Against adding to playoff

[Cdog923]

We may just have to agree to disagree, but let me try to convince you:  SOS, win loss record, and margin of victory are (with maybe the exception of SOS) factual data.  The problem is when you interpret and weigh the data subjectively. 

For one, you left out a lot of data that other teams or people might consider important, for instance what about defensive ranking or offensive ranking, home or away records, etc.  You subjectively picked out the three pieces of data you wanted to use in your calculations. 

Second, who gets to determine how each of your three facts get weighed?  Why should a team who went 11-1 (with a SOS #15) get to go over a team that went 12-0 (with a SOS #25) or vice versa?  Do you see how it can get really dicey depending on how you want to weigh each piece of your data?  Which you must admit is pure human bias.

It sounds to me like you want to bring back the BCS, without the coaches or AP poll, just the computer rankings, what you forget is that the BCS formula, was designed by humans with bias from the start.  You might be interested to read about the debate over whether to include margin of victory in the rankings (https://www.si.com/college-football/2018/07/11/bcs-computer-rankings-polls-formula-sagarin-billingsley).  Definitely not my flavor of Gatorade.  8 team playoff, 5 P5 conference championship winners, 3 chosen among the rest allows far more teams to settle it on the field.

By going to the 5 + 3 method, you actually make it less subjective than the current format. 

 

[Cdog923]

And with very little human bias.

You want to play for a National Championship? Your first hurdle is winning your Conference. Having 3 at large bids lets non-P5 teams have a chance (UCF anyone?) and allows for a conference to have more than one team in the playoff. A good example of that could be, two undefeated teams meet in a CCG and both end up in the playoff, one as the champion and one as a wildcard or "at large berth".

I believe that is his objective. 

I was stating my agreement

 

[Big Red 40]

Far to open to human bias. Human bias is why we have "mythical national champions" instead of National Champions. How can we claim to have a "true 1 vs 2" when human bias is involved in deciding who goes and who stays?

How is using data collected over the season human bias ? Numbers don’t lie . If you played a high sos and you won all your games you have a legitimate claim to #1 or #2 if you didn’t then you don’t . 

 

[jager]

SOS is biased. It is based on your perception of a team or possibly the teams rank. Sometimes the preseason rank is used, the time of the game rank is used, or the most recent rank is used. Everyone knows the preseason rank is bull. Listen to pundits through out the year and they will use the ranking that best promotes their agenda.

 

[Big Red 40]

They only "got it right" in years where there were exactly 2 undefeated teams and they played each other in a bowl game.  Pretty sure that didn't happen too often.  How about 1994?  How about 1997?  I think the Huskers were the better teams those years but you don't really know unless you play it on the field.  How do you know they "get it right" if there are three undefeated teams?  How about if there is one undefeated team and several one-loss teams?

The four team playoff has been around all of for years and the #4 seed has won it twice.  The #1 seed has yet to win.  There is no justification for your opinion other than that's what you want to believe.

Use strength of schedule and other data gathered over the entire season to pick the best one loss team. 

I already stated there were times when other teams had a legitimate claim but that was the exception not the norm . I can’t think of a single year the #8 team in the country had a legit argument that they deserved to be #1 or #2 , or in the national championship game, under any scenario. 

 

[Big Red 40]

SOS is biased. It is based on your perception of a team or possibly the teams rank. Sometimes the preseason rank is used, the time of the game rank is used, or the most recent rank is used. Everyone knows the preseason rank is bull. Listen to pundits through out the year and they will use the ranking that best promotes their agenda.

How else would you decide how good the opponents were ?  Would you say a team that wins the all their games against unranked opponents Is better than a team with a 1 loss season playing 4 or 5 top ten teams ? I wouldn’t 

 

[jager]

I agree with you. I’m just saying that stats can be manipulated. Look at when FSU lost their quarterback. After that they weren’t good, but it didn’t affect anyone’s SOS. How is that objective. Look at the SEC, it’s always biased. Yes, they have some tough teams, but the also have easy teams. Those teams affect schedules less compared to playing the weaker B10 teams.

 

[Cdog923]

How else would you decide how good the opponents were ?  Would you say a team that wins the all their games against unranked opponents Is better than a team with a 1 loss season playing 4 or 5 top ten teams ? I wouldn’t 

Without looking, in 2016, who was the better team: Nebraska or UCLA? 

 

[Big Red 40]

Without looking, in 2016, who was the better team: Nebraska or UCLA? 

I’d say UCLA mostly because I don’t remember us being good at all during the Riley years lol. Seriously though I do remember us having a very nice bowl win over them in 2016 yes . Not sure what the point is ? 

 

[Cdog923]

I’d say UCLA mostly because I don’t remember us being good at all during the Riley years lol. Seriously though I do remember us having a very nice bowl win over them in 2016 yes . Not sure what the point is ? 

The point is, UCLA went 4-8 in 2016, but had the 27th ranked SOS. Nebraska went 9-4, but had the 47th ranked SOS. 

Knowing that, who is the better team? The one who played a harder schedule and lost, or the one who played an easier schedule but won? 

 

[HS_Coach_C]

Use strength of schedule and other data gathered over the entire season to pick the best one loss team. 

I already stated there were times when other teams had a legitimate claim but that was the exception not the norm . I can’t think of a single year the #8 team in the country had a legit argument that they deserved to be #1 or #2 , or in the national championship game, under any scenario. 

The problem is, data is inherently biased.  The inputs, outputs, intent, the collection methods, weight of certain factors over others, the use (or exclusion) of additional variables such as home/away/injuries/etc.

It's not as black and white as you'd like it to be.

 

[Big Red 40]

The problem is, data is inherently biased.  The inputs, outputs, intent, the collection methods, weight of certain factors over others, the use (or exclusion) of additional variables such as home/away/injuries/etc.

It's not as black and white as you'd like it to be.

The “selection committee “ for the playoff system is human beings. Humans have biases feelings etc . If the goal is to remove that , computers are a much better way . They have no feelings or bias . 

 

[Big Red 40]

The point is, UCLA went 4-8 in 2016, but had the 27th ranked SOS. Nebraska went 9-4, but had the 47th ranked SOS. 

Knowing that, who is the better team? The one who played a harder schedule and lost, or the one who played an easier schedule but won? 

That year we also beat them head to head though right ? Knowing that we get the nod . 

Without that in the equation I actually agree with Bohunk lol lots of subjectivity in that answer. 

 

[Mavric]

The “selection committee “ for the playoff system is human beings. Humans have biases feelings etc . If the goal is to remove that , computers are a much better way . They have no feelings or bias . 

Who programs the computers to put how much weight on which factors?

 

[brophog]

If the goal is to remove that , computers are a much better way . They have no feelings or bias . 

Completion percentage is a number, a ratio, no inherent bias. If I write an algorithm that double weights completion percentage to compute qb rating, then I've inserted bias towards qbs with that statistic.

One of the failings of the bcs was calling them "computers" when they're really human created data models. That's why there were multiple models to account for multiple bias.