5.5___0.676<br />NYJT___10.9___6.1___0.641<br />BALT___10.9___6.1___0.640<br />ATLA____9.3___6.7___0.578<br />CHIC____9.2___6.8___0.574<br />SEAT____4.9__12.1___0.291<br /><br />Heroically assuming the thing still to be proven (but which seems plausible so far), that these numbers are a realistic estimate of relative team strength, then applying <a href="http://www.chancesis.com/2010/10/03/the-origins-of-log5/" rel="nofollow">Log5</a> gives these probable winning percentages for neutral-field games between these teams...<br /><br />NEWE__0.613<br />NYJT__0.387<br /> <br />PITT__0.551<br />BALT__0.449<br /> <br />CHIC__0.767<br />SEAT__0.233<br /> <br />GBAY__0.604<br />ATLA__0.396<br /><br />If you can convert these to point spreads and adjust for home field advantage, well, feel free "entertain" yourself with all the games all weekend. <br /><br />Disclaimer: The data and analysis going into it all are guaranteed fully sound and error-free up to the amount of cash you have paid me for it, minus a $100 deductible. [tbc]Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-53759781713748174402011-01-13T16:55:16.633-05:002011-01-13T16:55:16.633-05:00Bruce: My 15-year playoff database is a kludge, t...Bruce: My 15-year playoff database is a kludge, the 2010 BigW-L% one runs easy. <br /><br />Here are the post-wild card games BigW% ratings for the remaining playoff teams:<br /><br />(I know this can't line up right)<br /><br />_________BW____BL____pct<br />NEWE___11.8___4.2___0.739<br />PITT___11.0___5.0___0.686<br />GBAY___11.5___5.5___0.676<br />NYJT___10.9___6.1___0.641<br />BALT___10.9___6.1___0.640<br />ATLA____9.3___6.7___0.578<br />CHIC____9.2___6.8___0.574<br />SEAT____4.9__12.1___0.291<br /><br />Heroically assuming the thing still to be proven (but which seems plausible so far), that these numbers are an improved realistic estimate of relative team strength, then applying <a href="http://www.chancesis.com/2010/10/03/the-origins-of-log5/" rel="nofollow">Log5</a> gives these probable winning percentages for neutral field games between these teams...<br /><br />NEWE__0.613<br />NYJT__0.387<br /> <br />PITT__0.551<br />BALT__0.449<br /> <br />CHIC__0.767<br />SEAT__0.233<br /> <br />GBAY__0.604<br />ATLA__0.396<br /><br />If you can convert these to point spreads and adjust for home field advantage, well, feel free "entertain" yourself with all the games all weekend. <br /><br />Disclaimer: The data and analysis going into it all are guaranteed fully sound and error-free up to the amount of cash you have paid me for it, minus a $100 deductible.<br /><br /><em>If you can give a quick example of how you factored SoS in, I'll rerun the program for various point differentials to see which is the most "entertaining". </em><br /><br />If you are proposing to test break points (other than 10) as in my 15-year playoff run, the SoS adjustment is easy and standard, I think.<br /><br />At the risk of telling what you already know (in an exercise like describing a spiral staircase without using my hands)...<br /><br />Count BigWs and BigLs with close games as ties, to give each team a normal W-L%. Call that its WinPct(0). Then its opponents' combined average win% (their avergage WinPct(0)) is computed. Figure the original team's adjusted win% against that strength of schedule, and the result is its WinPct(1), and do for all teams. Then substitute all the WinPct(1)s for the WinPct(0)s in figuring average opponent strength for each team, etc. Rinse and repeat until the numbers stabilize.<br /><br />For instance, using the simple arithmetic method, say Team A has a 60% winning% against oppnents with an average 46% winning%. As 46% is 4 below 50% (league average) drop Team A's adjusted winning% by 4 points to 56% -- winning 60% against .460 teams is equivalent to winning 56% against .500 teams. Now team A's 56% winning% is used instead of its orginal 60% in the next iteration of figuring every other team's strength of schedule. Repeat until the numbers stop changing. Hope all the combined team strengths and SoSes the average out to .500.<br /><br />The arithmetic method is very simple and fine as long as team win%s are clustered around 50%, say from 35% to 65% (as in MLB) but since it is a linear appoximation of a non-linear reality at the extremes errors become significant -- it can give teams like NE and CAR winning over 100%/less than 0% of their games. So instead I use Log5 in the iterations, which is complex for pencil-and-paper but easy for a spreadsheet. The little extra coding is a cheap price to pay to get out of having to convert a >100% chance of winning into a point spread.<br /><br />Have fun!Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-72403052221049884482011-01-13T10:11:12.071-05:002011-01-13T10:11:12.071-05:00Jim,
I've written a program using your BigWin...Jim,<br /><br />I've written a program using your BigWin formula that connects to a database.<br /><br />It'll be easy to check the various point differences as stated above, the only thing missing is the SoS factor.<br /><br />If you can give a quick example of how you factored SoS in, I'll rerun the program for various point differentials to see which is the most "entertaining".Bruce D.http://i60200nfl.clanteam.comnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-41395240673380408542011-01-12T19:59:00.141-05:002011-01-12T19:59:00.141-05:00Bruce, thanks for the kind words.
I definitely pl...Bruce, thanks for the kind words.<br /><br />I definitely plan to check numbers other than 10 as the break point, but it will have to wait until the off season due to (1) lack of time, and (2) call it "legacy problems" with my spreadsheet. <br /><br />This started merely as a very quick-and-dirty look at the playoff records of teams that win "close games" in the regular season, just for my own interest. I picked 10 points to define "close" because of Brian's post a while back saying half of games are determined by luck, and the median point margin in games last year was 10 points.<br /><br />"The value of winning close games" was settled for me when I found that over the past 15 years the 15 teams with the best records in close games during the regular season -- a combined 103-11 in them, pretty dang impressive! -- in the playoffs then went 15-14, 52%. That was what I was looking for. Done, case closed. Then I noticed the Big Wins business. <br /><br />I wasn't thinking of looking at "Big" wins and losses at all, sure wasn't thinking of examining them to write about. So the spreadsheet is a big kludge I keep adding to ad hoc, with the 10 points sort of hard-coded in, not manipulation friendly.<br /><br />This year the median point margin is less, so even by the initial simple logic 10 points is too many, too many games are being treated as BigTies, not enough as BWs and BLs. And now Brian says it's closer to 40% games determined by luck. So clearly the calibration is a long way from right -- but still, for a crude version 0.1 attempt, I find the results interesting.<br /><br />My intuition is that to seriously reduce luck in results requires a point differential of more than one score, I'm thinking 8.5 points. But intuition is only a starting point, from there it takes test test test to find what produces the best result. I plan to re-write the program code and use much more robust data to do that in the offseason, real-life permitting.<br /><br />And hey, feel free to steal all you want, and I'll steal from you. That's what a community is for!Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-71625725745484322242011-01-12T14:55:26.962-05:002011-01-12T14:55:26.962-05:00Jim,
Nice follow-up to your previous post.
Very ...Jim,<br /><br />Nice follow-up to your previous post.<br /><br />Very "out of the box" and innovating. I plan on stealing, um, borrowing your ideas here. <br /><br />SoS seems to add a lot of credibility.<br /><br />Brian Burke asked in your previous post:<br />"Awesome job. I wonder though if 10 points is too many to be called a close game. If the cutoff is reduced to 7 points or less, would the results be robust? Do the samples get too small? Perhaps it would be even more condemning for the clutch narrative."<br /><br />+1 for that(barring family emergencies of course)Bruce D.http://i60200nfl.clanteam.comnoreply@blogger.com