Bama vs FSU chances is 42.7% according to The Prediction Machine

[Mike30142]

Undefeated Odds Update (11/11/2013) - Alabama, Baylor, Ohio State, Florida State @ PredictionMachine.com

 

[Shanemansj13]

Baylor would be favored against tOhio St. Yes! Bring it on!

 

[sakau2007]

i find it interesting that
a) ohio state would be an underdog to aTm (not very surprising; last year they would have been an underdog to 5 or 6 teams in the SEC depending on which oddsmaker you asked)
b) ohio state is less than 50/50 to go undefeated (a little surprising i suppose)

 

[Mike30142]

Did anyone notice the strength of schedules?

 

[BucksFanInGA]

i find it interesting that
a) ohio state would be an underdog to aTm (not very surprising; last year they would have been an underdog to 5 or 6 teams in the SEC depending on which oddsmaker you asked)
b) ohio state is less than 50/50 to go undefeated (a little surprising i suppose)

SEC


SEC


SEC

 

[Shanemansj13]

LOL@ those predictions

 

[Shanemansj13]

I don't look too hard at SOS, don't get me wrong it is important. But the way SEC teams are ranked... USCe is a perfect example.

 

[sakau2007]

I don't look too hard at SOS, don't get me wrong it is important. But the way SEC teams are ranked... USCe is a perfect example.

you do realize that SOS calculations come from computers, not those super biased human rankings that people keep railing on, right? south carolina beat central florida on the road. that's more impressive than anything ohio state has done in 2+ seasons.

 

[nolehusker]

you do realize that SOS calculations come from computers, not those super biased human rankings that people keep railing on, right? south carolina beat central florida on the road. that's more impressive than anything ohio state has done in 2+ seasons.

You do realize that those computer rankings are designed by humans and can be easily manipulated. Hence, some give conference rankings which then impact the SOS. Some also use preseason rankings to determine things even as the season progresses.

I'm not saying that humans are less biased, just that computer rankings are only as biased as the set of inputs they are given, whether those be intentional biases or not.

 

[ellupo]

you do realize that SOS calculations come from computers, not those super biased human rankings that people keep railing on, right? south carolina beat central florida on the road. that's more impressive than anything ohio state has done in 2+ seasons.

And they also lost to TN, is that more impressive also?

 

[Shanemansj13]

you do realize that SOS calculations come from computers, not those super biased human rankings that people keep railing on, right? south carolina beat central florida on the road. that's more impressive than anything ohio state has done in 2+ seasons.

Humans use computers, you do know that right The inforamtion doesn't magically input itself.

Of course, you bring tOSU into it. They are still undefeated, they are doing what they sre supposed to do. USCe has two losses.

USCe lost to Tenn. Got lucky and won against Mizz, and UCF. So they have a bad loss and two quality wins. Top 10 for that. Also close win against UK.

 

[Mike30142]

The Predictalator is the most advanced sports forecasting software available today. The technology has the ability to account for all of the statistical interactions of the players (playing or not playing/injured), coaches, officials, fans (homefield advantage) and weather in each game. The Predictalator plays every game 50,000 times before it's actually played. This provides PredictionMachine.com the ability to assign probabilities to the likelihood of just about any outcome occurring in any event as well as to project individual statistics and more including straight-up, against-the-spread and over/under pick percentages for each game.

In sports wagering, money management is critical. Knowing the likelihood of success of any play (SU, ATS, O/U, or even Futures, Parlays, Teasers or other exotics) is of utmost importance – not just for deciding what to wager, but how much. By playing the game 50,000 times before it's actually played, all of our picks come with a specified level of confidence (no dimes, no stars – just the facts) and a Play Value Key and Calculator to decipher what that means to you.

One of the keys to the Predictalator is its use of strength-of-schedule-adjusted numbers. Not only is it important to know what the players and teams have done, but against whom they have done it.

Along those lines, the Predictalator factors in every relevant piece of information that it can to come up with the final figures that it incorporates into the analysis.

That starts with the actual players who are expected to be playing, if they are playing hurt, how they are utilized by the team, what they have done in every measurable realm of the game, whether what have they done is mostly due to "luck" or "fluke" and how they are (and should be) progressing in their careers. Recent performance is weighted more heavily than previous performance, but everything a player has ever done is considered.

But it also considers coaching styles and play-calling, the tendencies of officials (especially in basketball), the specific effects of the homefield/homecourt advantage on outcomes (or on individual statistics as in baseball) and if weather will play a role in the game.

Sample size is also very important. Many quoted statistics tend to fall into the sample size trap. Knowing that a baseball player hit .400 against left-handed pitchers on the road in the seventh inning means absolutely nothing if that is 2-for-5 over an entire season. Over-complicating a stat in this way usually leads to irrelevant and unnecessary results with very little to base implications. The saying, that a player "plays to the back of the baseball card" is generally true – in the long-term, a player is who he is and the numbers will even out. In the event that the Predictalator does not have ample data, complex algorithms are used to come up with numbers based on historically similar athletes. In the past, this approach has had great success for the Predictalator when applied to NFL rookies.

And what about heart? Or a player's performance in the "clutch?" They're in the numbers. It's not like the Predictalator ignores certain parts of the game or just uses a player's 40-time or height to come up with its results. There have to be reasons why players succeed and teams win. The numbers will tell the story and the Predictalator will factor those numbers into its calculations.

Accounting for all of this allows the Predictalator to utilize the most complete and unbiased inputs when it runs every play of every game 50,000 times.

With those items in mind, the Predictalator makes use of the probabilities that occur when all of the pieces of a play are interacting. Every game can be broken down into a "play." Within that play are certain decisions that have to be made. The Predictalator goes through each of those decisions, factoring all of the data above and comes up with result for the play. It keeps doing this until the game is over. Then it does all of that again 49,999 more times. Results in each game can be different because there can be different outcomes for each decision of each play. (ex. If a team is determined to have an even run-pass split for a situation, half the time the play will be a run and half a pass – that outcome affects the rest of the game.) The most important statistics are on a per play basis – strength-of-schedule adjusted if you can find them – not on the aggregate. (What does ranked fifth in defense mean? According to whom? Ambiguous rankings are fairly arbitrary.)

From the 50,000 games, the Predictalator can provide average scores and individual statistics as well as the probability of either team winning straight-up or against-the-spread.

http://predictionmachine.com/Register

 

[Used 2 B Hu]

Humans use computers, you do know that right The inforamtion doesn't magically input itself.

Of course, you bring tOSU into it. They are still undefeated, they are doing what they sre supposed to do. USCe has two losses.

USCe lost to Tenn. Got lucky and won against Mizz, and UCF. So they have a bad loss and two quality wins. Top 10 for that. Also close win against UK.

You might call the Missouri win "lucky" based on the Tigers missing a chip-shot FG in OT, but to be down 17-0 that late in the game and come back, that wasn't luck. It was a combo of conservative coaching from Gary Pinkel and a tremendous effort from Connor Shaw off the bench.

I think South Carolina could beat Ohio State head to head. Two losses and all. I'm not saying "definitely," or "I guarantee it," or "we would wipe the floor with you," but I would like our chances against anybody in D-1 outside of 3 or four schools, based on matchup issues. It won't happen in a bowl game, but I'd like to see it.

 

[UNA Lion]

Teams that would be favored over Alabama on a neutral field: None

 

[ellupo]

The Predictalator is the most advanced sports forecasting software available today. The technology has the ability to account for all of the statistical interactions of the players (playing or not playing/injured), coaches, officials, fans (homefield advantage) and weather in each game. The Predictalator plays every game 50,000 times before it's actually played. This provides PredictionMachine.com the ability to assign probabilities to the likelihood of just about any outcome occurring in any event as well as to project individual statistics and more including straight-up, against-the-spread and over/under pick percentages for each game.

In sports wagering, money management is critical. Knowing the likelihood of success of any play (SU, ATS, O/U, or even Futures, Parlays, Teasers or other exotics) is of utmost importance – not just for deciding what to wager, but how much. By playing the game 50,000 times before it's actually played, all of our picks come with a specified level of confidence (no dimes, no stars – just the facts) and a Play Value Key and Calculator to decipher what that means to you.

One of the keys to the Predictalator is its use of strength-of-schedule-adjusted numbers. Not only is it important to know what the players and teams have done, but against whom they have done it.

Along those lines, the Predictalator factors in every relevant piece of information that it can to come up with the final figures that it incorporates into the analysis.

That starts with the actual players who are expected to be playing, if they are playing hurt, how they are utilized by the team, what they have done in every measurable realm of the game, whether what have they done is mostly due to "luck" or "fluke" and how they are (and should be) progressing in their careers. Recent performance is weighted more heavily than previous performance, but everything a player has ever done is considered.

But it also considers coaching styles and play-calling, the tendencies of officials (especially in basketball), the specific effects of the homefield/homecourt advantage on outcomes (or on individual statistics as in baseball) and if weather will play a role in the game.

Sample size is also very important. Many quoted statistics tend to fall into the sample size trap. Knowing that a baseball player hit .400 against left-handed pitchers on the road in the seventh inning means absolutely nothing if that is 2-for-5 over an entire season. Over-complicating a stat in this way usually leads to irrelevant and unnecessary results with very little to base implications. The saying, that a player "plays to the back of the baseball card" is generally true – in the long-term, a player is who he is and the numbers will even out. In the event that the Predictalator does not have ample data, complex algorithms are used to come up with numbers based on historically similar athletes. In the past, this approach has had great success for the Predictalator when applied to NFL rookies.

And what about heart? Or a player's performance in the "clutch?" They're in the numbers. It's not like the Predictalator ignores certain parts of the game or just uses a player's 40-time or height to come up with its results. There have to be reasons why players succeed and teams win. The numbers will tell the story and the Predictalator will factor those numbers into its calculations.

Accounting for all of this allows the Predictalator to utilize the most complete and unbiased inputs when it runs every play of every game 50,000 times.

With those items in mind, the Predictalator makes use of the probabilities that occur when all of the pieces of a play are interacting. Every game can be broken down into a "play." Within that play are certain decisions that have to be made. The Predictalator goes through each of those decisions, factoring all of the data above and comes up with result for the play. It keeps doing this until the game is over. Then it does all of that again 49,999 more times. Results in each game can be different because there can be different outcomes for each decision of each play. (ex. If a team is determined to have an even run-pass split for a situation, half the time the play will be a run and half a pass – that outcome affects the rest of the game.) The most important statistics are on a per play basis – strength-of-schedule adjusted if you can find them – not on the aggregate. (What does ranked fifth in defense mean? According to whom? Ambiguous rankings are fairly arbitrary.)

From the 50,000 games, the Predictalator can provide average scores and individual statistics as well as the probability of either team winning straight-up or against-the-spread.

I did not realize that statistical interactions won games. Interesting.