Any simulations where Rice wins more tha four games?

Sent from my SM-A600A using Tapatalk

NC State loses and the playoff is rather non-controversial. Sure, Texas A&M has 2 losses, but the only power team with fewer is NC State from the (this sim) middling ACC. The Cotton Bowl is an all-Sun Belt replay, as Louisiana tries (and fails) to redeem the one loss they suffered during the regular season. Yes, 11-1 Marshall (with 2 Top 25 wins, including #19 Notre Dame) gets the lone at large spot instead of 12-1 NC State (with only 1 Top 25 victory, over #22 Wake Forest).

In the expanded playoffs, Cincinnati ousts P5 Champ Pittsburgh, largely because of having 2 fewer losses, and being from a conference ranked not too far below the ACC. Marshall and Louisiana both do some P5 damage before each falling to eventual champion Alabama. In both expanded formats, 9-3 Texas makes a surprise appearance in the Rose Bowl in a rivalry game (vs. A&M in the 6+3, vs. Oklahoma in the 6+6).

(05-04-2022 11:22 AM)loki_the_bubba Wrote:  Any simulations where Rice wins more tha four games?

Sent from my SM-A600A using Tapatalk

I have 3-9 as their most likely record. 1 "likely" win against their FCS opponent and 4 toss-ups (La Tech, Charlotte, UTEP, UNT). Still, their bowl odds are set to 13%, so not impossible.

(04-19-2022 09:22 AM)Edgebrookjeff Wrote:  

(04-12-2022 01:18 PM)ArmoredUpKnight Wrote:  I'm surprised to see Cincy at the top. Lots of talent players heading to the NFL. Key losses: QB Desmond Ridder, CB Ahmad "Sauce" Gardner, RB Jerome Ford, DE Myjai Sanders, and WR Alec Pierce.

I would think the top of the AAC would be:
1. Houston
2. UCF
3. ECU
4. Cincy

Its not uncommon for UCF to be favored in most models. The undefeated streak tends to skew models. So Cincy and UCF having recent undefeated regular seasons outweighs the progress teams like Houston and ECU have made.

Yes, Cincy has more guys going to the NFL than ever before in their history. They also have a good many guys with lots of experience coming back for another year. Plus they will benefit from a few portal guys coming in as well as some highly rated recruits that are ready for more PT. They have a very favorable schedule as they have neither Memphis nor Houston on the schedule. It would not be a surprise if they come close to repeating the success from last year.

Last year, Cincy had the big cap-feather of having beat Notre Dame. IMO that is what got them in to the playoffs. We could almost see it coming - ND themselves had a weak schedule, so they were in position to beat everyone but Cincy, thus looking great on Cincy's resume, which is how it played out.

This year, Cincy doesn't seem to have that. Their schedule looks weak up and down the line.

The one possible wild card is the opener vs Arkansas. If they win that, and then Arkansas, which has been improving, goes on a run and ends up as a top SEC team, then they would fill the bill. But that IMO is very unlikely, given the withering competition of the SEC.

I will be very surprised if Cincy can make the playoffs with this schedule. I will also be surprised if there is ever a single CFP rankings that doesn't include an SEC team.

I'm not sure there ever has been?

Also, just curious - what was your final pre-season simulation prediction for the four playoff teams last year?

(05-07-2022 11:40 AM)quo vadis Wrote:  Also, just curious - what was your final pre-season simulation prediction for the four playoff teams last year?

Last year, this was an extended (week-by-week) simulation:
https://csnbbs.com/thread-922993.html
and here is a thread similar to this current one with multiple sims:
https://csnbbs.com/thread-924679.html
My '21 pre-season Top 4 most likely playoff teams were:
Ohio State, Alabama, Notre Dame, Clemson
This year they are:
Ohio State, Alabama, Notre Dame, Oklahoma
(replace ND with Clemson for the most likely set of 4)

To all, I would be very interested in delivering data in threads like this which is of interest to fans. So if there are better (or, simply, "more") lists or what-ifs or stats that would be of interest, make mention and I can try and include them.

(05-07-2022 09:59 AM)Crayton Wrote:  

(05-04-2022 11:22 AM)loki_the_bubba Wrote:  Any simulations where Rice wins more tha four games?

Sent from my SM-A600A using Tapatalk

I have 3-9 as their most likely record. 1 "likely" win against their FCS opponent and 4 toss-ups (La Tech, Charlotte, UTEP, UNT). Still, their bowl odds are set to 13%, so not impossible.

That matches my data, Loki.
You might have seen that I have Rice with a >10% chance to make a bowl.

Also noticed that in Sim 125b Rice gets only its second win by beating Ranked UTSA (#25) at home by a FG.

So if we don’t make a bowl I’ll take a win over a ranked team!

(05-08-2022 04:33 PM)owl at the moon Wrote:  I would take those odds. I figure we have 10-25% chance of stringing together 6 regular season W’s.

And probably the same 10-20% chance of an 0-fer or 1-11 campaign.

(04-26-2022 02:23 PM)Crayton Wrote:  

(04-26-2022 02:05 PM)jimrtex Wrote:  How do you do your sim?

If you had a set of teams and a schedule could you project the results?

What does 125b indicate?

I've got a database with the teams and schedules. There is also strength data pulled mostly from past years. I run a Monte Carlo simulation 10,000 times. I prefer Monte Carlo to a projection because results in one game often influence others (tie-breakers, correlation of strength, etc) Each "sim" gives each team a different strength value, loosely based around their expected strength. Each game of the sim is then assigned a point spread based on that strength and a random value along a normal distribution of that spread is assigned to the game. A crude algorithm then turns that point spread into a football-ish score.

The original purpose of the software was to rank teams. The results of each sim are then incorporated into the ranking function and every team is assigned a rank from 1-131. Division/Conference tie-breakers are incorporated and postseason games are also simmed. Technically the sim only completes the postseason games of: CCGs, semis, and National Championship; I usually hand-sim other games for the results I post here.

"125b" means of the 10,000 sims, this is #125. If I want to go back weeks from now and reference that sim, I know which one to pull up. "b" I used here because I have over-written the original set of 10,000 sims to incorporate the changes mentioned in my last post. "125" is merely the default number in the system; out of laziness I usually look at that one first.

I am learning SQL because I want the 10,000 years to be accessible online, so that users can filter the results to see "what if" results.

Is the "strength" comparable to a Sagarin rating, where the difference in ratings along with a home-field adjustment is the mean expected point spread?

For each run, you assign a different strength value to the team. What does "loosely" mean. I assume this is to give more variability to the results, and that your strength ratings are necessarily inaccurate (past results are indicative, not predictive), and perhaps to represent events that are difficult or impossible to model, such as a starting QB breaking his arm in first game or a coach having an Urban Meyer event. By varying the season strength you may increase the chance of a team running the table, that would not happen if you applied strength on a game by game basis.

What is the standard deviation for each game? I read that it is 14 points for an NFL game. If it were very large (say 30), it would be like they came out an held the coin toss. "Rams call head", (coin toss), "It is heads, the Rams win the coin toss and the game. Good luck next week gentlemen." If it were 3 points, it would be too predictable.

Do you adjust the strength based on the results of the game? If a game between East-West State and North-South University were a pick, it might still end up a 28-point rout. In your simulation you would roll the dice and they come up snake-eyes or box-cars. Improbable, but not impossible. Though there is no skill involved in rolling the dice, we can assume a 28-point win by E-W is in part due to E-W being the better team. An adjustment in strength would also increase the chance of an undefeated seasons.

Sim #500 Week 14 and Top 14
#1 Michigan (12-0) @ #11 Wisconsin (10-2)
#2 Ohio St (11-1) **at large**
#3 Oklahoma (12-0) @ #7 OK State (11-1)
#4 Clemson (12-0) @ #15 Pittsburgh (10-2)
#5 Alabama (12-0) @ #14 Georgia (10-2)
#6 Penn State (10-2) **at large**
#8 Navy (10-1) @ #22 East Carolina (9-3)
#9 Notre Dame (9-3) **at large**
#10 Western Michigan (11-1) @ #77 Ohio (7-5)
#12 Auburn (9-3) **at large**
#13 Boise St (11-1) @ #45 San Diego St (9-3)
#18 Oregon (10-2) @ #49 Arizona St (8-4)
#36 WKU (11-2) @ #42 UTSA (9-3)
#37 App St (9-3) @ #47 Louisiana (10-2)

This one looks fun. 4 undefeated Power teams, but the committee did not drop #1 Ohio State far after their 2-point loss to Michigan. Of the other 3 teams, Oklahoma has the strongest resume and eye-test. Clemson edges Alabama on the resume, 'Bama the Tigers on the eyes. Georgia is a much bigger test than Pittsburgh, despite the similar rankings, so I would expect Clemson to be the odd-team out if it came down to it.

Navy is leading the G5 in the rankings, but a win over East Carolina (really?) won't put them in the club house yet. There is still the Army-Navy game. Western Michigan and Boise are looking for a piece of the action and there is a slight chance they'll still get it as a NY6 at large.

After 9-0 Oregon, the Pac-12 and its new divisionless structure was a traffic jam of 5 teams finishing 6-3. ASU was the only 3-1 team and beat the only other team (USC) with 1 loss. Early season losses by the Ducks to BYU (by 8) and Georgia (by 52) have likely doomed the Pac-12's hope for fielding a "Top 6" Champ.

(05-15-2022 01:05 PM)jimrtex Wrote:  

(04-26-2022 02:23 PM)Crayton Wrote:  

(04-26-2022 02:05 PM)jimrtex Wrote:  How do you do your sim?

If you had a set of teams and a schedule could you project the results?

What does 125b indicate?

I've got a database with the teams and schedules. There is also strength data pulled mostly from past years. I run a Monte Carlo simulation 10,000 times. I prefer Monte Carlo to a projection because results in one game often influence others (tie-breakers, correlation of strength, etc) Each "sim" gives each team a different strength value, loosely based around their expected strength. Each game of the sim is then assigned a point spread based on that strength and a random value along a normal distribution of that spread is assigned to the game. A crude algorithm then turns that point spread into a football-ish score.

The original purpose of the software was to rank teams. The results of each sim are then incorporated into the ranking function and every team is assigned a rank from 1-131. Division/Conference tie-breakers are incorporated and postseason games are also simmed. Technically the sim only completes the postseason games of: CCGs, semis, and National Championship; I usually hand-sim other games for the results I post here.

"125b" means of the 10,000 sims, this is #125. If I want to go back weeks from now and reference that sim, I know which one to pull up. "b" I used here because I have over-written the original set of 10,000 sims to incorporate the changes mentioned in my last post. "125" is merely the default number in the system; out of laziness I usually look at that one first.

I am learning SQL because I want the 10,000 years to be accessible online, so that users can filter the results to see "what if" results.

Is the "strength" comparable to a Sagarin rating, where the difference in ratings along with a home-field adjustment is the mean expected point spread?

For each run, you assign a different strength value to the team. What does "loosely" mean. I assume this is to give more variability to the results, and that your strength ratings are necessarily inaccurate (past results are indicative, not predictive), and perhaps to represent events that are difficult or impossible to model, such as a starting QB breaking his arm in first game or a coach having an Urban Meyer event. By varying the season strength you may increase the chance of a team running the table, that would not happen if you applied strength on a game by game basis.

What is the standard deviation for each game? I read that it is 14 points for an NFL game. If it were very large (say 30), it would be like they came out an held the coin toss. "Rams call head", (coin toss), "It is heads, the Rams win the coin toss and the game. Good luck next week gentlemen." If it were 3 points, it would be too predictable.

Do you adjust the strength based on the results of the game? If a game between East-West State and North-South University were a pick, it might still end up a 28-point rout. In your simulation you would roll the dice and they come up snake-eyes or box-cars. Improbable, but not impossible. Though there is no skill involved in rolling the dice, we can assume a 28-point win by E-W is in part due to E-W being the better team. An adjustment in strength would also increase the chance of an undefeated seasons.

Yes it is comparable to Sagarin. Multiply his by 20 to approximate mine. His home field advantage, I see, is 30% larger than mine. Perhaps I should investigate updating mine.

You are correct in defining my "loosely" term. A team's assigned strength also follows a normal distribution based on that "past results" strength... 90% of the time. 10% of the time the strength a team will begin a sim with is based on a broader range of their conference's (/opponents') strengths. This, as you note, is to simulate the freak occurrences where a team's QB breaks his arm or a darkhorse Heisman candidate appears, etc. Because it is based on opponents' strength it does not result in CUSA Rice rampaging past USC by four scores.

Strength IS adjusted after each game. This is a new addition this off-season. If Clemson has an 89% chance of defeating Georgia Tech to open the season, that number should not still be 89% if Georgia Tech surprises all and wins the Atlantic at 10-2. Related, if Georgia suffers another early season loss to South Carolina, that 'loss' should linger in their performance subsequent weeks. The attempt here was to simulate momentum and you are correct that this adjustment is made on expected point-spread, where a "rout" even over a team you were favored against (or eeking out a win over a patsy) will result in a momentum change for subsequent games.

Going on a three week roadtrip/campingtrip with the fam. So this'll be my last post here for a bit. My son, God bless'im, is a Seminole fan and we may see their Week 0 game on the way back home. I may or may not post 1 more sim then, but will definitely post my first in-season sim after Week 1.

Sim 500: Michigan loses in the Big Ten finale yet stays in the Top 4, along with fellow non-Big Ten Champ Ohio State (11-1), who is given the top seed. The other playoff teams are the only other undefeateds Oklahoma and Clemson. In the 4-team scenario, these two ascend to the National Championship game in SoFi.

In the 6+3, 75% of the Final Four are Big Ten teams. In the 6+6, 75% of the first-round game are upsets (Boise over #1 Ohio State!). Makes for somewhat of a dud of a second round, save for Georgia who tries to parlay their first ever SEC Championship win over Alabama into a National Championship; in both expanded formats Oklahoma knocks them off. In the 6+3, Big Ten champ Wisconsin claims the crown.