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[Results] r/JustStart Affiliate Marketing Survey
After around 45 days of data collection, the results are in!
OverviewWe received 37 responses that were almost entirely complete. I was hoping for 50, but 37 is a very healthy number to see some trends.
Thank you to everyone who took the time to respond! I really appreciate it, and everyone reading this (even those who did not respond) will benefit from your contributions.
A note about anonymity. I did my best to only report results in the aggregate, but if anyone who responded feels uncomfortable with how I summarized things, please message me.
All data was manipulated and visualized in R using RStudio and the ggplot2 package. In case you're curious.
I created 20 charts to summarize the major data points. There's a lot more detail available since this was a 45+ question survey, but it took me around 12 hours to put this together and, well, that's a lot of time. If there's something you're curious about that I didn't explicitly show, just ask in the comments.
I'll try to keep this post short while also giving my $0.02 about the results. I encourage you all to ask questions and have discussions in the comments.
Part 1: Respondent DemographicsChart 1: Where are survey respondents from?
The 37 respondents came from 7 different countries with the United States being the overwhelming majority (81.0%). We even had one person from Zimbabwe!
Chart 2: What is your high-level niche category?
The 37 respondents represented 11 different niches. These categories were my attempt at rolling things up to a level that was high enough to make people feel comfortable while still being useful. 41.0% of respondents stated their niche was "Outdoors, sports, recreational, pets" so maybe I rolled that one up too much, but I honestly didn't expect 2 of every 5 people to have that one category.
Chart 3: Are you a full-time affiliate marketer?
Only 5 of 37 (14.0%) respondents stated they were full-time affiliate marketers, but almost half (18, 49.0%) stated they were either full-time or trying to be full-time.
Chart 4: How many sites do you operate?
Only 25 of 37 respondents answered this question. The chart breaks down the number of sites operated by whether the respondent was full-time, trying to be full-time, or just doing this on the side. Overall 8 of 25 (32.0%) only operate 1 affiliate website, 17 of 25 (68.0%) operate no more than 3 websites, and the most someone operates is 10. Among the 4 full-time affiliate marketers who responded, the median/mode/average was 3 websites.
Part 2: Affiliate Program ParticipationChart 5: How many affiliate programs do you participate in?
Two people did not answer this question. Of those that did answer, around half (19, 54.3%) report they only participate in one affiliate program. 7 of 35 (20.0%) report they participate in more than 3 affiliate programs. The max was 10.
Chart 6: What percent of your affiliate revenue is from Amazon?
This was an Amazon-centric survey because it's the plurality/majority affiliate program used in this sub. However, as Chart 5 showed, it's not the only affiliate program people use. Only 29 of 37 respondents provided enough information on the affiliate revenue distribution among programs, but 21 of 29 (72.4%) have at least 85% of their affiliate revenue come from Amazon. 3 respondents (10.3%) have none of their affiliate revenue come from Amazon.
Chart 7: How much of your shipped item revenue comes from your target niche?
I also asked users to estimate what percentage of their shipped item revenue comes from their target niche versus from unrelated niches. I only included 15 respondents who reported data and had at least $2,500 in shipped item revenue (my arbitrary cut-off to exclude outliers). The majority of respondents reported between 70%-90% of their referrals coming from within their niche with 80% (33.3% of respondents) being the plurality.
Part 3: Post & Traffic StatsChart 8: How many words per post?
Overall the median reported post length was 1,464 words. This was calculated by dividing the reported Total Word Count by the Total Post Count. Since 41.0% of respondents stated their site was in the "Outdoors, recreation, sports, pets" niche, I wanted to break up post lengths for that niche versus all other niches combined. Median word count for the "Outdoors" group was 1,210 while the "non-Outdoors" group was 1,500. Small sample size? Probably. If you need help reading a boxplot, click here.
Chart 9: How much organic traffic per post?
For this chart I plotted Domain Age (Months) vs Organic Traffic (Sessions) to see if there was a trend. Unsurprisingly there is with an R2 of 0.30. If you don't know anything about R2, this is a good link. Basically it means that 30% of the variation in Organic Traffic Per Post is explained by Domain Age (Months). The other 70% is other factors, such as keyword selection, SEO, UX signals, etc. I have no idea if that 0.30 is anywhere near what the actual number should be, but with our small sample of 37 responses, that's what we get. Honestly, I was surprised it was this high.
Chart 10: How much affiliate revenue per money post?
I asked respondents to estimate what percent of their posts were money posts and used that to calculate the amount of affiliate revenue per money post for the month. Note that some info posts do make money with affiliate links and a lot of posts are a hybrid of the two. This isn't going to be perfect. Still, we got another R2 of 0.31 when plotting Domain Age (Months) vs. Affiliate Revenue Per Money Post.
This is a good place to mention that I converted all money numbers to USD. Every respondent provided the currency they were using with the exception of the first 3 respondents (I hadn't added the question then). However, the first 3 respondents were all from the US.
Chart 11: What is the relationship between all traffic and affiliate revenue per money post?
Next I looked at the amount of total traffic (not just organic) versus affiliate revenue per money posts. Again, I used self-reported percent of posts that were money posts to estimate the traffic share to these posts. This assumes the same traffic to info and money posts, which may not be true. This produced an R2 of 0.79, which is very high but also not very surprising. This means 79% of the variation in affiliate revenue per post is determined by the amount of traffic each posts gets. The other 21% of variation in affiliate revenue per posts is things like how well you sell your content to the reader, the commission of the affiliate program used, how often they click on links in the post, etc.
Chart 12: Do you outsource content?
23 of 37 respondents (62.2%) reported they don't outsource any of their content. On the other end of the spectrum, 5 people (13.5%) reported they outsource all of their content. On the "How much do you pay per word?" side, I'm now realizing I'm an idiot for leaving the "$0.00" in for those who said they don't outsource content. Oh well. Still, I'm shocked how many people report they pay only $0.02-$0.03 per word for content -- 7 of the 14 people (50.0%) who provided data. No one paid more than $0.15 per word.
Part 4: AdsChart 13: Do you display ads?
Overall the ad data reported was the most incomplete section of the survey. 35.1% of respondents display ads and only 1 person (2.7%) reported that they sell ads directly to companies. It seems most other people use a network with Google Adsense being the most reported. But again, the ads data was pretty incomplete.
Part 5: More Revenue & Expense StuffChart 14: Total monthly revenue and expense by site
I also wanted to throw together some high-level stuff on overall revenue and expenses. Not a ton to really say about this.
Part 6: Backlinks & Linking StrategiesChart 15: Domain age vs number of dofollow referring domains
I had the most fun working with the backlinks data. This plot shows an R2 of 0.12 between domain age and the number of dofollow referring domains, indicating there are a lot of other variables affecting how many dofollow backlinks a site gets.
Chart 16: Domain age and dofollow referring domains, natural vs built
I also asked people to estimate the number of referring domains they had earned naturally versus built through various efforts. Interestingly (but unsurprisingly) the R2 between domain age and manually built backlinks was 0.00. When you isolate only naturally earned backlinks, the R2 between domain age and dofollow referring domains increases to 0.18.
Chart 17: Popular backlink building strategies
The survey listed 11 different backlink building methods and asked people to rate how often they used each from 1 (very rarely) to 5 (very frequently). There was also a "Never" option. Using those scores I ranked each strategy from most to least used by assigning each 5 response a score of 5, each 4 response a score of 4, etc. The most possible points a strategy could score if everyone used it very frequently was 170. According to the responses from 34 people, the 3 most popular backlink strategies are Comments/Forums (50), Guest Posting (48), and Skyscraper Outreach (38). The 3 least popular backlink strategies are Blog Roundups (8), Scholarships (8), and Broken Links (11).
Also note that I forgot to add PBNs as a strategy until half of the responses came in, so I extrapolated the points PBNs earned over the entirety of responses.
Chart 18: How frequently is each link building strategy used?
This chart takes the link building strategies from Chart 17 and provides more detail. It reads from top-left (Comments/Forums, most popular) to bottom-right (Blog Roundup, least popular) and quantifies the responses each got. 17 of 34 (50.0%) of respondents reported using Comments/Forum links at least sometimes. Guest Posting got more 5s (very frequently) than any other backlink strategy. Only 1 person (2.7%) gave Inforgraphics more than a 2 (somewhat rarely).
Chart 19: How white hat is your site?
I asked people to rate how white hat their site was from 1 (not at all white hat) to 10 (totally white hat). 16 of 37 respondents (43.2%) gave their site a 10. 28 of 37 (65.6%) gave their site either a 9 or 10. Only 3 of 37 (8.1%) gave their sites less than a 5. One person (2.7%) gave their site a 1.
Chart 20: Backlink strategies vs white hat rating
Lastly, I was curious how people's backlink strategies aligned with how white hat they rated their site. The 1 person who gave their site a white hat rating of 1 gave their PBN usage a 5 (very frequently). Guest Posting is frequently used by people with sites of all white hat ratings. I'm not really sure what else to glean from this data. I just find the chart I made to be pretty cool.
That's ItI hope you enjoyed that and got something from it. It was interesting aggregating all the results and seeing some trends. Thanks again to everyone who participated!
EditChart 21: Stats for domains that outsource vs domains that don't
u/rwiman asked about how stats such as revenue, traffic, and word count vary between domains that outsource and domains that don't. Great question. For this chart I looked only at domains older than 6 months and grouped them as "Outsource = Yes" if the respondent reported that they personally created less than 100% of the site content. There were 27 domains that fit this criteria: 13 outsourced and 14 did not. Nice even split. The domains had roughly the same median age as well: 17 months for those that outsourced, 18 months for those that didn't. Pretty good apples to apples comparison in terms of sample size and domain age. For the domains that outsourced, the median amount of outsourced content was 30%, so these domains are still producing (or historically did produce) a lot of their own content.
Interestingly, the domains that outsourced had much higher median revenue per post ($29 vs $5) with longer content on average (median 1,667 words vs 1,205 words) and more traffic per post (252 sessions vs 166 sessions). My thoughts? (1) There's sort of a prerequisite that your site make money before you outsource. Of course, that doesn't have to be true, but it's possible these sites outsource because they make more money...not that they make more money because they outsource. (2) I can't tell if the higher median word count on the outsourced side means the outsourced content is longer -- remember, median amount of outsourced content was only 30% -- but I find it interesting. Maybe people who create 100% of their own content get into a rut and just push out shorter stuff because they're bored/frustrated? Maybe the people who outsource content value the content more (enough to pay for it) and thus place emphasis on more in-depth stuff? Totally just throwing things out there.
Also note, however, that the top 4 sites in revenue per post do not outsource. I don't know what to make of that haha.
Thanks u/rwiman for the request!
Examining the Differences In The College And NFL Overtime Systems And the “Fairness” of Each
Both the college and the NFL overtime system are currently under fire for recent events. For the NFL, the Patriots again took an overtime kickoff and scored, there is a fan uprising over the “unfairness” of it. For the NCAA, the governing body says it’s open to changes to prevent games from going into multiple overtime games like LSU and Texas A&M’s 7 overtime contest in November. If any changes will be made to either system is yet to be determined. However, the debate rages with the fans.
For several years now, people have debated over which system is better and which system is more “fair” (however we measure that). As I’ve a fan of the college overtime system since the early 80s, I have followed some of these debates with interest, often amazed at the misinformation spread in these debates and accepted without pause. As I’ve collected a large share of college football overtime information including details of over 2,600 college football overtime games, I have more than average knowledge on the subject.
Among the things I want to address myths regarding the advantage the coin toss has in the college system. There are myths at both extremes, with people believing that there is no advantage in college (“because both teams get the ball”) and the widely held belief that the college system favors the coin toss winner by a much large margin than the pro game. The winning percentage of coin toss winners in college and pro is very similar (53.89% for in college FBS games and 54.58% for the new NFL system) but you constantly see people take a stand that one system is much fairer than the other. Of course, “fair” to some people means both teams having the ball, which is a valid point of view, but we’ll get into that later.
Given that there have only been 120 NFL games using the new system, which started in 2010 for the playoffs and 2012 in the regular season, it seemed pretty simple to gather the information I needed to assess how each system compares to the other. I used the game finder queries at pro-football-reference.com along with their play-by-play logs. Before I get into the results, I want to address a few common things I’ve seen people say which I feel stifle debate, rather than enhance it.
A lot of the debates I’ve followed discussion gets bogged down in people engaging in hyperbole, “facts” seemingly out of the blue, and pointless observations. Before I get down to the facts in this case, I want to give examples of some of those.
Every time a tie happens in the NFL, someone opines that the NFL needs to change to the college overtime system. This comment is pointless, as the NFL system is equally capable of breaking a tie as the college system is, it’s just that the NFL limits the length of the overtime and calls it a tie at that point. It seems unlikely that the NFL is going to want to have 7+ overtime games like has happened in college so it would have to put a cap on that system as well and you’d still have ties. I don’t see the NFL completely changing the system to end up with the same problem. I’m not saying that the NFL wouldn’t benefit from going to the college system, but just saying they should change without giving what the benefits or drawbacks are is a pointless statement.
Every time a major NFL games has the coin toss winner score on the first drive you see comments that run along the line of “the game was decided by a coin toss”. What does that even mean? If the coin toss decided the game, why even play the overtime, just toss the coin. Given that the coin toss loser has won nearly half the games, it clearly doesn’t decide the game. When people who throw this comment out bother to explain what they mean, they either actually believe that the toss winners win most games, or they are upset that the kicking team never got the ball. The first school of thought is nonsense, while the second is a valid point of view worth discussion. When people use the generic “decided by a coin toss” line, you don’t know if they are using a throwaway line or have a point worth exploring.
Facts From Nowhere
The final thing I want to address is the practice that some people have of just quoting some “facts” without giving the source or the context of it. While there are many examples of this, I will focus on this gem from Ross Tucker which, at last check, got over 5,100 retweets and over 6,800 likes. The tweet was as follows:
“Overtime coin toss winner winning % NFL: 52.7% CFB: 54.9% Please RT for the sake of humanity.”
The first observation I will make is that he made no point at all, which might have bailed him out from blowing both “facts”. If his point was that the NFL coin toss is not way out of line, precise numbers aren’t necessary. If his point was college is more out of line than the NFL, than he got that wrong because that’s not really the case. However, let’s focus on where the numbers came from.
I didn’t have to research were he got the 54.9%. This came from a study done 12 years ago by Peter A. Rosen and Rick L. Wilson titled “An Analysis of the Defense First Strategy in College Football Overtime Games”. While the study was not 100% accurate (no study of this scope ever is), it did an excellent job of breaking things down. I have two issues in using this information without giving the source, one minor and one major. The minor one is that they were not tracking the “coin toss winner”, as stated, but the team who went second. Though the two numbers are similar, I prefer people to be accurate in their statements. The major problem I have is that the study is 12 years old, which is certainly information that would seem relevant if you’re giving a “fact”. It’s as if we are to believe these numbers are static and never change, and while the current percentage is similar these numbers fluctuate over time, as I will address.
Figuring out where the NFL number came from took a little more effort. Eventually, I figured it must have come from Mike Sando who gave this tweet after the Patriots/Chiefs games. He seems to have gotten it from somewhere, but I couldn’t determine where it came from.
He states the record of the coin toss winner is 56-50-7 since 2012. This does not match my research, and I’m confident that the mistake is not mine. First of all, Pro Football Reference, shows 118 overtime games since 2012, so unless they mistakenly put in 5 extra games, complete with play-by-plays for overtime, than 56-50-7 cannot be right. Calculating the game-by-game records, at no point in time was the record of the “coin toss winner” 56-50-7. I do see that after October 21, 2018, the record of teams receiving the overtime kickoff was 56-50-7, so it’s probably that the information is old and again not the “coin toss winner”. Not every “coin toss winner” has elected to receive as this research apparently assumes. In college, it’s a minor difference, but since selecting which goal to defend can be a strategic advantage, as I’ll get into later, it seems important to me to correctly label it. Since these numbers got repeated many, many times, that they accepted as absolute fact shows how getting the numbers right can move the debate along properly rather than stifling it.
Why You Should Not Trust The NFL Numbers
There have only been 120 NFL games using the newer overtime system. With that little data, the percentage can change quickly over just a few games. Over the last 40 games, the winning percentage of coin toss winners has range from as high as 57.74% to 53.24%. To show how unreliable small amounts of games can be, I will focus on another college study.
In 2013, Kevin Rudy did this analysis of college football overtime. While Rosen and Wilson focused on teams that played defense last in the first overtime period, Rudy calculated the winning percentage of teams that played defense last in the final overtime period. His study was from the start of the 2008 season until October of 2013, just over 5 years. Given that he just kind of decided to do this one day, the study had some data problems. He had 156 games in the study, 3 of which should not have been counted and he missed 31 others over that period. Still, it works as a random dataset of 156 games.
Among his determinations were that home teams won 56% of their overtime games and teams that played defense last won 61.5% of those games. These were mostly accurate for his dataset, but are actually quite a bit off the all-time numbers and even the period he studies.
The actual winning percentage of home teams over the period studied was 51.7%. The reason he missed so badly was that most of the games he missed, the visiting team won. All-time in FBS overtime games, the home team has won 52.4%. He wasn’t wrong in his dataset, but his dataset was so far off the norm that his conclusion was an erroneous one.
The actual winning percentage of the team playing defense last in the final overtime over that period was 63.59%, even higher than what he found. However, had he done his study over the 5 previous years, he would have come up with a winning percentage of 47.40%, a losing record. All-time, that percentage is 53.35%.
I often see quoted that the rate of teams winning the toss in college is 60%, and it’s usually because the numbers from this study has stuck in some people’s head. If he had come up with the actual percentage over the period, the number quoted would now even be higher, but if the study had been done of the 5 previous years, the narrative would have been the disadvantage of losing the toss.
Why are the percentages so different? Either the style of play changed drastically, or the dataset is too small to give an accurate picture. Plus, the NFL percentage is still changing at a regular rate ranging from a high of 57.74% over the last 40 games to a low of 53.24%. Over the last 7 games, it has risen a full percentage point. More than likely dataset is too small since we only have 120 NFL games to measure the new NFL system with.
Coin Toss Comparison
If you’ve bothered to read everything so far, you understand that there are a lot of ways to look at the college system. There are the ones mentioned and a few more besides so it would not be hard to come up with a number to fit a wide variety of narratives. Confusing things farther is the fact that one team gets to choose “defense, offense or end of field” in every single overtime period, not just the first one. So while games usually go with one team on offense first in the first period, than the other in the second and so on, it doesn’t always happen that way. Probably the only true comparison to the NFL would be to take the winning percentage of the teams that won the coin toss, so I’ll start there.
In the 120 games the NFL has played with their new overtime system (field goal on a first drive doesn’t end the game), 62 have been won by the team that won the toss, 51 by the team that loss the toss and 7 games ended in a tie. A 62-51-7 record computes to a winning percentage of 54.58% (if you count ties as half win/half loss). In the 746 overtime games involving FBS teams, the toss winner won 402 and the toss loser won 344. A 402-344 record computes to a winning percentage of 53.89%. If we wanted to do both since 2012, the year the NFL started the new system in the year the NFL started the new system in the regular season, the NFL would be 60-51-7 (53.81%) and FBS would be 135-124 (52.12%).
If we want to ignore the toss winner and choices and simply go by who received the overtime kickoff in the NFL and who played defense last in FBS college games (the most common choices), the percentages are slightly different. In the 120 NFL games, you’d get 61-52-7 (53.75%), in college you’d get 406-340 (54.42%), or 59-52-7 (52.97%) and 136-123 (52.51%) since 2012.
All-time (NFL New System):
|League||Coin Toss||Common Choice|
|NFL||62-51-7 (54.58%)||61-52-7 (53.75%)|
|FBS||402-344 (53.89%)||406-340 (54.42%)|
|League||Coin Toss||Common Choice|
|NFL||60-51-7 (53.81%)||59-52-7 (52.97%)|
|FBS||135-124 (52.12%)||136-123 (52.51%)|
College Division Breakdown
Another interesting aspect of the college game is that the defense last strategy shows up to be a bigger advantage at the top level of the game than it is at other levels. While the team going second in the first overtime does win 54.42% of their games, in non-FBS college overtime games, the team going second wins 51.48% of their games (of the games I have).
|Division||Def First First Per||Def First Last Per|
The most interesting thing I found in this information is the division II numbers that have the defense first teams winning a higher percentage of games in multiple overtime games. The teams who played defense last in the first overtime, won over 60% of the games that ended in 2 periods. Of the 13 games that went 4 overtimes, 10 games were won by the team playing defense first.
Further College Breakdown
To get an idea of how the college overtime flows, I have some further breakdowns. Since the division stats very and the FBS level is closest to the NFL, I’ll just give games involving FBS teams.
Multiple Overtime Games: 252 (33.78% of the games)
Periods: 1148 (1.54 per game)
Drives: 2292 (3.07 per game)
Points scored by the offense on each drive:
NFL Coin Toss Info
Of the 120 NFL overtime games using the new system, 62 were won by the team that won the toss, 51 by the team that loss the toss, 7 were ties. Calculating the ties as half wins/half losses, that comes to a 54.48% winning percentage. If we toss out the ties, the percentage becomes 54.86%. Clearly, the people that think there’s a huge advantage to winning the toss, aren’t looking at the data. I have a table of the NFL data, and meant to include it here, but Reddit space limits and formatting caused to many issues. If you want to see the data, you can go to my original blog post here.
NFL Drive Breakdown
The first thing I want to look at is the result of the first drive by both the team taking the kickoff. The following chart shows a breakdown of the result of the first drive of overtime and the record with each result. Also, I included just regular season results as well.
|4 Down Fail||3||0-3-0||2.50%||4 Down Fail||3||0-3-0||2.68%|
|Missed FG||4||2-1-1||3.33%||Missed FG||4||2-1-1||3.57%|
|4 Down Fail||9||0-9-0||9.38%||4 Down Fail||9||0-9-0||9.68%|
|Missed FG||7||2-4-1||7.29%||Missed FG||7||2-4-1||7.53%|
|4 Down Fail||9||0-9-0||40.91%||4 Down Fail||9||0-9-0||52.94%|
|Missed FG||1||0-1-0||4.55%||Missed FG||1||0-1-0||5.88%|
|4 Down Fail||0||0-0-0||0.00%||4 Down Fail||0||0-0-0||0.00%|
|Missed FG||2||1-1-0||11.11%||Missed FG||2||1-1-0||11.76%|
While people rightfully focus on the advantage of receiving the overtime kickoff, the advantages of being the kickoff team are never mentioned. The first thing to mention is the 18 times that the team receiving the kickoff has turned the ball over. When a team starts with the ball deep in their own end and turns the ball over, it almost always ends in a score, which would end the game in overtime. The average field position after the receiving team turns the ball over is the opponents’ 45 yard line. The average number of plays that occur before these turnovers is 3.44, with the turnover occurring in the first 3 plays 13 times.
Another advantage is the second team gets better field position. The average starting field position of the team receiving the kickoff is their own 23 yard line. The average starting field position of the kicking team after a stop or a field goal is their own 31. While 8 yards isn’t a huge advantage, it’s also noteworthy that the kicking team only needs a field goal to win, while the receiving team can’t end the game with a field goal.
The last advantage I’ll mention is that the team kicking off, if the stop their opponent from scoring a touchdown, knows what they need to do. They number of fourth down failures shows how often a team had gone for it on fourth down. They have done so because the receiving team has kicked a field goal and they know they need to score. If they aren’t in field goal range, they know they have to go for it.
While these advantages don’t outweigh the advantage of receiving the kickoff, they do counter them to the point where kicking off isn’t the huge disadvantage that some people think it is. In fact, there are likely times where the advantageous choice would be to choose which end zone to defend, rather than kicking the ball, usually in bad weather situations. If it is harder to move the ball on offense, it would be worth the risk of the other team scoring, for the likely better field position, particularly if it means kicking with or against the wind.
NFL Plays Vs FBS Plays
The NFL states that it wants to shorten games for player safety. If so, changing to the college system would apparently do that. The current system has overtimes lasting an average of 17.36 plays per overtime game. Interestingly, since the NFL shortened overtime games to 10 minutes or less, the number of plays actually increased to 17.55. In the last 43 games involving an FBS team, the plays per overtime game have averaged 13.42, 4 less than the NFL.
As such, the NFL could have fewer plays if they changed to the college system, though that clearly is not in the works. Among other things, I suspect that the run time for multiple overtime games in college run longer than NFL overtimes. Each overtime period has one timeout per team and in between period, the coin toss options have to be repeated, and the end of field usually changes, not to mention all the extra points. Since run time will affect television, the NFL probably wouldn’t choose that, though they would never state that is the reason.
The Kansas City Proposal
The most common criticism of the NFL overtime is that it’s possible for one team to never possess the ball. As such, the commonly suggested solution is that even if the first team scores a touchdown, give the second team a chance to match. This is a simple and seemingly fair solution, in fact, one that seemed good to me, until I saw these numbers. Unfortunately, though being “fair” in that it gives both teams the ball, it would might give the coin toss winner a bigger advantage, which is the stated reason that most of those people claim is the reason why they want change.
The advantages each team has in the current system are as follows:
Can end the game with a touchdown without the other team possessing.
First crack at any score after both teams have possessed the ball.
If the other team turns the ball over, might get great field position.
Gets better field position on average.
Could get great field position on turnover.
Selects end of field.
Knows what they need to score to continue game.
Need only field goal after a stop.
Take away the advantage of ending the game with a touchdown for the receiving team, you remove almost the only advantage the receiving team would get. The kicking team already, on average, starts with better field position, and has a change at great field position with a turnover, but would also have the advantage of knowing what they need to score on their drive. It’s this latter aspect that gives the defense last team an advantage in the college overtime. Given that you add to the advantage of kicking, you might never see a coach elect to receive an overtime kick again. Let’s try to see how much of an impact this would make.
If we assume the results we have so far reflect reality (a large assumption, but it’s all we got), under the current system, teams receiving the kickoff have gone 61-52-7 for a winning percentage of 53.75%. When failing to score a touchdown they have gone 37-52-7 for a winning percentage of 42.19%. It follows if the kicking team gets a possession if they allow a touchdown, the percentage would be between those two marks, the question being how many of those 24 games would the kicking team manage to win after allowing a touchdown. We know that teams trying to score on the first possession have scored touchdowns in 20% of their drives, which is a good place to start. Those teams also score a field goal on 18.33% of their drives, which indicates they were nearing the goal when they were stopped by a fourth down. It follows that if that a team would not kick a field goal if they needed a touchdown, nor would they punt, so that’s it’s going to be higher than that 20%. Teams responding to a field goal have 11 of the 22 times that it has happened. Likely the number of games that a team responding to a touchdown would win would be somewhere between 5 and 10.
So, taking 5 wins away from the 61-52-7 would result in a 56-57-7 record. Taking 10 away would result in a 51-62-7 record. Likely a worse case would give the kicking team a 45.42% winning percentage. Not far off the advantage the receiving team has now, and that’s worse case. And since the likely choice would be to kick, that means the receiving team would also get to choose the end to defense. This is probably not a bad option, considering it would give both teams a chance to have the ball, which is the most common complaint. It would not solve the “coin toss deciding the game” factor though in that it would still be an advantage to win the toss.
The most common other options people give for changing to the NFL system is to use the college system, or a derivative of it. I can only fall back on the data I’ve given for the college system and that it would give the NFL what it claims to want in fewer plays, though people asking for the starting position to be moved back would change that. There seems to be little change that the NFL would change to the college system for the reasons many others have given.
Currently, the NCAA is looking at a proposal that would change the overtime rules if a game goes into a 5th overtime. Instead of continuing as they had in the first 4 overtimes, the game would go into alternating 2 point conversions, ending when one team scores and the other doesn’t.
It seems likely to me that this is going to pass. Just over 1 in 100 overtime games get to the 5th overtime. This would happen very rarely and it would solve the stated problem for the NCAA. It’s a shame to see any change, but under the banner of “player safety”, this does make sense. The only question I have is would they count as separate overtime periods in the record book. It would be a shame to see the NCAA record 8 overtime game get dethroned by this change.
Another (fan) suggestion that would seem to curb the coin toss advantage is to eliminate all kicking from the overtime periods. Either you score a touchdown, or fail on fourth down. Either you make a 2 point conversion, or you fail in the attempt. Since both teams are doing their best to put the ball in the end zone, the advantage of choosing to kick or not is gone. This would cause it to lose some of its charm, though, in my opinion.
The last (fan) proposal I’ll mention is the one to have each team go for 2 every time they score a touchdown, not just past the 2nd overtime. What would limit the effectiveness of this is that it only would come into play when both teams scored a touchdown. This happens barely 30% of the time. Of the 1005 periods in which teams were not required to go for 2, only 305 of them had both teams scoring a touchdown. Of those 305, 47 were decided when one of the teams went for 2 and 30 were decided when one team made the extra point kick and the other missed it. So of the 305 games where it would have factored, 77 were already decided by current rules.
Of the 143 periods in which teams were required to go for 2, 43 had both teams scoring a touchdown. Of those 43 games, 19 were decided when one team made the 2 point conversion when the other failed, which is 44.19%. If we had a big enough sample, you’d figure that 50% of those games would likely be decided using this method, so I’ll assume that. On a side note, both the 5th and 6th periods have never had a winner decide by this method.
Okay, if you have 305 period where this would applied and assume that 50% were decide by a 2 point system, that means that 152.5 of those periods would have decided that game. Given that 77 were already decided, you’d have a net increase of 75.5 games that this method would decide, or 7.51% of the periods. It seems a rather small percentage of games would be affected by this rule change, though if a 7 overtime game is decided in the first, you could save 6 periods, so it’s difficult to determine how many periods would be saved. It seems a rather large change to make for such a small impact, but that’s my opinion.
Neither the NFL nor the NCAA seems likely to move toward the other’s overtime system, but both are looking at possible changes. Both the NFL and college give an advantage to the coin toss winner, but the winning percentage is less than 55% for both systems neither system seems to give a much larger advantage than the other (despite what is often said).
For the NFL, it seems to make sense to have both teams a chance to possess the ball, even if the first team scores a touchdown. A large number of fans seem to want it and though it flips the odds to favor the kicking team, it the coin toss advantage would likely stay the same. The obvious drawback to this is that the number of plays in the overtime would increase, which is opposite of the NFL’s stated goals.
For the NCAA, the more commonly discussed changes would likely not make much of an impact. The proposed rule change of doing a 2 point conversion shoot out after 4 overtime games would only effect about 1% of the total games. This would eliminate the 7 overtime marathons that occasionally come up, while having no change at all on most of the games.
I started this project in hopes that it would open up some discussion. I feel too often people have tried to stop any constructive conversation by claiming that one system is “more fair” than the other using incorrect coin toss statistics, or just general misunderstandings. I know it was a pointless effort, but it was fun doing it.
I'm curious what opinions people have on what either the NFL or the NCAA should do with overtime.