Expectations and Realities: Week 3 AP Top 25 College Football Teams

Welcome to the 3rd week of breaking down the top 25 teams’ performance compared to their expectations. Here are the results of week 1 and week 2 if want to see previous performance comparisons. In that article, you will find comparisons of vegasinsider.com’s lines for the AP top 25 teams to the actual scores for that week’s games. Here are the results of week 3.

This week Nebraska and North Carolina fell out of the top 25 and will not be included in this week’s results.

In the Table 1 and Figure 1 below you will find the 25 teams ranked in the AP preseason poll, the predicted spread, the actual result, and the percent error. The teams are sorted based on how well the team performed based on the spread for that game. Teams with a positive percent error performed better than expected, or covered the spread, while teams with a negative percent error underperformed, or did not cover the spread. Note: this is not a measure if a team won or lost a game, rather a measure of how well the team won or lost the game. For example, Oklahoma was expected to beat Tennessee by 21 but outscored them by 24 points. Oklahoma performed 14% better than predicted.

Rank Team Opponent Spread Actual %Error
24 South Carolina Georgia 6.5 -9.5 246%
20 Missouri Central Florida -10.5 -28 167%
22 Ohio State Kent State -31 -66 113%
14 Ole Miss LA-Lafayette -27 -41 52%
8 Baylor Buffalo -33.5 -42 25%
15 Stanford Army -30 -35 17%
4 Oklahoma Tennessee -21 -24 14%
10 LSU Louisiana Monroe -31 -31 0%
3 Alabama Southern Miss -46 -40 -13%
7 Texas A&M Rice -32.5 -28 -14%
16 Arizona State Colorado -16.5 -14 -15%
2 Oregon Wyoming -43.5 -34 -22%
21 Louisville East Carolina -14.5 -10 -31%
11 Notre Dame Purdue -30 -16 -47%
25 Brigham Young Houston -17 -8 -53%
12 UCLA Texas -8.5 -3 -65%
9 USC Boston College -17 23 -235%
6 Georgia South Carolina -6.5 9.5 -246%
17 Virginia Tech East Carolina -10 17 -270%

Spreads come from vegasinsider.com.

Florida State, Auburn, Michigan State, Wisconsin, Clemson, and Kansas State each had bye weeks. They are not included in the table. LSU is the first team in 3 weeks to push the spread. South Carolina exceeded predictions by the largest margin with the upset over Georgia. Virginia Tech had the worst performance after being upset by East Carolina.

Now that we are in the 3rd week of college football we have enough data for some statistics. Teams with byes this week will not be included in this analysis due to insufficient data. Georgia is also excluded since they had a bye week last week. A single factor analysis of variance (ANOVA) reveals that there is no statistical difference in percent error across the first three weeks of play (p=0.556). The results of head to head Tukey multiple comparison statistical matchups can be found here. This means that, on average, all teams have performed in a similar manner relative to their expectations.

While there may not be any differences now, there might be some practical information to be taken from the data. Below are four boxplots to aid in understanding the data. Figure 2 shows the percent error for top 25 teams that have played at least three games. Typically, I would not show this sort of figure. The graph is crowded. However, notice how BYU’s average percent error dwarfs the other teams. This is skewed due to their week two game vs Texas. They were expected to lose by one point but ended up winning by a convincing 34 points. For now, this is considered an outlier and BYU is removed from the analysis. The remaining 17 teams are portrayed in Figures 3 through 5.

Figure 2 – Boxplots of Percent Error for AP Top 25 Teams that Have Played 3 or More Games

There are three important details in Figure 3. Firstly, that the percent error is relatively variable, except for Baylor. Baylor has a median error rate of 36% and has exceeded expectations for all three weeks. Next notice that, so far, Alabama has been underperforming without a single positive error rate in the first three weeks. Finally, LSU has been meeting or exceeding expectations in all of three weeks.

Figure 3 – Boxplots of Percent Error for Alabama, Arizona State, Baylor, Louisville and LSU

The data in Figure 4 is more straightforward. Of the six teams pictured, only Ole Miss is consistently winning against the spread with percent errors of 120%, 105%, and 52% for weeks one, two and three respectively. The percent errors for the other teams in Figure 4 are too variable for any discernible patterns.

Figure 4 – Boxplots of Percent Error for Missouri, Notre Dame, Ohio State, Oklahoma, Ole Miss, and Oregon

With the exception of UCLA, the percent error for teams in Figure 5 is too great. Each team has had weeks were they beat the spread and others where they did not. However, UCLA has consistently not beaten the spread with a median percent error of -65%.

Figure 5 – Boxplots of Percent Error for South Carolina, Stanford, Texas A&M, UCLA, USC, and Virginia Tech

This week’s takeaways: If Baylor, LSU, and Ole Miss keep the same pace, bet against the spread. Bet that Alabama or UCLA won’t cover the spread if they continue the same pattern.

 

Important notes:

  1. Spreads can be found at vegasinsider.com.
  2. Percent error is calculated as (Spread-Actual)/Abs(Spread)
  3. I understand that spreads are typically used for gambling purposes and that the lines move. However, it is important for the spreads to reasonably accurate in order for the house or bookie to make money. Lines are a consistent source of weekly predictions.

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