tag:blogger.com,1999:blog-5204092591876211047.post4383292396771545605..comments2016-07-29T03:02:20.310-04:00Comments on Advanced NFL Stats Community: Betting Market Power RankingsUnknownnoreply@blogger.comBlogger31125tag:blogger.com,1999:blog-5204092591876211047.post-74520216457500326702011-12-07T23:47:35.206-05:002011-12-07T23:47:35.206-05:00My point is that I was mainly interested in estima...My point is that I was mainly interested in estimating how the bookmakers would set the line in a hypothetical game between any two teams. For example, my ratings estimate that if Green Bay and New England played on a neutral field right now, the Packers would be favored by 2.5 points. So basically, I'm letting the bookmakers do the work for me and using their expertise in setting lines. Future spreads are more up-to-date than past spreads and, in theory, should be more accurate in terms of predictive power (if for no other reason than they account for recent injuries).<br /><br />Trying to predict future spreads based on past spreads is a similar but not identical exercise. That's closer to what bookmakers actually do, and such a model would be particularly useful if you were applying for a job with Las Vegas Sports Consultants (the company that provides initial lines to most books).<br /><br />FYI, my rankings for week 14 are up. The MAE for the week is 0.41, which is particularly low because the teams are pretty well-connected--no bye weeks this week or previous two weeks.Jim Ahttp://www.nutshellsports.com/armstrong.htmlnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-89454473243279688632011-12-07T19:48:07.151-05:002011-12-07T19:48:07.151-05:00to JIm A, I don't quite follow your reasonaing...to JIm A, I don't quite follow your reasonaing. You say you do not factor in last weeks results (but this weeks pointspreads).<br />You state "At the time I implemented this, I didn't really care about predicting the point spreads or measuring the market reaction to results as you have. Rather, I only wanted to compare teams that weren't playing each other this week." In what way are your trying to compare them, then? by talent difference? I guess I miss your point.<br /><br />and to j holz, you said to look at future spreads. Well, that to me seems to defeat the purpose. The whole purpose is to predict point spreads based on past point spreads, isn’t it? Yeah, a future one will essentially tell you what the bookies are thinking, but you want to see how you can predict what the bookies are thinking.<br />For the purpose intended, I would have done exactly what the author did with perhaps only weighting type differences.The Wizardhttp://www.wizardofathousandkings.com/noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-54968615096628116322011-12-06T00:53:07.156-05:002011-12-06T00:53:07.156-05:00Mike, my ratings ended up on nutshellsports.com af...Mike, my ratings ended up on nutshellsports.com after I found a similar betting market system on that site. Some interesting discussions of methodology resulted in that site's owner asking me to contribute my own system to his site. This was around November 2009, as I recall. So I don't even claim to be the first to publish such a system. It wouldn't surprise me if there are others out there, too.<br /><br />I've always thought I or someone else could come up with more accurate results. In particular, I wondered how much using SRS limited the results as opposed to a more complex computer rating system. Your previous work on opponent adjustments may be useful in this regard. I kind of lost interest in working on this myself after the initial thrill and have since moved on to other projects. Feel free to use my ratings as a benchmark or in any way that is helpful. Maybe I'll look at this again if I get a chance; as I recall my system's MAE was in the 0.7-0.8 range, but again my goal was slightly different than yours and using future games is, in a sense, cheating. It would be interesting to see a more detailed analysis of how the systems compare. I definitely look forward to seeing what you come up with next.Jim Anoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-88976659000477831942011-12-05T18:04:05.005-05:002011-12-05T18:04:05.005-05:00I don't have anything handy on error distribut...I don't have anything handy on error distribution, but here's the MAE for the past 4 seasons:<br /><br />2010 - 1.8<br />2009 - 1.7<br />2008 - 1.5<br />2007 - 1.6Michael Beuoyhttps://www.blogger.com/profile/03960600491528993233noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-79407808296035683672011-12-05T17:24:54.599-05:002011-12-05T17:24:54.599-05:00I Would be interested in seeing this weeklyI Would be interested in seeing this weeklyWhamphttps://www.blogger.com/profile/16006695529627865508noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-54502404096232076362011-12-05T15:05:26.478-05:002011-12-05T15:05:26.478-05:00I guess what I meant was how far back you tested. ...I guess what I meant was how far back you tested. I'm interested in your error distribution. Do you have that data handy?SportsGuyhttps://www.blogger.com/profile/02900787022759289513noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-8041588630439676322011-12-05T14:19:50.593-05:002011-12-05T14:19:50.593-05:00SportsGuy - Sorry about that. I was being loose w...SportsGuy - Sorry about that. I was being loose with my terminology. That should read "Point Spread = Home Team GPF - Visiting Team GPF + 2.5".<br /><br />If you're asking if I backtested the approach, the answer is yes (it's how I arrived at optimized credibility coefficient and number of weeks).<br /><br />Running the algorithm on past data generates a Mean Absolute Error (MAE) of about 1.7 points in predicting the spread for the upcoming week. Unfortunately, I had no benchmark to compare that to.Michael Beuoyhttps://www.blogger.com/profile/03960600491528993233noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-43114404550270890542011-12-05T12:55:11.824-05:002011-12-05T12:55:11.824-05:00"Point Spread = Home Team Rank - Visiting Tea..."Point Spread = Home Team Rank - Visiting Team Rank + 2.5"<br /><br />That is kinda where I get the idea you're figuring ranks first then converting to points.<br /><br />Have you run your algorithm on past data?SportsGuyhttps://www.blogger.com/profile/02900787022759289513noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-65146921600136669072011-12-04T19:18:02.537-05:002011-12-04T19:18:02.537-05:00Jim Glass, in all my work on NFL stats I have foun...Jim Glass, in all my work on NFL stats I have found no evidence that the strength of a spread is total dependent, this is also true of the NBA and NHL. From my observations this is due to covariance between team scoring.Tomnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-55723247287003499172011-12-04T16:20:00.499-05:002011-12-04T16:20:00.499-05:00SportsGuy - That's exactly what I did. The mo...SportsGuy - That's exactly what I did. The model output is the "Generic Points Favored". The points are converted to a ranking, not the other way around.Michael Beuoyhttps://www.blogger.com/profile/03960600491528993233noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-51052920471725787322011-12-04T15:26:14.136-05:002011-12-04T15:26:14.136-05:00I've been doing spread ratings since the mid-8...I've been doing spread ratings since the mid-80s. The error distribution you'll see over the long haul is precisely the shape you're seeing this week, recency adjustments or not.<br /><br />I just don't understand the obsession with using ranks. Why convert ranks to points when you can use points to start with?SportsGuyhttps://www.blogger.com/profile/02900787022759289513noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-19072351507293162992011-12-04T12:27:28.760-05:002011-12-04T12:27:28.760-05:00Market Price snapshot an hour before kickoff (with...Market Price snapshot an hour before kickoff (with win probability estimated based on no juice moneyline in parenthesis..as taken from prominent offshore locale)<br /><br />Buffalo -1 over Tennessee (Buffalo 52%)<br />Chicago -8 over Kansas City (Chicago 78%)<br />Miami -3 (-120) over Oakland (Miami 62%)<br />Pittsburgh -7 over Cincy (Pittsburgh 75%)<br />Baltimore -6.5 over Cleveland (Balt 74%)<br />NY Jets -3 over Washington (NYJ 60%)<br />Atlanta -1 over Houston (Atlanta 52%)<br />Carolina -2 over Tampa Bay (Carolina 56%)<br />***Note that Freeman is out for TB<br />New Orleans -8.5 over Detroit (NO 78%)<br />Minnesota -1 over Denver (Minnesota 52%)<br />San Francisco -14 over St. Louis (SF 89%)<br />Dallas -4 over Arizona (Dallas 65%)<br />***Kolb is back for Arizona<br />Green Bay -6.5 over NYG (Green Bay 71%)<br />New England -20 over Indy (NE 95%)<br /><br />The TB/Carolina line was TB -3 at home earlier this week, suggesting equality. A 5-point move would mean TB with Josh Johnson is 5 points worse than Carolina, and wherever anyone had them with Freeman.<br /><br />The Dallas/AZ line was Dallas -6.5 when it was thought Skelton was still playing. So, Arizona is 2.5 points better with Kolb than Skelton in the market's view...<br /><br />Looks like Yates got a little respect in the market today, as Houston is now only +1 instead of +2. They should be 4 points worse than Atlanta in a market snapshot at the moment rather than just one or two I'd think.<br /><br />Don't have time at the moment to compare differences here to MB's very interesting work up above...or to the win probabalities for the week from BB (life can be busy in the hour before kickoffs!). Wanted to throw down a live market look in the last hour since so many have posted interest in this kind of material. Might influence future discussions at the very least. Enjoy the games!Jeff Foglenoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-67022528590140927352011-12-04T11:41:15.817-05:002011-12-04T11:41:15.817-05:00Mike D - View the point spreads as stock prices. ...Mike D - View the point spreads as stock prices. If you wanted to know the state of Google right now, you would look at their stock price today, you wouldn't average their stock price over the past few years.<br /><br />Basically, all I'm doing here is trying to figure out the "stock price" of each team. But instead of getting direct quotes off of the NYSE, all I have available is the difference in stock prices between different companies. And those companies change each day.<br /><br />By necessity, I'm forced to look back to "old" stock prices just so I have enough connections between the various teams in order to get a proper comparison.Michael Beuoyhttps://www.blogger.com/profile/03960600491528993233noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-17119940802020131672011-12-04T11:29:15.307-05:002011-12-04T11:29:15.307-05:00This should definitely be a weekly feature. Good ...This should definitely be a weekly feature. Good stuff!Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-19387281640917460492011-12-04T11:05:35.372-05:002011-12-04T11:05:35.372-05:00I'd like to see them posted weekly as well.
I...I'd like to see them posted weekly as well.<br /><br />I have some suggestions/questions (full disclosure: I am NOT a statistician & not even 100% sure I spelled it correctly).<br />Wouldn't it be possible to tweek the equation to its highest probability of accuracy by using only historical data (i.e., NFL 2010) then applying it to NFL 2011 to see if it translates? Instead of tweeking it week to week or including the previous 4 or 5 gms, is it possible to use the now static historical data as something like a laboratory conditions to create a better model?<br /><br />Is there a scientific reason why the reality of static historical data isn't the primary source? Scientifically, is comparing NFL 2009 & 2010 or 2010 & 2011 like comparing apples & oranges instead of apples to apples?<br /><br />Just a lay person chiming in...Mike Dnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-11230737324459499122011-12-04T09:53:19.006-05:002011-12-04T09:53:19.006-05:00Great stuff as always. I would love to see weekly...Great stuff as always. I would love to see weekly breakdowns as well as any R code.The Wizardhttp://www.wizardofathousandkings.com/noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-4056927375741746802011-12-03T22:52:14.724-05:002011-12-03T22:52:14.724-05:00Outstanding concepts. I would love to see this as ...Outstanding concepts. I would love to see this as a regular featureAnonymousnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-37981021368652243362011-12-03T19:46:38.704-05:002011-12-03T19:46:38.704-05:00Thanks to everybody for the feedback.
First off, ...Thanks to everybody for the feedback.<br /><br />First off, my source for the spread information is killersports.com. It's also a useful site for game by game statistical information as well.<br /><br />I'm glad there's interest in this. I will try to get these submitted to the site each week soon after the weekly ANS Efficiency rankings are published.<br /><br />Here is a link to the R code. Any thoughts or suggestions on the methodology are welcome. Link: https://docs.google.com/document/d/1CFfQcnithQA2MXhCB2cUCCDwSdnAkDnEGPMTK_hJ7zk/edit<br /><br />Jim A - I did some googling while I was developing this approach to see if something like this existed. I wasn't able to find anything, but I'm not too surprised that I wasn't the first to try this. <br />I ran into the exact same difficulty when it came to bye weeks. Teams on bye seemed to have their ranking magnified (good teams moved up, bad teams moved down). The approach I eventually settled on was to normalize each team's weights to 1.0. If a team was missing a week due to a bye, the weights for the other weeks would get magnified to compensate. I wasn't thrilled with the solution, but it seemed to work well enough.<br />I took a look at your rankings. Your rankings better match the NO-NYG spread than mine, but mine got closer on the IND-NE spread. Care to put our approaches head to head for upcoming weeks? :) <br />I will try your weighting approach to see if I get a better fit. Like I said, mine was developed by trial and error and it's very possible I missed a better approach.<br /><br />Jim Glass - I completely agree. I knew my approach was not perfect, and modelling error of a point or two was unavoidable. Fine tuning each decimal point was not my goal.<br /><br />However, I am intrigued by the idea of using future weeks spreads to the extent they're available. I may take a deeper look at that.Michael Beuoyhttps://www.blogger.com/profile/03960600491528993233noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-84413033542661738392011-12-03T18:33:10.893-05:002011-12-03T18:33:10.893-05:00This is all very excellent, and I second the idea ...This is all very excellent, and I second the idea that it would be nice to see this ranking posted somewhere every week, making the weekly changes in Vegas's opion visible. <br /><br />One minor suggestion: remember when converting the Vegas point spread to win probability the over/under matters. A 6-point spread with an over/under of 37-points expected to be scored has a higher win probability than one with 47 points expected to be scored. So when converting the spread to win probability I don't use the exponential function but instead figure the projected score (spread applied to over-under) and then take the Pythagorean win expectation. <br /><br />The difference can realistically be equivalent to a couple points of spread (more at the extremes) in a given game. Over a few weeks it washes out so I wouldn't worry about it. But if one is using a small sample of only three or four weeks plus over-weighting the last, it might make a visible difference for some teams.<br /><br />That said, I probably still wouldn't worry about it. A difference significant enough to be visible isn't necessarily significant enough to be significant. False precision is something always to beware against.<br /><br />IMHO, the value of objective ranking/rating systems like this isn't their great precision (which is impossible in a season of only 16 games, even less so only part way through the 16) but how they can make plainly visible to the naked eye something one might have missed otherwise. If "Vegas ratings" (or ANFL Stats ratings) rate a team by a bunch a points different than I would that's interesting, if by 1/2 a point or 1 1/2 points that's not so interesting.<br /><br />So while I think it probably doesn't make any practical difference, I metion it just for the sake of logical consistency and because one might want to check the scale of the difference it makes, to be sure. After all, using a home field advantage of 2 points or 3 points doesn't make much difference and gets washed out quickly too, but people put a lot of effort into calculating that. (But then, they probably gamble a lot more money than I do.)Jim Glassnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-1763368868420057222011-12-03T10:53:43.604-05:002011-12-03T10:53:43.604-05:00I would be +1 for making this a weekly feature :)!...I would be +1 for making this a weekly feature :)!Justinhttp://sotsohockey.blogspot.comnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-48603881399713925892011-12-03T08:25:48.677-05:002011-12-03T08:25:48.677-05:00j holz, that was my thought, too, but there isn...j holz, that was my thought, too, but there isn't enough "interconnected" data between teams not playing each other to use only future lines. One idea I had that might help is to incorporate the odds to win the Super Bowl in addition to the current week's spreads, although you'd have to be careful to recognize that divisional/conference alignments can affect those. The tradeoff is you can either treat the future games spreads as representing immutable fact regarding the relative strengths of those teams and fit the rest of the teams around that the best you can. Or you can spread the errors around more evenly, which is the method I chose.<br /><br />I found that my 3-2-1 weighting of 50% future lines and 50% past lines yielded a reasonably accurate approximation.Jim Anoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-16621118451996354942011-12-03T07:27:37.794-05:002011-12-03T07:27:37.794-05:00To avoid including stale information, it's bes...To avoid including stale information, it's best to look at future lines, not past ones. Some websites and Vegas sportsbooks offer odds on next week's games and "games of the year"; these odds will reflect the cutler and schaub injuries and also whatever we learned by watching last week's games.<br /><br />This is a good concept, but you're measuring the wrong target.j holzhttps://www.blogger.com/profile/13428814047654767163noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-1052467758133333382011-12-03T05:11:35.196-05:002011-12-03T05:11:35.196-05:00I use the same pointspread to moneyline conversion...I use the same pointspread to moneyline conversion as you ml=exp(ps/7) or ps=7*ln(ml) in my power rankings (pointshare) however this means that if team A is 5 points better than B and B is 5 points than C then if you say a is 10 points better than c the moneyline odds would give you a different answer if you used a gwp or log 5 approach then converted that win prob to a pointspread. As the relationship is not linear so a moneyline approach would have team a as less than 10 point favourites. Could you redo your analysis first converting to odds then working out the rankings then convert back to pointspreads.Machttps://www.blogger.com/profile/15278455276778626892noreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-77450208999093994782011-12-03T00:21:17.695-05:002011-12-03T00:21:17.695-05:00I've been publishing a similar ranking system ...I've been publishing a similar ranking system based on the point spreads for the last few years (see link on my name). My methodology is just to use iterative SRS (as described on the p-f-r blog) with point spreads rather than game results. For the recency issue I found that a simple 3-2-1 weighting of the last three games gave the best fit. Any farther back made the fit worse in my experiments.<br /><br />I believe another difference is that I don't factor in last week's game results, but I do include this week's point spreads. At the time I implemented this, I didn't really care about predicting the point spreads or measuring the market reaction to results as you have. Rather, I only wanted to compare teams that weren't playing each other this week. One problem this presents is dealing with bye weeks. I didn't want to simply carry over a team's rating through its bye week because, in theory, incorporating updated spreads for non-bye teams should give you additional information about the strengths of the bye teams via opponent adjustments. In practice, bye week teams' ratings tend to be a little too volatile. Another weakness is that the largest individual game spreads seem to skew the ratings more than they should and those teams tend to have the greatest error relative to the actual spreads. I never got around to playing with various solutions to these problems.<br /><br />Anyway, I found this to be a very interesting exercise and am glad to see someone else has too!Jim Ahttp://www.nutshellsports.com/armstrong.htmlnoreply@blogger.comtag:blogger.com,1999:blog-5204092591876211047.post-21261107625218356822011-12-02T22:55:44.260-05:002011-12-02T22:55:44.260-05:00And by bias, I mean the fact that the bookies will...And by bias, I mean the fact that the bookies will put the juice where they think makes most sense, skewing the probabilities if you just split the juice evenly.Tomnoreply@blogger.com