College Football Week 11 Score Predictions: Projected Scores for the Biggest Games

College football Week 11 brings several important matchups as teams enter the final stretch of the 2026 regular season. With conference races tightening and College Football Playoff positioning becoming increasingly important, every Saturday result carries additional weight. This week's schedule features Texas at LSU, Missouri at Georgia, USC at Indiana, Utah at Arizona, and Ole Miss at Oklahoma, among other games that could reshape the national picture.

Our college football Week 11 score predictions break down the biggest matchups with projected final scores, expected offensive production, defensive considerations, and potential game scripts. Rather than treating a single score as a guaranteed outcome, these projections consider team strength, home-field advantage, matchup characteristics, scoring efficiency, and the uncertainty surrounding college football.

The central question is not simply which team should win. It is how the matchup is likely to develop, how many points each offense could produce, and which factors might push the actual result away from the initial projection.

Table of Contents

• College Football Week 11 Projected Scores

• Biggest Matchups and Score Predictions

• Other Week 11 Games Worth Watching

• What Could Change These Predictions

• How Football Simulations Estimate Final Scores

• Using Probability to Evaluate Week 11 Projections

• Final Outlook for College Football Week 11

• Frequently Asked Questions

College Football Week 11 Projected Scores

The Week 11 schedule runs from November 10 through November 14, 2026, with conference games and nationally relevant matchups spread across the week. The Saturday slate includes several contests between ranked teams, while Friday features important games involving UCLA and Houston.

Here are our projected scores for selected games on the schedule.

These are independent editorial score estimates, not confirmed ATSwins model outputs or guaranteed results. They are intended as matchup projections based on the available schedule and team context.

The closest projected contests include Wake Forest-SMU, Tennessee-Texas A&M, Ole Miss-Oklahoma, Texas-LSU, and Utah-Arizona. Those games deserve extra attention because a single turnover, red-zone mistake, or late defensive stop could change the winner.

Biggest Matchups and Score Predictions

Texas at LSU: Projected Score 24-27

Texas traveling to LSU is one of the most interesting games of the weekend. The matchup brings together two programs with the talent to compete at a high level, but the environment in Baton Rouge adds another variable.

Texas should look to establish offensive balance early. A productive running game would make it easier to control down-and-distance situations and prevent LSU from consistently attacking obvious passing situations. The Longhorns also need to protect the football because short fields can quickly change the scoring outlook.

LSU gets the benefit of playing at home. Its ability to generate pressure, create difficult third downs, and take advantage of scoring opportunities could be decisive.

Our projection favors LSU by three points. A 27-24 result suggests a competitive game that could remain undecided until the fourth quarter.

Missouri at Georgia: Projected Score 17-34

Georgia enters this matchup with a strong national profile and the advantage of playing at Sanford Stadium. Missouri faces a difficult assignment against a team capable of applying pressure on both sides of the football.

The most important factor is likely to be Missouri's ability to maintain offensive efficiency against Georgia's defensive front. If the Tigers struggle to generate consistent rushing production, their passing game could face predictable situations.

Georgia's offense has a more favorable path if it can establish the run and avoid unnecessary turnovers. Sustained drives would also put additional pressure on Missouri's defense as the game progresses.

Our projected score is Georgia 34, Missouri 17. The Bulldogs are the clear selection in this matchup, although the margin could narrow if Missouri creates explosive plays or wins the turnover battle.

USC at Indiana: Projected Score 24-31

USC against Indiana is another major Big Ten matchup with potential postseason implications. Indiana gets home-field advantage, while USC brings an offense that could make this game uncomfortable for the home team.

Indiana's path to victory starts with controlling the pace. The Hoosiers should benefit from sustaining drives, protecting their quarterback, and avoiding situations where USC can dictate the game through offensive tempo.

USC needs to turn its scoring opportunities into touchdowns rather than field goals. Against a competitive opponent, leaving points on the field can become a major problem.

Our projection gives Indiana a 31-24 victory. The expected difference is relatively small, so this is not a matchup where the home team should be considered certain to win.

Ole Miss at Oklahoma: Projected Score 27-24

This matchup could become one of the more entertaining games of Week 11. Ole Miss has the offensive potential to create problems through pace and explosive plays, while Oklahoma will look to make the game more physical and take advantage of its home environment.

Oklahoma's defensive performance is especially important. If the Sooners can disrupt the timing of Ole Miss's passing attack, they have a better chance of controlling the game. However, allowing repeated explosive plays could put the defense under pressure throughout the afternoon.

Ole Miss receives the narrow edge in our projection, primarily because of its offensive scoring potential. The projected final score is 27-24.

The margin is small enough that a late turnover or successful fourth-down conversion could easily reverse the prediction.

Utah at Arizona: Projected Score 30-27

Utah against Arizona deserves attention because it presents a different type of challenge from the SEC and Big Ten headliners.

Arizona has the home-field advantage, but Utah's physical approach could make it difficult for the Wildcats to control the game. The Utes should look to establish their running game, create favorable passing situations, and limit Arizona's opportunities to generate momentum.

Arizona's best route is to produce enough offensive efficiency to prevent Utah from slowing the game down. If the Wildcats can create explosive plays and capitalize on defensive mistakes, they have a realistic opportunity to win.

Our projection favors Utah 30-27. This is one of the more uncertain games on the schedule, and the projected three-point difference should not be interpreted as a substantial advantage.

Tennessee at Texas A&M: Projected Score 24-28

Tennessee and Texas A&M have the ingredients for a close SEC contest. Both teams have reasons to favor an aggressive approach, but the location gives Texas A&M an important advantage.

The Aggies should benefit from playing at Kyle Field, particularly if they can establish control of the line of scrimmage. Tennessee needs to find a way to keep its offense productive without giving Texas A&M too many opportunities to attack after short possessions.

One important matchup will be Tennessee's ability to handle defensive pressure. If the Volunteers struggle to protect the quarterback, their offensive rhythm could disappear for extended stretches.

Our projection gives Texas A&M a 28-24 win. The home team gets the narrow advantage, but this is another matchup where the result could come down to fourth-quarter execution.

Duke at Miami: Projected Score 20-34

Miami enters this game with a significant advantage in overall team expectations. Duke is capable of making the contest competitive, but playing against Miami on the road presents a difficult challenge.

The Hurricanes should look to use their offensive athleticism to create favorable matchups. If Miami establishes an early lead, Duke may be forced to become more aggressive with its passing game. That could create additional opportunities for Miami's defense.

Duke's best chance is to control possession, limit mistakes, and prevent Miami from generating explosive scoring drives.

Our projected final score is Miami 34, Duke 20. Miami is the stronger selection, although the margin depends heavily on how effectively Duke handles the opening stages of the game.

Illinois at UCLA: Projected Score 20-30

Friday night's meeting between Illinois and UCLA offers an interesting contrast in styles. UCLA has home-field advantage and enters the game with a strong national ranking, while Illinois has the opportunity to make a statement against a highly regarded opponent.

The projected scoring environment is relatively controlled compared with some of the weekend's SEC matchups. Illinois will need to stay disciplined defensively and avoid giving UCLA easy scoring opportunities.

UCLA gets the edge because of its home environment and projected offensive advantage. Our score prediction is 30-20 in favor of the Bruins.

The key question is whether Illinois can maintain enough offensive consistency to keep the game close. If the Illini fall behind early, UCLA could have more freedom to dictate the pace.

Other Week 11 Games Worth Watching

The headline matchups will attract most of the attention, but several other games deserve consideration when evaluating college football Week 11 predictions.

Houston at Colorado: Projected Score 31-20

Houston gets the advantage in our projection. The Cougars should have opportunities to create scoring drives against Colorado, particularly if they can establish offensive consistency. Colorado needs to protect the football and avoid allowing Houston to control field position.

Wake Forest at SMU: Projected Score 31-28

This is one of the most competitive games on the schedule. Wake Forest receives a narrow advantage, but SMU's home-field environment makes the matchup difficult to separate. Offensive efficiency and third-down conversions could determine the result.

Alabama at Vanderbilt: Projected Score 34-20

Alabama is the projected winner because of its overall team strength and ability to create advantages across multiple phases. Vanderbilt should have opportunities to compete, but maintaining offensive consistency against Alabama will be a significant challenge.

Baylor at BYU: Projected Score 21-30

BYU gets the home-field advantage and the stronger projected result. Baylor will need to prevent the Cougars from establishing offensive rhythm. If BYU controls the early possessions, the home team could gradually create separation.

Texas Tech at Oklahoma State: Projected Score 34-24

Texas Tech receives the advantage in this Big 12 matchup. The Red Raiders should have opportunities to create scoring drives, but Oklahoma State's home-field advantage could make the game more competitive than the projected margin suggests.

Florida State at Pittsburgh: Projected Score 21-27

Pittsburgh gets the edge at home. Florida State needs to produce enough offensive consistency to prevent the Panthers from controlling the game. A low-scoring first half would increase the importance of turnovers and field position.

These additional projections are useful because they show that not every Week 11 matchup should be evaluated in the same way. Some games favor the stronger offense, while others depend more heavily on defensive efficiency, pace, and home-field conditions.

What Could Change These Predictions

A projected score is a starting point, not a final answer. College football games contain several variables that are difficult to measure perfectly before kickoff.

Injuries are among the biggest concerns. A starting quarterback playing through an injury can affect passing efficiency, mobility, and decision-making. The absence of an offensive lineman can also influence an entire game by changing how frequently the quarterback faces pressure.

Weather deserves attention as well. Rain, strong winds, and poor field conditions can reduce passing efficiency and change the expected scoring environment. A team that normally depends on explosive passing plays may need to adjust its approach.

Turnovers are another major source of uncertainty. An interception returned for a touchdown or a fumble recovered near the opponent's goal line can produce a scoring swing that ordinary offensive efficiency numbers do not fully capture.

Coaching decisions also matter. Fourth-down attempts, red-zone play calling, clock management, and defensive adjustments can change the outcome even when two teams perform close to their expected statistical levels.

There are also differences between a team's overall record and its actual matchup strength. A team with a strong win-loss record may have benefited from an easier schedule, while another program may have faced several highly rated opponents.

For this reason, score predictions should consider more than rankings and recent results.

A useful pregame evaluation should examine:

• Offensive and defensive efficiency against comparable opponents.

• Quarterback availability and offensive-line health.

• Rushing production and defensive run prevention.

• Turnover rates and red-zone performance.

• Home-field advantage and travel conditions.

• Weather and expected game tempo.

The purpose is to understand why a score projection makes sense and which assumptions could cause it to change.

How Football Simulations Estimate Final Scores

A football simulation approaches score predictions differently from a traditional pick based primarily on team reputation or recent results.

Instead of selecting a winner first and assigning a score afterward, a statistical model estimates how the matchup could develop through individual possessions, expected scoring opportunities, and team performance characteristics.

The process generally begins with offensive and defensive ratings. These measurements estimate how effectively each team moves the football and prevents its opponent from doing the same.

A model may also incorporate pace, opponent strength, quarterback performance, rushing efficiency, passing efficiency, turnovers, field position, and home-field advantage.

These inputs create an expected scoring baseline.

For example, consider a hypothetical matchup in which Team A has an expected scoring output of 28 points and Team B has an expected scoring output of 24 points.

The initial projection would be:

Team A: 28 points

Team B: 24 points

However, a simulation does not need to stop there. It can generate thousands of possible game scenarios by varying possession outcomes, scoring opportunities, and other uncertain events.

Some simulations might produce a 35-17 victory for Team A. Others could generate a 24-27 loss. A smaller portion might result in overtime.

The important output is the distribution of possible scores.

Example of a hypothetical simulation distribution

These figures are illustrative rather than actual Week 11 model results.

A model with a 58% win probability for Team A does not mean Team A will win 58% of the time in one particular game. It means that, across comparable simulated scenarios under the model's assumptions, Team A wins approximately 58% of outcomes.

That distinction matters when interpreting college football score projections.

A projected score is simply the most convenient way to communicate the expected matchup. The underlying probability distribution provides a more complete picture of the uncertainty.

Why Expected Scores and Actual Results Differ

Football scoring is not perfectly predictable. A team might generate several productive drives but fail to score touchdowns because of red-zone mistakes.

Another team could produce fewer total yards but score through a defensive touchdown and a short-field opportunity.

These situations create differences between expected performance and the final scoreboard.

Models therefore need to account for more than average offensive production. They should also consider scoring volatility, defensive performance, possession count, and the possibility of unusual events.

A strong simulation framework should produce a range of plausible results rather than suggesting that one exact score is certain.

Using Probability to Evaluate Week 11 Projections

One of the biggest mistakes in sports forecasting is confusing a likely winner with a guaranteed winner.

A team might have a 65% estimated chance of winning, but that still leaves a meaningful possibility of losing. Even strong teams experience unexpected results because football games involve turnovers, injuries, penalties, and unpredictable scoring sequences.

This is why probabilities are often more useful than standalone score predictions.

Consider the Texas-LSU projection. Our estimated score is LSU 27, Texas 24. That suggests a narrow LSU advantage, but it does not establish that LSU is automatically a better betting option.

The available market price would need to be evaluated separately.

For example, if a hypothetical model assigns LSU a 55% win probability, its corresponding fair decimal odds would be approximately 1.82. That number represents the theoretical price before accounting for sportsbook margin.

If the available price is significantly shorter, the potential value may be limited. If the market offers a better price than the model's fair estimate, further evaluation may be worthwhile.

The same principle applies to projected totals and point spreads.

A predicted score of 31-24 produces a combined total of 55 points and a seven-point margin. However, neither number automatically establishes an over, under, or spread opportunity. The available betting line must be compared against the forecast.

This is where closing-line value becomes relevant. Comparing an earlier projected price with the eventual closing market can provide another way to evaluate whether a forecast identified a useful number before the market adjusted.

ATSwins.ai approaches sports forecasting through projected scores, game simulations, probability estimates, fair lines, and market comparisons. Its college football analytics can be explored alongside historical simulation results and other forecasting information.

The platform's NCAAF historical simulation results also provide a reference for examining projected scores against actual outcomes. Historical results should be evaluated over meaningful samples rather than used as proof that future predictions will perform similarly.

Final Outlook for College Football Week 11

Week 11 offers a strong collection of games with important implications for conference races and the College Football Playoff picture. Georgia against Missouri, Texas against LSU, USC against Indiana, and Ole Miss against Oklahoma stand out as particularly interesting matchups.

Our projections favor Georgia, Miami, Indiana, BYU, and Houston in several of the weekend's more prominent games. However, the closer contests deserve just as much attention. Utah-Arizona, Texas-LSU, Wake Forest-SMU, and Tennessee-Texas A&M could all produce results that differ from their initial projected scores.

The most useful way to approach these predictions is to consider the expected score alongside the probability of different outcomes. Team strength, matchup efficiency, injuries, and home-field conditions all influence the forecast, but none removes the uncertainty associated with college football.

ATSwins.ai provides a data-driven environment for evaluating game projections, simulations, and fair-line estimates. Comparing those numbers with actual market prices offers a more disciplined way to assess each matchup instead of relying on a single predicted final score.

Frequently Asked Questions

1. What are the college football Week 11 score predictions for 2026?

Some of our featured projections include Georgia defeating Missouri 34-17, Indiana beating USC 31-24, Miami defeating Duke 34-20, LSU beating Texas 27-24, and Utah defeating Arizona 30-27. These are independent editorial estimates for the November 10-14 schedule, not guaranteed outcomes or official ATSwins model predictions.

2. What is the biggest game in college football Week 11?

Texas at LSU is one of the biggest matchups because of its potential implications for the SEC race and national rankings. Other important games include Missouri at Georgia, USC at Indiana, Ole Miss at Oklahoma, and Utah at Arizona. Each presents a different matchup challenge, making the weekend particularly interesting for football forecasting.

3. How accurate are college football score predictions?

Accuracy depends on the quality of the data, modeling approach, and evaluation method. Even sophisticated forecasting systems cannot consistently identify exact final scores. A better evaluation examines predicted margins, total scoring error, win probabilities, and calibration over a large sample of games. One correct or incorrect prediction says relatively little about a model's long-term reliability.

4. How do AI models predict college football scores?

AI sports prediction models analyze information such as offensive and defensive efficiency, quarterback performance, opponent strength, rushing production, passing efficiency, turnovers, and home-field advantage. Simulation methods can then generate thousands of possible game outcomes. The resulting projections estimate likely scores and winning probabilities rather than identifying a guaranteed final result.

5. Why do projected scores change before kickoff?

Projected scores change when important information becomes available. Quarterback injuries, lineup announcements, weather conditions, changes in team performance, and updated opponent evaluations can all affect a forecast. Market movement may also reveal that the broader market is responding to new information. Updating projections helps keep the analysis relevant as kickoff approaches.