tag:blogger.com,1999:blog-38600807.post4532419199915637157..comments2023-11-05T04:16:44.937-05:00Comments on Advanced Football Analytics (formerly Advanced NFL Stats): Momentum Part 3: After Failed 4th Down Conversion AttemptsUnknownnoreply@blogger.comBlogger9125tag:blogger.com,1999:blog-38600807.post-54245047269041118392013-12-05T14:50:52.527-05:002013-12-05T14:50:52.527-05:00So Ive been thinking about it for a while...
The w...So Ive been thinking about it for a while...<br />The weighted team average calculated in this article shows that overall bad teams are attempting more of the 4th downs than good teams..basically, right? <br />I was wondering if there is a bias as to where on the "WP estimated" axis they fall (where on the x axis on the graph above). What if most of the data comes from the right side of the x axis (or even the FAR right side of the x axis)? This would open up the possibility that the left side of the x axis is filled with the good teams. So - overall - the sample is made up of more bad teams attempting 4th downs, but they are all concentrated on the right side of the x axis. The good teams can actually outnumber the bad teams for all the 4th down conversions attempted when their WP is above 50%. So, lets say that after a failed 4th down, the team receiving the ball has an actual WP of ~10% (red line on the graph above). This is lower than the WP model says you should have (~15%) - but its somewhat expected if most of the teams that attempted the conversion are good teams. <br />I really don't think that effect can be very big, since you can see on the far right hand of the x axis that there is essentially no difference between a 4th down stop and receiving a punt..i.e, no momentum effect. Over here, on the far right hand side we know there are mostly bad teams attempting these 4th downs so the bias is gone.Andyhttps://www.blogger.com/profile/13033784825091918451noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-14194865545352375572013-12-04T09:27:19.592-05:002013-12-04T09:27:19.592-05:00Teams turning the ball over on downs have to have ...Teams turning the ball over on downs have to have made the decision to go for it on 4th down. From many of your other articles, this tends to be an area where coaches throw away WP - so perhaps there's a bias where the teams turning the ball over on downs are more likely to win because in the course of the remainder of the game they will throw away less WP than their opponent on subsequent 4th down decisions.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-84154646004404621802013-12-03T21:16:19.703-05:002013-12-03T21:16:19.703-05:00Excel is often used to keep a common look and feel...Excel is often used to keep a common look and feel, but the analysis is almost always custom coded.<br /><br />The next 2 installments (and hopefully last 2) will use a completely different approach.Brian Burkehttps://www.blogger.com/profile/12371470711365236987noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-45704108089068594042013-12-03T14:43:04.363-05:002013-12-03T14:43:04.363-05:00Excellent analysis as always. One thing I do find ...Excellent analysis as always. One thing I do find that consistently nags me, however, is that when making comparisons like this you don't provide statistical measures of uncertainty. It would be very helpful in interpreting this graph if you provided a p-value for the difference between the two trendlines. I'd be interested to know if there really is a statistically significant difference (in the opposite direction as that predicted by the "momentum" theory) between the win expectancy of teams that take over on downs and those that receive a punt, or if difference is not significant, in which case we would simply fail to reject the null hypothesis of there being no difference beyond what is already predicted by the current game state (I would suspect the latter but would be interested to see the numbers).<br /><br />I find that this criticism seems to apply to many of the comparative analyses done at ANS. If you're using Excel to make these graphs, the relevant p-values or confidence intervals should be easy to calculate and include, and I think that doing so would shed a lot of light on the (otherwise excellent) analysis you do here over and above what we're already able to derive.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-13872579527309692632013-12-03T14:33:09.902-05:002013-12-03T14:33:09.902-05:00As a wonderful example of how momentum is overused...As a wonderful example of how momentum is overused, check out the BBC feed from the Murray v Djokovic Wimbledon final (http://www.bbc.co.uk/sport/0/tennis/23101783)<br /><br />Two sets to love down, two-love down in the third set, Djokovic wins four games in a row to lead the set 4-2. The quote from Tim Henman on the feed is "Momentum is a funny thing, we can't see it but we can feel it, and the momentum is certainly with Djokovic in this set."<br /><br />From that point, Murray wins four games on the bounce of his own, takes the set and the championship. Momentum eh? She's a fickle ally.Ian Simcoxhttps://www.blogger.com/profile/01518825067469269377noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-67332258353198799672013-12-03T14:19:01.621-05:002013-12-03T14:19:01.621-05:00NateTG - Whenever the punters (i.e. those who beli...NateTG - Whenever the punters (i.e. those who believe in punting on 4th and 1) are presented with the numbers, their argument is nearly always that going for it and failing loses 'momentum'. Seeing as we've already told them what the WP would be if they fail, we can only assume what they mean is that they think WP following a failed 4th down would be even lower. What this is testing is whether there is any evidence that teams that fail on 4th down lose more often than teams that find themselves in the same situation without turning the ball over (spoiler - there is not)Ian Simcoxhttps://www.blogger.com/profile/01518825067469269377noreply@blogger.comtag:blogger.com,1999:blog-38600807.post-61345814032249424032013-12-03T12:23:19.333-05:002013-12-03T12:23:19.333-05:00@Anonymous: No, momentum says "when things ar...@Anonymous: No, momentum says "when things are going good, things will continue to go better than they normally would; and conversely, when things are going bad, things will continue to go worse than they normally would". You measure "when things are going good/bad" using anything, say, the derivative of WP versus time (That is, if your chance of winning is going up, things are good.) or your SR% at any given time (That is, if you are succeeding at the plays you are running, things are good.)Xnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-71961490867329601662013-12-03T11:58:03.894-05:002013-12-03T11:58:03.894-05:00All these articles rely on "momentous" t...All these articles rely on "momentous" turnovers as a way of measuring momentum, but isn't momentum really saying "when things are going good, results tend to be good" and "when things are going band, results tend to be bad." I know "results" can be measure but I'm not sure how you would measure "when things are going good/bad"Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-38600807.post-37349102025606482892013-12-03T10:46:26.245-05:002013-12-03T10:46:26.245-05:00I don't want to be overly critical, but it see...I don't want to be overly critical, but it seems like the larger notion of momentum is a poorly defined hypothesis. These articles testing various notions of momentum seems like an intellectual game of whack-a-mole.<br /><br />"... For example, if NE (.710 win%) and IND (.660 win%) are the teams with all the failed 4th down attempts, they would naturally win more often just because they were good teams. ..."<br /><br />Controlling for team quality might make for more powerful EP and WP models in the general context. As with other NFL stuff, the data set is probably a bit sparse.<br /><br />NateTGnoreply@blogger.com