
04212018, 04:01 PM  #46 
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I read something you said wrong, looking at it now
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04212018, 04:21 PM  #47 
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Ok, so you weren't wrong to say 8 of the top 10 in PPG were top 15 in Punts inside the 20%. However, this theory doesn't really hold up in other years in the data set. Also, I've shown no strong correlation. There also isn't a strong correlation between YDS/G and Punts inside the 20 %.
Despite the fact that we haven't found any strong indicator that punting affects defense in a meaningful way this did prompt me to do one more data set, and that is Win % versus Punt IN 20 % and that has given the strongest correlation of all data.
Spoiler!
This is surprising, because one would expect that if they did affect W% they'd also affect Defensive performance. That really doesn't seem to be the case.
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04212018, 04:44 PM  #48 
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Number of teams in the top 10 in % of Punts IN 20 that were also in top 15 in PTS/G
2017: 8 2016: 6 2015: 5 2014: 6 2013: 5 What this tells me is that if you have a punter that is in the top 30% of the league in % IN 20, then you've got about a 60% chance of being in the top half the league in PTS/G. But, if you have one of the 70% of the rest of the punters, you still have a 40% shot... However, 2017 skews the data and if we were to regress that value more towards the mean (6), we'd end up closer to a 55%/45% split. More to follow...
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04212018, 05:46 PM  #49  
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The Texas punter was the MVP of that bowl game. He’s a helluva punter. 

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04212018, 06:16 PM  #50  
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The point of it all, to me, is to ask yourself whether or not it's actually worth spending high dollars on a "great" punter when, statistically, it really doesn't make much of a (if any) difference? If you can spend low draft capital, or even sign a punter as an UDFA, and he will perform in line with NFL standards at the position, there isn't a clear statistic that I've found that correlates it being any worse than the best in the game over the longhaul. One thing I'm looking into right now is the differences between punters that are 1 standard deviation below the mean in terms of IN 20 % and those that are 1 standard deviation above the mean. This should give me a better idea of impact of a bad punter versus great punter.
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04212018, 07:13 PM  #51 
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Number of Punters above and below one standard deviation from the mean by year:
2017: >1STDEV = 5, <1STDEV = 3 2016: >1STDEV = 6, <1STDEV = 4 2015: >1STDEV = 6, <1STDEV = 6 2014: >1STDEV = 7, <1STDEV = 8 2013: >1STDEV = 2, <1STDEV = 5 Here are the approximate ranges within 1STDEV of the mean, so the data I worked with were for punters above and below these: 2017 1STDEV = 42.98% to 30.42% 2016 1STDEV = 44.69% to 29.99% 2015 1STDEV = 42.29% to 27.85% 2014 1STDEV = 41.68% to 28.90% 2013 1STDEV = 41.41% to 28.59% I combined these into 1 list to get some data, and so I ended up with 26 punters in total in each category. >1STDEV Average %IN20 = 46.4% (Min 42.5%) Average W% = 59.6% Average PTS/G = 20.9 Average YDS/G = 344.4 <1STDEV Average %IN20 = 26.1% (Max 30.0%) Average W% = 35.1% Average PTS/G = 24.2 Average YDS/G = 352.6 If I take the data set as a whole I get the following correlations: IN20% to YDS/G = 0.140 IN20% to PTS/G = 0.430 IN20% to W% = 0.565 What I've learned. It is better to have a great punter in terms of %IN20 than an absolutely shitty punter. That said, overall that means you need to find a punter that is within 1STDEV of the mean or better. It may indicate as well that if you have a punter that is better than one standard deviation from the mean, he is an advantage. In case you're curious, here's the best and worst through the past 5 years in terms of %IN 20.
Spoiler!
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04212018, 07:47 PM  #52 
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Looking into Net Average, in the same way I looked at %IN 20, I came away with results that indicate Net Average is not a factor.
Number of Punters above and below one standard deviation from the mean by year: 2017: >1STDEV = 5, <1STDEV = 4 2016: >1STDEV = 5, <1STDEV = 4 2015: >1STDEV = 5, <1STDEV = 5 2014: >1STDEV = 5, <1STDEV = 6 2013: >1STDEV = 5, <1STDEV = 3 Here are the approximate ranges within 1STDEV of the mean, so the data I worked with were for punters above and below these: 2017 1STDEV = 42.30 to 39.14 2016 1STDEV = 42.21 to 38.44 2015 1STDEV = 41.68 to 38.16 2014 1STDEV = 41.30 to 37.73 2013 1STDEV = 41.55 to 37.48 I combined these into 1 list to get some data, and so I ended up with 25 punters above and 22 punters below 1STDEV. >1STDEV Average Net = 42.9 Average W% = 50.75% Average PTS/G = 22.4 Average YDS/G = 348.2 <1STDEV Average Net = 37.2 Average W% = 47.44% Average PTS/G = 22.8 Average YDS/G = 337.7 If I take the data set as a whole I get the following correlations: NetAve to YDS/G = 0.164 NetAve to PTS/G = 0.080 NetAve to W% = 0.065 After seeing all of this data, I can pretty well conclude that the only real factor you need to look at with a punter is % inside the 20. That seems to be the only factor that has any significant impact on games and it seems to do so in terms of PTS/G and W%. Now the question is, at what point is there a cutoff where it no longer matters? This would tell me a reasonable break in what should define a punter "worth paying" versus one that isn't. My next question would be, at what point is the cutoff for bad punters? I'm working on formulating how I want to tackle these questions. Basically, I have to continue to include punters by %IN 20 in tiers until I reach a 50% mark for win percentage, IMO. If anyone has a suggestion, feel free to chip in.
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04212018, 09:01 PM  #53  
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With Mahomes now at QB with still a very suspect defense you need finesse punting more than ever pinning back the opposing offense giving your suspect defense more of a chance for a stop and why Colquit got paid. Veatch recognizes his defense needs help in the field position game for sure.
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04212018, 10:03 PM  #54  
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We also discussed looking at how offensive performance affects Punt Inside 20 %. I think there's a solid chance it is a factor. Just looking at the data I've poured through and the teams I see having punters at the worst end of that, like Buffalo, Cleveland, New York, etc, I just want to see how it looks. I've put way too much time into this tonight and I'm not any further. I'm looking for more beyond Dustin Colquitt though in all of this. I want to see if there really is a reason to pay a punter good money and when that situation should exist from a statistical perspective. I think I'm close to that answer now, but I don't know if I can take it much further. It's been forever since I got my undergrad degree in mathematics. I haven't used it for a very long time, especially anything beyond basic algebra because of the field I'm in. I don't' have the time nor do I remember much on multivariate analysis. I'd love to see someone that does take this to another level. It'd be interesting to see it.
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