Football Outsiders

Innovative Statistics, Intelligent Analysis

Schedule Strength in College Football

Guest column by Akshay Ramprakash

In college football, strength of schedule (SoS) has generally referred to the cumulative/average/weighted strength of all the relevant opponents of a team during a given football season. Generally speaking, this strength has been calculated as a function of the performance of these opponents (and, to an extent, the performance of the opponents of these opponents).

But what if we turn this understanding of what the SoS should be on its head? What if, instead of calculating the strength of each opponent based on its past performances, we calculate the strength based on the opponent's current resources?

In order to do this, we need to identify and quantify the "resources" available to each team. And the biggest resource on any team's roster is, well, the roster. The players are the team's biggest resources. There is generally a positive correlation between the quality of a team's student-athletes and its performances. This is an obvious observation. What is also true is that it is not the only "resource" that matters. Coaching, facilities, institute budget, etc., all influence the final performances. But for this article, we will be considering a team's student-athlete resources as they were initially quantified, and not how they are transformed (good or bad) through the school's coaching system.

In order to quantify the quality of the student-athletes in any given college football team, we need only to look at the different recruiting services that rate every single high school football player in this country as a prospect, and then list which team each player joined. is one such service which has publicly available information on the player ratings, team rankings, and class scores for every year since 2003. Using this information, we will be able to quantify the quality of the players in each team for the 2018 college football season.

Team Roster Score

The 247Sports Composite Rating is a proprietary algorithm that compiles prospect "rankings" and "ratings" listed in the public domain by the major media recruiting services. It converts average industry ranks and ratings into a linear composite index capping at 1.0000, which indicates a consensus No. 1 prospect across all services.

Each year, 247Sports provides a team's recruiting class with a composite rating based on the composite index for each player in that class. Here, each recruit is weighted according to a Gaussian distribution formula that gives the most points to the team's best recruits. This ensures that all student-athletes contribute some value to the team (with the best ones contributing the most), without heavily rewarding teams that simply have several more recruits than others. (No recruiting service is going to perfectly predict how well a given athlete is going to perform in the field over three to five years in college. Some may overperform and some may underperform. But this "error" will not be restricted to any specific school/conference/state, and will instead be spread out across all schools and conferences.)

A team's roster at any point consists of student-athletes from the previous five recruiting classes. This includes players who played in their freshman season and those who may have been redshirted (thus resulting in a fifth year of eligibility). We can quantify the overall team roster's quality with a weighted average of the composite ratings of these five recruiting classes. The exact weights for each of the previous five recruiting classes are given in the table below:

Weights Assigned to Each Recruiting Class for the 2018 Team Roster
Recruiting Year Weight (%) Current Status
2014 25% Redshirt Senior
2015 30% Redshirt Junior/True Senior
2016 25% Redshirt Sophomore/True Junior
2017 15% Redshirt Freshman/True Sophomore
2018 5% True Freshman
Attrition prior to Senior year, or incoming transfers from other programs, are not weighted in this model. However, it is to be noted that the overall sensitivity of the TRS to changing weights from one year to another is very low as the class recruiting score typically does not change drastically from one year to the next for a given team.

The above weights reflect the generally expected relative contributions of a recruiting class based on their experience and availability. For instance, true freshmen rarely make a noticeable impact, with the best players often being redshirted. (Players like Tua Tagovailova and Trevor Lawrence are anomalies.) On the other hand, players who are two or three years into a program will be far more likely to make the starting roster and make consistent and meaningful impacts. That being said, there is bound to be attrition in the senior class, with the best players leaving to the NFL early. This is reflected in the slight reduction in the weights assigned for the senior class.

Using the above weights, we can now calculate each school's Team Roster Score (TRS). The TRS will thus quantify the available resources to any given team. The 2018 Team Roster Scores for all the Power 5 Colleges (along with Notre Dame and BYU) are listed below.

Summary of Team Roster Scores for 2018
Rank School Conference Team Roster Score
1 Alabama SEC 311.6
2 Ohio State Big 10 292.9
3 LSU SEC 289.8
4 Florida State ACC 288.7
5 USC Pac-12 287.0
6 Georgia SEC 284.8
7 Auburn SEC 273.2
8 Tennessee SEC 265.4
9 Clemson ACC 261.4
10 Texas A&M SEC 260.3
11 Texas Big 12 259.5
12 Notre Dame IND 258.9
13 UCLA Pac-12 252.2
14 Oklahoma Big 12 251.2
15 Florida SEC 251.0
Rank School Conference Team Roster Score
16 Ole Miss SEC 246.3
17 Michigan Big 10 242.9
18 Penn State Big 10 240.2
19 Miami (FL) ACC 238.0
20 Stanford Pac-12 237.3
21 Oregon Pac-12 233.1
22 South Carolina SEC 229.4
23 Michigan State Big 10 221.9
24 Arizona State Pac-12 221.4
25 Mississippi State SEC 218.0
26 Arkansas SEC 216.9
27 Washington Pac-12 213.5
28 Nebraska Big 10 212.8
29 North Carolina ACC 211.4
30 Virginia Tech ACC 209.4
Rank School Conference Team Roster Score
31 Kentucky SEC 206.8
32 Texas Christian Big 12 206.1
33 Baylor Big 12 201.0
34 Missouri SEC 200.1
35 Wisconsin Big 10 197.9
36 Oklahoma State Big 12 197.5
37 Louisville ACC 196.9
38 North Carolina State ACC 195.4
39 Maryland Big 10 195.4
40 Pitt ACC 194.2
41 West Virginia Big 12 193.3
42 Arizona Pac-12 191.3
43 Texas Tech Big 12 190.8
44 California Pac-12 189.0
45 Utah Pac-12 186.7
Rank School Conference Team Roster Score
46 Duke ACC 182.0
47 Virginia ACC 181.4
48 Washington State Pac-12 180.8
49 Georgia Tech ACC 178.3
50 Vanderbilt SEC 178.3
51 Northwestern Big 10 178.2
52 Iowa Big 10 178.1
53 Indiana Big 10 175.5
54 Minnesota Big 10 174.8
55 Syracuse ACC 173.6
56 Rutgers Big 10 173.6
57 Iowa State Big 12 171.8
58 Oregon State Pac-12 170.3
59 Kansas State Big 12 169.9
60 Wake Forest ACC 168.6
Rank School Conference Team Roster Score
61 Illinois Big 10 168.3
62 Brigham Young IND 167.5
63 Colorado Pac-12 165.5
64 Boston College ACC 164.8
65 Kansas Big 12 163.0
66 Purdue Big 10 157.4

Strength of Schedule

With the quantification of all Power 5 team resources, we can now turn our attention to the problem of how exactly to characterize each team's strength of schedule. The broad approach would be to calculate the cumulative or average TRS of all of a team's opponents, with a higher score indicating a more resourceful pool of opponents, and thereby a tougher schedule. This would be the crux of our new approach. Following this, some additional layers of complexity and intrigue can be added to the numbers.

But first, a few specific rules and assumptions need to be established:

1. The SoS applies only to Power 5 teams. For the purposes of this analysis, Power 5 teams will include all current teams playing in the ACC, SEC, Big 12, Big 10, and Pac-12 conferences. It will also include independent schools Notre Dame and BYU.

2. Matches between a Power 5 team and a non-Power 5 team are not counted towards the Power 5 team's SoS. A Power 5 school is always expected to beat a non-Power 5 opponent. No, this is not completely true. Fans of Central Florida will probably be very upset. And obviously nobody told this to Old Dominion before they beat Virginia Tech. But the overall percentage of non-Power 5 teams beating Power 5 opponents is extremely small. Hence this assumption is generally valid.

3. The total SoS of a Power 5 team will be the sum of the TRS scores of all the Power 5 opponents it faces. This will be the "Power 5 SoS."

4. The average SoS of a Power 5 team will be the average TRS of all the Power 5 opponents it faces. This will be the "Average Power 5 SoS Score."

The following chart should help illustrate the number of non-Power 5 teams that each Power 5 team faces. Note that this does not include BYU, which faces seven non-Power 5 teams.

Let us now come to the meat of our analysis. Who has the toughest schedule? To start off, let us define the strength of schedule as simply the "Average Power 5 SoS Score." The higher the Average Power 5 SoS Score, the tougher the schedule is considered to be. But since not all teams play the same number of Power 5 opponents, it would be prudent to incorporate the number of non-Power 5 teams played into any discussion or illustration of the SoS of all Power 5 teams.

The chart below shows the Average Power 5 SoS Score for each team plotted against the team's own TRS. Additionally, each data point (school) is color-coded to illustrate how many non-Power 5 teams it plays this season. For instance, Mississippi State and Arkansas both have approximately the same TRS and Average Power 5 SoS Score, but since Arkansas plays four non-Power 5 teams vs. three for Mississippi State, it can be construed that Mississippi State has a tougher schedule than Arkansas.

So Auburn and Iowa have the toughest and easiest SoS among all the Power 5 schools respectively. The full list of SoS based on the Average TRS of all Power 5 opponents is listed in the table below. Note that this does not differentiate between number of non-Power 5 teams played.

Strength of Schedule based on Team Roster Scores of Power 5 Opponents
SoS Rank School Conference Team Roster Score Avg Power 5 SoS Score
1 Auburn SEC 273.2 256.3
2 LSU SEC 289.8 255.6
3 Texas A&M SEC 260.3 250.4
4 Mississippi State SEC 218.0 247.3
5 Arkansas SEC 216.9 247.2
6 Ole Miss SEC 246.3 240.9
7 Alabama SEC 311.6 240.8
8 Florida SEC 251.0 240.2
9 Vanderbilt SEC 178.3 240.0
10 South Carolina SEC 229.4 239.4
11 Tennessee SEC 265.4 236.5
12 Missouri SEC 200.1 233.5
13 Kentucky SEC 206.8 231.6
14 Georgia SEC 284.8 230.3
15 Oregon State Pac-12 170.3 221.2
SoS Rank School Conference Team Roster Score Avg Power 5 SoS Score
16 Stanford Pac-12 237.3 219.3
17 Georgia Tech ACC 178.3 217.7
18 UCLA Pac-12 252.2 217.6
19 Arizona State Pac-12 221.4 215.9
20 Notre Dame IND 258.9 215.8
21 Syracuse ACC 173.6 215.6
22 Utah Pac-12 186.7 215.0
23 Michigan Big 10 242.9 214.7
24 Louisville ACC 196.9 213.1
25 Wake Forest ACC 168.6 212.9
26 Maryland Big 10 195.4 212.8
27 Florida State ACC 288.7 211.8
28 Miami (FL) ACC 238.0 211.1
29 Virginia Tech ACC 209.4 210.8
30 Colorado Pac-12 165.5 210.5
SoS Rank School Conference Team Roster Score Avg Power 5 SoS Score
31 USC Pac-12 287.0 210.3
32 California Pac-12 189.0 210.0
33 Boston College ACC 164.8 209.9
34 Texas Christian Big 12 206.1 209.1
35 Michigan State Big 10 221.9 209.0
36 Washington State Pac-12 180.8 208.2
37 Rutgers Big 10 173.6 207.6
38 Washington Pac-12 213.5 207.0
39 West Virginia Big 12 193.3 206.5
40 Texas Tech Big 12 190.8 206.0
41 Indiana Big 10 175.5 205.8
42 Arizona Pac-12 191.3 205.4
43 Kansas State Big 12 169.9 205.2
44 Oregon Pac-12 233.1 204.7
45 North Carolina State ACC 195.4 204.5
SoS Rank School Conference Team Roster Score Avg Power 5 SoS Score
46 Pitt ACC 194.2 204.2
47 Penn State Big 10 240.2 204.1
48 Clemson ACC 261.4 203.8
49 Texas Big 12 259.5 202.5
50 Duke ACC 182.0 202.2
51 Kansas Big 12 163.0 201.5
52 Iowa State Big 12 171.8 201.1
53 Oklahoma State Big 12 197.5 200.8
54 Oklahoma Big 12 251.2 200.5
55 Baylor Big 12 201.0 198.5
56 Virginia ACC 181.4 197.9
57 Nebraska Big 10 212.8 197.8
58 Ohio State Big 10 292.9 197.3
59 Purdue Big 10 157.4 196.8
60 Northwestern Big 10 178.2 196.7
SoS Rank School Conference Team Roster Score Avg Power 5 SoS Score
61 Brigham Young IND 167.5 195.7
62 Minnesota Big 10 174.8 195.2
63 North Carolina ACC 211.4 193.5
64 Illinois Big 10 168.3 189.8
65 Wisconsin Big 10 197.9 189.4
66 Iowa Big 10 178.1 187.2

Some observations from the above chart and table:

1. SEC teams pretty much rule the roost when it comes to strength of schedule. This should come as no surprise as they are also the teams with the highest Team Roster Scores and they all play each other.

2. There is no Big 12 team in the top 30 of that list.

3. Big 12 and especially Big 10 teams dominate the bottom of the list.

4. Ohio State and Wisconsin have two of the easiest schedules among all Power 5 conference teams. Let that sink in.

5. While Auburn has the toughest schedule in the SEC, Oregon State (Pac-12), Georgia Tech (ACC), Michigan (Big 10), and TCU (Big 12) have the toughest overall schedules in their respective conferences. This includes all games and is not limited to conference schedules.

Team Resource Ratio

The above table gives a measure of each Power 5 team's strength of schedule based on the current resources available to all its opponents, but this list doesn't tell the entire story. There are many dimensions we must examine to fully understand and interpret a team's strength of schedule in terms of each team's own resources and those of its opponents. The Average Power 5 SoS Score is one such perspective. It gives us a broad picture of the resources of a team's opponents over the course of a season. But how do we determine how well (or poorly) equipped each team is to play their schedule?

Take the cases of Auburn, Vanderbilt, and Ohio State. Auburn had the highest Average Power 5 SoS at 256.3, followed by Vanderbilt (240.0) and then Ohio State (197.3). However, Auburn is well-equipped to take on such a difficult schedule with a TRS of 273.2. Compare that to Vanderbilt which has significantly lower resources (178.3) to play its schedule, which is nearly as difficult as Auburn's. Meanwhile, Ohio State's resources (292.9) are an overkill for the schedule they play.

So instead of just stopping at the Average Power 5 SoS Score as the sole measure of a team's SoS, we should also consider how capable each team is of playing its own schedule. One method to measure this would be to measure the ratio of each team's Average Power 5 SoS Score to its own Team Roster Score. A "Resource Ratio" for each team can thus be defined as the ratio of a team's Average Power 5 SoS Score to its Team Roster Score.

Consider wrestling as a sport. The athletes in wrestling are restricted to compete against opponents within their own weight class. This prevents obvious mismatches (such as a 200-pound athlete going up against a 110-pound athlete). And if the weight of an athlete were to be treated as a resource, it can be argued that any athlete can always bulk up and move to a higher weight class out of his or her own volition and ability. However, unlike wrestling, the resources that any team can procure in college football are not completely in their control. A number of different factors determine a school's recruiting prowess. This can lead to a number of highly competitive match-ups and a significant number of lopsided ones.

The Team Resource Ratio is a way to broadly determine how well a given team is matched up against its opponents. Looking at it another way, it can be interpreted as a way to establish how well or poorly equipped each team is to play its own schedule. This tells us not only which teams are generally punching above (or below) their own weight, but also by how much. A Resource Ratio of more than 1.00 would indicate that the team would generally be playing opponents that have better resources than themselves, while a ratio of less than 1.00 would indicate that the team is generally playing opponents that have lesser resources than themselves.

The table below lists all the Power 5 teams in decreasing order of their Resource Ratio

Strength of Schedule based on a Team's Resource Ratio
SoS Rank School Conference Resource Ratio
1 Vanderbilt SEC 1.35
2 Oregon State Pac-12 1.30
3 Boston College ACC 1.27
4 Colorado Pac-12 1.27
5 Wake Forest ACC 1.26
6 Purdue Big 10 1.25
7 Syracuse ACC 1.24
8 Kansas Big 12 1.24
9 Georgia Tech ACC 1.22
10 Kansas State Big 12 1.21
11 Rutgers Big 10 1.20
12 Indiana Big 10 1.17
13 Iowa State Big 12 1.17
14 Brigham Young IND 1.17
15 Missouri SEC 1.17
SoS Rank School Conference Resource Ratio
16 Utah Pac-12 1.15
17 Washington State Pac-12 1.15
18 Arkansas SEC 1.14
19 Mississippi State SEC 1.13
20 Illinois Big 10 1.13
21 Kentucky SEC 1.12
22 Minnesota Big 10 1.12
23 California Pac-12 1.11
24 Duke ACC 1.11
25 Northwestern Big 10 1.10
26 Virginia ACC 1.09
27 Maryland Big 10 1.09
28 Louisville ACC 1.08
29 Texas Tech Big 12 1.08
30 Arizona Pac-12 1.07
SoS Rank School Conference Resource Ratio
31 West Virginia Big 12 1.07
32 Pitt ACC 1.05
33 Iowa Big 10 1.05
34 North Carolina State ACC 1.05
35 South Carolina SEC 1.04
36 Oklahoma State Big 12 1.02
37 Texas Christian Big 12 1.01
38 Virginia Tech ACC 1.01
39 Baylor Big 12 0.99
40 Ole Miss SEC 0.98
41 Arizona State Pac-12 0.98
42 Washington Pac-12 0.97
43 Texas A&M SEC 0.96
44 Wisconsin Big 10 0.96
45 Florida SEC 0.96
SoS Rank School Conference Resource Ratio
46 Michigan State Big 10 0.94
47 Auburn SEC 0.94
48 Nebraska Big 10 0.93
49 Stanford Pac-12 0.92
50 North Carolina ACC 0.92
51 Tennessee SEC 0.89
52 Miami (FL) ACC 0.89
53 Michigan Big 10 0.88
54 LSU SEC 0.88
55 Oregon Pac-12 0.88
56 UCLA Pac-12 0.86
57 Penn State Big 10 0.85
58 Notre Dame IND 0.83
59 Georgia SEC 0.81
60 Oklahoma Big 12 0.80
SoS Rank School Conference Resource Ratio
61 Texas Big 12 0.78
62 Clemson ACC 0.78
63 Alabama SEC 0.77
64 Florida State ACC 0.73
65 USC Pac-12 0.73
66 Ohio State Big 10 0.67

Based on the above table, we can formulate a new parameter that can estimate the degree of mismatch in a team's Power 5 schedule. In an ideal situation, all teams would be playing with the same resources, thereby leading to a Resource Ratio of 1.00 for all schools. This new parameter takes each team's Resource Ratio and subtracts the hypothetical average of 1.00. For instance, Michigan would have a degree of mismatch of 0.12 (in favor) whereas Iowa State would have a Degree of Mismatch of 0.17 (against).

In order to visualize this better, the chart below plots the degree of mismatch in each Power % team's schedule with respect to its own resources.

Some observations from the above chart and table:

1. Vanderbilt is the team that is most heavily mismatched in terms of being too poorly equipped to play its schedule. As we move down the table (and to the right in the chart) from Vanderbilt, the degree of mismatch reduces as we approach Virginia Tech and Baylor, who each have a Resource Ratio of about 1.00 (indicating a well-matched schedule of opponents). Moving farther down in the table (and to the right in the chart) only increases the degree of mismatch, but this time in favor of the team. And so, at the other end of the spectrum, Ohio State is the most heavily mismatched team in terms of being too well equipped for the schedule it plays.

2. SEC teams are generally better equipped for their schedules, while Big 10 and Big 12 teams are not that well equipped. But this approach treats the better equipped SEC teams playing each other in the same way as the lesser equipped Big 12 teams playing each other, thereby implying more even matchups in both cases. And this is also why teams like Vanderbilt and Ohio State stand out -- Vanderbilt is very poorly equipped in an otherwise high-resource conference, while Ohio State is extremely well-equipped in a conference whose schools generally have much lower resources.

3. With most of the games in any team's schedule played against teams from the same conference, the above table indicates that there is significant variation in the distribution of resources within each conference. Therefore there is no one conference that dominates either the top or bottom of this list.

4. Iowa may have had the easiest schedule among all Power 5 teams (as per the Average Power 5 SoS Score), but they are so poorly equipped that they are still generally punching above their own weight with a 1.05 Team Resource Ratio.

5. Most of the teams that you would generally expect to make the Top 25 are just so well equipped that they are almost always punching below their weight -- except when they play each other.

Team Resource Ratio gives us a way to broadly determine how well or poorly matched a team is with its schedule. However, it only gives us information about the average degree of mismatch, without going into specific matchups. For instance, Virginia Tech has a Team Resource Ratio of 1.01, indicating that its schedule is evenly matched. But that does not tell us anything about the distribution of its opponents. Are there an equal number of Power 5 teams with more and lesser resources? Or are there a small number of high resource teams and a relatively larger number of teams with moderately lesser resources? This information can be useful, as it can tell us in which games a given team can be considered the favorite. It also gives us an overall picture of where a team stands with regard to its schedule.

One way to tie all the ideas we have discussed here together is to come up with a table or a chart that lists the number of games each team plays in its schedule where it has more resources than its opponent. This would be a simple evaluation of a team's own TRS against each of its opponent's TRS. The final tally of such matchups should tell us how many games a given team is favored over its opponents in terms of its primary resource. Plotting this against the Average Power 5 SoS Score will also provide a useful comparative context to evaluate different teams.

To calculate the number of games a team plays an opponent of lesser resources than itself, let us make the following simple assumptions:

1. A Power 5 team will always be assumed to have more resources than a non-Power 5 team.

2. All teams have 12 regular season matches (including games against Non-Power 5 teams). Conference championship games are not counted here.

Based on the above assumptions, the table below lists all the Power 5 teams in tiers according to the number of games a team plays against opponents of lesser resources than itself.

Resource and Schedule Based Rankings at End of Regular Season
School Conf. No. of
with Lower

School Conf. No. of
with Lower

School Conf. No. of
with Lower
Alabama SEC 12 Tennessee SEC 9 California Pac-12 5
Florida State ACC 12 Texas Christian Big 12 9 Indiana Big 10 5
Ohio State Big 10 12 Wisconsin Big 10 9 Minnesota Big 10 5
USC Pac-12 12 Arizona Pac-12 8 Missouri SEC 5
Clemson ACC 11 Florida SEC 8 Syracuse ACC 5
Georgia SEC 11 North Carolina State ACC 8 Texas Tech Big 12 5
LSU SEC 11 Ole Miss SEC 8 Utah Pac-12 5
Texas Big 12 11 Texas A&M SEC 8 Virginia ACC 5
UCLA Pac-12 11 Virginia Tech ACC 8 Washington State Pac-12 5
Miami (FL) ACC 10 Arizona State Pac-12 7 West Virginia Big 12 5
Michigan Big 10 10 Brigham Young IND 7 Boston College ACC 4
North Carolina ACC 10 Iowa Big 10 7 Illinois Big 10 4
Notre Dame IND 10 Maryland Big 10 7 Iowa State Big 12 4
Oklahoma Big 12 10 Northwestern Big 10 7 Rutgers Big 10 4
Oregon Pac-12 10 Oklahoma State Big 12 7 Wake Forest ACC 4
Penn State Big 10 10 Pitt ACC 7 Georgia Tech ACC 3
Auburn SEC 9 Washington Pac-12 7 Kansas State Big 12 3
Baylor Big 12 9 Arkansas SEC 6 Oregon State Pac-12 3
Louisville ACC 9 Duke ACC 6 Vanderbilt SEC 3
Michigan State Big 10 9 Kentucky SEC 6 Colorado Pac-12 2
Nebraska Big 10 9 Mississippi State SEC 6 Kansas Big 12 2
Stanford Pac-12 9 South Carolina SEC 6 Purdue Big 10 1

And to visualize this even better, below is the chart that ties everything together.

Some observations from the above chart:

1. The primary takeaway from this chart is how two (or more) teams can have a widely different strength of schedule (based on the Average Power 5 SoS Score), and still have the same number of opponents with lower Team Roster Scores than themselves. Take for example the following teams: Wisconsin, Baylor, Stanford, and Auburn. They all play the same number of opponents that have lower TRS than themselves (nine). However, the Average Power 5 SoS Scores of these teams tells us a different story. It tells us that a potentially nine-win Wisconsin team has nowhere near the same Strength of Schedule as a potentially nine-win Auburn team (with Baylor and Stanford falling somewhere between them).

2. Another thing to note is the number of Power 5 opponents a team plays. Take the case of Texas and Georgia. They are both favored in terms of their resources 11 times this season. If both these teams end up with 11-win regular seasons, one can argue that Georgia will have played a much tougher schedule and should therefore be rated higher. However, the Longhorns play two more Power 5 opponents than the Bulldogs, and that definitely should count for something.

3. Generally speaking, the top right quadrant consists of teams that have some of the highest Avg Power 5 SoS Scores AND who are sufficiently equipped themselves to play that tough schedule – or in other words, most of the SEC.

4. The bottom right quadrant consists of teams that play a tough schedule but are not sufficiently equipped themselves – or in other words, the rest of the SEC.

5. However, you don’t necessarily need to have a significantly high Team Roster Score in order to be playing a large number of lower TRS opponents. Take North Carolina for instance. They are ranked 29th in the nation in Team Roster Score (211.4) -- well below teams like South Carolina, Michigan State, Stanford, and Arkansas. But because they also have one of the lowest (63rd) Avg Power 5 SoS Scores in the nation, they are actually favored in 11 games with regard to TRS. Wisconsin (and to an extent Iowa) also fall into this category. What this means is that if a team has a sufficiently low Avg Power 5 SoS Score, it can be favored with regard to TRS for a large number of games despite its own TRS being relatively low.

It is important to note that just because a team has a higher TRS than its opponents does not necessarily mean that it is expected to win. The quality of coaching, game-day play calling, and many more factors influence how athletes perform on any given day. So the above chart and table should not be treated as a prediction of win/loss records for 2018. Instead this should be interpreted -- in hindsight -- as a way to determine which games can be treated as upsets, which teams are overperforming, and which teams are underperforming. Take Nebraska and UCLA for instance – two teams that were favored in their TRS 11 and nine times respectively this season. But as we know, both are winless after five games, including losses to non-Power 5 opponents. With the quantification of the team’s resources, we know that these two teams are definitely underperforming and that there is a clear gap in the utilization of their resources.

The X Factor

Which brings me to the final point of this post: the X factor. There are always intangible aspects involved in any team's performance over the course of a season -- coaching, player selection, fitness levels, play calling, among many others. A combination of a team's margin of victory and the difference its resources and those of its opponents can lead us to a parameter that could help assign a number to that X factor, making the intangible aspect now a measurable quantity. But this can only be calculated at the end of the season and in hindsight.

Akshay Ramprakash is a civil engineer by profession who discovered American football 10 years ago at Virginia Tech. He religiously supports and follows Juventus FC, the Virginia Tech Hokies, and the Indian National Cricket team. He largely rejects social media and instead blogs at


2 comments, Last at 13 Oct 2018, 10:07am

1 Re: Schedule Strength in College Football

by Subrata Sircar // Oct 12, 2018 - 5:07pm

Interesting, and probably tracks well over time. The principal error sources here will be (in no particular order)
- transcendent players. Peyton Manning or Charles Woodson make impacts far outweighing even their lofty rankings.
- coaching and development. One three-star is not the same as all others, especially if one is being coached by a staff with a track record for success.
- attrition. Injuries and transfers will take their toll, as will coaching transitions and the like.
- discrete versus continuous. In particular, the top teams in the country will only have one or two games that determine their season. OSU might stumble once and still get by, but if they lose to Michigan they're almost certainly not making the B1G title game or the playoff. Thus the season results will largely be determined not by strength of schedule but by the relatively difficulty of one or two opponent.

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2 Re: Schedule Strength in College Football

by akshaynr // Oct 13, 2018 - 10:07am

A lot of what you are pointing out is what I have tried to allude to in the very last section as the X Factor. Some players overperform their initial ranking whereas some will underperform. The coaching and a lot of other intangible aspects will dictate that. And formulating and computing that X Factor will help quantity that.

And taking the example of Michigan and Ohio state, you can actually see the difference in their resources and why Ohio St is always considered the favorite - regardless of whether Mr. Harbaugh lives up to the lofty expectations the football community has of him.

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